On outcome-dependent sampling designs for longitudinal binary response data with time-varying covariates

Biostatistics

Volumn 9, Issue 4, 2008, Pages 735-749

  Schildcrout, Jonathan S ^a   Heagerty, Patrick J ^b  

^a VANDERBILT UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Binary data; Longitudinal data analysis; Marginal models; Marginalized models; Outcome dependent sampling; Time dependent covariates

Indexed keywords

OZONE;

ADOLESCENT; AIR POLLUTION; ALGORITHM; ARTICLE; ASTHMA; CHILD; EPIDEMIOLOGY; FEMALE; HUMAN; LONGITUDINAL STUDY; MALE; MONTE CARLO METHOD; OUTCOME ASSESSMENT; REGRESSION ANALYSIS; SEASON; STATISTICAL MODEL; STATISTICS; UNITED STATES;

ADOLESCENT; AIR POLLUTION; ALGORITHMS; ASTHMA; CALIFORNIA; CHILD; EPIDEMIOLOGIC RESEARCH DESIGN; FEMALE; HUMANS; LIKELIHOOD FUNCTIONS; LONGITUDINAL STUDIES; MALE; MODELS, STATISTICAL; MONTE CARLO METHOD; OUTCOME ASSESSMENT (HEALTH CARE); OZONE; REGRESSION ANALYSIS; SAMPLING STUDIES; SEASONS; SELECTION BIAS;

EID: 52449090741     PISSN: 14654644     EISSN: 14684357     Source Type: Journal    
DOI: 10.1093/biostatistics/kxn006     Document Type: Article

References (28)

1. ANDERSON, J. A. (1972). Separate sample logistic discrimination. Biometrika 59, 19-35.
2. AZZALINI, A. (1994). Logistic regression for autocorrelated data with application to repeated measures. Biometrika 81, 767-775.
3. BELL, M., DOMINICI, F. AND SAMET, J. (2005). A meta-analysis of time-series studies of ozone and mortality with comparison to the national morbidity, mortality, and air pollution study. Epidemiology 16, 436-445.
4. BRESLOW, N. E. AND CLAYTON, D. G. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 88, 9-25.
5. DIGGLE, P., HEAGERTY, P. J., LIANG, K.-Y. AND ZEGER, S. L. (2002). Analysis of Longitudinal Data. New York: Oxford University Press.
6. FITZMAURICE, G. M. (1995). A caveat concerning independence estimating equations with multivariate binary data. Biometrics 51, 309-317.
7. GENT, J., TRICHE, E., HOLFORD, T., BELANGER, K., BRACKEN, M., BECKETT, W. AND LEADERER, B. (2003). Association of low-level ozone and fine particles with respiratory symptoms in children with asthma. Journal of the American Statistical Association 290, 1859-1867.
8. HEAGERTY, P. J. (1999). Marginally specified logistic-normal models for longitudinal binary data. Biometrics 55, 688-698.
9. HEAGERTY, P. J. (2002). Marginalized transition models and likelihood inference for longitudinal categorical data. Biometrics 58, 342-351.
10. HEAGERTY, P. J. AND ZEGER, S. L. (2000). Marginalized multilevel models and likelihood inference (with comments and a rejoinder by the authors). Statistical Science 15, 1-26.
11. LIANG, K.-Y. AND ZEGER, S. L. (1986). Longitudinal data analysis using g linear models. Biometrika 73, 13-22.
12. MANCL, L. A. AND LEROUX, B. G. (1996). Efficiency of regression estimates for clustered data. Biometrics 52, 500-511.
13. MORTIMER, K., NEAS, L., DOCKERY, D., REDLINE, S. AND TAGER, I. (2002). The effect of air pollution on inner-city children with asthma. European Respiratory Journal 19, 699-705.
14. NEUHAUS, J. M. AND JEWELL, N. P. (1990). The effect of retrospective sampling on binary regression models for clustered data. Biometrics 46, 977-990.
15. NEUHAUS, J. M. AND KALBFLEISCH, J. D. (1998). Between- and within-cluster covariate effects in the analysis of clustered data. Biometrics 54, 638-645.
16. NEUHAUS, J. M. AND LESPERANCE, M. L. (1996). Estimation efficiency in a binary mixed-effects model setting. Biometrika 83, 441-446.
17. PALTA, M. AND YAO, T.-J. (1991). Analysis of longitudinal data with unmeasured confounders. Biometrics 47, 1355-1369.
18. PETERS, J. M., AVOL, E., GAUDERMAN, W. J., LINN, W. S., NAVIDI, W., LONDON, S. J., MARGOLIS, H., RAPPAPORT, E., VORA, H., GONG, H. J. and others (1999a). A study of twelve Southern California communities with differing levels and types of air pollution. II. Effects on pulmonary function. American Journal of Respiratory and Critical Care Medicine 159, 768-775.
19. PETERS, J. M., AVOL, E., NAVIDI, W., LONDON, S. J., GAUDERMAN, W. J., LURMANN, F., LINN, W. S., MARGOLIS, H., RAPPAPORT, E., GONG, H. and others (1999b). A study of twelve Southern California communities with differing levels and types of air pollution. I. Prevalence of respiratory morbidity. American Journal of Respiratory and Critical Care Medicine 159, 760-767.
20. PFEIFFER, R., RYAN, L., LITONJUA, A. AND PEE, D. (2005). A case-cohort design for assessing covariate effects in longitudinal studies. Biometrics 61, 982-991.
21. PRENTICE, R. L. AND PYKE, R. (1979). Logistic disease incidence models and case-control studies. Biometrika 66, 403-412.
22. QAQISH, B. F., ZHOU, H. AND CAI, J. (1997). On case-control sampling of clustered data. Biometrika 84, 983-986.
23. SCHILDCROUT, J. S. AND HEAGERTY, P. J. (2005). Regression analysis of longitudinal binary response data with time-dependent environmental covariates: bias and efficiency. Biostatistics 6, 633-652.
24. SCHILDCROUT, J. S. AND HEAGERTY, P. J. (2007). Marginalized models for moderate to long series of longitudinal binary response data. Biometrics 63, 322-331.
25. SHEPPARD, L. (2003). Insights on bias and information in group-level studies. Biostatistics 4, 265-278.
26. STIRATELLI, R., LAIRD, N. AND WARE, J. H. (1984). Random-effects models for serial observations with binary response. Biometrics 40, 961-971.
27. WHITE, H. (1982). Maximum likelihood estimation of misspecified models. Econometrica 50, 1-26.
28. YU, O., SHEPPARD, L., LUMLEY, T., KOENIG, J. AND SHAPIRO, G. (2000). Effects of ambient air pollution on symptoms of asthma in Seattle-area children enrolled in the CAMP study. Environmental Health Perspectives 108, 1209-1214.