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December 2022 Newsletter

The Biometrics Section of the American Statistical Association invites submissions for early career paper awards. We will consider papers from two categories: Biometric Methodology and Biometric Practice. Papers in Biometric Methodology should propose novel statistical methodology addressing a problem relevant to the biosciences. Applications in Biometric Practice should demonstrate innovative applications of an existing method in a novel context, re-examine statistical practices from a new perspective, or propose innovative and practical data analysis strategies.

Travel awards in the amount of $1,000 will be awarded to the most outstanding papers in each category to help cover conference expenses for the 2023 Joint Statistical Meetings. The best overall paper will be awarded the David P. Byar Early Career Award which commemorates the late David Byar, a biostatistician who made significant contributions to the development and application of statistical methods and was esteemed as an exceptional mentor during his career at the National Cancer Institute. The winner of the Byar award will receive a $2,000 award.


All applicants for the early career awards must meet all of the following criteria:

  • Have held a doctorate in statistics, biostatistics, or a related quantitative field for three years or less as of May 2022, or be currently enrolled as a student. The committee encourages individuals with e.g., caretaking responsibilities, illness, and other considerations to apply and extend their eligibility by an additional year or more.
  • Be a current member of the Biometrics Section (applicant may join at the time of submission). Please note that membership in ASA does not automatically confer Section membership; ASA members must join individual sections in addition to their generic membership.
  • Be the first author of the paper. The paper may be unsubmitted, submitted or under review, but may not have already appeared in a journal either online or in print at the time of the application or have been accepted for publication as of December 15, 2022.
  • Be scheduled to present the same paper submitted for the Award at the 2023 Joint Statistical Meetings as either a talk or poster.
  • Have submitted the paper to no more than one other ASA section 2023 student or early career award competition. (Note that in the event of a paper winning two competitions, the author is permitted to accept only one of the two awards.)
  • Have not been a previous Byar award or Biometrics section paper award winner.

 

Applicants must submit their JSM abstracts to the Biometrics Section, which will organize a series of Topic Contributed sessions to highlight the awards winners. Applicants should submit an anonymized copy of the paper (i.e., author names and institutions removed as well as any references within text to the authors prior work that is not anonymized). The paper must be a maximum of 25 double-spaced pages (with max of 25 lines per page, at least 1/2" margins and font size no smaller than 11pt) including references but not including tables and figures and submitted as a PDF. Appendices are not permitted and should not be included. Papers that are not within these restrictions will not be considered for an award. Submissions must be submitted online by December 15, 2022. Questions should be sent to the 2023 Byar Award Chair, Sebastien Haneuse, shaneuse@hsph.harvard.edu.



October 2022 Newsletter
The Biometrics Section would like to solicit candidates to the following open positions for 2023 elections. In addition to elected positions, we also have several appointed positions and volunteer opportunities available. Please contact 2023 Chair-Elect Jennifer Bobb (Jennifer.F.Bobb@kp.org) for questions or further information.

Elected positions:

Chair elect (terms would be Chair Elect 2024/Chair 2025/Past Chair 2026)

Representative to the Council of Sections (for 3-year term 2024-2026)

 

Appointed positions in the Executive Committee:

Biometrics Section Representative to the ENAR Program Committee (for ENAR 2024)

Biometrics Section Representative to the JSM Program Committee (for JSM 2024)

 

Other appointed positions and volunteer opportunities:

Byar award committee member


August 2022 Newsletter

The Biometrics Section would like to highlight the following events at the 2022 JSM Conference:

  • JSM Biometrics Section Business Meeting and Mixer
  • JSM 2022 Invited sessions sponsored by the Biometrics Section


Biometrics Section Business Meeting and Mixer

Come join us at JSM for our annual Biometrics Section Mixer on Monday August 8 at 5:30-7:30pm EDT in M-Monument. We will announce this year’s talented group of Student Paper Award Winners and the Annie T. Randall Innovator Award recipient. Drop by to learn about the groundbreaking work from our young scholars. If anyone is interested in volunteering for the Section or if you had any suggestions for other Section activities, we would love to hear from you!

Invited Sessions Sponsored by the Biometrics Section

Sunday, August 7

4:00 p.m. – 5:50 p.m.
Recent Developments of Statistical Methods for Microbiome Research
Organizer(s): Gen Li, University of Michigan

Monday, August 8
8:30 a.m. – 10:20 a.m.
Dealing with Error-Prone Electronic Health Record Data via Validation Sampling
Organizer(s): Bryan Shepherd, Vanderbilt University Medical Center

Tuesday, August 9
10:30 a.m. – 12:20 p.m.
Novel Methods in Curve Registration for Functional Data
Organizer(s): Julia Wrobel, Colorado School of Public Health

Wednesday, August 10
10:30 a.m. – 12:20 p.m.
Advances in Statistical Methods for Wearable and Mobile Health Data Analysis
Organizer(s): Ekaterina Smirnova, Virginia Commonwealth University


June 2022 Newsletter
2021 Annie T. Randall Innovator Award Winner

The ASA Biometrics and Mental Health Statistics Sections are delighted to announce that Dr. Adji Bousso Dieng is the 2022 Annie T. Randall Innovator Award winner! The Annie T. Randall Innovator Award was established in 2020 in honor of Black female statistician Annie T. Randall to recognize early-career statistical innovators across all job sectors and with any level of educational attainment.

Dr. Dieng is an Assistant Professor in the Department of Computer Science at Princeton University. She is recognized for her innovative statistical methods on artificial intelligence and probabilistic machine learning, and for her focus on the societal impact as the founder and president of the non-profit group 'The Africa I Know' that aims to positively change narratives about Africa and provide opportunities to young Africans in STEM.



April 2022 Newsletter
JEDI April Workshop
Dr. Loni Tabb, our 2021 Annie T. Randall Innovator Awardee, will speak at the April JEDI (Justice, Equivalence, Diversity, and Inclusion Outreach Group) Workshop on April 28, 2022 at 2:00 pm ET. Dr. Tabb is Associate Professor of Biostatistics in the Department of Epidemiology and Biostatistics at Drexel University’s Dornsife School of Public Health. Her talk is titled ‘The Power of Representation: My Journey to the Field of Biostatistics (The Journey Continues...) - From Patsie to Annie’. She will discuss her journey in biostatistics and the importance of representation. The workshop can be registered at https://amstat.zoom.us/webinar/register/WN_KLKZwZXbT7uK8sOgl2OuWQ. A detailed abstract can be found below.

The Power of Representation:
My Journey to the Field of Biostatistics (The Journey Continues...) 
From Patsie to Annie
 

Representation matters. Representation in the field of biostatistics, as well as other science, technology, engineering, and math (STEM) related fields, is not only necessary to ensure the pipeline of talented students is as diverse and inclusive as possible, but, without it, our field will be limited in being able to make tremendous advancements in addressing the many public health, medical, and social challenges that plague our societies. During this lecture, I will share my journey to the field of biostatistics and the pivotal role of women – especially Black women – during my training and career. Not only will this space allow for transparency in the process of my growth as a biostatistician, but it will hopefully serve as a platform to shed light into the various peaks and valleys of my own career development. Topics of discussion will not stop at the importance of representation – the significance of mentoring and fostering inclusive environments as a cultural norm will also be discussed. Lastly, the lecture and Q & A will allow for discussions around the role systems and structures play in all of these aspects to ensure our field is better positioned moving forward in addressing the inequities in our own field – from education and training to even retirement.


February 2022 Newsletter
2022 JSM Paper Award Winners
This year’s David P. Byar Early Career Award goes to Andrew Ying from University of Pennsylvania Department of Statistics and Data Science at the Wharton School.

Paper awards winners for JSM 2022 are:

  • Bryan Blette from the Center for Causal Inference, Perelman School of Medicine at the University of Pennsylvania,
  • Sunyi Chi from the Department of Biostatistics, the University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences,
  • Souvik Seal from the Department of Biostatistics and Informatics at Colorado School of Public Health, University of Colorado Anschutz Medical Campus,
  • Satwik Acharyya from the Department of Biostatistics at the University of Michigan.

