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Updated: May 18, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
Published on: July 4, 2007
Monte Carlo simulation of the cost-effectiveness of sample size maintenance programs revealed the need to consider
Michael C David1, Mark Bensink, Hideki Higashi
1School of Population Health, The University of Queensland, Herston, Queensland 4006, Australia. michael.david@uqconnect.edu.au
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.
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