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Assessment and implication of prognostic imbalance in randomized controlled trials with a binary outcome--a
Rong Chu1, Stephen D Walter, Gordon Guyatt
1Department of Clinical Epidemiology and Biostatistics, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada. chur@mcmaster.ca
Plos One
|May 26, 2012
Summary
Randomized controlled trials can have chance imbalance in prognostic factors, especially with small sample sizes. Adjusting for strong prognostic factors improves treatment effect estimation and statistical power, making it crucial for accurate results.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Epidemiology
Background:
- Chance imbalance in baseline prognostic factors (PF) can bias treatment effect estimates in randomized controlled trials (RCTs).
- This bias is more pronounced in trials with smaller sample sizes.
- Prognostic imbalance can lead to over or underestimation of treatment effects.
Purpose of the Study:
- To evaluate the probability of prognostic factor imbalance between treatment arms in RCTs.
- To investigate the impact of prognostic imbalance on treatment effect estimation.
- To examine the effect of sample size on prognostic imbalance and treatment effect estimation.
Main Methods:
- Simulated data from parallel-group trials with varying outcome risks, treatment effects, PF prevalence, and sample sizes (n).
- Compared logistic regression models with and without PF adjustment.
- Assessed bias, standard error, confidence interval coverage, and statistical power.
Main Results:
- A 5% imbalance in a common PF (0.5 prevalence) occurred in 42% of trials with 125 participants/arm.
- Ignoring a strong PF (RR=5) underestimated moderate treatment effects, independent of sample size (n>50/arm).
- Adjusting for strong PFs increased statistical power and reduced bias; adjustment for weak PFs had minimal impact.
Conclusions:
- Substantial prognostic imbalance is probable in small RCTs.
- Covariate adjustment for strong prognostic factors enhances estimation accuracy and statistical power.
- Performing covariate adjustment is recommended when strong PFs are identified.
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