 

Application Invited for the Annie T. Randall Innovator Award

ASA Biometrics Section calls for nominations of this year’s Annie T. Randall Innovator Award. Named in honor of pathbreaking Black female statistician Annie T. Randall for her pioneering career in government amid pervasive racial discrimination, the award was established by the Biometrics Section in 2020 and co-sponsored by the Mental Health Statistics Section to recognize early-career statistical innovators across all job sectors and with any level of educational attainment. The award description and last year’s winner could be found on the ASA website at https://www.amstat.org/ASA/Your-Career/Awards/Annie-T-Randall-Innovator-Award.aspx.


Selection Criteria

Winners are selected by the Annie T. Randall Innovator Award Committee. To be eligible for the award, candidates should be in the early phase of their professional statistical careers. While no more than 10 years into their career is a guideline, career interruptions and transitions would not be included in this count. The committee appreciates that nontraditional paths are common for trailblazers, and thus there is no firm cutoff for the definition of early phase. There are also no degree requirements for this award. A personal statement or nomination letter should discuss how the candidate has pushed boundaries in statistics toward the betterment of the field and society, as well as how they embody Annie T. Randall’s tenacious and resolute commitment to excellence. How the candidate meets the broad definition of early career described above should also be addressed in the personal statement or nomination letter.

Award Recipient Responsibilities

The award recipient is responsible for providing a current photograph and general personal information the year the award is presented. The American Statistical Association uses this information to publicize the award and prepare the check and certificate.


Nominations

Self-nomination or nomination by someone other than the candidate is welcome. Individuals from underrepresented and historically excluded groups in statistics are encouraged to apply.

Submissions are due by March 15, 2022 and the following materials should be sent to award committee chair, Sherri Rose <sherrirose@stanford.edu>.

  • Candidate résumé or CV
  • Candidate personal statement or nomination letter (1–2 pages)



November 2021 Newsletter
Submissions Invited for 2022 JSM Early Career Paper Awards

The Biometrics Section of the American Statistical Association invites submissions for early career paper awards. We will consider papers from two categories: Biometric Methodology and Biometric Practice. Papers in methodology should propose novel statistical methodology addressing a problem relevant to the biosciences. Applications in Biometric Practice should demonstrate innovative applications of an existing method in a novel context, re-examine statistical practices from a new perspective, or propose innovative and practical data analysis strategies. Travel awards in the amount of $1,000 will be awarded to the most outstanding papers in each category to help cover conference expenses for the 2022 Joint Statistical Meetings. The best overall paper will be awarded the David P. Byar Early Career Award. The award commemorates the late David Byar, a biostatistician who made significant contributions to the development and application of statistical methods and was esteemed as an exceptional mentor during his career at the National Cancer Institute. The winner of the Byar award will receive a $2,000 award.

All applicants for the early career awards must meet all of the following criteria:

  • Have held a doctorate in statistics, biostatistics, or a related quantitative field for three years or less as of May 2021 or be currently enrolled as a student. The committee encourages individuals with e.g., caretaking responsibilities, illness, and other considerations to apply and extend their eligibility by an additional year or more.
  • Be a current member of the Biometrics Section (applicant may join at the time of submission). Please note that membership in ASA does not automatically confer Section membership; ASA members must join individual sections in addition to their generic membership.
  • Be the first author of the paper. The paper may be unsubmitted, submitted or under review, but may not have already appeared in a journal either online or in print at the time of the application or have been accepted for publication as of December 15, 2021.
  • Be scheduled to present the same paper submitted for the Award at the 2022 Joint Statistical Meetings as either a talk or poster.
  • Have submitted the paper to no more than one other ASA section 2022 student or early career award competition. (Note that in the event of a paper winning two competitions, the author is permitted to accept only one of the two awards.)
  • Have not been a previous Byar award or Biometrics section paper award winner.

Applicants must submit their JSM abstracts to the Biometrics Section, which will organize a series of Topic Contributed sessions to highlight the awards winners.

Applicants should submit an anonymized copy of the paper (i.e., author names and institutions removed as well as any references within text to the authors prior work that is not anonymized). The paper must be a maximum of 25 double-spaced pages (with max of 25 lines per page and at least 1/2"; margins) including references but not including tables and figures and submitted as a PDF. Appendices are not permitted and should not be included. Papers that are not within these restrictions will not be considered for an award.

Submissions must be submitted online by December 15, 2021. Questions should be sent to the 2022 Byar Award Chair, Pamela Shaw, at pamela.a.shaw@kp.org.

 

The ASA Biometrics Section Strategic Initiatives Grant Spotlight (2nd Series)

Dr. Esther Drill, Principal Biostatistician at Memorial Sloan Kettering Cancer Center, is the recipient of the 2020 ASA Biometrics Strategic Initiatives Grant. Her project, titled “Developing the Next Generation of Biostatisticians”, in alignment with the "Bridge to Biostats" program, proposed a "Biostat days" that will introduce underserved and underrepresented high school students to the field of Biostatistics. Here Dr. Drill provides an overview of the project and discuss the challenges and progress on the impact of Biostatistics outreach amidst the COVID-19 pandemic.

“As chair of Memorial Sloan Kettering’s Bridge to Biostats Committee, I proposed to develop a suite of interactive “statistical thinking” exercises for use as part of our “Biostats Day” awareness program, which introduces STEM-interested New York City high school students from underrepresented minority (URM) groups to the field of Biostatistics. We present this hour-long program over Zoom to students from existing NYC-area organizations who serve our target audience. The proposed “statistical thinking” activities summarize and visualize data from students in real time, which allows for active participation in learning about introductory statistical concepts, including sampling, sample mean, and sources of error. To date, we have completed (1) the “Population and Sample Mean” activity, which compares the CDC’s 45,000-person sample mean height with our student group’s mean height, and (2) “The F-test” activity, in which all students have two minutes to count the number of F’s in the same paragraph, and are presented with a histogram of the results demonstrating measurement error. We have included these activities in “Biostats Day” presentations to 70 students from three NYC-based STEM exposure organizations: BEAM NYC (Bridge to Enter Advanced Mathematics), the Einstein Enrichment Program (a New York-state funded Science Technology Entry Program), and Memorial Sloan Kettering’s Summer Exposure Program (SEP). In all, 70 students have participated in the program so far. While only 26% of students had heard of Biostatistics before our presentation, 80% expressed interest in learning more about Biostatistics at the end; “the interactive activities” was one of the most frequent answers to “What is your favorite part of the Biostats Day?”

 

Our third “statistical thinking” activity explores other aspects of error in the context of aging estimation and is under active R shiny development. We expect the “Guessing Ages” activity to be ready for use in our next “Biostats Day” presentation, as well as our BEAM Saturday enrichment class in Spring 2022, and our 2nd cohort of SEP Biostatistics summer students in Summer 2022. We are in the process of creating a GitHub repository for all of our “Biostats Day” materials, including access to and instructions for these “statistical activities,” and hope it will be a valuable resource for groups interested in performing their own outreach.”



October 2021 Newsletter

The ASA Biometrics Section Strategic Initiatives Grant Spotlight

The strategic initiatives grant is an effort funded annually by the ASA Biometrics Section to support projects developing innovative outreach focused on enhancing awareness of biostatistics among quantitatively talented US students. The project timeline is typically 1.5-2 years, and the award recipients must be an ASA member and Biometrics section member before project initiation. We will be featuring the projects conducted by our past and current awardees.

 Dr. Michelle Shardell, Professor at the Institute for Genome Science at the University of Maryland Baltimore, was the recipient of the 2019 award. Her project, titled “Introducing Biostatistics to Diverse Pathways in Technology Early College (P-TECH) Students Through Microbiome Data Analysis,” aimed to develop and deliver a module introducing the field of biostatistics and techniques to analyze microbiome data to quantitatively talented students enrolled in the P-TECH program at Dunbar High School in Baltimore, Maryland. Here Dr. Shardell discusses the launch of the project and the challenges and progress made on the impact of Biostatistics outreach amidst the COVID-19 pandemic.

