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1.  Estimating the diagnostic likelihood ratio of a continuous marker 
Biostatistics (Oxford, England)  2010;12(1):87-101.
The diagnostic likelihood ratio function, DLR, is a statistical measure used to evaluate risk prediction markers. The goal of this paper is to develop new methods to estimate the DLR function. Furthermore, we show how risk prediction markers can be compared using rank-invariant DLR functions. Various estimators are proposed that accommodate cohort or case–control study designs. Performances of the estimators are compared using simulation studies. The methods are illustrated by comparing a lung function measure and a nutritional status measure for predicting subsequent onset of major pulmonary infection in children suffering from cystic fibrosis. For continuous markers, the DLR function is mathematically related to the slope of the receiver operating characteristic (ROC) curve, an entity used to evaluate diagnostic markers. We show that our methodology can be used to estimate the slope of the ROC curve and illustrate use of the estimated ROC derivative in variance and sample size calculations for a diagnostic biomarker study.
doi:10.1093/biostatistics/kxq045
PMCID: PMC3006125  PMID: 20639522
Biomarker; density estimation; diagnosis; logistic regression; rank invariant; risk prediction; ROC–GLM
2.  Biomarker evaluation and comparison using the controls as a reference population 
Biostatistics (Oxford, England)  2008;10(2):228-244.
The classification accuracy of a continuous marker is typically evaluated with the receiver operating characteristic (ROC) curve. In this paper, we study an alternative conceptual framework, the “percentile value.” In this framework, the controls only provide a reference distribution to standardize the marker. The analysis proceeds by analyzing the standardized marker in cases. The approach is shown to be equivalent to ROC analysis. Advantages are that it provides a framework familiar to a broad spectrum of biostatisticians and it opens up avenues for new statistical techniques in biomarker evaluation. We develop several new procedures based on this framework for comparing biomarkers and biomarker performance in different populations. We develop methods that adjust such comparisons for covariates. The methods are illustrated on data from 2 cancer biomarker studies.
doi:10.1093/biostatistics/kxn029
PMCID: PMC2648906  PMID: 18755739
Biomarker; Classification; Covariate adjustment; Percentile value; ROC; Standardization
3.  Estimating the capacity for improvement in risk prediction with a marker 
Biostatistics (Oxford, England)  2008;10(1):172-186.
Consider a set of baseline predictors X to predict a binary outcome D and let Y be a novel marker or predictor. This paper is concerned with evaluating the performance of the augmented risk model P(D = 1|Y,X) compared with the baseline model P(D = 1|X). The diagnostic likelihood ratio, DLRX(y), quantifies the change in risk obtained with knowledge of Y = y for a subject with baseline risk factors X. The notion is commonly used in clinical medicine to quantify the increment in risk prediction due to Y. It is contrasted here with the notion of covariate-adjusted effect of Y in the augmented risk model. We also propose methods for making inference about DLRX(y). Case–control study designs are accommodated. The methods provide a mechanism to investigate if the predictive information in Y varies with baseline covariates. In addition, we show that when combined with a baseline risk model and information about the population distribution of Y given X, covariate-specific predictiveness curves can be estimated. These curves are useful to an individual in deciding if ascertainment of Y is likely to be informative or not for him. We illustrate with data from 2 studies: one is a study of the performance of hearing screening tests for infants, and the other concerns the value of serum creatinine in diagnosing renal artery stenosis.
doi:10.1093/biostatistics/kxn025
PMCID: PMC2639345  PMID: 18714084
Biomarker; Classification; Diagnostic likelihood ratio; Diagnostic test; Logistic regression; Posterior probability

Results 1-3 (3)