Abstract
BackgroundAn increasing number of randomized controlled trials (RCTs) have measured the impact of interventions on work productivity loss. Productivity loss outcome is inflated at zero and max loss values. Our study was to compare the performance of five commonly used methods in analysis of productivity loss outcomes in RCTs.MethodsWe conducted a simulation study to compare Ordinary Least Squares (OLS), Negative Binominal (NB), two-part models (the non-zero part following truncated NB distribution or gamma distribution) and three-part model (the middle part between zero and max values following Beta distribution). The main number of observations each arm, Nobs, that we considered were 50, 100 and 200. Baseline productivity loss was included as a covariate.ResultsAll models performed similarly well when baseline productivity loss was set at the mean value. When baseline productivity loss was set at other values and Nobs = 50 with ≤5 subjects having max loss, two-part models performed best if the proportion of zero loss> 50% in at least one arm and otherwise, OLS performed best. When Nobs = 100 or 200, the three-part model performed best if the two arms had equal scale parameters for their productivity loss outcome distributions between zero and max values.ConclusionsOur findings suggest that when treatment effect at any given values of one single covariate is of interest, the model selection depends on the sample size, the proportions of zero loss and max loss, and the scale parameter for the productivity loss outcome distribution between zero and max loss in each arm of RCTs.
Highlights
An increasing number of randomized controlled trials (RCTs) have measured the impact of interventions on work productivity loss
Before transformed into monetary amount, work productivity loss due to health is usually first expressed as work time loss, i.e., counting the days missed from work, the hours lost due to reduced productivity while working
Among patients with arthritis, a study found that the frequency of ‘0’ value varied by the measurement questionnaires: 61% if presenteeism was measured using the Health Labour Questionnaire, 5% using Work Limitations Questionnaire, 16% using the World Health Organization Health and Work Performance Questionnaire, and 27% using Work Productivity and Activity Impairment Questionnaire (WPAI) [25]
Summary
An increasing number of randomized controlled trials (RCTs) have measured the impact of interventions on work productivity loss. Studies have shown that the proportion of Group I is very high, i.e., zero loss [24,25,26,27] Such a distribution applies to the sum of time loss from all the three subcomponents and the time loss from absenteeism and presenteeism, respectively. A clinical trial among employed patients with early rheumatoid arthritis showed that about 50–70% did not have any paid work productivity loss (sum of all three subcomponents) depending on the follow-up time point, and that about 5– 10% stopped working due to health problems at Week 13 or cumulatively at Week 52 [27], which contributed to a high proportion of Group I and Group III, i.e., inflated zero value and maximum value
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