Registration is now open for the following Webinar sponsored by the Mental Health Statistics Section:
Title: Small Data, N-of-1 Trials, and Personalized Medicine
Presenters: Naihua Duan (Columbia University) and Richard Kravitz (UC Davis)
Date and Time: Tuesday, December 5, 2017, 12:00 p.m. – 2:00 p.m. Eastern time
Sponsor: Mental Health Statistics Section
Registration Deadline: Friday, December 1, at 12:00 p.m. Eastern time
Description:
Advances in biomedical science have led to rapid replacement of "one size fits all" therapeutic strategies by a more individualized approach. While big data analytics have been deployed extensively in recent years for applications in personalized medicine, small data studies such as N-of-1 trials have the potential to further advance the methodological underpinnings of personalized medicine.
In this webinar, we will discuss the conceptual framework for small data studies, and the design and implementation of N-of-1 trials – multiple cross-over trials within individual patients to inform each individual's own clinical or lifestyle decision-making. Broad applications of small data studies, including N-of-1 trials, have become feasible in recent years with advances in personal communication and information technologies. It is timely for the biostatistics community to begin to serve the needs of savvy consumers wanting to take an active role in enhancing their own health, as demonstrated in the extensive practice of self-tracking and self-experimentation among members of QuantifiedSelf.com.
This is an emerging area in which biostatisticians can help transform health care delivery by supplementing the traditional "top down" organization of knowledge production and deployment with a new "bottom up" paradigm, in which biostatistical methods are applied directly in day-to-day clinical care and lifestyle decisions for individual patients. This new paradigm may in turn expand the constituency for biostatistics, empowering and engaging the lay population to participate actively and directly in the practice of creating and harvesting small data using personalized biostatistics tools and apps.
Each registration is allowed one web connection. Sound is received via audio streaming from your computer's speakers. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).
For additional details and to register, go to http://www.amstat.org/education/weblectures/index.cfm.
Registration Fees:
Member of the Mental Health Statistics Section: $60
ASA Member: $90
Nonmember: $110
Also, you can still register for the following webinar taking place next Thursday:
Title: Network Analysis in Cross-sectional Data Using R
Presenter: Eiko Fried
Date and Time: Thursday, October 19, 2017, 12:00 p.m. – 2:00 p.m. Eastern time
Sponsor: Mental Health Statistics Section
Registration Deadline: Tuesday, October 17, at 12:00 p.m. Eastern time
Description:
Analysis of mental health data is usually based on sum-scores of symptoms or the estimation of factor models. Both types of analyses disregard direct associations among symptoms that are well-understood in clinical practice: mental disorders can be conceptualized as vicious circles of problems that are hard to escape. A novel research framework, the network perspective on psychopathology, understands mental disorders as complex networks of interacting symptoms. Despite its comparably recent debut, with conceptual foundations in 2008 and empirical foundations in 2010, the framework has received considerable attention and recognition in recent years.
In this webinar, we will use R to learn about (1) network estimation, (2) network inference, and (3) network stability in cross-sectional data. Regarding network estimation, the state-of-the-art network model for cross-sectional data is the pairwise Markov Random Field or regularized partial correlation network that estimates the conditional dependence relations among items. We will learn to estimate appropriate network models for our data: the Ising Model for binary data, and the Gaussian Graphical Model for metric data. In this first section, we will also cover regularization methods that avoid the estimation of false positive associations in networks. The second topic, network inference, covers graph theoretical measures such as centrality that allow us to interpret networks. What symptoms are most connected with other symptoms? Finally, network stability allows us to gain insight into the robustness of our networks. We conclude the webinar with advanced methods such as the statistical comparison of networks, and how to deal with ordinal and mixed data. Is it noteworthy that network analysis is not limited to psychopathology data, but has been employed to study other psychological constructs such as intelligence, personality traits, and attitudes.
Each registration is allowed one web connection. Sound is received via audio streaming from your computer's speakers. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).
For additional details and to register, go to http://www.amstat.org/education/weblectures/index.cfm.
Registration Fees:
Member of the Mental Health Statistics Section: $60
ASA Member: $90
Nonmember: $110
Other upcoming MHS sponsored ASA webinars:
- 1/17/18, 12-2pm EST: Danny Almirall will be comparing embedded adaptive interventions in a SMART based on a longitudinal outcome.
- 3/21/18, 12-2pm EST: Don Hedeker will be discussing Ecological Momentary Assessment (EMA).
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Douglas Gunzler, PhD
Assistant Professor of Medicine
Case Western Reserve University
Cleveland, Ohio
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