Dataset Information

A simulation study on estimating biomarker-treatment interaction effects in randomized trials with prognostic variables.

ABSTRACT:

Background

To individualize treatment decisions based on patient characteristics, identification of an interaction between a biomarker and treatment is necessary. Often such potential interactions are analysed using data from randomized clinical trials intended for comparison of two treatments. Tests of interactions are often lacking statistical power and we investigated if and how a consideration of further prognostic variables can improve power and decrease the bias of estimated biomarker-treatment interactions in randomized clinical trials with time-to-event outcomes.

Methods

A simulation study was performed to assess how prognostic factors affect the estimate of the biomarker-treatment interaction for a time-to-event outcome, when different approaches, like ignoring other prognostic factors, including all available covariates or using variable selection strategies, are applied. Different scenarios regarding the proportion of censored observations, the correlation structure between the covariate of interest and further potential prognostic variables, and the strength of the interaction were considered.

Results

The simulation study revealed that in a regression model for estimating a biomarker-treatment interaction, the probability of detecting a biomarker-treatment interaction can be increased by including prognostic variables that are associated with the outcome, and that the interaction estimate is biased when relevant prognostic variables are not considered. However, the probability of a false-positive finding increases if too many potential predictors are included or if variable selection is performed inadequately.

Conclusions

We recommend undertaking an adequate literature search before data analysis to derive information about potential prognostic variables and to gain power for detecting true interaction effects and pre-specifying analyses to avoid selective reporting and increased false-positive rates.

SUBMITTER: Haller B

PROVIDER: S-EPMC5819679 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Publications

A simulation study on estimating biomarker-treatment interaction effects in randomized trials with prognostic variables.

Haller Bernhard B Ulm Kurt K

Trials 20180220 1

<h4>Background</h4>To individualize treatment decisions based on patient characteristics, identification of an interaction between a biomarker and treatment is necessary. Often such potential interactions are analysed using data from randomized clinical trials intended for comparison of two treatments. Tests of interactions are often lacking statistical power and we investigated if and how a consideration of further prognostic variables can improve power and decrease the bias of estimated biomar ...[more]

PMID: 29463271

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Project description:ObjectiveRandomised controlled trials (RCTs) are often considered as the gold standard for assessing new health interventions. Patients are randomly assigned to receive an intervention or control. The effect of the intervention can be estimated by comparing outcomes between groups, whose prognostic factors are expected to balance by randomisation. However, patients' non-compliance with their assigned treatment will undermine randomisation and potentially bias the estimate of treatment effect. Through simulation, we aim to compare common approaches in analysing non-compliant data under different non-compliant scenarios.SettingsBased on a real study, we simulated hypothetical trials by varying three non-compliant factors: the type, randomness and degree of non-compliance. We compared the intention-to-treat (ITT), as-treated (AT), per-protocol (PP), instrumental variable (IV) and complier average casual effect (CACE) analyses to estimate large (50% improvement over the control), moderate (25% improvement) and null (same as the control) treatment effects. Different approaches were compared by the bias of estimate, mean square error (MSE) and 95% coverage of the true value.ResultsFor a large or moderate treatment effect, the ITT estimate was considerably biased in all scenarios. The AT, PP, IV and CACE estimates were unbiased when non-compliant behaviours were random. The IV estimate was unbiased when non-compliant behaviours were symmetrically dependent on patients' conditions. The PP estimate was mostly unbiased when patients in the control group did not have access to the intervention. When the intervention was not different from the control, the ITT was less biased than the other approaches. Similar results were found when comparing the MSE and 95% coverage.ConclusionsThe standard ITT analysis under non-compliance is biased when the intervention has a moderate or large effect. Alternative analyses can provide unbiased or less biased estimates. Based on the results, we make some suggestions on choosing optimal approaches for analysing specific non-compliant scenarios.

Dataset Information

A simulation study on estimating biomarker-treatment interaction effects in randomized trials with prognostic variables.

Background

Methods

Results

Conclusions

Publications

A simulation study on estimating biomarker-treatment interaction effects in randomized trials with prognostic variables.

Similar Datasets

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