Weighted estimating equations for longitudinal studies with death and non-monotone missing time-dependent covariates and outcomes

Statistics in Medicine

Volumn 27, Issue 7, 2008, Pages 1008-1025

  Shardell, Michelle ^a   Miller, Ram R ^a  

^a UNIVERSITY OF MARYLAND SCHOOL OF MEDICINE   (United States)

Author keywords

Death; Longitudinal data; Non monotone missing data; Weighted estimating equations

Indexed keywords

AGED; AGING; ARTICLE; COHORT ANALYSIS; CONVALESCENCE; DATA ANALYSIS; FEMALE; FOLLOW UP; FUNCTIONAL DISEASE; HIP FRACTURE; HUMAN; LONGITUDINAL STUDY; MATHEMATICAL VARIABLE; MORTALITY; OUTCOME ASSESSMENT; STATISTICAL ANALYSIS;

AGED; BALTIMORE; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; DEATH; FEMALE; GERIATRICS; HIP FRACTURES; HUMANS; LONGITUDINAL STUDIES; MODELS, STATISTICAL; REGRESSION ANALYSIS;

EID: 40849094464     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.2964     Document Type: Article

References (25)

1. Hawkes WG, Williams GR, Zimmerman S, Lapuerta P, Li T, Orwig D, Wehren L, Hebel JR, Magaziner J. A clinically meaningful difference was generated for a performance measure of recovery from hip fracture. Journal of Clinical Epidemiology 2004; 57:1019-1024.
2. Yu-Yahiro JA, Michael RH, Dubin NH, Fox KM, Sachs M, Hawkes WG, Hebel JR, Zimmerman SI, Shapiro J, Magaziner J. Serum and urine markers of bone metabolism during the year after hip fracture. Journal of the American Geriatric Society 2001; 49:877-883.
3. Miller RR, Cappola AR, Shardell M, Hawkes WG, Yu-Yahiro JA, Hebel JR. Magaziner J. Persistent changes in Interleukin-6 and lower extremity function following hip fracture. Journal of Gerontology: Medical Sciences 2006; 61:1053-1058.
4. Robins JM, Rotnitzky A, Zhao LP. Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association 1994; 89:846-866.
5. Robins JM, Rotnizky A, Zhao LP. Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association 1995; 90(429):106-121.
6. Rubin DB. Inference and missing data. Biometrika 1976; 63:581-592.
7. Parzen M, Lipsitz SR, Fitzmaurice GM, Ibrahim JG, Troxel A. Pseudo-likelihood methods for longitudinal binary data with non-ignorable missing responses and covariates. Statistics in Medicine 2006; 25:2784-2796.
8. Troxel AB, Lipsitz SR, Harrington DP. Marginal models for the analysis of longitudinal measurements with nonignorable non-monotone missing data. Biometrika 1998; 85:661-672.
9. Minini P, Chavance M. Sensitivity analysis of longitudinal binary data with non-monotone missing values. Biostatistics 2004; 5:531-544.
10. Lin H, McCulloch CE, Rosenheck RA. Latent pattern mixture models for informative intermittent missing data in longitudinal studies. Biometrics 2004; 60:295-305.
11. Shen C, Weissfeld L. A copula model for repeated measurements with non-ignorable non-monotone missing outcome. Statistics in Medicine 2006; 25:2427-2440.
12. Yun S, Lee Y. Robust estimation in mixed linear models with non-monotone missingness. Statistics in Medicine 2006; 25:3877-3892.
13. Kurland BF, Heagerty PJ. Directly parameterized regression conditioning on being alive: analysis of longitudinal data truncated by deaths. Biostatistics 2005; 6:241-258.
14. Dufouil C, Brayne C, Clayton D. Analysis of longitudinal studies with death and drop-out: a case study. Statistics in Medicine 2004; 23:2215-2226.
15. Pauler DK, McCoy S, Moinpour C. Pattern mixture models for longitudinal quality of life studies in advanced stage disease. Statistics in Medicine 2003; 22:795-809.
16. Ribaudo HJ, Thompson SG, Allen-Mersh TG. A joint analysis of quality of life and survival using a random effect selection model. Statistics in Medicine 2000; 19:3237-3250.
17. Shen C, Gao S. A mixed-effects model for cognitive decline with non-monotone non-response from a two-phase longitudinal study of dementia. Statistics in Medicine 2007; 26:409-425.
18. Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika 1986; 73:13-22.
19. Parzen M, Lipsitz SR, Ibrahim JG, Lipshultz S. A weighted estimating equation for linear regression with missing covariate data. Statistics in Medicine 2002; 21:2421-2436.
20. Scharfstein DO, Rotnitzky A, Robins JM. Adjusting for nonignorable drop-out using semiparametric nonresponse models (with discussion). Journal of the American Statistical Association 1999; 94:1096-1146.
21. McCullagh P, Nelder JA. Generalized Linear Models (2nd edn). Chapman & Hall: New York, 1989.
22. Huber P. The behavior of maximum likelihood estimates under non-standard conditions. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. I. University of California Press: Berkeley, 1967; 221-223.
23. White EA. A two stage design for the study of the relationship between a rare exposure and a rare disease. American Journal of Epidemiology 1982; 115:119-128.
24. Little RJA. Regression with missing X's: a review. Journal of the American Statistical Association 1992; 87:1227-1237.
25. Lin H, Scharfstein DO, Rosenheck RA. Analysis of longitudinal data with irregular, informative follow-up. Journal of the Royal Statistical Society Series B 2004; 66:791-813.