||5R01CA074112-03 Interpret this number
||Harvard University (Medical School)
||Measurement Error in Occupational Cohort Studies
DESCRIPTION (Adapted from the Investigators Abstract): Large errors in
measurement of the exposure variable are frequently encountered in
occupational cancer studies. In general, these errors lead to bias of
effect estimates towards the null and underestimation of the width of
confidence intervals. Although statisticians have proposed numerous methods
for treating this problem, most of these methods are not appropriate for
occupational cancer studies, because; a) they make unrealistic assumption
about the process generating error in the measurement of the exposure
variable, b) they are not applicable to the survival data methods required
for the analysis of cohort studies, and c) they do not consider interval
estimation, but only address point estimation. In addition, the few
developments which do not have the above limitations, have not made their
way into standard practice because; a) they have not considered the
particular issues which arise when a cumulative exposure variable is of
primary interest, and b) the proper use of these methods requires a detailed
understanding of the statistical literature and few realistic examples exist
of practical applications.
The work proposed in this grant, will develop new methods and adapt existing
ones for use in occupational cancer studies with cumulative exposure as the
variable of interest with none of the above limitations. Fully parametric
likelihood methods, as originally proposed by Prentice in (1982), and new,
semi-parametric methods, as elaborated by Robins et al. (1994, 1995), will
both be considered. These methods will be applied to important data sets
taken from occupational epidemiology: Samet et al. s 1991 study of the
effects of occupational exposure to radon gas in relation to lung cancer
mortality among New Mexico uranium miners, and Savitz et al. s 1995 study of
the effects of occupational exposure to magnetic fields in relation to
mortality from leukemia and brain cancer among workers in five electric
power plants. Both of these studies have detailed validation data from
which parametric and non-parametric measurement error models will be
constructed, and used to obtain point and interval estimates of the exposure
effect which are not contaminated by measurement error. In addition to
contributing to the advancement of statistical methods available in this
setting, the investigators will advance scientific understanding of the
extent of the exposure-disease relationships investigated in these two
studies, and provide a more realistic quantification of the uncertainty
around this understanding.
Regression Calibration With Heteroscedastic Error Variance
, Logan R.
, Grove D.
The International Journal Of Biostatistics, 2011; 7(1), p. 4.
Inference In Randomized Studies With Informative Censoring And Discrete Time-to-event Endpoints
, Robins J.M.
, Eddings W.
, Rotnitzky A.
Biometrics, 2001 Jun; 57(2), p. 404-13.
Methods For Conducting Sensitivity Analysis Of Trials With Potentially Nonignorable Competing Causes Of Censoring
, Scharfstein D.
, Su T.L.
, Robins J.
Biometrics, 2001 Mar; 57(1), p. 103-13.
Efficient Regression Calibration For Logistic Regression In Main Study/internal Validation Study Designs With An Imperfect Reference Instrument
, Carroll R.J.
, Kipnis V.
Statistics In Medicine, 2001-01-15 00:00:00.0; 20(1), p. 139-160.