Modern case-control studies typically involve the collection of data on a large number of variables, often at considerable logistical and monetary expense. These data are of potentially great value to subsequent researchers, who although not concerned with the disease that was the subject of the original study, may want to use the available information towards an analysis of the effects of an exposure on a secondary outcome other than the disease that defined the original case series. A difficulty with re-using data in this way is that the case-control sampling scheme used in the original study will likely induce bias in estimates of log odds ratios and other parameters in the secondary study, if conventional analytical approaches are used. In this proposal, we plan to develop novel statistical methodology for making robust and highly efficient inferences on the effects of an exposure on a secondary outcome under case-control sampling on a different outcome. An important advance is that the proposed methodology applies whether or not sampling probabilities are known to the investigator, and is particularly useful when, as in most observational studies, one needs to adjust for a moderate to large number of confounders. We will use the proposed new methodology to accurately evaluate the effects of lead exposure on the risk of osteoporosis and on decline in cognitive function, using data from the Nurses'Health Study case-control study of lead and hypertension.

Public Health Relevance

Modern case-control studies typically involve the collection of data on a large number of variables, often at considerable logistical and monetary expense. These data are of potentially great value to subsequent researchers, who although not concerned with the disease that was the subject of the original study, may want to use the available information towards an analysis of the effects of an exposure on a secondary outcome other than the disease that defined the original case series. A difficulty with re-using data in this way is that the case-control sampling scheme used in the original study will likely induce bias in estimates of regression parameters in the secondary study, if conventional analytical approaches are used. In this proposal, we plan to develop novel statistical methodology for making robust and highly efficient inferences on the effects of an exposure on a secondary outcome of a categorical or continuous nature, under case-control sampling on a different outcome. An important advance is that the proposed methodology applies whether or not sampling probabilities are known to the investigator. Furthermore, the methods are particularly useful when, as in most observational studies, one needs to adjust for a moderate to large number of confounders, and data on some key covariates may be missing for a subset of subjects. The new methodology will be evaluated via extensive simulation studies, and will be used to accurately assess the effects of lead exposure on the risk of osteoporosis and on decline in cognitive function using data from the Nurse's Health Study case-control study of lead and hypertension.

Agency
National Institute of Health (NIH)
Institute
National Institute of Environmental Health Sciences (NIEHS)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21ES019712-02
Application #
8214692
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Dilworth, Caroline H
Project Start
2011-02-01
Project End
2014-01-31
Budget Start
2012-02-01
Budget End
2014-01-31
Support Year
2
Fiscal Year
2012
Total Cost
$213,763
Indirect Cost
$78,395
Name
Harvard University
Department
Public Health & Prev Medicine
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
Wirth, Kathleen E; Tchetgen Tchetgen, Eric J (2014) Accounting for selection bias in association studies with complex survey data. Epidemiology 25:444-53
Power, Melinda C; Korrick, Susan; Tchetgen Tchetgen, Eric J et al. (2014) Lead exposure and rate of change in cognitive function in older women. Environ Res 129:69-75
Young, Jessica G; Tchetgen Tchetgen, Eric J (2014) Simulation from a known Cox MSM using standard parametric models for the g-formula. Stat Med 33:1001-14
Stephens, Alisa; Tchetgen Tchetgen, Eric; De Gruttola, Victor (2014) Locally efficient estimation of marginal treatment effects when outcomes are correlated: is the prize worth the chase? Int J Biostat 10:59-75
Tchetgen Tchetgen, Eric (2014) The control outcome calibration approach for causal inference with unobserved confounding. Am J Epidemiol 179:633-40
Weuve, Jennifer (2013) Magnitude matters: beyond detection in the presence of selection in research on socioeconomic inequalities in health. Epidemiology 24:10-3
Tchetgen Tchetgen, Eric J (2013) On a closed-form doubly robust estimator of the adjusted odds ratio for a binary exposure. Am J Epidemiol 177:1314-6
Tchetgen Tchetgen, Eric J; Walter, Stefan; Glymour, M Maria (2013) Commentary: building an evidence base for mendelian randomization studies: assessing the validity and strength of proposed genetic instrumental variables. Int J Epidemiol 42:328-31
Cornelis, Marilyn C; Tchetgen, Eric J Tchetgen; Liang, Liming et al. (2012) Gene-environment interactions in genome-wide association studies: a comparative study of tests applied to empirical studies of type 2 diabetes. Am J Epidemiol 175:191-202
Glymour, M Maria; Tchetgen, Eric J Tchetgen; Robins, James M (2012) Credible Mendelian randomization studies: approaches for evaluating the instrumental variable assumptions. Am J Epidemiol 175:332-9

Showing the most recent 10 out of 11 publications