EB Research: Biomarker/Analytical Development

Biomarkers are, and will continue to be, an integral part of epidemiological research, making substantial contributions to our understanding of disease pathways and processes.  New and emerging biomarkers are essential to this continued understanding.  Biomarkers vary greatly in their relation to human disease etiology, but also in measurement techniques and analytic methods.  Measurement error can occur in a variety of measurement-specific or more general ways including intra-individual variability and instrument sensitivity, among other causes. Acknowledging, evaluating, and adjusting for these errors is crucial for the correct assessment of individual, as well as population, risk, as measurement error is a consideration for measurement of all biomarkers.  Division researchers continue to inform the epidemiologic community of sources and effects of measurement error, but also with developing and implementing methodologies that maximize statistical efficiency while properly accounting for measurement error.

Methods that compare biomarker diagnostic effectiveness and novel study designs that reduce cost and leverage statistical efficiency are also a major focus of Division researchers. These methods, originally created for receiver operating characteristic (ROC) curves, have been adapted and found to have equally useful application to gene-environment interactions.

Researchers here have diligently investigated the sources of laboratory measurement errors by gaining a laboratory perspective on the measurement process ranging from sample storage and preparation to the calibrations and measurement processes of assay equipment.  This understanding has provided insight to data issues commonly present, yet largely ignored, in epidemiological research.  Researchers have the goal of providing the methodological tools necessary to assess and address issues related to study design, biomarker measurement, and biomarker analytic assessment.

Principal Investigators

DIPHR Collaborators

Biomarker Analytical Development Publications

  • Lash TL, Schisterman EF. New Designs for New Epidemiology. Epidemiology. 2018; 29(1):76-77.  PMID: 29068839
  • Schildcrout JS, Schisterman EF, Aldrich MC, Rathouz PJ. Outcome-related, Auxiliary Variable Sampling Designs for Longitudinal Binary Data. Epidemiology. 2018; 29(1):58-66.  PMID: 29068841.  PMCID: PMC5718926
  • Schildcrout JS, Schisterman EF, Mercaldo ND, Rathouz PJ, Heagerty PJ. Extending the Case-Control Design to Longitudinal Data: Stratified Sampling Based on Repeated Binary Outcomes. Epidemiology. 2018; 29(1):67-75.  PMID: 29068838.  PMCID: PMC5718932
  • Danaher MR, Albert PS, Roy A, Schisterman EF. Estimation of interaction effects using pooled biospecimens in a case-control study. Statistics in Medicine. 2016; 35(9):1502-1513.  PMID: 26553532.  PMCID: PMC4821703
  • Lyles RH, Mitchell EM, Weinberg CR, Umbach DM, Schisterman EF. An efficient design strategy for logistic regression using outcome- and covariate-dependent pooling of biospecimens prior to assay. Biometrics. 2016; 72(3):965-975.  PMID: 26964741.  PMCID: PMC5014596
  • McMahan CS, McLain AC, Gallagher CM, Schisterman EF. Estimating covariate-adjusted measures of diagnostic accuracy based on pooled biomarker assessments. Biometrical Journal. 2016; 58(4):944-961.  PMID: 26927583
  • Mitchell EM, Plowden TC, Schisterman EF. Estimating relative risk of a log-transformed exposure measured in pools. Statistics in Medicine. 2016; 35(29):5477-5494.  PMID: 27530506.  PMCID: PMC5118194
  • Perkins NJ, Mitchell EM, Lyles RH, Schisterman EF. Case‐control data analysis for randomly pooled biomarkers. Biometrical Journal. 2016; 58(5):1007-1020. PMID: 26824757. PMCID: PMC5588030
  • Mitchell EM, Lyles RH, Manatunga AK, Schisterman EF. Semiparametric regression models for a right-skewed outcome subject to pooling. American Journal of Epidemiology. 2015; 181(7):541-548. PMID: 25737248. PMCID: PMC4371765
  • Mitchell EM, Lyles RH, Schisterman EF. Positing, fitting, and selecting regression models for pooled biomarker data. Statistics in Medicine. 2015; 34(17):2544-2558. PMID: 25846980. PMCID: PMC4490092
  • Lynch KE, Mumford SL, Schliep KC, Whitcomb BW, Zarek SM, Pollack AZ, Bertone-Johnson ER, Danaher M, Wactawski-Wende J, Gaskins AJ, Schisterman EF. Assessment of anovulation in eumenorrheic women: comparison of ovulation detection algorithms. Fertility and Sterility. 2014; 102(2):511-518.e2. PMID: 24875398. PMCID: PMC4119548
  • Mitchell EM, Lyles RH, Manatunga AK, Danaher M, Perkins NJ, Schisterman EF. Regression for skewed biomarker outcomes subject to pooling. Biometrics. 2014; 70(1):202-211. PMID: 24521420. PMCID: PMC3988986
  • Schisterman EF, Mumford SL, Sjaarda LA. Failure to consider the menstrual cycle phase may cause misinterpretation of clinical and research findings of cardiometabolic biomarkers in premenopausal women. Epidemiology Reviews. 2014; 36:71-82. PMID: 24042431. PMCID: PMC3873842
  • Danaher MR, Roy A, Chen Z, Mumford SL, Schisterman EF. Minkowski-Weyl Priors for Models with Parameter Constraints: An Analysis of the BioCycle Study. Journal of the American Statistical Association. 2012; 107(500):1395-1409. PMID: 27099406. PMCID: PMC4834988
top of pageBACK TO TOP