A Comparison of Estimand and Estimation Strategies for Clinical Trials in Early Parkinson’s Disease

Noci, Alessandro; Wolbers, Marcel; Abt, Markus; Baayen, Corine; Burger, Hans Ulrich; Jin, Man; Robieson, Weining

doi:10.1080/19466315.2022.2116476

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…However, our approach and other approaches based on missing data imputation still play an important role for implementing more complex MAR assumptions. As an example, Noci et al 26 implemented a treatment policy strategy by including interaction terms between the treatment group and two time‐varying covariates in the imputation model to account for the impact of treatment discontinuations and initiations of symptomatic treatment, respectively, on outcomes. Completed data sets based on this more complex imputation model were then analyzed using a standard ANCOVA analysis model.…”

Section: Discussionmentioning

confidence: 99%

Standard and reference‐based conditional mean imputation

Wolbers

Noci

Delmar

et al. 2022

Pharmaceutical Statistics

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Clinical trials with longitudinal outcomes typically include missing data due to missed assessments or structural missingness of outcomes after intercurrent events handled with a hypothetical strategy. Approaches based on Bayesian random multiple imputation and Rubin's rules for pooling results across multiple imputed data sets are increasingly used in order to align the analysis of these trials with the targeted estimand. We propose and justify deterministic conditional mean imputation combined with the jackknife for inference as an alternative approach. The method is applicable to imputations under a missing-at-random assumption as well as for reference-based imputation approaches. In an application and a simulation study, we demonstrate that it provides consistent treatment effect estimates with the Bayesian approach and reliable frequentist inference with accurate standard error estimation and type I error control. A further advantage of the method is that it does not rely on random sampling and is therefore replicable and unaffected by Monte Carlo error.

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Section: Discussionmentioning

confidence: 99%

Standard and reference‐based conditional mean imputation

Wolbers

Noci

Delmar

et al. 2022

Pharmaceutical Statistics

Self Cite

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“…For specific CNS disorders, some publications on the thinking process for defining an estimand [ 4 ], or on the methods of estimation aligned to certain strategies [ 5 , 6 ] exist, and some regulatory guidance that includes strategies to account for intercurrent events has been given [ 7 , 8 ]. However, the experience in constructing and reporting estimands is in general limited [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Estimands in CNS trials – A review of strategies for addressing intercurrent events

Mészáros,

Lasch,

Delafont

et al. 2024

Contemporary Clinical Trials Communications

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Estimation of Treatment Policy Estimands for Continuous Outcomes Using Off‐Treatment Sequential Multiple Imputation

Drury,

Abellan,

Best

et al. 2024

Pharmaceutical Statistics

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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 late‐stage clinical trials, it is common to handle IEs like ‘treatment discontinuation’ using the treatment policy strategy and target the treatment effect on outcomes regardless of treatment discontinuation. For continuous repeated measures, this type of effect is often estimated using all observed data before and after discontinuation using either a mixed model for repeated measures (MMRM) or multiple imputation (MI) to handle any missing data. In basic form, both these estimation methods ignore treatment discontinuation in the analysis and therefore may be biased if there are differences in patient outcomes after treatment discontinuation compared with patients still assigned to treatment, and missing data being more common for patients who have discontinued treatment. We therefore propose and evaluate a set of MI models that can accommodate differences between outcomes before and after treatment discontinuation. The models are evaluated in the context of planning a Phase 3 trial for a respiratory disease. We show that analyses ignoring treatment discontinuation can introduce substantial bias and can sometimes underestimate variability. We also show that some of the MI models proposed can successfully correct the bias, but inevitably lead to increases in variance. We conclude that some of the proposed MI models are preferable to the traditional analysis ignoring treatment discontinuation, but the precise choice of MI model will likely depend on the trial design, disease of interest and amount of observed and missing data following treatment discontinuation.

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A Comparison of Estimand and Estimation Strategies for Clinical Trials in Early Parkinson’s Disease

Cited by 5 publications

References 24 publications

Standard and reference‐based conditional mean imputation

Standard and reference‐based conditional mean imputation

Estimands in CNS trials – A review of strategies for addressing intercurrent events

Estimation of Treatment Policy Estimands for Continuous Outcomes Using Off‐Treatment Sequential Multiple Imputation

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