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Analysis of longitudinal randomised controlled trials is frequently complicated because patients deviate from the protocol. Where such deviations are relevant for the estimand, we are typically required to make an untestable assumption…

Methodology · Statistics 2018-05-16 Suzie Cro , James R Carpenter , Michael G Kenward

In clinical trials, an experimental treatment is sometimes added on to a standard of care or control therapy in multiple treatment phases (e.g., concomitant and maintenance phases) to improve patient outcomes. When the new regimen provides…

Methodology · Statistics 2020-11-19 Sudipta Bhattacharya , Jyotirmoy Dey

The estimand framework provides guidance on handling intercurrent events, such as treatment discontinuation, in the analysis of clinical trial responses. Under ICH E9(R1), the treatment policy (TP) strategy incorporates post-discontinuation…

Methodology · Statistics 2026-04-07 Myeongjong Kang , Sangyoon Yi

The estimands framework outlined in ICH E9 (R1) describes the components needed to precisely define the effects to be estimated in clinical trials, which includes how post-baseline "intercurrent" events (IEs) are to be handled. In…

Methodology · Statistics 2023-08-28 Thomas Drury , Juan J Abellan , Nicky Best , Ian R. White

A draft addendum to ICH E9 has been released for public consultation in August 2017. The addendum focuses on two topics particularly relevant for randomized confirmatory clinical trials: estimands and sensitivity analyses. The need to amend…

Methodology · Statistics 2023-04-17 Kaspar Rufibach

We consider estimating the "de facto" or effectiveness estimand in a randomised placebo-controlled or standard-of-care-controlled drug trial with quantitative outcome, where participants who discontinue an investigational treatment are not…

Methodology · Statistics 2017-05-15 Ian R. White , Royes Joseph , Nicky Best

This article provides a novel approach to assess the importance of specific treatment phases within a treatment regimen through tipping point analyses (TPA) of a time-to-event endpoint using rank-preserving-structural-failure-time (RPSFT)…

Methodology · Statistics 2021-07-06 Sudipta Bhattacharya , Jyotirmoy Dey

The estimand framework proposes different strategies to address intercurrent events. The treatment policy strategy seems to be the most favoured as it is closely aligned with the pre-addendum intention-to-treat principle. All data for all…

Methodology · Statistics 2025-07-21 Sunita Rehal , Nicky Best , Sarah Watts , Thomas Drury

In clinical trials, multiple outcomes of different priorities commonly occur as the patient's response may not be adequately characterized by a single outcome. Win statistics are appealing summary measures for between-group difference at…

Methodology · Statistics 2025-06-04 Ying Cui , Bo Huang , Gaohong Dong , Ryuji Uozumi , Lu Tian

The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) E9 (R1) Addendum provides a framework for defining estimands in clinical trials. Treatment policy strategy is the mostly used…

Applications · Statistics 2024-10-10 Rong Liu , Yongming Qu

Individualized treatment rules can lead to better health outcomes when patients have heterogeneous responses to treatment. Very few individualized treatment rule estimation methods are compatible with a multi-treatment observational study…

Estimands using the treatment policy strategy for addressing intercurrent events are common in Phase III clinical trials. One estimation approach for this strategy is retrieved dropout whereby observed data following an intercurrent event…

The ICH E9(R1) Addendum (International Council for Harmonization 2019) suggests treatment-policy as one of several strategies for addressing intercurrent events such as treatment withdrawal when defining an estimand. This strategy requires…

Methodology · Statistics 2023-08-28 Suzie Cro , James H Roger , James R Carpenter

Observational studies of recurrent event rates are common in biomedical statistics. Broadly, the goal is to estimate differences in event rates under two treatments within a defined target population over a specified followup window.…

Methodology · Statistics 2024-11-13 Arman Oganisian , Anthony Girard , Jon A. Steingrimsson , Patience Moyo

Missing data is a systemic problem in practical scenarios that causes noise and bias when estimating treatment effects. This makes treatment effect estimation from data with missingness a particularly tricky endeavour. A key reason for this…

Machine Learning · Statistics 2023-02-27 Jeroen Berrevoets , Fergus Imrie , Trent Kyono , James Jordon , Mihaela van der Schaar

Time-to-event estimands are central to many oncology clinical trials. The estimand framework (addendum to the ICH E9 guideline) calls for precisely defining the treatment effect of interest to align with the clinical question of interest…

Applications · Statistics 2023-07-24 Jonathan Siegel , Hans-Jochen Weber , Stefan Englert , Feng Liu

Missing values in multivariate time series data can harm machine learning performance and introduce bias. These gaps arise from sensor malfunctions, blackouts, and human error and are typically addressed by data imputation. Previous work…

Machine Learning · Computer Science 2025-03-04 Mohammad Rafid Ul Islam , Prasad Tadepalli , Alan Fern

Mid-study design modifications are becoming increasingly accepted in confirmatory clinical trials, so long as appropriate methods are applied such that error rates are controlled. It is therefore unfortunate that the important case of…

Applications · Statistics 2014-05-08 Dominic Magirr , Thomas Jaki , Franz Koenig , Martin Posch

Many epidemiological and clinical studies aim at analyzing a time-to-event endpoint. A common complication is right censoring. In some cases, it arises because subjects are still surviving after the study terminates or move out of the study…

Methodology · Statistics 2024-01-10 Andrew Ying

This paper addresses the problem of identifying and estimating the causal effect of a treatment in the presence of unmeasured confounding and various types of right-censoring. Examples of these censoring mechanisms are administrative…

Statistics Theory · Mathematics 2025-03-19 Ilias Willems , Sara Rutten , Gilles Crommen , Ingrid Van Keilegom
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