Toward improved statistical methods for analyzing Cotinine-Biomarker health association data

We compared commonly used methods for analyzing censored serum cotinine data using parametric and non-parametric techniques employing data from the 1999-2004 National Health and Nutrition Examination Surveys (NHANES). To illustrate the differences in associations obtained by various analytic methods, we compared parameter estimates for the association between cotinine and the inflammatory marker homocysteine using complete case analysis, single and multiple imputation, "reverse" Kaplan-Meier, and logistic regression models.Parameter estimates and statistical significance varied according to the statistical method used with censored serum cotinine values. Single imputation of censored values with either 0, LOD or LOD/√2 yielded similar estimates and significance; multiple imputation method yielded smaller estimates than the other methods and without statistical significance. Multiple regression modelling using the "reverse" Kaplan-Meier method yielded statistically significant estimates that were larger than those from parametric methods.Analyses of serum cotinine data with values below the LOD require special attention. "Reverse" Kaplan-Meier was the only method inherently able to deal with censored data with multiple LODs, and may be the most accurate since it avoids data manipulation needed for use with other commonly used statistical methods. Additional research is needed into the identification of optimal statistical methods for analysis of SHS biomarkers subject to a LOD.A biomarker is a laboratory measure of a biological process [1]. The lowest quantity of a biomarker that can be distinguished from the lack of that biomarker is the biomarker's limit of detection (LOD), below which the level of biomarker cannot be accurately measured. One important yet unresolved issue in analyzing biomarker data arises when biomarker measurements fall below the LOD (i.e. "non-detects", "left-censored").Statistical analyses of data that include biomarker measurements below the