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How to analyze work productivity loss due to health problems in randomized controlled trials? A simulation study
Wei Zhang1,2, Huiying Sun3
1School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada. wzhang@cheos.ubc.ca.
BMC Medical Research Methodology
|June 24, 2021
Summary
Choosing the best statistical model for analyzing work productivity loss in randomized controlled trials (RCTs) depends on sample size and data distribution. Our simulation shows two-part models excel with many zero losses, while three-part models perform best with larger sample sizes and specific data characteristics.
Area of Science:
- Health Economics
- Biostatistics
- Clinical Trial Analysis
Background:
- Randomized controlled trials (RCTs) increasingly measure work productivity loss.
- Productivity loss data often exhibit inflation at zero and maximum values.
- Comparing analytical methods for these outcomes is crucial.
Purpose of the Study:
- To compare the performance of five common statistical methods for analyzing work productivity loss in RCTs.
- To evaluate how sample size and data characteristics influence method selection.
Main Methods:
- A simulation study was conducted.
- Methods compared include Ordinary Least Squares (OLS), Negative Binomial (NB), two-part models (truncated NB or gamma), and a three-part model (Beta distribution for intermediate values).
- Sample sizes of 50, 100, and 200 were simulated, with baseline productivity loss as a covariate.
Main Results:
- All models performed similarly when baseline productivity loss was at the mean.
- With N=50 and few max losses, two-part models were best for >50% zero loss; otherwise, OLS performed best.
- With N=100 or 200, the three-part model was superior if scale parameters between zero and max loss were equal across arms.
Conclusions:
- Model selection for productivity loss in RCTs is contingent upon sample size, proportions of zero/max loss, and distribution parameters.
- Consider these factors for accurate treatment effect estimation.
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