This is a reminder that the deadline for submission to this special
issue is in two weeks.
Best,
Jun Yan
On Mon, Jun 27, 2022 at 7:39 AM Jun Yan <
jyan.statistics@gmail.com> wrote:
>
> (Apologies for cross-postings)
>
> Dear Colleagues, the deadline of this call for contributions has been
> extended to October 31, 2022. Attached is a pdf flyer for ease of
> sharing with people who might be interested.
>
> Best,
> Jun Yan
>
>
> On Fri, Feb 11, 2022 at 12:50 PM Jun Yan <
jyan.statistics@gmail.com> wrote:
> >
> > Deadline: June 30, 2022
> >
> > Journal of Data Science (http://jds-online.org) invites submissions to
> > a special issue on "Data Science Approaches to Vaccine Effect
> > Analysis."
> >
> > Vaccine is an essential tool for stopping the spread of COVID-19.
> > Evaluation of the effectiveness of the vaccines is important for the
> > success of vaccines. Vaccine effect analysis consists of assessing the
> > efficacy of vaccines in clinical trials and the effectiveness of
> > vaccines under real world conditions. The Centers for Disease Control
> > and Prevention (CDC)'s guidance on vaccine effectiveness analysis
> > include (1) analysis for specific subpopulations, (2) reducing the
> > risk of infection, (3) protection against mild COVID-19 illness, (4)
> > preventing hospitalization, (5) reducing the spread of illness, (6)
> > assessing duration of protection, (7) evaluating the impact of the
> > virus new variants, (8) evaluating the effectiveness of a single dose
> > and delayed second dose, and (9) evaluating impact of population host
> > factors.
> >
> > The effect analysis may include two kinds of study designs, (1)
> > randomized clinical trials and (2) observational studies. Since the
> > infection and progression of COVID-19 are stochastic and dynamic, many
> > unknown factors may play an important role, making accurate estimation
> > of the vaccine effectiveness a great challenge in both randomized
> > clinical trials and population-based observational studies. To
> > stimulate discussions, we illustrate (incompletely) some challenges
> > and recently developed statistical and machine learning methods for
> > treatment effect estimation as follows.
> >
> > Methods for clinical trials:
> >
> > Recently, some investigators challenged the view that randomization
> > implies unconfoundedness and claims that randomization and
> > unconfoundedness are two separate concepts. We need further
> > validations on both theoretical and numerical analyses about the
> > relationships between randomization and unconfoundedness.
> >
> > The Cox proportional-hazards model is widely viewed as a causal model
> > and used for treatment effect estimation in both randomized clinical
> > trials and observational studies. However, some researchers concluded
> > that the Cox hazard ratio is not causally interpretable and the Cox
> > model still may suffer selection bias.
> >
> > Methods for observation data:
> >
> > Estimating the vaccine effectiveness in observational data raises two
> > principal challenges. First, the treatment assignment mechanism is not
> > known a priori. Therefore, there might be confounders, affecting both
> > the vaccine and protection outcomes, which lead to selection bias. The
> > second challenge is censoring. Censoring might be informative, which
> > also may lead to bias.
> >
> > Machine learning (ML) methods for estimating the effects of
> > treatments, including tree-based methods, the nonparametric Random
> > Survival Forest, Bayesian Additive regression trees, Gaussian
> > processes and, in particular, neural networks have grown rapidly. To
> > address the challenges of selection and censoring biases,
> > counterfactual survival analysis has been developed to incorporate
> > censoring and balanced representation for individual treatment effect
> > (ITE) prediction. These methods can be applied to COVID-19 vaccine
> > effect analysis and their merits and limitations need to be
> > investigated.
> >
> > To overcome the bias of Cox proportional-hazards model, existing
> > methods for estimating treatment-specific hazard functions are able to
> > address the challenges due to the potential presence of multiple
> > sources of covariate shift: (i) non-randomized treatment assignment
> > (confounding), (ii) informative censoring, and (iii) event-induced
> > covariate shift.
> >
> > The effect of the vaccine may depend on the true patient status. The
> > true patient status, however, is unknown and hidden. In addition, the
> > confounding variables that affect both the potential outcomes and
> > treatments are also hidden. The variational auto-encoder (VAE) that
> > models the hidden variables can be integrated with the Cox model for
> > unbiased treatment effect estimation. VAE-Cox model can be explored
> > for COVID-19 vaccine effect analysis.
> >
> > We welcome original research articles (methods or computing), case
> > studies, tutorials, reviews, and perspective articles. The formats are
> > flexible. For example, short opinion or commentary articles are
> > welcome regarding misleading discoveries in the literature and
> > potential misuses of statistical or data science methods. Such
> > articles may be as impactful as full-length research articles.
> >
> > Since 2003, Journal of Data Science has published research works on a
> > wide range of topics that involve understanding and making effective
> > use of field data. The journal has been reformed since July 2020 to
> > better serve the data science community in the era of data science.
> > Attractive features of the journal are completely free access, fast
> > review, and reproducible data science. We look forward to your
> > submissions.
> >
> > Guest Editors:
> >
> > Wenjiang Fu, Department of Mathematics, University of Houston
> > Annie Qu, Department of Statistics, University of California, Irvine
> > Momiao Xiong, Department of Biostatistics and Data Science, University
> > of Texas Health Science Center at Houston</
jyan.statistics@gmail.com></
jyan.statistics@gmail.com>