 

How it Started...

 Our original proposal planned to host an in-person “hands on” quantitative activity for students enrolled in the Pathways in Technology Early College (P-TECH) program at Dunbar High School in Baltimore, of which the University of Maryland, Baltimore (UMB) is a partner. P-TECH is a national program where students obtain a high-school diploma and a two-year associate degree in a STEM field at no cost (www.ptech.org). The idea was to collaborate with microbiologist colleagues to build on an existing program in which approximately 30 P-TECH students come to the UMB campus to collect soil samples as part of a biology fieldwork experience, where the samples are used to generate soil microbiome data. This way, students can move from literally “touching” their data to performing simple data analysis as a palpable introduction to the field of biostatistics.

 

...How it’s Going

Once COVID-19 led to the cancelation of all in-person learning activities and a struggle for K-12 educators to cover their required curriculum, let alone enrichment activities, we needed a new plan. The first opportunity came in the summer of 2020 when the leaders of multiple University of Maryland School of Medicine summer enrichment programs that have historically focused on in-person laboratory experiences switched to a remote model focused on computational biology and bioinformatics content. Given that these learners ranged from undergraduate students to medical students, we developed an activity to meet their needs. First, we introduced students to the role of biostatistics as the information science of biomedical and public health research. Next, to complement their exposure to biomedical research in the programs’ journal club, we introduced learners to p-values and the debate on multiple comparisons as well as introductory R programming. To reinforce the content, we developed a web-based digital “escape room.” The first three students who completed the escape room won e-gift cards and special recognition. By the Spring of 2021, when K-12 students and faculty had become virtual learning veterans, we were able to deliver a virtual introduction to biostatistics to sophomores enrolled in the P-TECH program. As in our first program, we initially introduced students to the field of biostatistics as the information science of public health and biomedical research. For these learners, we focused more on the training and job sectors common to biostatisticians as well as internship opportunities in the Baltimore/Washington DC area for high school students interested in biostatistics. We also adapted the web-based digital escape room to emphasize the definitions, training, and careers relevant to biostatisticians (again with gift card prizes). Lastly, we also connected with another program, the UMB Continuing Umbrella of Research Experiences (CURE) Scholars Program, which aims to mentor students from West Baltimore from grade six through graduation. Notably, the UMB-CURE Scholars Program is the subject of a PBS documentary series, with the fourth installment scheduled to air in October 2021. For these learners, we covered generally the same topics as we did for the P-TECH program; however, to be consistent with UMB-CURE program goals, we put greater emphasis on the role of biostatistics in interpreting COVID-19 data. 

 

Impact: By the Numbers

Although we were not able to deliver a cool hands-on fieldwork activity in conjunction with biostatistics content, we were able to connect with a greater number and breadth of students than we had originally planned. Because of this outreach initiative, 83 more high-school students know what biostatistics is, and 41 more undergraduate students and 69 more medical students can explain the multiple comparisons debate. Upon evaluation, over 90% of responders would recommend the session, and interest in pursuing additional coursework or degree programs in biostatistics increased. Learners indicated that the digital escape room increased their ability to interpret p-values and address multiple comparisons, where one learner said “I really liked the post-lecture online activity! It was so cute and I think it was a great way to really reinforce the concepts that we'd just learned. Definitely one of my favorite activities that we've done so far :).” 

 

Future Plans

The lectures for summer undergraduate and medical student programs were recorded and are now available for students in subsequent cohorts. Moreover, now that we have created content and built relationships with multiple local UMB partner programs, we aim to continue outreach work. These sessions will likely remain virtual in the near term for flexibility but may transition to something resembling the original plan.

    


July 2021 Newsletter

2021 ANNIE T. RANDALL INNOVATOR AWARD WINNER
The ASA Biometrics and Mental Health Statistics Sections are delighted to announce that Dr. Loni Philip Tabb is the 2021 Annie T. Randall Innovator Award Winner! The Annie T. Randall Innovator Award was established in 2020 in honor of Black female statistician Annie T. Randall to recognize early-career statistical innovators across all job sectors and with any level of educational attainment.

Dr. Tabb is an Associate Professor at the Department of Epidemiology and Biostatistics at the Drexel University Dornsife School of Public Health. She is recognized for her outstanding contributions to statistical methods and dedication to building a diverse health workforce.



February 2021 Newsletter

2021 JSM PAPER AWARD WINNERS
This year’s David P. Byar Early Career Award goes to Aaron Hudson from the University of Washington Department of Biostatistics for the paper entitled "Honest Uncertainty Quantification for Infinite-Dimensional Risk Minimizers via the Restricted Gradient Test".

Paper awards winners for JSM 2021 are:

  • Hunyong Cho from the University of North Carolina for the paper entitled "Multi-stage Optimal Dynamic Treatment Regimes for Survival Outcomes with Dependent Censoring",
  • Chan Park from the University of Wisconsin-Madison for the paper entitled "Analysis of Cluster Randomized Trials of Infectious Diseases: Effect Heterogeneity, Noncompliance, and Spillover Effects",
  • Kalins Banerjee from the Pennsylvania State University for the paper entitled "An Adaptive and Powerful Multivariate Test for Microbiome Association Analysis via Feature Selection",
  • Arielle Marks-Anglin from the University of Pennsylvania for the paper entitled "Surrogate-assisted Sampling for Cost-efficient Validation of Electronic Health Record outcomes".


APPLICATION INVITED FOR THE ANNIE T. RANDALL INNOVATOR AWARD

ASA Biometrics Section is pleased to announce the Annie T. Randall Innovator Award, established in 2020 to recognize early-career statistical innovators across all job sectors and with any level of educational attainment. The award was named in honor of pathbreaking Black female statistician Annie T. Randall for her pioneering career in government amid pervasive racial discrimination. Her powerful story and legacy in statistics are an inspiration to future generations of trailblazers. The award provides a $2,000 prize each year. The announcement could also be found on the ASA website at https://www.amstat.org/ASA/Your-Career/Awards/Annie-T-Randall-Innovator-Award.aspx.

 

Selection Criteria

Winners are selected by the Annie T. Randall Innovator Award Committee. To be eligible for the award, candidates should be in the early phase of their professional statistical careers. While no more than 10 years into their career is a guideline, career interruptions and transitions would not be included in this count. The committee appreciates that nontraditional paths are common for trailblazers, and thus there is no firm cutoff for the definition of early phase. There are also no degree requirements for this award.

A personal statement or nomination letter should discuss how the candidate has pushed boundaries in statistics toward the betterment of the field and society, as well as how they embody Annie T. Randall’s tenacious and resolute commitment to excellence. How the candidate meets the broad definition of early career described above should also be addressed in the personal statement or nomination letter.

 

Award Recipient Responsibilities

The award recipient is responsible for providing a current photograph and general personal information the year the award is presented. The American Statistical Association uses this information to publicize the award and prepare the check and certificate.

 

Nominations

Self-nomination or nomination by someone other than the candidate is welcome. Individuals from underrepresented and historically excluded groups in statistics are encouraged to apply.

 

Submissions are due by March 15 each year and the following materials should be sent to award committee chair, Sherri Rose <sherrirose@stanford.edu>.

