Monte Carlo simulation of the cost-effectiveness of sample size maintenance programs revealed the need to consider substitution sampling

Abstract

Objective: To assess the cost-effectiveness of sample size maintenance programs in a prospective cohort. Study Design and Setting: The Living with Diabetes Study in Queensland, Australia is a longitudinal survey providing a comprehensive examination of health care utilization and disease progression among people with diabetes. Data from this study were used to compare the cost-effectiveness of a program incorporating substitution sampling with two alternative programs: “no follow-up” and “usual practice.” Results: A program involving substitution sampling was shown to be the most effective with an additional 3,556 complete responses (compared with a “no follow-up” program) and an additional 2,099 complete responses (compared with “usual practice”). An incremental analysis through a Monte Carlo simulation found substitution sampling to be the most cost-effective option for maintaining sample size with an incremental cost-effective ratio of $54.87 (95% uncertainty interval $52.68–$57.25) compared with $87.58 ($77.89–$100.09) for “usual practice.” Conclusions: Based on the available data, a program involving substitution sampling is economically justified and should be considered in any approach with the aim of maintaining sample size. There is, however, a continuing need to evaluate the effectiveness of this option on other outcome measures, such as bias.