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Biomarker Assessment (biomarker + assessment)

Distribution by Scientific Domains

Mathematics and Statistics	50%

Selected Abstracts

Measurement error bias in pharmaceutical cost-effectiveness analysis

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 5-6 2006
*Article first published online: 20 DEC 200, Ian C. Marschner
Abstract Drug development in the pharmaceutical industry is increasingly influenced by measures of cost-effectiveness, such as cost per life-year gained, and some governments make decisions about whether to pay for drugs based on cost-effectiveness considerations. While cost per life-year gained is a key measure of cost-effectiveness, costs associated with the intermediate outcome of improving a biomarker, such as cholesterol level or blood pressure, provide important supplementary information, particularly where mortality data may be limited. In this case, cost-effectiveness can be interpreted as the additional cost per unit time of achieving an additional beneficial biomarker response to treatment. A problem in this context is that biomarker assessment is typically subject to measurement error which leads to bias in assessing the benefit of a drug, and hence in the assessment of its cost-effectiveness. We discuss the adjustment of cost-effectiveness analyses for measurement error and consider the potential magnitude of bias that can arise. Using example calculations in the context of cholesterol-lowering therapy, it is demonstrated that such biases can be significant, leading to costs being overestimated by in excess of 25%. Ignoring measurement error in cost-effectiveness analyses can, therefore, have a substantial effect on the interpretation of such analyses. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Characterization and peripheral blood biomarker assessment of anti,Jo-1 antibody,positive interstitial lung disease

ARTHRITIS & RHEUMATISM, Issue 7 2009
Thomas J. Richards
Objective Using a combination of clinical, radiographic, functional, and serum protein biomarker assessments, this study was aimed at defining the prevalence and clinical characteristics of interstitial lung disease (ILD) in a large cohort of patients with anti,Jo-1 antibodies. Methods A review of clinical records, pulmonary function test results, and findings on imaging studies determined the existence of ILD in anti,Jo-1 antibody,positive individuals whose data were accumulated in the University of Pittsburgh Myositis Database from 1982 to 2007. Multiplex enzyme-linked immunosorbent assays (ELISAs) for serum inflammation markers, cytokines, chemokines, and matrix metalloproteinases in different patient subgroups were performed to assess the serum proteins associated with anti,Jo-1 antibody,positive ILD. Results Among the 90 anti,Jo-1 antibody,positive individuals with sufficient clinical, radiographic, and/or pulmonary function data, 77 (86%) met the criteria for ILD. While computed tomography scans revealed a variety of patterns suggestive of underlying usual interstitial pneumonia (UIP) or nonspecific interstitial pneumonia, a review of the histopathologic abnormalities in a subset of patients undergoing open lung biopsy or transplantation or whose lung tissue was obtained at autopsy (n = 22) demonstrated a preponderance of UIP and diffuse alveolar damage. Analysis by multiplex ELISA yielded statistically significant associations between anti,Jo-1 antibody,positive ILD and elevated serum levels of C-reactive protein (CRP), CXCL9, and CXCL10, which distinguished this disease entity from idiopathic pulmonary fibrosis and anti,signal recognition particle antibody,positive myositis. Recursive partitioning further demonstrated that combinations of these and other serum protein biomarkers can distinguish these disease subgroups at high levels of sensitivity and specificity. Conclusion In this large cohort of anti,Jo-1 antibody,positive individuals, the incidence of ILD approached 90%. Multiplex ELISA demonstrated disease-specific associations between anti,Jo-1 antibody,positive ILD and serum levels of CRP as well as the interferon-,,inducible chemokines CXCL9 and CXCL10, highlighting the potential of this approach to define biologically active molecules contributing to the pathogenesis of myositis-associated ILD. [source]

Design and Inference for Cancer Biomarker Study with an Outcome and Auxiliary-Dependent Subsampling

BIOMETRICS, Issue 2 2010
Xiaofei Wang
Summary In cancer research, it is important to evaluate the performance of a biomarker (e.g., molecular, genetic, or imaging) that correlates patients' prognosis or predicts patients' response to treatment in a large prospective study. Due to overall budget constraint and high cost associated with bioassays, investigators often have to select a subset from all registered patients for biomarker assessment. To detect a potentially moderate association between the biomarker and the outcome, investigators need to decide how to select the subset of a fixed size such that the study efficiency can be enhanced. We show that, instead of drawing a simple random sample from the study cohort, greater efficiency can be achieved by allowing the selection probability to depend on the outcome and an auxiliary variable; we refer to such a sampling scheme as,outcome and auxiliary-dependent subsampling,(OADS). This article is motivated by the need to analyze data from a lung cancer biomarker study that adopts the OADS design to assess epidermal growth factor receptor (EGFR) mutations as a predictive biomarker for whether a subject responds to a greater extent to EGFR inhibitor drugs. We propose an estimated maximum-likelihood method that accommodates the OADS design and utilizes all observed information, especially those contained in the likelihood score of EGFR mutations (an auxiliary variable of EGFR mutations) that is available to all patients. We derive the asymptotic properties of the proposed estimator and evaluate its finite sample properties via simulation. We illustrate the proposed method with a data example. [source]