Reference‐based sensitivity analysis for time‐to‐event data

Atkinson, Andrew; Kenward, Michael G.; Clayton, Tim; Carpenter, James

doi:10.1002/pst.1954

Cited by 24 publications

(38 citation statements)

References 49 publications

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“…For survival data, we need to define the reference-based assumptions. This has been done in Atkinson (2018), which also contains simulation results suggesting promising information anchoring properties for Rubin's rules in this setting.…”

Section: Discussionmentioning

confidence: 99%

Information-Anchored Sensitivity Analysis: Theory and Application

Cro

Carpenter

Kenward³

2018

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary Analysis of longitudinal randomized clinical 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 about post‐deviation behaviour to perform our primary analysis and to estimate the treatment effect. In such settings, it is now widely recognized that we should follow this with sensitivity analyses to explore the robustness of our inferences to alternative assumptions about post‐deviation behaviour. Although there has been much work on how to conduct such sensitivity analyses, little attention has been given to the appropriate loss of information due to missing data within sensitivity analysis. We argue that more attention needs to be given to this issue, showing that it is quite possible for sensitivity analysis to decrease and increase the information about the treatment effect. To address this critical issue, we introduce the concept of information‐anchored sensitivity analysis. By this we mean sensitivity analyses in which the proportion of information about the treatment estimate lost because of missing data is the same as the proportion of information about the treatment estimate lost because of missing data in the primary analysis. We argue that this forms a transparent, practical starting point for interpretation of sensitivity analysis. We then derive results showing that, for longitudinal continuous data, a broad class of controlled and reference‐based sensitivity analyses performed by multiple imputation are information anchored. We illustrate the theory with simulations and an analysis of a peer review trial and then discuss our work in the context of other recent work in this area. Our results give a theoretical basis for the use of controlled multiple‐imputation procedures for sensitivity analysis.

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Section: Discussionmentioning

confidence: 99%

Information-Anchored Sensitivity Analysis: Theory and Application

Cro

Carpenter

Kenward³

2018

Journal of the Royal Statistical Society Series A: Statistics in Society

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“…This can be implemented using the R package InformativeCensoring. For survival data, Atkinson has proposed a collection of reference‐based assumptions using a proportional hazards model 61,62 . Lu et al 63 compared a δ adjusted MI method and a reference based MI method for survival data.…”

Section: Discussionmentioning

confidence: 99%

Sensitivity analysis for clinical trials with missing continuous outcome data using controlled multiple imputation: A practical guide

Cro

Morris

Kenward³

et al. 2020

Statistics in Medicine

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Missing data due to loss to follow‐up or intercurrent events are unintended, but unfortunately inevitable in clinical trials. Since the true values of missing data are never known, it is necessary to assess the impact of untestable and unavoidable assumptions about any unobserved data in sensitivity analysis. This tutorial provides an overview of controlled multiple imputation (MI) techniques and a practical guide to their use for sensitivity analysis of trials with missing continuous outcome data. These include δ‐ and reference‐based MI procedures. In δ‐based imputation, an offset term, δ, is typically added to the expected value of the missing data to assess the impact of unobserved participants having a worse or better response than those observed. Reference‐based imputation draws imputed values with some reference to observed data in other groups of the trial, typically in other treatment arms. We illustrate the accessibility of these methods using data from a pediatric eczema trial and a chronic headache trial and provide Stata code to facilitate adoption. We discuss issues surrounding the choice of δ in δ‐based sensitivity analysis. We also review the debate on variance estimation within reference‐based analysis and justify the use of Rubin's variance estimator in this setting, since as we further elaborate on within, it provides information anchored inference.

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“…The approach was originally proposed in the context of a repeatedly measured continuous endpoint assuming a multivariate normal model (Carpenter et al (2013)). Subsequently the idea has been extended to other endpoint types, including recurrent events (Keene et al (2014)) and survival times (Atkinson et al (2019)). To help make the following arguments regarding congeniality concrete yet (relatively) simple, we first review the jump to reference (J2R) approach for a repeatedly measured continuous endpoint, following Carpenter et al (2013).…”

Section: Reference-based Multiple Imputation and Congenialitymentioning

confidence: 99%

Reference-Based Multiple Imputation—What is the Right Variance and How to Estimate It

Bartlett

2021

Statistics in Biopharmaceutical Research

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Reference-based multiple imputation methods have become popular for handling missing data in randomised clinical trials. Rubin's variance estimator is well known to be biased compared to the reference-based imputation estimator's true repeated sampling (frequentist) variance. Somewhat surprisingly given the increasing popularity of these methods, there has been relatively little debate in the literature as to whether Rubin's variance estimator or alternative (smaller) variance estimators targeting the repeated sampling variance are more appropriate. We review the arguments made on both sides of this debate, and argue that the repeated sampling variance is more appropriate. We review different approaches for estimating the frequentist variance, and suggest a recent proposal for combining bootstrapping with multiple imputation as a widely applicable general solution. At the same time, in light of the consequences of reference-based assumptions for frequentist variance, we believe further scrutiny of these methods is warranted to determine whether the strength of their assumptions is generally justifiable.

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Reference‐based sensitivity analysis for time‐to‐event data

Cited by 24 publications

References 49 publications

Information-Anchored Sensitivity Analysis: Theory and Application

Information-Anchored Sensitivity Analysis: Theory and Application

Sensitivity analysis for clinical trials with missing continuous outcome data using controlled multiple imputation: A practical guide

Reference-Based Multiple Imputation—What is the Right Variance and How to Estimate It

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