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Intercurrent events, such as treatment switching, rescue medication, dropout, or truncation by death, frequently complicate intention-to-treat analyses in randomized clinical trials. Existing causal inference frameworks typically target…

Methodology · Statistics 2026-03-12 Georgi Baklicharov , Kelly Van Lancker , Stijn Vansteelandt

Many clinical studies evaluate the benefit of a treatment based on both survival and other continuous/ordinal clinical outcomes, such as Quality of Life scores. In these studies, when subjects die before the follow-up assessment, the…

Applications · Statistics 2023-09-06 Qingyan Xiang , Ronald J. Bosch , Judith J. Lok

In both observational studies and randomized trials, post-treatment events such as dropout, nonadherence, and truncation by death occur frequently. In some studies, conditioning on post-treatment variables is a deliberate strategy to…

Methodology · Statistics 2026-04-24 Marco Piccininni , Mats J. Stensrud

Although randomized controlled trials have long been regarded as the ``gold standard'' for evaluating treatment effects, there is no natural prevention from post-treatment events. For example, non-compliance makes the actual treatment…

Methodology · Statistics 2025-04-25 Qinqing Liu , Xiang Peng , Tao Zhang , Yuhao Deng

It is common that in multiarm randomized trials, the outcome of interest is "truncated by death," meaning that it is only observed or well defined conditioning on an intermediate outcome. In this case, in addition to pairwise contrasts, the…

Methodology · Statistics 2016-11-22 Linbo Wang , Thomas S. Richardson , Xiao-Hua Zhou

In neoadjuvant trials on early-stage breast cancer, patients are usually randomized into a control group and a treatment group with an additional target therapy. Early efficacy of the new regimen is assessed via the binary pathological…

Methodology · Statistics 2022-04-04 Xiaoqing Tan , Judah Abberbock , Priya Rastogi , Gong Tang

Principal stratification is a causal framework to analyze randomized experiments with a post-treatment variable between the treatment and endpoint variables. Because the principal strata defined by the potential outcomes of the…

Statistics Theory · Mathematics 2015-07-22 Zhichao Jiang , Peng Ding , Zhi Geng

In cluster-randomized crossover (CRXO) trials, groups of individuals are randomly assigned to two or more sequences of alternating treatments. Since clusters serve as their own control, the CRXO design is typically more statistically…

Methodology · Statistics 2026-01-30 Dane Isenberg , Michael O. Harhay , Andrew B. Forbes , Paul J. Young , Fan Li , Nandita Mitra

When longitudinal outcomes are evaluated in mortal populations, their non-existence after death complicates the analysis and its causal interpretation. Where popular methods often merge longitudinal outcome and survival into one scale or…

In prevalent cohort studies with delayed entry, time-to-event outcomes are often subject to left truncation where only subjects that have not experienced the event at study entry are included, leading to selection bias. Existing methods for…

Methodology · Statistics 2025-12-25 Yuyao Wang , Andrew Ying , Ronghui Xu

Truncation by death, a prevalent challenge in critical care, renders traditional dynamic treatment regime (DTR) evaluation inapplicable due to ill-defined potential outcomes. We introduce a principal stratification-based method, focusing on…

Machine Learning · Statistics 2025-10-10 Sihyung Park , Wenbin Lu , Shu Yang

In causal inference, the correct formulation of the scientific question of interest is a crucial step. Here we apply the estimand framework to a comparison of the outcomes of patient-level clinical trials and observational data to help…

Recent research in causal inference has made important progress in addressing challenges to the external validity of trial findings. Such methods weight trial participant data to more closely resemble the distribution of effect-modifying…

Methodology · Statistics 2024-07-18 Justin M. Clark , Kollin W. Rott , James S. Hodges , Jared D. Huling

The principal stratification has become a popular tool to address a broad class of causal inference questions, particularly in dealing with non-compliance and truncation-by-death problems. The causal effects within principal strata which…

Methodology · Statistics 2022-06-20 Shanshan Luo , Wei Li , Wang Miao , Yangbo He

The International Council for Harmonization (ICH) E9 (R1) addendum provides the estimand framework to formulate treatment effects in a clinical trial. One of the attributes of an estimand the framework describes is intercurrent events.…

Intercurrent (post-treatment) events occur frequently in randomized trials, and investigators often express interest in treatment effects that suitably take account of these events. A naive conditioning on intercurrent events does not have…

Methodology · Statistics 2021-11-17 Mats J. Stensrud , Oliver Dukes

This dissertation focuses on modern causal inference under uncertainty and data restrictions, with applications to neoadjuvant clinical trials, distributed data networks, and robust individualized decision making. In the first project, we…

Methodology · Statistics 2023-01-24 Xiaoqing Tan

Many research questions concern treatment effects on outcomes that can recur several times in the same individual. For example, medical researchers are interested in treatment effects on hospitalizations in heart failure patients and sports…

Methodology · Statistics 2024-12-31 Matias Janvin , Jessica G. Young , Pål C. Ryalen , Mats J. Stensrud

The issue of "truncation by death" commonly arises in clinical research: subjects may die before their follow-up assessment, resulting in undefined clinical outcomes. To address this issue, we focus on survival-incorporated quantiles --…

Detecting heterogeneity in treatment response enriches the interpretation of gerontologic trials. In aging research, estimating the effect of the intervention on clinically meaningful outcomes faces analytical challenges when it is…

Applications · Statistics 2026-01-08 Changjun Li , Heather Allore , Michael O. Harhay , Fan Li , Guangyu Tong