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Estimation of covariate effects with current status data and differential mortality
Alberto Palloni1, Jason R Thomas
1Center for Demography and Ecology, University of Wisconsin-Madison, Madison, WI 53706-1393, USA. palloni@ssc.wisc.edu
Demography
|November 20, 2012
Summary
This study addresses biases in chronic condition prevalence data from surveys. We developed an adjustment method using empirical data and microsimulation to correct for mortality bias, significantly reducing inaccuracies.
Area of Science:
- Epidemiology
- Biostatistics
- Health Economics
Background:
- Population surveys assessing chronic conditions face challenges.
- Self-reported data may contain errors (false positives/negatives).
- Survivorship bias due to excess mortality affects prevalence estimates.
Purpose of the Study:
- To quantify bias in chronic condition prevalence due to differential mortality.
- To propose and evaluate a statistical adjustment procedure for this bias.
- To improve the accuracy of socioeconomic impact assessments on chronic diseases.
Main Methods:
- Utilized a combination of empirical data and microsimulation modeling.
- Assessed the magnitude of bias introduced by differential mortality in survey respondents.
- Developed and tested an adjustment procedure to correct for selection bias.
Main Results:
- The proposed adjustment procedure significantly reduces bias stemming from differential mortality.
- Microsimulation effectively modeled the impact of excess mortality on reported prevalence.
- Empirical data supported the effectiveness of the bias correction method.
Conclusions:
- The developed adjustment procedure offers a viable solution to mitigate survivorship bias in chronic disease prevalence studies.
- Accurate assessment of socioeconomic determinants requires addressing mortality-related biases.
- This methodology enhances the reliability of population survey data for public health research.
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