19 citations · 43 across the 11 of their papers we have counts for
13 papers
Surrogacy Validation for Time-to-Event Outcomes with Illness-Death Frailty Models
Emily K. Roberts, Michael R. Elliott, Jeremy M. G. Taylor
A common practice in clinical trials is to evaluate a treatment effect on an intermediate endpoint when the true outcome of interest would be difficult or costly to measure. We con…
Variance as a predictor of health outcomes: Subject-level trajectories and variability of sex hormones to predict body fat changes in peri- and post-menopausal women
Irena Chen, Zhenke Wu, Siobán D. Harlow +3
Longitudinal biomarker data and cross-sectional outcomes are routinely collected in modern epidemiology studies, often with the goal of informing tailored early intervention decisi…
Robust Model-based Inference for Non-Probability Samples
Ali Rafei, Michael R. Elliott, Carol A. C. Flannagan
With the ubiquitous availability of unstructured data, growing attention is paid as how to adjust for selection bias in such non-probability samples. The majority of the robust est…
Robust and Efficient Bayesian Inference for Non-Probability Samples
Ali Rafei, Michael R. Elliott, Carol A. C. Flannagan
The declining response rates in probability surveys along with the widespread availability of unstructured data has led to growing research into non-probability samples. Existing r…
Incorporating baseline covariates to validate surrogate endpoints with a constant biomarker under control arm
Emily Roberts, Michael Elliott, Jeremy M. G. Taylor
A surrogate endpoint S in a clinical trial is an outcome that may be measured earlier or more easily than the true outcome of interest T. In this work, we extend causal inference a…
Robust Bayesian Inference for Big Data: Combining Sensor-based Records with Traditional Survey Data
Ali Rafei, Carol A. C. Flannagan, Brady T. West +1
Big Data often presents as massive non-probability samples. Not only is the selection mechanism often unknown, but larger data volume amplifies the relative contribution of selecti…
Using Multiple Imputation to Classify Potential Outcomes Subgroups
Yun Li, Irina Bondarenko, Michael R. Elliott +2
With medical tests becoming increasingly available, concerns about over-testing and over-treatment dramatically increase. Hence, it is important to understand the influence of test…
Deriving Priors for Bayesian Prediction of Daily Response Propensity in Responsive Survey Design: Historical Data Analysis vs. Literature Review
Brady T. West, James Wagner, Stephanie Coffey +1
Responsive Survey Design (RSD) aims to increase the efficiency of survey data collection via live monitoring of paradata and the introduction of protocol changes when survey errors…
Accounting for selection bias due to death in estimating the effect of wealth shock on cognition for the Health and Retirement Study
Yaoyuan Vincent Tan, Carol A. C. Flannagan, Lindsay R. Pool +1
The Health and Retirement Study is a longitudinal study of US adults enrolled at age 50 and older. We were interested in investigating the effect of a sudden large decline in wealt…
"Robust-squared" Imputation Models Using BART
Yaoyuan V. Tan, Carol A. C. Flannagan, Michael R. Elliott
Examples of "doubly robust" estimator for missing data include augmented inverse probability weighting (AIPWT) models (Robins et al., 1994) and penalized splines of propensity pred…
P-splines with an l1 penalty for repeated measures
Brian D. Segal, Michael R. Elliott, Thomas Braun +1
P-splines are penalized B-splines, in which finite order differences in coefficients are typically penalized with an norm. P-splines can be used for semiparametric regress…
Predicting human-driving behavior to help driverless vehicles drive: random intercept Bayesian Additive Regression Trees
Yaoyuan Vincent Tan, Carol A. C. Flannagan, Michael R. Elliott
The development of driverless vehicles has spurred the need to predict human driving behavior to facilitate interaction between driverless and human-driven vehicles. Predicting hum…
Fast Approximation of Small p-values in Permutation Tests by Partitioning the Permutations
Brian Segal, Thomas Braun, Michael Elliott +1
Researchers in genetics and other life sciences commonly use permutation tests to evaluate differences between groups. Permutation tests have desirable properties, including exactn…