Bayesian methods for correcting misclassification: an example from birth defects epidemiology.
BACKGROUND
Cleft lip with or without cleft palate (CL/P) and cleft palate only (CPO) are common congenital malformations. Numerous epidemiologic studies have shown an increased risk for orofacial clefts among children whose mothers smoked during early pregnancy; however, there is concern that the results of these studies may have been biased because of exposure misclassification. The purpose of this study is to use previous research on the reliability of self-reported cigarette smoking to produce corrected point estimates (and associated credible intervals) of the effect of maternal smoking on children's risk of clefts.


METHODS
We accounted for misclassification using 4 Bayesian models that made different assumptions about the sensitivity and specificity of self-reported maternal smoking data. We used results from previous studies to specify the prior distributions for sensitivity and specificity of reporting and used Markov chain Monte Carlo algorithms to calculate the posterior distribution of the effect of maternal smoking on children's risk for CL/P and CPO.


RESULTS
After correcting for potential sources of misclassification in data from the National Birth Defects Prevention Study, we found an increased risk of CL/P among children born to mothers who smoked during early pregnancy (posterior odds ratio [OR] = 1.6, 95% credible interval = 1.1-2.2). The posterior effect of smoking on CPO provided less evidence of effect (posterior OR = 1.1, 95% credible interval = 0.7-1.7).


CONCLUSIONS
Our results lend some credibility to the hypothesis that periconceptional maternal smoking increases the risk of a child being born with CL/P. The results concerning CPO provide no overall evidence of effect, although the estimates were relatively imprecise. We suggest that future research should emphasize validity studies, especially those of differential reporting, rather than replicating existing analyses of the relationship between maternal smoking and clefts. We discuss how our approach is also applicable to evaluating misclassification in a wide range of exposure-outcome scenarios.
MacLehose
Richard F
RF
Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA. mac10029@umn.edu
Olshan
Andrew F
AF
Herring
Amy H
AH
Honein
Margaret A
MA
Shaw
Gary M
GM
Romitti
Paul A
PA
D000328:Adult; D001499:Bayes Theorem; D015982:Bias; D002971:Cleft Lip / Q000209:etiology*; D002972:Cleft Palate / Q000209:etiology*; D005260:Female; D006801:Humans; D007407:Interviews as Topic; D016017:Odds Ratio; D011159:Population Surveillance; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000706:statistics & numerical data; D012680:Sensitivity and Specificity; D012907:Smoking / Q000009:adverse effects; D014481:United States