|Grant Number:||5R01CA050597-17 Interpret this number|
|Primary Investigator:||Spiegelman, Donna|
|Organization:||Harvard School Of Public Health|
|Project Title:||Measurement Errors in Cancer Epidemiology|
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.
Optimal allocation of resources in a biomarker setting.
Authors: Rosner B, Hendrickson S, Willett W
Source: Stat Med, 2015 Jan 30;34(2), p. 297-306.
EPub date: 2014 Oct 24.
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 SR, Spiegelman D, Zhao BB, Moshfegh A, Baer DJ, Willett WC
Source: Am J Epidemiol, 2011 Mar 15;173(6), p. 683-94.
EPub date: 2011 Feb 22.
Regression calibration with heteroscedastic error variance.
Authors: Spiegelman D, Logan R, Grove D
Source: Int J Biostat, 2011;7(1), p. 4.
EPub date: 2011 Jan 6.
Measurement error correction for the cumulative average model in the survival analysis of nutritional data: application to Nurses' Health Study.
Authors: Qiu W, Rosner B
Source: Lifetime Data Anal, 2010 Jan;16(1), p. 136-53.
EPub date: 2009 Sep 16.
Measurement error correction for nutritional exposures with correlated measurement error: use of the method of triads in a longitudinal setting.
Authors: Rosner B, Michels KB, Chen YH, Day NE
Source: Stat Med, 2008 Aug 15;27(18), p. 3466-89.
Interval estimation for rank correlation coefficients based on the probit transformation with extension to measurement error correction of correlated ranked data.
Authors: Rosner B, Glynn RJ
Source: Stat Med, 2007 Feb 10;26(3), p. 633-46.
Measurement error and confidence intervals for ROC curves.
Authors: Tosteson TD, Buonaccorsi JP, Demidenko E, Wells WA
Source: Biom J, 2005 Aug;47(4), p. 409-16.
Inference for the proportional hazards model with misclassified discrete-valued covariates.
Authors: Zucker DM, Spiegelman D
Source: Biometrics, 2004 Jun;60(2), p. 324-34.
Comparisons of three alternative breast modalities in a common phantom imaging experiment.
Authors: Li D, Meaney PM, Tosteson TD, Jiang S, Kerner TE, McBride TO, Pogue BW, Hartov A, Paulsen KD
Source: Med Phys, 2003 Aug;30(8), p. 2194-205.
Power and sample size calculations for generalized regression models with covariate measurement error.
Authors: Tosteson TD, Buzas JS, Demidenko E, Karagas M
Source: Stat Med, 2003 Apr 15;22(7), p. 1069-82.
Segmented regression in the presence of covariate measurement error in main study/validation study designs.
Authors: Staudenmayer J, Spiegelman D
Source: Biometrics, 2002 Dec;58(4), p. 871-7.
Statistical analysis of nonlinearly reconstructed near-infrared tomographic images: Part II--Experimental interpretation.
Authors: Song X, Pogue BW, Tosteson TD, McBride TO, Jiang S, Paulsen KD
Source: IEEE Trans Med Imaging, 2002 Jul;21(7), p. 764-72.
Statistical analysis of nonlinearly reconstructed near-infrared tomographic images: Part I--Theory and simulations.
Authors: Pogue BW, Song X, Tosteson TD, McBride TO, Jiang S, Paulsen KD
Source: IEEE Trans Med Imaging, 2002 Jul;21(7), p. 755-63.
Matrix methods for estimating odds ratios with misclassified exposure data: extensions and comparisons.
Authors: Morrissey MJ, Spiegelman D
Source: Biometrics, 1999 Jun;55(2), p. 338-44.
Design of validation studies for estimating the odds ratio of exposure-disease relationships when exposure is misclassified.
Authors: Holcroft CA, Spiegelman D
Source: Biometrics, 1999 Dec;55(4), p. 1193-201.
Efficient regression calibration for logistic regression in main study/internal validation study designs with an imperfect reference instrument.
