Abstract

Epidemiologic follow-up studies have become common in the last 5-10 years.
The aims of this proposal pertain to the evaluation of robustness, development of robust procedures, and detection of misspecification in regression models for the analysis of longitudinal data arising from such studies. Because of the recency of many multivariate regression methods used with unbalanced data these aspects of modelling, which are important for practical application, have not yet been fully explored. Two common examples of model misspecification are omitted covariates and incorrect assumptions on the mathematical form of the relationship. These problems are closely related since omitted covariates can lead to change in the form of regression equations. Conversely, for example quadratic terms in regression equations can be viewed as omitted covariates. Both forms of model violations may lead to differences in across and within individual regression coefficients, an often observed phenomenon. One example of such a situation has been modelled by letting the correlation between the covariate of interest x, and an omitted confounder z be different across than within individuals. Such situations are common (e.g. cohort and period effects in epidemiologic studies of the effect of aging). Extension of the results for this model form the basis for the proposed investigation. The data generating structure will be generalized in several directions, and the impact on longitudinal data analysis will be examined. The investigation of the sensitivity of various existing and novel analysis methods to specific model violations, will serve as a guide to choice and further development of robust methods. Since the impact of model misspecification differs by analysis approach, this will also be investigated as a potential method for detecting model violation. The methods developed in this proposal will be applied in the statistical analysis of data from several ongoing epidemiologic follow-up studies.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA053786-02
Application #
3198419
Study Section
Epidemiology and Disease Control Subcommittee 2 (EDC)
Project Start
1992-02-01
Project End
1995-01-31
Budget Start
1993-02-01
Budget End
1994-01-31
Support Year
2
Fiscal Year
1993
Total Cost
Indirect Cost

  Institution

Name
University of Wisconsin Madison
Department
Type
Schools of Medicine
DUNS #
161202122
City
Madison
State
WI
Country
United States
Zip Code
53715

  Publications

Vardeny, Orly; Peppard, Paul E; Finn, Laurel A et al. (2011) ?2 adrenergic receptor polymorphisms and nocturnal blood pressure dipping status in the Wisconsin Sleep Cohort Study. J Am Soc Hypertens 5:114-22
Shao, Jun; Zhong, Bob (2006) On the treatment effect in clinical trials with dropout. J Biopharm Stat 16:25-33
Hu, Bo; Palta, Mari; Shao, Jun (2006) Properties of R(2) statistics for logistic regression. Stat Med 25:1383-95
Li, Liang; Shao, Jun; Palta, Mari (2005) A longitudinal measurement error model with a semicontinuous covariate. Biometrics 61:824-30
Li, Liang; Wang, William W B; Chan, Ivan S F (2005) Correlation coefficient inference on censored bioassay data. J Biopharm Stat 15:501-12
Li, Liang; Palta, Mari; Shao, Jun (2004) A measurement error model with a Poisson distributed surrogate. Stat Med 23:2527-36
Shao, Jun; Zhong, Bob (2003) Last observation carry-forward and last observation analysis. Stat Med 22:2429-41
Wang, Jin; Shao, Jun; Palta, Mari (2002) Testing model fit in longitudinal data analysis against alternatives with omitted covariates. Stat Med 21:729-41
Park, Soomin; Palta, Mari; Shao, Jun et al. (2002) Bias adjustment in analysing longitudinal data with informative missingness. Stat Med 21:277-91
Tao, H; Palta, M; Yandell, B S et al. (1999) An estimation method for the semiparametric mixed effects model. Biometrics 55:102-10

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