Design and analytic considerations for single-armed studies with misclassification of a repeated binary outcome

Area of Science:

• Biostatistics
• Clinical Trial Design
• Epidemiology

Background:

• Clinical studies often use binary outcome variables prone to misclassification due to cost or logistics.
• Single-armed studies with screening-selected participants face bias from misclassification at screening and outcome measurement.
• Ethical considerations sometimes necessitate noncomparative study designs.

Purpose of the Study:

• To propose a robust study design for mitigating bias in treatment effect estimation.
• To introduce a method for collecting internal validation data using a gold standard outcome measure.
• To optimize the allocation of observations within a validation study to minimize variance.

Main Methods:

• Likelihood-based analysis for treatment effect estimation.
• Development of a study design incorporating internal validation data.
• Identification and analysis of four types of validation study observations.
• Exploration of efficiency considerations for ratio and difference measures of treatment effect.

Main Results:

• The proposed design effectively addresses bias from screening and outcome misclassification.
• Optimal allocation of validation data is crucial for minimizing estimated treatment effect variance.
• The optimal allocation strategy depends on whether a ratio or difference measure is used.
• Numerical illustrations and a real-life example demonstrate the proposed methods.

Conclusions:

• The proposed study design enhances the accuracy of treatment effect estimation in challenging clinical settings.
• Internal validation data collection and optimal allocation are key to reliable results.
• The choice of treatment effect measure significantly influences the optimal design strategy.