Assessing the impact of selection bias on test decisions in trials with a time‐to‐event outcome

Rückbeil, Marcia Viviane; Hilgers, Ralf-Dieter; Heussen, Nicole

doi:10.1002/sim.7299

Cited by 16 publications

(16 citation statements)

References 33 publications

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“…Studying impact of selection or chronological bias on statistical properties of various randomization designs is increasingly common . Recently, Hilgers et al published the Evaluation of Randomization procedures for Design Optimization template that provides a formal framework for evaluating different competing randomization procedures.…”

Section: Discussionmentioning

confidence: 99%

“…Studying impact of selection or chronological bias on statistical properties of various randomization designs is increasingly common. [47][48][49][50][51] Recently, Hilgers et al 52 published the Evaluation of Randomization procedures for Design Optimization template that provides a formal framework for evaluating different competing randomization procedures. It will be interesting to assess inferential properties of the designs considered in this paper using both parametric test procedures (eg, ANOVA F -test) and randomization-based tests (as was done, for example, in Galbete and Rosenberger 53 ), in the presence of selection and chronological biases, using Evaluation of Randomization procedures for Design Optimization template.…”

Section: Discussionmentioning

confidence: 99%

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A comparative study of restricted randomization procedures for multiarm trials with equal or unequal treatment allocation ratios

Ryeznik

Sverdlov²

2018

Statistics in Medicine

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Randomization designs for multiarm clinical trials are increasingly used in practice, especially in phase II dose-ranging studies. Many new methods have been proposed in the literature; however, there is lack of systematic, head-to-head comparison of the competing designs. In this paper, we systematically investigate statistical properties of various restricted randomization procedures for multiarm trials with fixed and possibly unequal allocation ratios. The design operating characteristics include measures of allocation balance, randomness of treatment assignments, variations in the allocation ratio, and statistical characteristics such as type I error rate and power. The results from the current paper should help clinical investigators select an appropriate randomization procedure for their clinical trial. We also provide a web-based R shiny application that can be used to reproduce all results in this paper and run simulations under additional user-defined experimental scenarios.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A comparative study of restricted randomization procedures for multiarm trials with equal or unequal treatment allocation ratios

Ryeznik

Sverdlov²

2018

Statistics in Medicine

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“…[18][19][20] A second attempt to explain the different results of MITRA-FR and COAPT is the relation of EROA and RV to LVEDV and to distinguish between proportionate and disproportionate FMR according to LV size. Selection or sampling bias towards moderate and mild FMR may have induced inaccurate quantification.…”

Section: Attempted Explanation For the Conflicting Echocardiographic mentioning

confidence: 99%

“…Selection or sampling bias towards moderate and mild FMR may have induced inaccurate quantification. [18][19][20] A second attempt to explain the different results of MITRA-FR and COAPT is the relation of EROA and RV to LVEDV and to distinguish between proportionate and disproportionate FMR according to LV size. 4,21 The more enlarged LV dimensions, the less reverse remodelling can be presumed after treatment.…”

Section: Attempted Explanation For the Conflicting Echocardiographic mentioning

confidence: 99%

Echocardiographic assessment of functional mitral regurgitation: opening Pandora's box?

Hagendorff¹,

Doenst

Falk

2019

ESC Heart Failure

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Two recent trials of transcatheter mitral‐valve repair in patients with functional mitral regurgitation (FMR) presented opposing results for the MitraClip® compared to medical therapy alone. The conflicting results gave rise to intensive discussions about assessment of mitral valve regurgitation (MR). A recent editorial viewpoint provided a potential explanation presenting a new pathophysiologic concept. However, the echocardiographic characterization of both trials' patients is inconsistent and the discussed concepts appear to suffer from plausibility weaknesses. It is well conceivable that limitations in the echocardiographic assessment of the trial patients introduced a bias regarding the selection of patients with severe (or less severe) MR that may be a more plausible explanation for the differences in outcome. We here illustrate our viewpoint regarding the two MitraClip trials and also illustrate the difficulties in assessing functional MR properly. It may indeed be “opening Pandora's box”, but we will also make an attempt to provide a solution.

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“…To mitigate the impact of chronological bias, it is recommended that a randomization design should balance treatment assignments over time, e.g., by means of some kind of restricted randomization (44,47). The potential negative impact of selection bias on statistical inference (test decisions) is acknowledged and well documented (48)(49)(50)(51). Strategies to reduce risk of selection bias exist (52,53); one recommendation is to use less restrictive randomization procedures, such as the maximal procedure (47,54).…”

Section: Robustness To Experimental Biasesmentioning

confidence: 99%

Implementing Optimal Designs for Dose–Response Studies Through Adaptive Randomization for a Small Population Group

Ryeznik

Sverdlov²,

Hooker

2018

AAPS J

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In dose-response studies with censored time-to-event outcomes, D-optimal designs depend on the true model and the amount of censored data. In practice, such designs can be implemented adaptively, by performing dose assignments according to updated knowledge of the dose-response curve at interim analysis. It is also essential that treatment allocation involves randomization-to mitigate various experimental biases and enable valid statistical inference at the end of the trial. In this work, we perform a comparison of several adaptive randomization procedures that can be used for implementing D-optimal designs for dose-response studies with time-to-event outcomes with small to moderate sample sizes. We consider single-stage, two-stage, and multi-stage adaptive designs. We also explore robustness of the designs to experimental (chronological and selection) biases. Simulation studies provide evidence that both the choice of an allocation design and a randomization procedure to implement the target allocation impact the quality of dose-response estimation, especially for small samples. For best performance, a multi-stage adaptive design with small cohort sizes should be implemented using a randomization procedure that closely attains the targeted D-optimal design at each stage. The results of the current work should help clinical investigators select an appropriate randomization procedure for their dose-response study.

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Assessing the impact of selection bias on test decisions in trials with a time‐to‐event outcome

Cited by 16 publications

References 33 publications

A comparative study of restricted randomization procedures for multiarm trials with equal or unequal treatment allocation ratios

A comparative study of restricted randomization procedures for multiarm trials with equal or unequal treatment allocation ratios

Echocardiographic assessment of functional mitral regurgitation: opening Pandora's box?

Implementing Optimal Designs for Dose–Response Studies Through Adaptive Randomization for a Small Population Group

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