Simulations showed that validation of database-derived diagnostic criteria based on a small subsample reduced bias.
OBJECTIVE
To evaluate alternative approaches to correct for bias due to inaccurate diagnostic criteria in database studies of associations.


METHODS
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.


CONCLUSIONS
Proposed methods help reducing bias due to sample contamination.
Abrahamowicz
Michal
M
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada. Michal@epimgh.mcgill.ca
Xiao
Yongling
Y
Ionescu-Ittu
Raluca
R
Lacaille
Diane
D
D001172:Arthritis, Rheumatoid / Q000175:diagnosis / Q000188:drug therapy; D015982:Bias; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D019992:Databases as Topic; D003951:Diagnostic Errors; D004185:Disability Evaluation; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D008297:Male; D017063:Outcome Assessment, Health Care / Q000379:methods*; D011379:Prognosis