Authors: Spiegelman D, Carroll RJ, Kipnis V
Source: Stat Med, 2001 Jan 15;20(1), p. 139-160.
Pooled analysis of prospective cohort studies on height, weight, and breast cancer risk.
Authors: van den Brandt PA, Spiegelman D, Yaun SS, Adami HO, Beeson L, Folsom AR, Fraser G, Goldbohm RA, Graham S, Kushi L, Marshall JR, Miller AB, Rohan T, Smith-Warner SA, Speizer FE, Willett WC, Wolk A, Hunter DJ
Source: Am J Epidemiol, 2000 Sep 15;152(6), p. 514-27.
Covariate measurement error and the estimation of random effect parameters in a mixed model for longitudinal data.
Authors: Tosteson TD, Buonaccorsi JP, Demidenko E
Source: Stat Med, 1998 Sep 15;17(17), p. 1959-71.
Correcting for bias in relative risk estimates due to exposure measurement error: a case study of occupational exposure to antineoplastics in pharmacists.
Authors: Spiegelman D, Valanis B
Source: Am J Public Health, 1998 Mar;88(3), p. 406-12.
Alcohol and breast cancer in women: a pooled analysis of cohort studies.
Authors: Smith-Warner SA, Spiegelman D, Yaun SS, van den Brandt PA, Folsom AR, Goldbohm RA, Graham S, Holmberg L, Howe GR, Marshall JR, Miller AB, Potter JD, Speizer FE, Willett WC, Wolk A, Hunter DJ
Source: JAMA, 1998 Feb 18;279(7), p. 535-40.
Measurement error correction for logistic regression models with an "alloyed gold standard".
Authors: Spiegelman D, Schneeweiss S, McDermott A
Source: Am J Epidemiol, 1997 Jan 15;145(2), p. 184-96.
Non-dietary factors as risk factors for breast cancer, and as effect modifiers of the association of fat intake and risk of breast cancer.
Authors: Hunter DJ, Spiegelman D, Adami HO, van den Brandt PA, Folsom AR, Goldbohm RA, Graham S, Howe GR, Kushi LH, Marshall JR, Miller AB, Speizer FE, Willett W, Wolk A, Yaun SS
Source: Cancer Causes Control, 1997 Jan;8(1), p. 49-56.
Cohort studies of fat intake and the risk of breast cancer--a pooled analysis.
Authors: Hunter DJ, Spiegelman D, Adami HO, Beeson L, van den Brandt PA, Folsom AR, Fraser GE, Goldbohm RA, Graham S, Howe GR
Source: N Engl J Med, 1996 Feb 8;334(6), p. 356-61.
Prediction in the presence of measurement error: general discussion and an example predicting defoliation.
Authors: Buonaccorsi JP
Source: Biometrics, 1995 Dec;51(4), p. 1562-9.
A two-stage validation study for determining sensitivity and specificity.
Authors: Tosteson TD, Titus-Ernstoff L, Baron JA, Karagas MR
Source: Environ Health Perspect, 1994 Nov;102 Suppl 8, p. 11-4.
Dietary fat and fiber in relation to risk of breast cancer. An 8-year follow-up.
Authors: Willett WC, Hunter DJ, Stampfer MJ, Colditz G, Manson JE, Spiegelman D, Rosner B, Hennekens CH, Speizer FE
Source: JAMA, 1992 Oct 21;268(15), p. 2037-44.
Effects of chain length on the immunogenicity in rabbits of group B Streptococcus type III oligosaccharide-tetanus toxoid conjugates.
Authors: Paoletti LC, Kasper DL, Michon F, DiFabio J, Jennings HJ, Tosteson TD, Wessels MR
Source: J Clin Invest, 1992 Jan;89(1), p. 203-9.
Cost-efficient study designs for binary response data with Gaussian covariate measurement error.
Authors: Spiegelman D, Gray R
Source: Biometrics, 1991 Sep;47(3), p. 851-69.