Selection Induced Contrast Estimate (SICE) Effect: An Attempt to Quantify the Impact of Some Patient Selection Criteria in Randomized Clinical Trials

TL;DR

This paper introduces the SICE effect to quantify how patient selection criteria influence observed treatment effects in clinical trials, highlighting implications for transparency and meta-analysis.

Contribution

The paper formulates the SICE effect, providing a new way to measure the impact of inclusion/exclusion criteria on treatment outcomes in clinical trials.

Findings

SICE effect can exist when treatment affects the correlation between selection markers and responses.

Simulations and real trial data demonstrate the presence of the SICE effect.

Formulating SICE enhances transparency and interpretation of clinical trial results.

Abstract

Defining the Inclusion/Exclusion (I/E) criteria of a trial is one of the most important steps during a trial design. Increasingly complex I/E criteria potentially create information imbalance and transparency issues between the people who design and run the trials and those who consume the information produced by the trials. In order to better understand and quantify the impact of a category of I/E criteria on observed treatment effects, a concept, named the Selection Induced Contrast Estimate (SICE) effect, is introduced and formulated in this paper. The SICE effect can exist in controlled clinical trials when treatment affects the correlation between a marker used for selection and the response of interest. This effect is demonstrated with both simulations and real clinical trial data. Although the statistical elements behind the SICE effect have been well studied, explicitly formulating and studying this effect can benefit several areas, including better transparency in I/E criteria, meta-analysis of multiple clinical trials, treatment effect interpretation in real-world medical practice, etc.