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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 .

Project description:Randomizing patients among treatments with equal probabilities in clinical trials is the established method to obtain unbiased comparisons. In recent years, motivated by ethical considerations, many authors have proposed outcome adaptive randomization, wherein the randomization probabilities are unbalanced, based on interim data, to favor treatment arms having more favorable outcomes. While there has been substantial controversy regarding the merits and flaws of adaptive versus equal randomization, there has not yet been a systematic simulation study in the multi-arm setting. A simulation study was conducted to evaluate four different Bayesian adaptive randomization methods and compare them to equal randomization in five-arm clinical trials. All adaptive randomization methods included an initial burn-in with equal randomization and some combination of other modifications to avoid extreme randomization probabilities. Trials either with or without a control arm were evaluated, using designs that may terminate arms early for futility and select one or more experimental treatments at the end. The designs were evaluated under a range of scenarios and sample sizes. For trials with a control arm and maximum same size 250 or 500, several commonly used adaptive randomization methods have very low probabilities of correctly selecting a truly superior treatment. Of those studied, the only adaptive randomization method with desirable properties has a burn-in with equal randomization and thereafter randomization probabilities restricted to the interval 0.10-0.90. Compared to equal randomization, this method has a favorable sample size imbalance but lower probability of correctly selecting a superior treatment. In multi-arm trials, compared to equal randomization, several commonly used adaptive randomization methods give much lower probabilities of selecting superior treatments. Aside from randomization method, conducting a multi-arm trial without a control arm may lead to very low probabilities of selecting any superior treatments if differences between the treatment success probabilities are small.

Project description:BackgroundMulti-Arm Multi-Stage designs aim at comparing several new treatments to a common reference, in order to select or drop any treatment arm to move forward when such evidence already exists based on interim analyses. We redesigned a Bayesian adaptive design initially proposed for dose-finding, focusing our interest in the comparison of multiple experimental drugs to a control on a binary criterion measure.MethodsWe redesigned a phase II clinical trial that randomly allocates patients across three (one control and two experimental) treatment arms to assess dropping decision rules. We were interested in dropping any arm due to futility, either based on historical control rate (first rule) or comparison across arms (second rule), and in stopping experimental arm due to its ability to reach a sufficient response rate (third rule), using the difference of response probabilities in Bayes binomial trials between the treated and control as a measure of treatment benefit. Simulations were then conducted to investigate the decision operating characteristics under a variety of plausible scenarios, as a function of the decision thresholds.ResultsOur findings suggest that one experimental treatment was less efficient than the control and could have been dropped from the trial based on a sample of approximately 20 instead of 40 patients. In the simulation study, stopping decisions were reached sooner for the first rule than for the second rule, with close mean estimates of response rates and small bias. According to the decision threshold, the mean sample size to detect the required 0.15 absolute benefit ranged from 63 to 70 (rule 3) with false negative rates of less than 2 % (rule 1) up to 6 % (rule 2). In contrast, detecting a 0.15 inferiority in response rates required a sample size ranging on average from 23 to 35 (rules 1 and 2, respectively) with a false positive rate ranging from 3.6 to 0.6 % (rule 3).ConclusionAdaptive trial design is a good way to improve clinical trials. It allows removing ineffective drugs and reducing the trial sample size, while maintaining unbiased estimates. Decision thresholds can be set according to predefined fixed error decision rates.Trial registrationClinicalTrials.gov Identifier: NCT01342692 .

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

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

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