(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>