  • Candidate résumé or CV
  • Candidate personal statement or nomination letter (1–2 pages)


December 2020 / January 2021 Newsletter

SUBMISSIONS INVITED FOR 2021 JSM EARLY CAREER PAPER AWARDS
The Biometrics Section of the American Statistical Association invites submissions for early career paper awards. We will consider papers from two categories: Biometric Methodology and Biometric Practice. Papers in methodology should propose novel statistical methodology addressing a problem relevant to the biosciences. Applications in Biometric Practice should demonstrate innovative applications of an existing method in a novel context, re-examine statistical practices from a new perspective, or propose innovative and practical data analysis strategies. Travel awards in the amount of $1,000 will be awarded to the most outstanding papers in each category to help cover conference expenses for the 2021 Joint Statistical Meetings. The best overall paper will be awarded the David P. Byar Early Career Award. The award commemorates the late David Byar, a biostatistician who made significant contributions to the development and application of statistical methods and was esteemed as an exceptional mentor during his career at the National Cancer Institute. The winner of the Byar award will receive a $2,000 award.

All applicants for the early career awards must meet all of the following criteria:

  • Have held a doctorate in statistics, biostatistics, or a related quantitative field for three years or less as of May 2020 or be currently enrolled as a student. The committee encourages individuals with e.g., caretaking responsibilities, illness, and other considerations to apply and extend their eligibility by an additional year or more.
  • Be a current member of the Biometrics Section (applicant may join at the time of submission). Please note that membership in ASA does not automatically confer Section membership; ASA members must join individual sections in addition to their generic membership.
  • Be the first author of the paper. The paper may be unsubmitted, submitted or under review, but may not have already appeared in a journal either online or in print at the time of the application or have been accepted for publication as of December 15, 2020.
  • Be scheduled to present the same paper submitted for the Award at the 2021 Joint Statistical Meetings as either a talk or poster.
  • Have submitted the paper to no more than one other ASA section 2021 student or early career award competition. (Note that in the event of a paper winning two competitions, the author is permitted to accept only one of the two awards.)
  • Have not been a previous Byar award or Biometrics section paper award winner.

Applicants must submit their JSM abstracts to the Biometrics Section, which will organize a series of Topic Contributed sessions to highlight the awards winners.

Applicants should submit an anonymized copy of the paper (i.e., author names and institutions removed as well as any references within text to the authors prior work that is not anonymized). The paper must be a maximum of 25 double-spaced pages (with max of 25 lines per page and at least 1/2"; margins) including references but not including tables and figures and submitted as a PDF. Appendices are not permitted and should not be included. Papers that are not within these restrictions will not be considered for an award.

Submissions must be submitted online by December 15, 2020 AoE. Questions should be sent to the 2021 Byar Award Chair, Sherri Rose, at sherrirose@stanford.edu.



October 2020 Newsletter

ASA Biometrics Section: Call for Developing the Next Generation of Biostatisticians

The ASA Biometrics Section invites applications for funding to support projects developing innovative outreach projects focused on enhancing awareness of biostatistics among quantitatively talented US students. We are interested in projects that will encourage students to pursue advanced training in biostatistics. We anticipate funding one project this year. The project timeline would be from 1.5-2 years. All educators and investigators are strongly encouraged to apply. The earliest start date is January 1, 2021.

The maximum funding amount is $3,000. The applicant must be a member of ASA and the ASA Biometrics Section. The application deadline is November 15, 2020.



February 2020 Newsletter
The Byar Award goes to Yi Zhao from Indiana University for the paper entitled "Multimodal Neuroimaging Data Integration and Pathway Analysis". 

Travel awards for JSM 2020 go to

  • Ting Ye from the University of Pennsylvania for the paper entitled "Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelian Randomization"
  • Jacob Maronge from the University of Wisconsin-Madison for the paper entitled "Generalized case-control sampling under generalized linear models"
  • Yinqiu He from the University of Michigan for the paper entitled "Asymptotically Independent U-Statistics in High-Dimensional Testing"
  • Lu Xia from the University of Michigan for the paper entitled "A Revisit to De-biased Lasso for Generalized Linear Models"
  • Dustin Rabideau from the Harvard T. H. Chan School of Public Health for the paper entitled "Randomization-Based Confidence Intervals for Cluster Randomized Trials"
  • Jialei Chen from the Georgia Institute of Technology for the paper entitled "Function-on-function kriging, with applications to 3D printing of aortic tissues"
  • Minjie Wang from Rice University for the paper entitled "Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data"
December 2019 / January 2020 Newsletter

The ASA Biometrics Strategic Initiatives Committee is proud to announce its funding support for a project led by Dr. Michelle Shardell, Professor at the Institute for Genome Science at the University of Maryland Baltimore.  Her project, titled “Introducing Biostatistics to Diverse Pathways in Technology Early College (P-TECH) Students Through Microbiome Data Analysis,” will include developing and delivering a module introducing the field of biostatistics to quantitatively talented students enrolled in the P-TECH program at Dunbar High School in Baltimore, Maryland. Students will learn techniques to analyze microbiome data derived from soil samples collected in Dr. Shardell’s prior fieldwork. Dr. Shardell will also administer student and teacher evaluations, and will submit results and data to the Journal of Statistics Education and present results in a National Science Foundation Grant application to expand outreach. 

We are excited about Dr. Shardell’s project and look forward to seeing its impact on biostatistics outreach! The ASA Biometrics Strategic Initiatives Committee was led by Dr. Tanya P. Garcia (Chair, Texas A&M University) and Dr. Milan Bimali (Co-Chair, University of Arkansas for Medical Sciences).



October / November 2019 Newsletter


APPLICATIONS INVITED FOR 2020 JSM EARLY-STAGE INVESTIGATOR AWARDS

Do you know an early-stage investigator who is planning to submit an abstract for the 2020 Joint Statistical Meetings (JSM)? If so, please alert them to the ASA Biometrics Section’s 2020 JSM early-stage investigator awards. We will be considering papers from two categories: Biometric Methodology and Biometric Practice. Papers in methodology should propose novel statistical methodology addressing a problem relevant to the biological sciences. Applications in Biometric Practice should demonstrate innovative applications of an existing method in a novel context, re-examine statistical practices from a new perspective, or propose innovative and practical data analysis strategies. Travel awards in the amount of $1,000 will be awarded to the most outstanding papers in each category to help cover conference expenses for JSM. The best overall paper will be awarded the David P. Byar Young Investigator Award. The award commemorates the late David Byar, a biostatistician who made significant contributions to the development and application of statistical methods and was esteemed as an exceptional mentor during his career at the National Cancer Institute. The winner of the Byar award will receive a $2,000 cash award.

All applicants for the early-stage investigator awards must meet all of the following criteria:

  • Have held a doctorate in statistics, biostatistics or a related quantitative field for three years or less as of April 1 of the current year, or be currently enrolled as a student in a program in statistics or biostatistics and in active pursuit of a degree.
  • Be a current member of the Biometrics Section (applicant may join at the time of submission for a $5 annual membership fee [$3 for students]). Please note that membership in ASA does not automatically confer Section membership; ASA members must join individual sections in addition to their generic membership.
  • Be first author of the paper. The paper may be unsubmitted, submitted or under review, but may not have already appeared in a journal either on-line or in print at the time of the application or have been accepted for publication as of December 5, 2019.
  • Be scheduled to present the same paper submitted for the Award at the 2020 Joint Statistical Meetings in Philadelphia, PA as either a talk or poster.
  • Have submitted the paper to no more than one other ASA section 2020 student or early-stage investigator award competition. (Note that in the event of a paper winning two competitions, the author is permitted to accept only one of the two awards.)
  • Have not been a previous Byar award or Biometrics section travel award winner.

Applicants must submit their JSM abstracts to the Biometrics Section, which will organize a series of Topic Contributed sessions to highlight the awards winners.

Applicants must complete their application by submitting the following materials:

  • A current CV.
  • A one-page cover letter that summarizes the paper’s content and contribution.
  • One PDF copy of the paper.

The paper must be a maximum of 25 double-spaced pages (with max of 25 lines per page) including references but not including tables and figures. Papers that are not within these restrictions will not be considered for an award. 

Deadline: All materials must be submitted electronically on or before December 1, 2019. The electronic submission website will be open by November 1, 2019.  Questions should be sent to the 2020 Byar Award Chair, Sheng Luo, at sheng.luo at duke.edu.

