Grant Number:	2R01CA050597-16 Interpret this number
Primary Investigator:	Spiegelman, Donna
Organization:	Harvard University D/B/A Harvard School Of Public Health
Project Title:	Measurement Errors in Cancer Epidemiology
Fiscal Year:	2011

DESCRIPTION (provided by applicant): Exposure measurement errors in cancer epidemiology poses special methodologic challenges. For example, nutritional and physical activity patterns form the basis for many etiologic hypotheses concerning cancer. However, nutrient intake and physical activity are difficult to measure precisely. It is the role of measurement error correction methods to validly and efficiently estimate the relationship between exposures and cancer outcomes. To accomplish this requires both a main study where disease and the surrogate exposure are measured and validation data to determine the extent of the measurement error. In this proposal, we seek to further expand our group's previous work on methods of corrections for measurement error and misclassification. A major focus is on nutritional studies based on intakes and activity measures reported at multiple questionnaires over time when the target exposure is long-term average diet and/or activity. Here, the dependent variable is time to cancer incidence or mortality; thus, Cox models are considered in which time-varying covariates such as cumulative averages and cumulative exposures are of primary interest. Modeling and variable selection issues in the context of real-life applications of measurement error correction methods will be carefully studied, with an aim to provide guidance for current and future users of the methodology; no such guidance currently exists. Application of methods to correct for measurement error in time-varying exposures such as cumulative averages requires validation studies in which repeated measures within study participants are repeatedly validated. Analysis of the currently ongoing HSPH Lifestyle Validation Study' which will make available validation of up to four repeated dietary and physical activity measurements, using state of the art biomarker technology including doubly labeled water and urinary nitrogen, will be another major focus of our work. Finally, our attention will turn to an entirely new area of research in this cycle: methods which consider measurement error and misclassification in the development and evaluation of models for the prediction of breast, colon and ovarian cancer risk. As previously, user friendly , public use software development will be a focus of all new methods of development. PUBLIC HEALTH RELEVANCE: Measurement error is a major source of bias in epidemiologic research aimed at elucidating the relationship between diet, physical activity, and cancer incidence and mortality. A competing continuation of a methodologic research effort ongoing since 1989 focusing on the development of methods and software to adjust for this bias in point and interval estimates of relative risk, the current proposal aims to develop methods for bias reduction and elimination in prospective cohort studies with repeated measures of exposure over time, for relative risks and in cancer risk prediction models. In addition, we will analyze the newly completed Lifestyle Validation Study, a unique resource with state of the art biomarkers and up to four repeated validations, and apply this information to the newly developed methods to cancer studies arising in the Nurses' Health Study, the Nurses' Health Study II, and the Health Professionals Follow-up Study.




Parametric Regression Analysis with Covariate Misclassification in Main Study/Validation Study Designs.
Authors: Yi G.Y. , Yan Y. , Liao X. , Spiegelman D. .
Source: The International Journal Of Biostatistics, 2018-12-15 00:00:00.0; 15(1), .
EPub date: 2018-12-15 00:00:00.0.
PMID: 30864410
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A test for gene-environment interaction in the presence of measurement error in the environmental variable.
Authors: Aschard H. , Spiegelman D. , Laville V. , Kraft P. , Wang M. .
Source: Genetic Epidemiology, 2018 Apr; 42(3), p. 250-264.
EPub date: 2018-02-08 00:00:00.0.
PMID: 29424028
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A Semiparametric Copula Method For Cox Models With Covariate Measurement Error
Authors: Kim S. , Li Y. , Spiegelman D. .
Source: Lifetime Data Analysis, 2016 Jan; 22(1), p. 1-16.
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Correcting AUC for Measurement Error.
Authors: Rosner B. , Tworoger S. , Qiu W. .
Source: Journal Of Biometrics & Biostatistics, 2015 Dec; 6(5), .
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The Impact Of Covariate Measurement Error On Risk Prediction
Authors: Khudyakov P. , Gorfine M. , Zucker D. , Spiegelman D. .
Source: Statistics In Medicine, 2015-07-10 00:00:00.0; 34(15), p. 2353-67.
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Functional And Structural Methods With Mixed Measurement Error And Misclassification In Covariates
Authors: Yi G.Y. , Ma Y. , Spiegelman D. , Carroll R.J. .
Source: Journal Of The American Statistical Association, 2015-06-01 00:00:00.0; 110(510), p. 681-696.
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Optimal allocation of resources in a biomarker setting.
Authors: Rosner B. , Hendrickson S. , Willett W. .
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PMID: 25346516
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Can Efficiency Be Gained By Correcting For Misclassification?
Authors: Wang M. , Liao X. , Spiegelman D. .
Source: Journal Of Statistical Planning And Inference, 2013-11-01 00:00:00.0; 143(11), .
PMID: 24187429
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Regression Calibration Is Valid When Properly Applied
Authors: Liao X. , Spiegelman D. , Carroll R.J. .
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A Regularization Corrected Score Method For Nonlinear Regression Models With Covariate Error
Authors: Zucker D.M. , Gorfine M. , Li Y. , Tadesse M.G. , Spiegelman D. .
Source: Biometrics, 2013 Mar; 69(1), p. 80-90.
