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Assessment and implication of prognostic imbalance in randomized controlled trials with a binary outcome--a simulation study.

ABSTRACT: Chance imbalance in baseline prognosis of a randomized controlled trial can lead to over or underestimation of treatment effects, particularly in trials with small sample sizes. Our study aimed to (1) evaluate the probability of imbalance in a binary prognostic factor (PF) between two treatment arms, (2) investigate the impact of prognostic imbalance on the estimation of a treatment effect, and (3) examine the effect of sample size (n) in relation to the first two objectives.We simulated data from parallel-group trials evaluating a binary outcome by varying the risk of the outcome, effect of the treatment, power and prevalence of the PF, and n. Logistic regression models with and without adjustment for the PF were compared in terms of bias, standard error, coverage of confidence interval and statistical power.For a PF with a prevalence of 0.5, the probability of a difference in the frequency of the PF?5% reaches 0.42 with 125/arm. Ignoring a strong PF (relative risk?=?5) leads to underestimating the strength of a moderate treatment effect, and the underestimate is independent of n when n is >50/arm. Adjusting for such PF increases statistical power. If the PF is weak (RR?=?2), adjustment makes little difference in statistical inference. Conditional on a 5% imbalance of a powerful PF, adjustment reduces the likelihood of large bias. If an absolute measure of imbalance ?5% is deemed important, including 1000 patients/arm provides sufficient protection against such an imbalance. Two thousand patients/arm may provide an adequate control against large random deviations in treatment effect estimation in the presence of a powerful PF.The probability of prognostic imbalance in small trials can be substantial. Covariate adjustment improves estimation accuracy and statistical power, and hence should be performed when strong PFs are observed.

SUBMITTER: Chu R

PROVIDER: S-EPMC3358303 | biostudies-other | 2012

REPOSITORIES: biostudies-other

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Assessment and implication of prognostic imbalance in randomized controlled trials with a binary outcome--a simulation study.

Chu Rong R Walter Stephen D SD Guyatt Gordon G Devereaux P J PJ Walsh Michael M Thorlund Kristian K Thabane Lehana L

PloS one 20120522 5

<h4>Background</h4>Chance imbalance in baseline prognosis of a randomized controlled trial can lead to over or underestimation of treatment effects, particularly in trials with small sample sizes. Our study aimed to (1) evaluate the probability of imbalance in a binary prognostic factor (PF) between two treatment arms, (2) investigate the impact of prognostic imbalance on the estimation of a treatment effect, and (3) examine the effect of sample size (n) in relation to the first two objectives.< ...[more]

PMID: 22629322

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Project description:IntroductionAdjudication of the primary outcome in randomised trials is thought to control misclassification. We investigated the amount of misclassification needed before adjudication changed the primary trial results.Patients (or materials) and methods: We included data from five randomised stroke trials. Differential misclassification was introduced for each primary outcome until the estimated treatment effect was altered. This was simulated 1000 times. We calculated the between-simulation mean proportion of participants that needed to be differentially misclassified to alter the treatment effect. In addition, we simulated hypothetical trials with a binary outcome and varying sample size (1000-10,000), overall event rate (10%-50%) and treatment effect (0.67-0.90). We introduced non-differential misclassification until the treatment effect was non-significant at 5% level.ResultsFor the five trials, the range of unweighted kappa values were reduced from 0.89-0.97 to 0.65-0.85 before the treatment effect was altered. This corresponded to 2.1%-6% of participants misclassified differentially for trials with a binary outcome. For the hypothetical trials, those with a larger sample size, stronger treatment effect and overall event rate closer to 50% needed a higher proportion of events non-differentially misclassified before the treatment effect became non-significant.DiscussionWe found that only a small amount of differential misclassification was required before adjudication altered the primary trial results, whereas a considerable proportion of participants needed to be misclassified non-differentially before adjudication changed trial conclusions. Given that differential misclassification should not occur in trials with sufficient blinding, these results suggest that central adjudication is of most use in studies with unblinded outcome assessment.ConclusionFor trials without adequate blinding, central adjudication is vital to control for differential misclassification. However, for large blinded trials, adjudication is of less importance and may not be necessary.

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Assessment and implication of prognostic imbalance in randomized controlled trials with a binary outcome--a simulation study.

Publications

Assessment and implication of prognostic imbalance in randomized controlled trials with a binary outcome--a simulation study.

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