Clinical trials: how to assess confounding and why so

Area of Science:

• Biostatistics
• Clinical Trials Methodology
• Epidemiology

Background:

• Randomized controlled trials (RCTs) typically have negligible covariate imbalance.
• Smaller studies face substantial risk of random imbalance, necessitating confounder assessment and adjustment.
• Failure to adjust for confounders can lead to biased treatment effect estimations.

Purpose of the Study:

• To review three methods for assessing and adjusting for confounding variables.
• To present these methods for a non-mathematical audience.
• To enhance the reliability of treatment comparisons in smaller studies.

Main Methods:

• Subclassification: Dividing the study population into subclasses based on shared characteristics, assessing treatment efficacy within each, and calculating a weighted average.
• Regression Modeling: Incorporating covariates as dependent variables in a multivariable regression model with treatment efficacy as the independent variable; non-significant covariates are removed.
• Propensity Scores: Calculating patient-specific odds ratios (ORs) based on covariate values, multiplying significant ORs to derive a propensity score, and using these scores for adjustment via subclassification or regression.

Main Results:

• Subclassification clearly visualizes empty subclasses but is limited to single confounders.
• Regression modeling accommodates multiple covariates, but the number of covariates is constrained by sample size.
• Propensity scores offer greater power with multiple covariates but can be affected by irrelevant covariates or extreme ORs.

Conclusions:

• Each method presents distinct advantages and limitations for confounding adjustment in clinical research.
• Subclassification is intuitive but limited in scope; regression modeling is flexible but sample-size dependent.
• Propensity scores are powerful for multiple confounders but require careful covariate selection. These methods do not assess interaction effects.