Dataset Information

Sample size recalculation based on the prevalence in a randomized test-treatment study.

ABSTRACT:

Background

Randomized test-treatment studies aim to evaluate the clinical utility of diagnostic tests by providing evidence on their impact on patient health. However, the sample size calculation is affected by several factors involved in the test-treatment pathway, including the prevalence of the disease. Sample size planning is exposed to strong uncertainties in terms of the necessary assumptions, which have to be compensated for accordingly by adjusting prospectively determined study parameters during the course of the study.

Method

An adaptive design with a blinded sample size recalculation in a randomized test-treatment study based on the prevalence is proposed and evaluated by a simulation study. The results of the adaptive design are compared to those of the fixed design.

Results

The adaptive design achieves the desired theoretical power, under the assumption that all other nuisance parameters have been specified correctly, while wrong assumptions regarding the prevalence may lead to an over- or underpowered study in the fixed design. The empirical type I error rate is sufficiently controlled in the adaptive design as well as in the fixed design.

Conclusion

The consideration of a blinded recalculation of the sample size already during the planning of the study may be advisable in order to increase the possibility of success as well as an enhanced process of the study. However, the application of the method is subject to a number of limitations associated with the study design in terms of feasibility, sample sizes needed to be achieved, and fulfillment of necessary prerequisites.

SUBMITTER: Hot A

PROVIDER: S-EPMC9317230 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAn adequate sample size calculation is essential for designing a successful clinical trial. One way to tackle planning difficulties regarding parameter assumptions required for sample size calculation is to adapt the sample size during the ongoing trial.This can be attained by adaptive group sequential study designs. At a predefined timepoint, the interim effect is tested for significance. Based on the interim test result, the trial is either stopped or continued with the possibility of a sample size recalculation.ObjectivesSample size recalculation rules have different limitations in application like a high variability of the recalculated sample size. Hence, the goal is to provide a tool to counteract this performance limitation.MethodsSample size recalculation rules can be interpreted as functions of the observed interim effect. Often, a "jump" from the first stage's sample size to the maximal sample size at a rather arbitrarily chosen interim effect size is implemented and the curve decreases monotonically afterwards. This jump is one reason for a high variability of the sample size. In this work, we investigate how the shape of the recalculation function can be improved by implementing a smoother increase of the sample size. The design options are evaluated by means of Monte Carlo simulations. Evaluation criteria are univariate performance measures such as the conditional power and sample size as well as a conditional performance score which combines these components.ResultsWe demonstrate that smoothing corrections can reduce variability in conditional power and sample size as well as they increase the performance with respect to a recently published conditional performance score for medium and large standardized effect sizes.ConclusionBased on the simulation study, we present a tool that is easily implemented to improve sample size recalculation rules. The approach can be combined with existing sample size recalculation rules described in the literature.

Project description:ObjectiveTo assess the influence of trial sample size on treatment effect estimates within meta-analyses.DesignMeta-epidemiological study.Data sources93 meta-analyses (735 randomised controlled trials) assessing therapeutic interventions with binary outcomes, published in the 10 leading journals of each medical subject category of the Journal Citation Reports or in the Cochrane Database of Systematic Reviews.Data extractionSample size, outcome data, and risk of bias extracted from each trial.Data synthesisTrials within each meta-analysis were sorted by their sample size: using quarters within each meta-analysis (from quarter 1 with 25% of the smallest trials, to quarter 4 with 25% of the largest trials), and using size groups across meta-analyses (ranging from <50 to ≥ 1000 patients). Treatment effects were compared within each meta-analysis between quarters or between size groups by average ratios of odds ratios (where a ratio of odds ratios less than 1 indicates larger effects in smaller trials).ResultsTreatment effect estimates were significantly larger in smaller trials, regardless of sample size. Compared with quarter 4 (which included the largest trials), treatment effects were, on average, 32% larger in trials in quarter 1 (which included the smallest trials; ratio of odds ratios 0.68, 95% confidence interval 0.57 to 0.82), 17% larger in trials in quarter 2 (0.83, 0.75 to 0.91), and 12% larger in trials in quarter 3 (0.88, 0.82 to 0.95). Similar results were obtained when comparing treatment effect estimates between different size groups. Compared with trials of 1000 patients or more, treatment effects were, on average, 48% larger in trials with fewer than 50 patients (0.52, 0.41 to 0.66) and 10% larger in trials with 500-999 patients (0.90, 0.82 to 1.00).ConclusionsTreatment effect estimates differed within meta-analyses solely based on trial sample size, with stronger effect estimates seen in small to moderately sized trials than in the largest trials.