The 2020 Awards Committee is composed of the 2020 current and past Section Chairs and Chair-Elect as well as additional individuals to be appointed by the Section Chairs prior to the competition. 

For the 2020 competition, the Byar Award and travel award winners will be announced by January 15, 2020.  Winners should contact the 2020 Section JSM Program Chair, Samrachana Adhikari (samrachana.adhikari at nyulangone.org), and submit a Topic Contributed abstract for JSM 2020. 

Information regarding this award is also available on the Section webpage at

 https://community.amstat.org/biometricsbiom/award/byaraward/2020byaraward.

Strategic Initivatives Grant:

The ASA Biometrics Section provides funding to support projects developing innovative outreach projects focused on enhancing awareness of biostatistics among quantitatively talented US students. We particularly are interested in projects that will encourage students to pursue advanced training in biostatistics. The project timeline is typically 1.5-2 years, and the Biometrics section provides support up to $3000. Award recipients must be an ASA member and Biometrics section member before project initiation.

The Strategic Initiatives Subcommittee Chair is Tanya Garcia and Co-Chair is Milan Bimali.

Please find more details of the application here: 

https://community.amstat.org/biometricsbiom/award/sig

Feel free to fill out either format. The application should be submitted electronically to the Committee Chair Tanya Garcia at tpgarcia at stat.tamu.edu.

September 2019 Newsletter

Call for Proposals: Developing the next generation of biostatisticians

The ASA Biometrics Section invites applications for funding to support projects developing innovative outreach projects focused on enhancing awareness of biostatistics among quantitatively talented US students. We particularly are interested in projects that will encourage students to pursue advanced training in biostatistics. We anticipate funding one project this year, with total funding of up to $3,000. The project timeline would be from 1.5-2 years. All investigators are encouraged to apply. Award recipients must be an ASA member and Biometrics section member before project initiation.

A three-page application is due by November 15, 2019, and should be in the following format: Title, Objectives and Specific Aims; Background, Significance, and/or Rationale; Design and Methods; Deliverables/Products, and Budget. The following types of expenditures are allowed: supplies, domestic travel (when necessary to carry out the project), professional expertise (e.g., instructional designer or webmaster) and cost of computer time. The following types of expenditures are not allowed: secretarial/administrative personnel, tuition, foreign travel, and honoraria and travel expenses for visiting lecturers to the investigator's home institution. A project period with a start date no earlier than January 1, 2020 and an end date no later than December 31, 2021 also should be specified.

Applications should be submitted electronically to the Strategic Initiatives Subcommittee Chair, Tanya Garcia at tpgarcia@stat.tamu.edu. All investigators will be expected to submit a brief report at the conclusion of the project to the Subcommittee Chair. Questions should be addressed either to the Subcommittee Chair or to the Subcommittee Co-Chair, Milan Bimali, at MBimali@uams.edu


June/July 2019 Newsletter

Below please find the following information:
JSM 2019 Topic contributed paper sessions sponsored by the Biometrics Section
JSM 2019 Invited sessions sponsored by the Biometrics Section
JSM 2019 Courses cosponsored by the Biometrics Section
ENAR 2020 invited session proposal

Topic contributed paper sessions sponsored by the Biometrics Section:

284 Tue, 7/30/2019, 8:30 AM - 10:20 AM CC-101 ASA Biometrics Section JSM Travel Awards (II) — Topic Contributed Papers Biometrics Section Organizer(s): Rebecca Hubbard, University of Pennsylvania Chair(s): Elizabeth Ogburn, Johns Hopkins Bloomberg School of Public Health 8:35 AM Integrated Principal Components Analysis Tiffany M Tang, University of California at Berkeley; Genevera Allen, Rice University 8:55 AM Are Clusterings of Multiple Data Views Independent? Lucy Gao, University of Washington; Daniela Witten, University of Washington; Jacob Bien, University of Southern California 9:15 AM High-dimensional Log-Error-in-Variable Regression with Applications to Microbial Compositional Data Analysis Pixu Shi, University of Wisconsin-Madison; Yuchen Zhou, University of Wisconsin-Madison; Anru Zhang, University of Wisconsin-Madison 9:35 AM A Spatial Bayesian Modeling Approach for Cortical Surface fMRI Data Analysis Amanda Mejia, IU; Yu Yue, The City University of New York; David Bolin, University of Gothenburg; Finn Lindgren, University of Edinburgh; Martin Lindquist, Johns Hopkins University 9:55 AM Tailored Optimal Post-Treatment Surveillance for Cancer Recurrence Rui Chen, UW-Madison Dept. of Statistics 10:15 AM Floor Discussion   

393 Tue, 7/30/2019, 2:00 PM - 3:50 PM CC-111 ASA Biometrics Section JSM Travel Awards (I) — Topic Contributed Papers Biometrics Section Organizer(s): Rebecca Hubbard, University of Pennsylvania Chair(s): Sheng Luo, Duke University Medical Center 2:05 PM Propensity Score Weighting for Causal Inference with Multiple Treatments Fan Li, Duke University; Fan Li, Department of Statistical Science, Duke University 2:25 PM Triplet Matching for Estimating Causal Effects with Three Treatment Arms and Extensions Giovanni Nattino, The Ohio State University; Bo Lu, The Ohio State University; Junxin Shi, The Research Institute of Nationwide Children's Hospital; Stanley Lemeshow, Ohio State University; Henry Xiang, The Research Institute of Nationwide Children's Hospital 2:45 PM Causal Isotonic Regression Ted Westling, University of Massachusetts Amherst; Marco Carone, University of Washington; Peter Gilbert, Fred Hutchinson Cancer Research Center 3:05 PM Stage-Wise Synthesis of Randomized Trials for Optimizing Dynamic Treatment Regimes Yuan Chen, Columbia University Mailman School of Public Health, Department of Biostatistics; Yuanjia Wang, Columbia University; Donglin Zeng, UNC Chapel Hill 3:25 PM Discussant: Rebecca Hubbard, University of Pennsylvania 3:45 PM Floor Discussion.

Invited sessions sponsored by the Biometrics Section:

48 * ! Sun, 7/28/2019, 4:00 PM - 5:50 PM CC-104 New Frontiers in High Dimensional and Complex Data Analyses — Invited Papers Biometrics Section, International Chinese Statistical Association, Section on Nonparametric Statistics Organizer(s): Yichuan Zhao, Georgia State University Chair(s): Lexin Li, University of California at Berkeley 4:05 PM Statistical Inference for High-Dimensional Models via Recursive Online-Score Estimation Runze Li, Penn State University 4:30 PM Dimension Reduction for High Dimensional Censored Data Shanshan Ding, University of Delaware; Wei Qian, University of Delaware; Lan Wang, University of Minnesota 4:55 PM Network Response Regression for Modeling Population of Networks with Covariates Emma Jingfei Zhang, University of Miami; Will Wei Sun, University of Miami; Lexin Li, University of California at Berkeley 5:20 PM Penalized Empirical Likelihood for the Sparse Cox Model Dongliang Wang, SUNY Upstate Medical University; Tong Tong Wu, University of Rochester; Yichuan Zhao, Georgia State University 5:45 PM Floor Discussion   

216 * ! Mon, 7/29/2019, 2:00 PM - 3:50 PM CC-704 Promises and Pitfalls of Making Decisions with Real World Data — Invited Papers Biometrics Section, ENAR, Health Policy Statistics Section Organizer(s): Yuanjia Wang, Columbia University Chair(s): Ying Liu, Medical College of Wisconsin 2:05 PM A Decision Theoretic Approach to Pre-emptive Genotyping Jonathan Schildcrout, Vanderbilt University Medical Center 2:25 PM Data Enriched Regression via Generalized Linear Models Ying Qing Chen, Fred Hutchinson Cancer Research Center; Sayan Dasgupta, Fred Hutchinson Cancer Research Center; Cheng Zheng, University of Wisconsin at Milwakee; Yuxiang Xie, University of Washington 2:45 PM Integrative Analysis of Multivariate Temporal Biomarkers in Electronic Health Records Donglin Zeng, UNC Chapel Hill 3:05 PM Learning Treatment Strategies from Randomized Trials Supplemented by Information in Electronic Health Records Yuanjia Wang, Columbia University 3:25 PM Risk Assessment with Imprecise EHR Data Tianxi Cai, Harvard University 3:45 PM Floor Discussion   

