Causal inference with recurrent and competing events

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 hospitalizati ...

Counterfactual Phenotyping with Censored Time-to-Events

Estimation of treatment efficacy of real-world clinical interventions
involves working with continuous outcomes such as time-to-death,
re-hospitalization, or a composite event that may be subject to c ...

Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models

Longitudinal observational patient data can be used to investigate the causal
effects of time-varying treatments on time-to-event outcomes. Several methods
have been developed for controlling for the ...

Estimating causal effects in the presence of competing events using regression standardisation with the Stata command standsurv

When interested in a time-to-event outcome, competing events that prevent the
occurrence of the event of interest may be present. In the presence of
competing events, various statistical estimands hav ...

Hypothetical estimands in clinical trials: a unification of causal inference and missing data methods

The ICH E9 addendum introduces the term intercurrent event to refer to events
that happen after randomisation and that can either preclude observation of the
outcome of interest or affect its interpre ...

Survival analysis for AdVerse events with VarYing follow-up times (SAVVY) -- comparison of adverse event risks in randomized controlled trials

Analyses of adverse events (AEs) are an important aspect of benefit-risk and
health-technology assessments of therapies. The SAVVY project aims to improve
the analyses of AE data in clinical trials th ...

Quantifying and Detecting Individual Level `Always Survivor' Causal Effects Under `Truncation by Death' and Censoring Through Time

The analysis of causal effects when the outcome of interest is possibly
truncated by death has a long history in statistics and causal inference. The
survivor average causal effect is commonly identif ...

Conditional separable effects

Researchers are often interested in treatment effects on outcomes that are
only defined conditional on a post-treatment event status. For example, in a
study of the effect of different cancer treatmen ...

Causal inference for semi-competing risks data

An emerging challenge for time-to-event data is studying semi-competing
risks, namely when two event times are of interest: a non-terminal event time
(e.g. age at disease diagnosis), and a terminal ev ...

Survivor average causal effects for continuous time: a principal stratification approach to causal inference with semicompeting risks

In semicompeting risks problems, nonterminal time-to-event outcomes such as
time to hospital readmission are subject to truncation by death. These settings
are often modeled with illness-death models ...