@article{zhang_tsiatis_davidian_pieper_mahaffey_2011, title={Inference on treatment effects from a randomized clinical trial in the presence of premature treatment discontinuation: the SYNERGY trial}, volume={12}, ISSN={["1465-4644"]}, DOI={10.1093/biostatistics/kxq054}, abstractNote={The Superior Yield of the New Strategy of Enoxaparin, Revascularization, and GlYcoprotein IIb/IIIa inhibitors (SYNERGY) was a randomized, open-label, multicenter clinical trial comparing 2 anticoagulant drugs on the basis of time-to-event endpoints. In contrast to other studies of these agents, the primary, intent-to-treat analysis did not find evidence of a difference, leading to speculation that premature discontinuation of the study agents by some subjects may have attenuated the apparent treatment effect and thus to interest in inference on the difference in survival distributions were all subjects in the population to follow the assigned regimens, with no discontinuation. Such inference is often attempted via ad hoc analyses that are not based on a formal definition of this treatment effect. We use SYNERGY as a context in which to describe how this effect may be conceptualized and to present a statistical framework in which it may be precisely identified, which leads naturally to inferential methods based on inverse probability weighting.}, number={2}, journal={BIOSTATISTICS}, author={Zhang, Min and Tsiatis, Anastasios A. and Davidian, Marie and Pieper, Karen S. and Mahaffey, Kenneth W.}, year={2011}, month={Apr}, pages={258–269} }
 @article{zhang_davidian_2008, title={"Smooth" semiparametric regression analysis for arbitrarily censored time-to-event data}, volume={64}, ISSN={["1541-0420"]}, DOI={10.1111/j.1541-0420.2007.00928.x}, abstractNote={Summary A general framework for regression analysis of time‐to‐event data subject to arbitrary patterns of censoring is proposed. The approach is relevant when the analyst is willing to assume that distributions governing model components that are ordinarily left unspecified in popular semiparametric regression models, such as the baseline hazard function in the proportional hazards model, have densities satisfying mild “smoothness” conditions. Densities are approximated by a truncated series expansion that, for fixed degree of truncation, results in a “parametric” representation, which makes likelihood‐based inference coupled with adaptive choice of the degree of truncation, and hence flexibility of the model, computationally and conceptually straightforward with data subject to any pattern of censoring. The formulation allows popular models, such as the proportional hazards, proportional odds, and accelerated failure time models, to be placed in a common framework; provides a principled basis for choosing among them; and renders useful extensions of the models straightforward. The utility and performance of the methods are demonstrated via simulations and by application to data from time‐to‐event studies.}, number={2}, journal={BIOMETRICS}, author={Zhang, Min and Davidian, Marie}, year={2008}, month={Jun}, pages={567–576} }
 @article{tsiatis_davidian_zhang_lu_2008, title={Covariate adjustment for two-sample treatment comparisons in randomized clinical trials: A principled yet flexible approach}, volume={27}, ISSN={["1097-0258"]}, DOI={10.1002/sim.3113}, abstractNote={Abstract}, number={23}, journal={STATISTICS IN MEDICINE}, author={Tsiatis, Anastasios A. and Davidian, Marie and Zhang, Min and Lu, Xiaomin}, year={2008}, month={Oct}, pages={4658–4677} }
 @article{zhang_tsiatis_davidian_2008, title={Improving efficiency of inferences in randomized clinical trials using auxiliary covariates}, volume={64}, ISSN={["1541-0420"]}, DOI={10.1111/j.1541-0420.2007.00976.x}, abstractNote={Summary The primary goal of a randomized clinical trial is to make comparisons among two or more treatments. For example, in a two‐arm trial with continuous response, the focus may be on the difference in treatment means; with more than two treatments, the comparison may be based on pairwise differences. With binary outcomes, pairwise odds ratios or log odds ratios may be used. In general, comparisons may be based on meaningful parameters in a relevant statistical model. Standard analyses for estimation and testing in this context typically are based on the data collected on response and treatment assignment only. In many trials, auxiliary baseline covariate information may also be available, and it is of interest to exploit these data to improve the efficiency of inferences. Taking a semiparametric theory perspective, we propose a broadly applicable approach to adjustment for auxiliary covariates to achieve more efficient estimators and tests for treatment parameters in the analysis of randomized clinical trials. Simulations and applications demonstrate the performance of the methods.}, number={3}, journal={BIOMETRICS}, author={Zhang, Min and Tsiatis, Anastasios A. and Davidian, Marie}, year={2008}, month={Sep}, pages={707–715} }