322 ! Tue, 7/30/2019, 10:30 AM - 12:20 PM CC-106 Time-to-event Models in Complex Observational Studies — Invited Papers Biometrics Section, ENAR, Biopharmaceutical Section Organizer(s): Soutrik Mandal, National Cancer Institute Chair(s): Ana Maria Ortega-Villa, National Institutes of Health 10:35 AM A Copula Model Approach for Regression Analysis of Informatively Interval-censored Failure Time Data (Tony) Jianguo Sun, University of Missouri 11:00 AM Validating risk prediction models with sub-samples of cohorts Ruth Pfeiffer, National Cancer Institute; Mitchell Henry Gail, National Cancer Institute, Division of Cancer Epidemiology and Genetics; Yei Eun Shin, National Cancer Institute 11:25 AM Cure Rate Frailty Models for Clustered Current Status Data with Informative Cluster Size Kejun He, Renmin University; Wei Ma, Renmin University; Tong Wang, Texas A&M University; Dipankar Bandyopadhyay, Virginia Commonwealth University; Samiran Sinha, Texas A&M University 11:50 AM Goodness-of-fit Tests for the Linear Transformation Models with Interval-censored Data Soutrik Mandal, National Cancer Institute; Suojin Wang, Texas A&M University; Samiran Sinha, Texas A&M University 12:15 PM Floor Discussion   

446 * ! Wed, 7/31/2019, 8:30 AM - 10:20 AM CC-101 New Statistical Methods in Evolutionary Biology — Invited Papers Biometrics Section, International Indian Statistical Association, WNAR Organizer(s): Arindam RoyChoudhury, Cornell University Chair(s): Arindam RoyChoudhury, Cornell University 8:35 AM Shannon information collapse for phylogenetic experimental design Jeffrey Peter Townsend, Yale University 9:00 AM Inferring tumor phylogenies using single-cell sequencing data Jing Peng, The Ohio State University; Laura Kubatko, The Ohio State University; Yuan Gao, The Ohio State University 9:25 AM Neutrality test on evolutionary tree topologies: Where statistics, physics, and geometric analysis meet Dan D. Erdmann-Pham, University of California, Berkeley; Yun S. Song, University of California, Berkeley; Jonathan Terhorst, University of Michigan 9:50 AM Discussant: Marc Suchard, UCLA 10:15 AM Floor Discussion   

592 * ! Thu, 8/1/2019, 8:30 AM - 10:20 AM CC-702 Evaluating Impact in Networks: Causal Inference with Interference — Invited Papers Biometrics Section, Section on Statistics in Epidemiology, ENAR Organizer(s): Michael Hudgens, University of North Carolina at Chapel Hill Chair(s): Michael Hudgens, University of North Carolina at Chapel Hill 8:35 AM Individualistic Effects in Randomized Trials Under Contagion Olga Morozova, Yale School of Public Health; Daniel Eck, Yale School of Public Health; Forrest W Crawford, Yale School of Public Health 8:55 AM Matching methods for networked causal inference Alexander Volfovsky, Duke University 9:15 AM Causal inference with misspecified exposure mappings Fredrik Sävje, Yale University 9:35 AM Auto-G-Computation of Causal Effects on a Network Eric Tchetgen Tchetgen, University of Pennsylvania 9:55 AM Discussant: Dean Eckles, MIT 10:15 AM Floor Discussion  

