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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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Statistical analysis of cost-effectiveness data from randomized clinical trials.

Andrew R Willan¹

¹SickKids Research Institute, Population Health Sciences, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada. andy@andywillan.com.

Expert Review of Pharmacoeconomics & Outcomes Research

|June 10, 2010

Summary

Statistical methods for cost-effectiveness analysis have advanced rapidly, focusing on incremental net benefit (INB) over the incremental cost-effectiveness ratio (ICER). Key parameters include differences in cost and effectiveness, with methods adapting to data complexities like censoring and covariates.

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Area of Science:

Health Economics
Biostatistics
Clinical Trial Analysis

Background:

Rapid development in statistical methods for cost-effectiveness analysis since the mid-1990s.
Driven by the availability of patient-level cost data in randomized clinical trials.
Shift in focus from incremental cost-effectiveness ratio (ICER) to incremental net benefit (INB) due to statistical challenges with ratio estimation.

Purpose of the Study:

To review and present statistical methods for cost-effectiveness data analysis.
To outline the estimation of five key parameters required for cost-effectiveness analysis.
To discuss the influence of data characteristics on the choice of statistical procedures.

Main Methods:

Estimation of between-treatment arm differences in mean effectiveness and mean cost.
Estimation of variances and covariance for cost and effectiveness.
Utilizing these parameters to plot cost-effectiveness acceptability curves and calculate confidence limits for ICER and INB.

Main Results:

Identified five essential parameters for cost-effectiveness analysis.
Demonstrated the utility of estimated parameters for plotting cost-effectiveness acceptability curves.
Highlighted the dependence of specific statistical procedures on data features such as censoring, covariates, and cost distributions.

Conclusions:

Statistical methods for cost-effectiveness analysis are adaptable to various data complexities.
The choice of statistical procedure is contingent upon specific trial data characteristics.
Comprehensive understanding of these methods is crucial for accurate economic evaluations in healthcare.