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Application of a repeat-measure biomarker measurement error model to 2 validation studies: examination of the effect of within-person variation in biomarker measurements.
Authors: Preis S.R. , Spiegelman D. , Zhao B.B. , Moshfegh A. , Baer D.J. , Willett W.C. .
Source: American Journal Of Epidemiology, 2011-03-15 00:00:00.0; 173(6), p. 683-94.
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Survival Analysis With Error-prone Time-varying Covariates: A Risk Set Calibration Approach
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Source: Biometrics, 2011 Mar; 67(1), p. 50-8.
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Regression calibration with heteroscedastic error variance.
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Measurement error correction for the cumulative average model in the survival analysis of nutritional data: application to Nurses' Health Study.
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Source: Lifetime Data Analysis, 2010 Jan; 16(1), p. 136-53.
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The Impact Of Gene-environment Dependence And Misclassification In Genetic Association Studies Incorporating Gene-environment Interactions
Authors: Lindström,S. , Yen,Y.C. , Spiegelman,D. , Kraft,P. .
Source: Human Heredity, 2009; 68(3), p. 171-81.
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Measurement error correction for nutritional exposures with correlated measurement error: use of the method of triads in a longitudinal setting.
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Corrected Score Estimation In The Proportional Hazards Model With Misclassified Discrete Covariates
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Interval estimation for rank correlation coefficients based on the probit transformation with extension to measurement error correction of correlated ranked data.
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Measurement error and confidence intervals for ROC curves.
Authors: Tosteson T.D. , Buonaccorsi J.P. , Demidenko E. , Wells W.A. .
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Correlated Errors In Biased Surrogates: Study Designs And Methods For Measurement Error Correction
Authors: Spiegelman,D. , Zhao,B. , Kim,J. .
Source: Statistics In Medicine, 2005-06-15 00:00:00.0; 24(11), p. 1657-82.
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Inference for the proportional hazards model with misclassified discrete-valued covariates.
Authors: Zucker D.M. , Spiegelman D. .
Source: Biometrics, 2004 Jun; 60(2), p. 324-34.
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Comparisons of three alternative breast modalities in a common phantom imaging experiment.
Authors: Li D. , Meaney P.M. , Tosteson T.D. , Jiang S. , Kerner T.E. , McBride T.O. , Pogue B.W. , Hartov A. , Paulsen K.D. .
Source: Medical Physics, 2003 Aug; 30(8), p. 2194-205.
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Power and sample size calculations for generalized regression models with covariate measurement error.
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Segmented regression in the presence of covariate measurement error in main study/validation study designs.
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Statistical analysis of nonlinearly reconstructed near-infrared tomographic images: Part I--Theory and simulations.
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Markers of low level arsenic exposure for evaluating human cancer risks in a US population.
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Comparisons Of Measures Of Interclass Correlations: The General Case Of Unequal Group Size
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Matrix methods for estimating odds ratios with misclassified exposure data: extensions and comparisons.
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Covariate measurement error and the estimation of random effect parameters in a mixed model for longitudinal data.
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Correcting for bias in relative risk estimates due to exposure measurement error: a case study of occupational exposure to antineoplastics in pharmacists.
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Regression Calibration Method For Correcting Measurement-error Bias In Nutritional Epidemiology
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Measurement error correction for logistic regression models with an "alloyed gold standard".
Authors: Spiegelman D. , Schneeweiss S. , McDermott A. .
Source: American Journal Of Epidemiology, 1997-01-15 00:00:00.0; 145(2), p. 184-96.
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Non-dietary factors as risk factors for breast cancer, and as effect modifiers of the association of fat intake and risk of breast cancer.
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Source: Cancer Causes & Control : Ccc, 1997 Jan; 8(1), p. 49-56.
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Measurement Error Models For Ordinal Exposure Variables Measured With Error
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Source: Statistics In Medicine, 1996-02-15 00:00:00.0; 15(3), p. 293-303.
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Cohort studies of fat intake and the risk of breast cancer--a pooled analysis.
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Source: The New England Journal Of Medicine, 1996-02-08 00:00:00.0; 334(6), p. 356-61.
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Reproducibility And Validity Of A Self-administered Physical Activity Questionnaire For Male Health Professionals
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Correction Of Logistic Regression Relative Risk Estimates And Confidence Intervals For Random Within-person Measurement Error
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Dietary fat and fiber in relation to risk of breast cancer. An 8-year follow-up.
Authors: Willett W.C. , Hunter D.J. , Stampfer M.J. , Colditz G. , Manson J.E. , Spiegelman D. , Rosner B. , Hennekens C.H. , Speizer F.E. .
Source: Jama, 1992-10-21 00:00:00.0; 268(15), p. 2037-44.
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Absolute Fat Mass, Percent Body Fat, And Body-fat Distribution: Which Is The Real Determinant Of Blood Pressure And Serum Glucose?
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Effects of chain length on the immunogenicity in rabbits of group B Streptococcus type III oligosaccharide-tetanus toxoid conjugates.
Authors: Paoletti L.C. , Kasper D.L. , Michon F. , DiFabio J. , Jennings H.J. , Tosteson T.D. , Wessels M.R. .
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