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The impact of selection bias in randomized multi-arm parallel group clinical trials.

ABSTRACT: The impact of selection bias on the results of clinical trials has been analyzed extensively for trials of two treatments, yet its impact in multi-arm trials is still unknown. In this paper, we investigate selection bias in multi-arm trials by its impact on the type I error probability. We propose two models for selection bias, so-called biasing policies, that both extend the classic guessing strategy by Blackwell and Hodges. We derive the distribution of the F-test statistic under the misspecified outcome model and provide a formula for the type I error probability under selection bias. We apply the presented approach to quantify the influence of selection bias in multi-arm trials with increasing number of treatment groups using a permuted block design for different assumptions and different biasing strategies. Our results confirm previous findings that smaller block sizes lead to a higher proportion of sequences with inflated type I error probability. Astonishingly, our results also show that the proportion of sequences with inflated type I error probability remains constant when the number of treatment groups is increased. Realizing that the impact of selection bias cannot be completely eliminated, we propose a bias adjusted statistical model and show that the power of the statistical test is only slightly deflated for larger block sizes.

SUBMITTER: Uschner D

PROVIDER: S-EPMC5792025 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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The impact of selection bias in randomized multi-arm parallel group clinical trials.

Uschner Diane D Hilgers Ralf-Dieter RD Heussen Nicole N

PloS one 20180131 1

The impact of selection bias on the results of clinical trials has been analyzed extensively for trials of two treatments, yet its impact in multi-arm trials is still unknown. In this paper, we investigate selection bias in multi-arm trials by its impact on the type I error probability. We propose two models for selection bias, so-called biasing policies, that both extend the classic guessing strategy by Blackwell and Hodges. We derive the distribution of the F-test statistic under the misspecif ...[more]

PMID: 29385190

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Project description:Clinical trials are the hallmark of evidence-based medicine, but recruitment is often challenging, especially in stroke trials investigating patients not being able to give informed consent. In some nations, ethics committees will not approve of inclusion in a clinical study via consent of a legal representative. The ethical dilemma of including or excluding those patients has not been properly addressed, as there is little data on the effect of stroke characteristics on the ability to give informed consent.To examine differences between patients able and unable to consent at inclusion to an acute stroke trial, we conducted a post-hoc analysis of monitoring records from a multicentric interventional trial. These records listed patients who gave informed consent by themselves and those who needed a legal representative to do so. This exemplary STRAWINSKI trial aimed at improving stroke outcome by biomarker-guided antibiotic treatment of stroke associated pneumonia and included patients within 40 h after stroke onset, suffering from MCA infarctions with an NIHSS score?>?9 at admission. Standard descriptive and associative statistics were calculated to compare baseline characteristics and outcome measures between patients who were able to consent and those who were not.We identified the person giving consent in 228 out of 229 subjects. Patients with inability to consent were older (p?<?0.01), suffered from more left-hemispheric (p?<?0.01) and more severe strokes (NIHSS, p?<?0.01), were more likely to die during hospitalisation (p?<?0.01) or have unfavourable outcome at discharge (mRS, p?<?0.01), to develop fever (p?<?0.01) and tended to be more susceptible to infections (p?=?0.06) during the acute course of the disorder.Demographics, stroke characteristics and outcomes significantly affect stroke patients in their ability to consent. Where selection criteria and primary outcome measures of a trial are significantly affected by ability to consent, excluding patients unable to consent might be unethical.URL http://www.clinicaltrials.gov . Unique identifier: NCT01264549 .

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Dataset Information

The impact of selection bias in randomized multi-arm parallel group clinical trials.

Publications

The impact of selection bias in randomized multi-arm parallel group clinical trials.

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