Courses cosponsored by the Biometrics Section: Saturday, July 27 CE_02C8:30 a.m. - 5:00 p.m.Reproducible ComputingInstructor(s): Colin RundelSponsor: Biometrics SectionSuccess in statistics and data science is dependent on the development of both analytical and computational skills. This workshop will cover:- Recognizing the problems that reproducible research helps address.- Identifying pain points in getting your analysis to be reproducible.- The role of documentation, sharing, version control, automation, and organization in making your research more reproducible.- Introducing tools to solve these problems, specifically R, RStudio, RMarkdown, git, GitHub, and make.- Strategies for scaling these tools and methods for larger more complex projects.Workshop attendees will work through several exercises and get first-hand experience with using relevant tool-chains and techniques, including R/RStudio, literate programming with R Markdown, automation with make, and collaboration and version control with git/GitHub. CE_07C8:00 a.m. - 12:00 p.m.Statistical and Computational Methods for Microbiome and Metagenomics Data AnalysisInstructor(s): Curtis Huttenhower and Hongzhe LeeSponsor: Biometrics SectionHigh throughput sequencing technologies enable individualized characterization of the microbiome composition and functions.  The  human microbiome, defined as community of microbes in and on the human body, impacts human health and risk of disease by dynamically interacting with host diet, genetics, metabolism and environment. The resulting data can potentially be used for personalized diagnostic assessment, risk stratification, disease prevention and treatment.  Microbiome has become one of the most active areas of research in biomedical sciences. New computational and statistical methods are being developed to understand the function of microbial communities.  In this short course,  we will give detailed presentations on the statistical and computational methods for measuring various important features of the microbiome  based on 16S rRNA and shotgun metagenomic sequencing data, and how these features are used as an outcome of an intervention, as a mediator of a treatment and as a covariate to be controlled for when studying disease/exposure associations.  The statistics underlying some of the most popular tools in microbiome data analysis will be presented, including bioBakery tools for meta'omic profiling and tools for microbial community profiling (MetaPhlAn, HUMAnN, Data2, DEMIC, etc), together with advanced methods for compositional data analysis and kernel-based association analysis. Sunday, July 28 CE_09C8:30 a.m. - 5:00 p.m.Regression Modeling StrategiesInstructor(s): Frank HarrellSponsor: Biometrics SectionAll standard regression models have assumptions that must be verified for the model to have power to test hypotheses and for it to be able to predict accurately. Of the principal assumptions (linearity, additivity, distributional), this course will emphasize methods for assessing and satisfying the first two. Practical but powerful tools are presented for validating model assumptions and presenting model results. This course provides methods for estimating the shape of the relationship between predictors and response using the widely applicable method of augmenting the design matrix using restricted cubic splines. Even when assumptions are satisfied, overfitting can ruin a model’s predictive ability for future observations. Methods for data reduction will be introduced to deal with the common case where the number of potential predictors is large in comparison with the number of observations. Methods of model validation (bootstrap and cross–validation) will be covered, as will auxiliary topics such as modeling interaction surfaces, variable selection, overly influential observations, collinearity, and shrinkage, and a brief introduction to the R rms package for handling these problems. The methods covered will apply to almost any regression model, including ordinary least squares, logistic regression models, ordinal regression, quantile regression, longitudinal data analysis, and survival models. CE_12C1:00 p.m. - 5:00 p.m.Functional Data Analysis for Wearables: Methods and ApplicationsInstructor(s): Vadim Zipunnikov and Jeff GoldsmithSponsor: Biometrics SectionTechnological advances have made many wearable devices available for use in large epidemiological cohorts, national biobanks, and clinical studies. This opens up a tremendous opportunity for clinical and public health researchers to unveil previously hidden but pivotal physiological and behavioral signatures and relate them to disability and disease. Therefore, understanding, interpreta- tion and analysis of complex multimodal and multilevel data produced by such devices becomes crucial.The main goal of this workshop is to present an overview of the functional data analysis methods for modeling physical activity data, review their strengths and limitations, and demonstrate their implementation in R packages refund and mgcv. We will also examine several non-functional approaches for extracting informative and interpretable features from wearable data. We will discuss applications in epidemiological studies such as Head Start Program and National Health and Nutrition Examination Survey and a clinical study of Congestive Heart Failure. Tuesday, July 30 CE_21C8:00 a.m. - 12:00 p.m.Measuring the Impact of Nonignorable Missing DataInstructor(s): Daniel Heitjan and Hui XieSponsor: Biometrics SectionThe popular but typically unverifiable assumption of ignorability greatly simplifies analyses with incomplete data, both conceptually and computationally. We say that missingness is ignorable when the probability that an observation is missing depends only on fully observed information, and nonignorable when the probability that an observation is missing depends on the value of the observation, even after conditioning on available design variables and covariates. For example, in a clinical trial the data are plausibly nonignorably missing when the subjects who drop out are those for whom the drug is either ineffective or excessively toxic. The possibility that the missing observations in a study are the result of a nonignorable mechanism casts doubt on the validity of conclusions based on the assumption of ignorability. Unfortunately, it is generally impossible to robustly assess the validity of this assumption with just the data at hand. One way to address this problem is to conduct a local sensitivity analysis: Essentially, re-compute estimated parameters of interest under models that slightly violate the assumption of ignorability. If the parameters change only modestly under violation of the assumption, then it is safe to proceed with an ignorable model. If they change drastically, then a simple ignorable analysis is of questionable validity. To conduct such a sensitivity analysis in a systematic and efficient way, we have developed a measure that we call the index of local sensitivity to nonignorability (ISNI), which evaluates the rate of change of parameter estimates in the neighborhood of an ignorable model. Computation of ISNI is straightforward and avoids the need to estimate a nonignorable model or to posit a specific magnitude of nonignorability. We have developed a suite of statistical methods for ISNI analysis, now implemented in an R package named isni. In this half-day short course we will describe these methods and train users to apply them to inform evaluations of the reliability of empirical findings when data are incomplete. CE_24C8:30 a.m. - 5:00 p.m.An Introduction to the Joint Modeling of Longitudinal and Survival Data, with Applications in RInstructor(s): Dimitris RizopoulosSponsor: Biometrics SectionIn follow-up studies, different types of outcomes are typically collected for each subject. These include longitudinally measured responses (e.g., biomarkers), and the time until an event of interest occurs (e.g., death, dropout). Often these outcomes are separately analyzed, but on many occasions, it is of scientific interest to study their association. This type of research question has given rise in the class of joint models for longitudinal and time-to-event data. These models constitute an attractive paradigm for the analysis of follow-up data that is mainly applicable in two settings: First, when focus is on a survival outcome, and we wish to account for the effect of endogenous time-dependent covariates measured with error, and second, when focus is on the longitudinal outcome and we wish to correct for non-random dropout. This full-day course is aimed at applied researchers and graduate students and will provide a comprehensive introduction to this modeling framework. We will explain when these models should be used in practice, which are the key assumptions behind them, and how they can be utilized to extract relevant information from the data. Emphasis is given on applications, and after the end of the course, participants will be able to define appropriate joint models to answer their questions of interest.*Necessary background for the course*: This course assumes knowledge of basic statistical concepts, such as standard statistical inference using maximum likelihood, and regression models. Also, basic knowledge of R would be beneficial but is not required. Participants are required to bring their laptop with the battery fully charged. Before the course instructions will be sent for installing the required software. CE_26C1:00 p.m. - 5:00 p.m.Adaptive treatment strategies: An introduction to statistical approaches for estimationInstructor(s): Erica MoodieSponsor: Biometrics SectionEvidence-based medicine relies on using data to provide recommendations for effective treatment decisions. However, in many settings, response is heterogeneous across patients. Patient response may also vary over time, and physicians are faced with the daunting task of making sequential therapeutic decisions having seen few patients with a given clinical history.Adaptive treatment strategies (ATS) operationalize the sequential decision-making process in the precision medicine paradigm, offering statisticians principled estimation tools that can be used to incorporate patient’s characteristics into a clinical decision-making framework so as to adapt the type, dosage or timing of treatment according to patients’ evolving needs.This half-day course will provide an overview of precision medicine from the statistical perspective. We will begin with a discussion of relevant data sources. We will then turn our attention to estimation, and consider multiple approaches – and their relative strengths and weaknesses – to estimating tailored treatment rules in a one-stage setting. Next, we will consider the multi-stage setting and inferential challenges in this area. Relevant clinical examples will be discussed, as well available software tools. 

ENAR 2020 invited session proposalThe Program Committee for the 2020 ENAR Spring Meeting (March 22-25, Nashville, TN) is soliciting formal proposals for invited paper sessions. Proposals on topics that have broad potential scientific impact are particularly encouraged. The submission deadline is June 15, 2019 at 11:59 pm EDT. The invited session proposals will be selected by the program committee.Please formally submit your invited session proposal by clicking the following link:ENAR 2020 Invited Session Proposals https://forms.gle/fdZEmF36J9W2HFbF7 ENAR 2020 Invited Session Proposal Formforms.glePlease provide details of your session in the form below. Use navigation buttons provided on this form and not the browser back button in order to avoid loss of data. Please contact the Program Chair, Juned Siddique, at siddique@northwestern.edu or Associate Chair, Chenguang Wang at cwang68@jhmi.edu, for any queries.
Concise, self-contained proposals with confirmed speakers and talk abstracts have a much better chance of being accepted!All invited sessions are scheduled for 105 minutes. We will consider different formats including a session with 4 speakers, a session with 3 speakers plus a discussant, or a panel discussion. Each participant may be a speaker/panelist in at most one invited or contributed session.Please contact the Program Chair, Juned Siddique (siddique@northwestern.edu) or the Biometrics section representative, Zheyu Wang (wangzy@jhu.edu) if you have any questions.


May 2019 Newsletter

Here are the courses cosponsored by the Biometrics Section that have been selected for JSM 2019:

Saturday, July 27 

CE_02C

8:30 a.m. - 5:00 p.m.

Reproducible Computing

Instructor(s): Colin Rundel

Sponsor: Biometrics Section

Success in statistics and data science is dependent on the development of both analytical and computational skills. This workshop will cover:

- Recognizing the problems that reproducible research helps address.

- Identifying pain points in getting your analysis to be reproducible.

- The role of documentation, sharing, version control, automation, and organization in making your research more reproducible.

- Introducing tools to solve these problems, specifically R, RStudio, RMarkdown, git, GitHub, and make.

- Strategies for scaling these tools and methods for larger more complex projects.

Workshop attendees will work through several exercises and get first-hand experience with using relevant tool-chains and techniques, including R/RStudio, literate programming with R Markdown, automation with make, and collaboration and version control with git/GitHub.

CE_07C

8:00 a.m. - 12:00 p.m.

Statistical and Computational Methods for Microbiome and Metagenomics Data Analysis

Instructor(s): Curtis Huttenhower and Hongzhe Lee

Sponsor: Biometrics Section

High throughput sequencing technologies enable individualized characterization of the microbiome composition and functions.  The  human microbiome, defined as community of microbes in and on the human body, impacts human health and risk of disease by dynamically interacting with host diet, genetics, metabolism and environment. The resulting data can potentially be used for personalized diagnostic assessment, risk stratification, disease prevention and treatment.  Microbiome has become one of the most active areas of research in biomedical sciences. New computational and statistical methods are being developed to understand the function of microbial communities.  In this short course,  we will give detailed presentations on the statistical and computational methods for measuring various important features of the microbiome  based on 16S rRNA and shotgun metagenomic sequencing data, and how these features are used as an outcome of an intervention, as a mediator of a treatment and as a covariate to be controlled for when studying disease/exposure associations.  The statistics underlying some of the most popular tools in microbiome data analysis will be presented, including bioBakery tools for meta'omic profiling and tools for microbial community profiling (MetaPhlAn, HUMAnN, Data2, DEMIC, etc), together with advanced methods for compositional data analysis and kernel-based association analysis.

