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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

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Improper analysis of trials randomised using stratified blocks or minimisation.

Brennan C Kahan¹, Tim P Morris

¹MRC Clinical Trials Unit, Aviation House, 125 Kingsway, London, WC2B 6NH, UK. brk@ctu.mrc.ac.uk

Statistics in Medicine

|December 6, 2011

Summary

Clinical trial randomisation using stratification or minimisation requires analysis adjustment. Ignoring this in statistical analysis leads to biased results, wider confidence intervals, and reduced power, impacting treatment effect validity.

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Area of Science:

Clinical trial methodology
Biostatistics
Medical research design

Background:

Clinical trials often use stratified blocks or minimisation for balanced treatment groups.
Statistical literature recommends adjusting analyses for these randomisation factors.
A recent review found many studies fail to adjust for stratification or minimisation variables.

Purpose of the Study:

To investigate the impact of unadjusted analyses in trials using stratified randomisation or minimisation.
To demonstrate the consequences of ignoring stratification/minimisation variables in statistical analysis.
To evaluate the validity of inference when randomisation design is not reflected in analysis.

Main Methods:

Simulation studies were conducted to explore the issue.
Analyses were performed for continuous, binary, and time-to-event outcomes.
The impact of stratified block randomisation and minimisation was assessed.

Main Results:

Unadjusted analyses result in upwardly biased standard errors for treatment effects.
This bias leads to overly wide 95% confidence intervals and reduced statistical power.
Type I error rates are lower than nominal levels when stratification is ignored.

Conclusions:

Adjusting statistical analyses for stratification or minimisation variables is crucial for valid clinical trial inference.
Failure to adjust analyses can lead to misleading conclusions about treatment efficacy.
Proper analysis reflecting study design ensures accurate standard errors and appropriate power.