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Simulations showed that validation of database-derived diagnostic criteria based on a small subsample reduced bias
- Source :
-
Journal of Clinical Epidemiology . Jun2007, Vol. 60 Issue 6, p600-609. 10p. - Publication Year :
- 2007
-
Abstract
- Abstract: Objective: To evaluate alternative approaches to correct for bias due to inaccurate diagnostic criteria in database studies of associations. Study Design and Settings: A simulation study of a hypothetical cohort of 10,000 subjects selected based on database-derived diagnostic criteria with positive predictive value (PPV) of either 53% or 80%. Analyses focus on the putative association between a drug and the time to a negative outcome. The association is confounded for “false positive” subjects, where the drug acts as a marker for unobserved frailty. First, we estimate the conventional multivariable Cox''s Model 1. We then assume having in-depth evaluation of a fraction of subjects, which permits estimating the probabilities of having the disease for all subjects in the cohort. Alternative correction methods use the estimated probability as a confounder (Model 2), a modifier of the drug effect (Model 3), or an importance weight (Model 4). Results: With a PPV of 53%, Models 1 and 2 induced about 50% underestimation bias for the drug effect. Interaction-based Model 3 yielded the least biased estimates (25% bias), whereas weighting by probability (Model 4) resulted in slightly more biased (33%), but more stable estimates. Conclusion: Proposed methods help reducing bias due to sample contamination. [Copyright &y& Elsevier]
- Subjects :
- *DATABASES
*DRUG efficacy
*FAILURE time data analysis
*SIMULATION methods & models
Subjects
Details
- Language :
- English
- ISSN :
- 08954356
- Volume :
- 60
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Journal of Clinical Epidemiology
- Publication Type :
- Academic Journal
- Accession number :
- 25083309
- Full Text :
- https://doi.org/10.1016/j.jclinepi.2006.07.016