Tuesday, Aug 4: 4:00 PM - 5:50 PM
1678
Topic-Contributed Paper Session
Thomas M. Menino Convention & Exhibition Center
Room: CC-257B
Applied
Yes
Main Sponsor
Section on Statistics in Epidemiology
Presentations
Discrete Bayesian networks (DBNs) provide a broadly useful framework for modeling dependence structures in multivariate categorical data. There is a vast literature on methods for inferring conditional probabilities and graphical structure in DBNs, but data sparsity and parametric assumptions are major practical issues. In this article, we detail a comprehensive Bayesian framework for learning DBNs. First, we propose a hierarchical prior for the conditional probabilities that enables complicated interactions between parent variables and stability in sparse regimes. We give a novel Markov chain Monte Carlo (MCMC) algorithm utilizing parallel Langevin proposals to generate exact posterior samples, avoiding the pitfalls of variational approximations. Moreover, we verify that the full conditional distribution of the concentration parameters is log-concave under mild conditions, facilitating efficient sampling. We then propose two methods for learning network structures, including parent sets, Markov blankets, and DAGs, from categorical data. The first cycles through individual edges each MCMC iteration, whereas the second updates the entire structure as a single step. We evaluate the accuracy, power, and MCMC performance of our methods on several simulation studies. Finally, we apply our methodology to uncover prognostic network structure from primary breast cancer samples.
Keywords
Directed acyclic graphs
Bayesian inference
Structure learning
Variable selection
Causal discovery
With the advent of effective pre-exposure prophylaxis agents, active-controlled HIV prevention trials have become a common study design. Nevertheless, estimating absolute efficacy relative to a placebo remains important. We introduce a novel application of proximal causal inference methods to estimate the counterfactual cumulative HIV incidence under placebo for participants in an active-controlled trial of cabotegravir, using external control data from a placebo-controlled trial with similar eligibility criteria. We leverage baseline sexually transmitted infection status and geographic region as negative control outcome and exposure variables, respectively. We address two key challenges: unmeasured differences in HIV risk between trials and statistical difficulties arising from low HIV incidence rates in both studies. To overcome these challenges, we develop two proximal inference approaches: (1) a semiparametric inverse probability of censoring weighting estimator, and (2) a two-stage regression-based strategy tailored to low-event-rate settings. Our theoretical and numerical investigations demonstrate these methods yield reliable estimates of the counterfactual one-year cumulative HIV incidence under placebo, and provide robust evidence of the superior efficacy of cabotegravir compared with placebo. These findings highlight the potential of proximal inference methods to estimate placebo-controlled effects in both single-arm and active-controlled trials by leveraging external controls.
Keywords
active-controlled trials
single-arm trials
censoring
data integration
causal inference
Speaker
Yilin Song
Co-Author(s)
Yinxiang Wu, University of Washington
Raphael Landovitz, David Geffen School of Medicine, University of California, Los Angeles
Susan Buchbinder, Bridge HIV, San Francisco Department of Public Health
Srilatha Edupuganti, Department of Medicine, Emory University
Lydia Soto-Torres, Division of AIDS, National Institute of Allergy and Infectious Diseases
Kendrick Li, St. Jude Children's Research Hospital
Xu Shi
Fei Gao, Fred Hutchinson Cancer Research Center
Deborah Donnell, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center
Holly Janes, Fred Hutchinson Cancer Research Center
Ting Ye, University of Washington
Suppose that a data analyst wishes to report the results of a least squares linear regression only if the overall null hypothesis is rejected. This practice, which I refer to as F-screening (since the overall null hypothesis is typically tested using an F-statistic), is in fact common practice across a number of applied fields. Unfortunately, it poses a problem: standard guarantees for the inferential outputs of linear regression, such as Type 1 error control of hypothesis tests and nominal coverage of confidence intervals, hold unconditionally, but fail to hold conditional on rejection of the overall null hypothesis. In this talk, I will present an inferential toolbox for the coefficients in a least squares model that are valid conditional on rejection of the overall null hypothesis. In particular, I will construct a selective p-value that leads to tests that are consistent and control the selective Type 1 error, i.e., the Type 1 error conditional on having rejected the overall null hypothesis. Furthermore, I will derive an expression for the Fisher information about the coefficients resulting from the proposed approach, and compare this to the Fisher information that results from an alternative approach that relies on sample splitting. Finally, I will investigate the proposed approach in simulation and via re-analysis of a dataset from the epidemiological literature.
Keywords
selective inference
linear models
p-values
ANOVA
Composite endpoints are frequently used in clinical trials to enhance the event rate and improve the statistical power. In the presence of a terminal event, the while-alive cumulative frequency measure offers a useful alternative to define composite survival outcomes, by relating the average event rate to the survival time. Although non-parametric methods have been proposed for two-sample comparisons, limited attention has been given to regression methods that directly address time-varying association effects in while-alive measures. We address this gap by developing a
regression framework for exposure-weighted while-alive measures for composite survival outcomes that include a terminal component event. Our regression approach uses splines to model time-varying association between covariates and a generalized while-alive loss rate of all component events, and can be applied to both independent and clustered data. We derive the asymptotic properties of the regression estimator under both independent data and cluster-correlated data settings, and study the operating characteristics of our methods through simulations. Finally, we apply our regression method to analyze data two randomized clinical trials. The proposed methods are implemented in the WAreg R package.
Speaker
Xi Fang, Yale University
Co-Author(s)
Fan Li, Yale School of Public Health
Hajime Uno, Dana-Farber Cancer Institute