Opening the Black Box of Biomarker Measurement Error

Epidemiology (Cambridge, Mass.) (Impact Factor: 6.2). 07/2010; 21 Suppl 4(Supplement):S1-3. DOI: 10.1097/EDE.0b013e3181dda514
Source: PubMed

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    • "We focus on the latter in this article. Although many ad hoc methods have been implemented in practice, more appropriate statistical methods for regression models with a covariate subject to limit of detection are yet to be thoroughly studied (Schisterman and Little, 2010). The complete case analysis, simply eliminating observations with values below limit of detection, yields consistent estimates of the regression coefficients (Nie et al., 2010; Little and Rubin, 2002), but loses efficiency. "
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    ABSTRACT: We consider generalized linear regression analysis with left-censored covariate due to the lower limit of detection. Complete case analysis by eliminating observations with values below limit of detection yields valid estimates for regression coefficients, but loses efficiency; substitution methods are biased; maximum likelihood method relies on parametric models for the unobservable tail probability distribution of such covariate, thus may suffer from model misspecification. To obtain robust and more efficient results, we propose a semiparametric likelihood-based approach for the estimation of regression parameters using an accelerated failure time model for the covariate subject to limit of detection. A two-stage estimation procedure is considered, where the conditional distribution of the covariate with limit of detection given other variables is estimated prior to maximizing the likelihood function. The proposed method outperforms the complete case analysis and the substitution methods as well in simulation studies. Technical conditions for desirable asymptotic properties are provided.
    Preview · Article · Dec 2014
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    • "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. "
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    ABSTRACT: ABSTRACT: Serum cotinine, a metabolite of nicotine, is frequently used in research as a biomarker of recent tobacco smoke exposure. Historically, secondhand smoke (SHS) research uses suboptimal statistical methods due to censored serum cotinine values, meaning a measurement below the limit of detection (LOD). 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.
    Full-text · Article · Oct 2011 · Tobacco Induced Diseases
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