Sunday, July 28

CE_09C

8:30 a.m. - 5:00 p.m.

Regression Modeling Strategies

Instructor(s): Frank Harrell

Sponsor: Biometrics Section

All standard regression models have assumptions that must be verified for the model to have power to test hypotheses and for it to be able to predict accurately. Of the principal assumptions (linearity, additivity, distributional), this course will emphasize methods for assessing and satisfying the first two. Practical but powerful tools are presented for validating model assumptions and presenting model results. This course provides methods for estimating the shape of the relationship between predictors and response using the widely applicable method of augmenting the design matrix using restricted cubic splines. Even when assumptions are satisfied, overfitting can ruin a model’s predictive ability for future observations. Methods for data reduction will be introduced to deal with the common case where the number of potential predictors is large in comparison with the number of observations. Methods of model validation (bootstrap and cross–validation) will be covered, as will auxiliary topics such as modeling interaction surfaces, variable selection, overly influential observations, collinearity, and shrinkage, and a brief introduction to the R rms package for handling these problems. The methods covered will apply to almost any regression model, including ordinary least squares, logistic regression models, ordinal regression, quantile regression, longitudinal data analysis, and survival models.

CE_12C

1:00 p.m. - 5:00 p.m.

Functional Data Analysis for Wearables: Methods and Applications

Instructor(s): Vadim Zipunnikov and Jeff Goldsmith

Sponsor: Biometrics Section

Technological advances have made many wearable devices available for use in large epidemiological cohorts, national biobanks, and clinical studies. This opens up a tremendous opportunity for clinical and public health researchers to unveil previously hidden but pivotal physiological and behavioral signatures and relate them to disability and disease. Therefore, understanding, interpreta- tion and analysis of complex multimodal and multilevel data produced by such devices becomes crucial.

The main goal of this workshop is to present an overview of the functional data analysis methods for modeling physical activity data, review their strengths and limitations, and demonstrate their implementation in R packages refund and mgcv. We will also examine several non-functional approaches for extracting informative and interpretable features from wearable data. We will discuss applications in epidemiological studies such as Head Start Program and National Health and Nutrition Examination Survey and a clinical study of Congestive Heart Failure.

Tuesday, July 30

CE_21C

8:00 a.m. - 12:00 p.m.

Measuring the Impact of Nonignorable Missing Data

Instructor(s): Daniel Heitjan and Hui Xie

Sponsor: Biometrics Section

The popular but typically unverifiable assumption of ignorability greatly simplifies analyses with incomplete data, both conceptually and computationally. We say that missingness is ignorable when the probability that an observation is missing depends only on fully observed information, and nonignorable when the probability that an observation is missing depends on the value of the observation, even after conditioning on available design variables and covariates. For example, in a clinical trial the data are plausibly nonignorably missing when the subjects who drop out are those for whom the drug is either ineffective or excessively toxic. The possibility that the missing observations in a study are the result of a nonignorable mechanism casts doubt on the validity of conclusions based on the assumption of ignorability. Unfortunately, it is generally impossible to robustly assess the validity of this assumption with just the data at hand. One way to address this problem is to conduct a local sensitivity analysis: Essentially, re-compute estimated parameters of interest under models that slightly violate the assumption of ignorability. If the parameters change only modestly under violation of the assumption, then it is safe to proceed with an ignorable model. If they change drastically, then a simple ignorable analysis is of questionable validity. To conduct such a sensitivity analysis in a systematic and efficient way, we have developed a measure that we call the index of local sensitivity to nonignorability (ISNI), which evaluates the rate of change of parameter estimates in the neighborhood of an ignorable model. Computation of ISNI is straightforward and avoids the need to estimate a nonignorable model or to posit a specific magnitude of nonignorability. We have developed a suite of statistical methods for ISNI analysis, now implemented in an R package named isni. In this half-day short course we will describe these methods and train users to apply them to inform evaluations of the reliability of empirical findings when data are incomplete.

CE_24C

8:30 a.m. - 5:00 p.m.

An Introduction to the Joint Modeling of Longitudinal and Survival Data, with Applications in R

Instructor(s): Dimitris Rizopoulos

Sponsor: Biometrics Section

In follow-up studies, different types of outcomes are typically collected for each subject. These include longitudinally measured responses (e.g., biomarkers), and the time until an event of interest occurs (e.g., death, dropout). Often these outcomes are separately analyzed, but on many occasions, it is of scientific interest to study their association. This type of research question has given rise in the class of joint models for longitudinal and time-to-event data. These models constitute an attractive paradigm for the analysis of follow-up data that is mainly applicable in two settings: First, when focus is on a survival outcome, and we wish to account for the effect of endogenous time-dependent covariates measured with error, and second, when focus is on the longitudinal outcome and we wish to correct for non-random dropout. This full-day course is aimed at applied researchers and graduate students and will provide a comprehensive introduction to this modeling framework. We will explain when these models should be used in practice, which are the key assumptions behind them, and how they can be utilized to extract relevant information from the data. Emphasis is given on applications, and after the end of the course, participants will be able to define appropriate joint models to answer their questions of interest.

*Necessary background for the course*: This course assumes knowledge of basic statistical concepts, such as standard statistical inference using maximum likelihood, and regression models. Also, basic knowledge of R would be beneficial but is not required. Participants are required to bring their laptop with the battery fully charged. Before the course instructions will be sent for installing the required software.

CE_26C

1:00 p.m. - 5:00 p.m.

Adaptive treatment strategies: An introduction to statistical approaches for estimation

Instructor(s): Erica Moodie

Sponsor: Biometrics Section

Evidence-based medicine relies on using data to provide recommendations for effective treatment decisions. However, in many settings, response is heterogeneous across patients. Patient response may also vary over time, and physicians are faced with the daunting task of making sequential therapeutic decisions having seen few patients with a given clinical history.

Adaptive treatment strategies (ATS) operationalize the sequential decision-making process in the precision medicine paradigm, offering statisticians principled estimation tools that can be used to incorporate patient’s characteristics into a clinical decision-making framework so as to adapt the type, dosage or timing of treatment according to patients’ evolving needs.

This half-day course will provide an overview of precision medicine from the statistical perspective. We will begin with a discussion of relevant data sources. We will then turn our attention to estimation, and consider multiple approaches – and their relative strengths and weaknesses – to estimating tailored treatment rules in a one-stage setting. Next, we will consider the multi-stage setting and inferential challenges in this area. Relevant clinical examples will be discussed, as well available software tools.


March/April 2019 Newsletter

Call for Proposals: Developing the next generation of Biostatisticians

The ASA Biometrics Section invites applications for funding to support projects developing innovative outreach projects focused on enhancing awareness of biostatistics among quantitatively talented US students. We particularly are interested in projects that will encourage students to pursue advanced training in biostatistics. We anticipate funding one project this year, with total funding of up to $3,000. The project timeline would be from 1.5-2 years. All investigators are encouraged to apply. Award recipients must be an ASA member and Biometrics section member before project initiation.

A three-page application is due by May 31, 2019, and should be in the following format: Title, Objectives and Specific Aims; Background, Significance, and/or Rationale; Design and Methods; Deliverables/Products, and Budget. The following types of expenditures are allowed: supplies, domestic travel (when necessary to carry out the project), professional expertise (e.g., instructional designer or webmaster) and cost of computer time. The following types of expenditures are not allowed: secretarial/administrative personnel, tuition, foreign travel, and honoraria and travel expenses for visiting lecturers to the investigator's home institution. A project period with a start date no earlier than July 1, 2019 and an end date no later than June 30, 2020 also should be specified.

Applications should be submitted electronically to the Strategic Initiatives Subcommittee Chair, Tanya Garcia at tpgarcia@stat.tamu.edu. All investigators will be expected to submit a brief report at the conclusion of the project to the Subcommittee Chair. Questions should be addressed either to the Subcommittee Chair or to the Subcommittee Co-Chair, Milan Bimali, at MBimali@uams.edu