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Assessing incremental value of biomarkers with multi-phase nested case-control studies.

ABSTRACT: Accurate risk prediction models are needed to identify different risk groups for individualized prevention and treatment strategies. In the Nurses' Health Study, to examine the effects of several biomarkers and genetic markers on the risk of rheumatoid arthritis (RA), a three-phase nested case-control (NCC) design was conducted, in which two sequential NCC subcohorts were formed with one nested within the other, and one set of new markers measured on each of the subcohorts. One objective of the study is to evaluate clinical values of novel biomarkers in improving upon existing risk models because of potential cost associated with assaying biomarkers. In this paper, we develop robust statistical procedures for constructing risk prediction models for RA and estimating the incremental value (IncV) of new markers based on three-phase NCC studies. Our method also takes into account possible time-varying effects of biomarkers in risk modeling, which allows us to more robustly assess the biomarker utility and address the question of whether a marker is better suited for short-term or long-term risk prediction. The proposed procedures are shown to perform well in finite samples via simulation studies.

SUBMITTER: Zhou QM

PROVIDER: S-EPMC4819437 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Assessing incremental value of biomarkers with multi-phase nested case-control studies.

Zhou Qian M QM Zheng Yingye Y Chibnik Lori B LB Karlson Elizabeth W EW Cai Tianxi T

Biometrics 20150720 4

Accurate risk prediction models are needed to identify different risk groups for individualized prevention and treatment strategies. In the Nurses' Health Study, to examine the effects of several biomarkers and genetic markers on the risk of rheumatoid arthritis (RA), a three-phase nested case-control (NCC) design was conducted, in which two sequential NCC subcohorts were formed with one nested within the other, and one set of new markers measured on each of the subcohorts. One objective of the ...[more]

PMID: 26195245

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Project description:RationaleCross-sectional studies demonstrate an association between metabolic syndrome and impaired lung function.ObjectivesTo define if metabolic syndrome biomarkers are risk factors for loss of lung function after irritant exposure.MethodsA nested case-control study of Fire Department of New York personnel with normal pre-September 11th FEV(1) and who presented for subspecialty pulmonary evaluation before March 10, 2008. We correlated metabolic syndrome biomarkers obtained within 6 months of World Trade Center dust exposure with subsequent FEV(1). FEV(1) at subspecialty pulmonary evaluation within 6.5 years defined disease status; cases had FEV(1) less than lower limit of normal, whereas control subjects had FEV(1) greater than or equal to lower limit of normal.Measurements and main resultsClinical data and serum sampled at the first monitoring examination within 6 months of September 11, 2001, assessed body mass index, heart rate, serum glucose, triglycerides and high-density lipoprotein (HDL), leptin, pancreatic polypeptide, and amylin. Cases and control subjects had significant differences in HDL less than 40 mg/dl with triglycerides greater than or equal to 150 mg/dl, heart rate greater than or equal to 66 bpm, and leptin greater than or equal to 10,300 pg/ml. Each increased the odds of abnormal FEV(1) at pulmonary evaluation by more than twofold, whereas amylin greater than or equal to 116 pg/ml decreased the odds by 84%, in a multibiomarker model adjusting for age, race, body mass index, and World Trade Center arrival time. This model had a sensitivity of 41%, a specificity of 86%, and a receiver operating characteristic area under the curve of 0.77.ConclusionsAbnormal triglycerides and HDL and elevated heart rate and leptin are independent risk factors of greater susceptibility to lung function impairment after September 11, 2001, whereas elevated amylin is protective. Metabolic biomarkers are predictors of lung disease, and may be useful for assessing risk of impaired lung function in response to particulate inhalation.

Project description:Pooling biomarker data across multiple studies allows for examination of a wider exposure range than generally possible in individual studies, evaluation of population subgroups and disease subtypes with more statistical power, and more precise estimation of biomarker-disease associations. However, circulating biomarker measurements often require calibration to a single reference assay prior to pooling due to assay and laboratory variability across studies. We propose several methods for calibrating and combining biomarker data from nested case-control studies when reference assay data are obtained from a subset of controls in each contributing study. Specifically, we describe a two-stage calibration method and two aggregated calibration methods, named the internalized and full calibration methods, to evaluate the main effect of the biomarker exposure on disease risk and whether that association is modified by a potential covariate. The internalized method uses the reference laboratory measurement in the analysis when available and otherwise uses the estimated value derived from calibration models. The full calibration method uses calibrated biomarker measurements for all subjects, including those with reference laboratory measurements. Under the two-stage method, investigators complete study-specific analyses in the first stage followed by meta-analysis in the second stage. Our results demonstrate that the full calibration method is the preferred aggregated approach to minimize bias in point estimates. We also observe that the two-stage and full calibration methods provide similar effect and variance estimates but that their variance estimates are slightly larger than those from the internalized approach. As an illustrative example, we apply the three methods in a pooling project of nested case-control studies to evaluate (i) the association between circulating vitamin D levels and risk of stroke and (ii) how body mass index modifies the association between circulating vitamin D levels and risk of cardiovascular disease.

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Assessing incremental value of biomarkers with multi-phase nested case-control studies.

Publications

Assessing incremental value of biomarkers with multi-phase nested case-control studies.

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