Lifting the veil off treatment effect heterogeneity.

Area of Science:

• Biostatistics
• Clinical Trials
• Health Services Research

Background:

• Clinicians suspect treatment effects vary individually, but lack evidence-based guidance on heterogeneity of treatment effects (HTE).
• Conventional statistical methods in randomized controlled trials (RCTs) are conservative, hindering the discovery of actionable HTE.
• The assumption of a common treatment effect in RCTs is rarely challenged, potentially leading to suboptimal patient care.

Purpose of the Study:

• To explore the historical context and limitations of statistical methods used in RCTs concerning HTE.
• To propose a novel graphical method for exploratory data analysis to identify potential HTE.
• To demonstrate how this graphical method can reveal discrepancies from the common-effect model.

Main Methods:

• Review of historical statistical methods in RCTs and their limitations regarding HTE.
• Development of a graphical exploratory data analysis technique comparing observed data with 'pseudo data' from HTE models.
• Illustration using artificial data to show the impact of ignoring HTE and the utility of the proposed graphical method.

Main Results:

• Conventional statistical methods are ill-equipped to detect or quantify HTE, reinforcing the belief that it is rare.
• The proposed graphical method provides visual evidence of HTE by comparing actual data distributions to expected distributions under a common-effect model.
• Discrepancies between observed and pseudo-data distributions serve as prima facie evidence for HTE, warranting further investigation.

Conclusions:

• The common-effect assumption in RCTs may obscure important individual variations in treatment response.
• A graphical approach offers a feasible and intuitive method for detecting potential HTE in clinical trial data.
• Identifying and understanding HTE is crucial for optimizing individualized patient treatment strategies.