A functional multiple imputation approach to incomplete longitudinal data

Statistics in Medicine

Volumn 30, Issue 10, 2011, Pages 1137-1156

  He, Yulei ^a   Yucel, Recai ^b   Raghunathan, Trivellore E ^c  

^a HARVARD MEDICAL SCHOOL   (United States)

^b UNIVERSITY AT ALBANY   (United States)

^c UNIVERSITY OF MICHIGAN SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Cubic smoothing spline; Functional data analysis; Gibbs sampling; Missing data; Multiple imputation; Panel Study of Income Dynamics

Indexed keywords

ARTICLE; CHILD; CHILD DEVELOPMENT; CHILD HEALTH; CONTROLLED STUDY; DATA ANALYSIS; FAMILY ASSESSMENT; FAMILY LIFE; FAMILY STRESS; FEMALE; HEALTH CARE SURVEY; HEALTH STATUS; HUMAN; INFORMATION PROCESSING; LEARNING ALGORITHM; LONGITUDINAL STUDY; LOWEST INCOME GROUP; MALE; MONTE CARLO METHOD; MULTIPLE IMPUTATION; POVERTY; PRESCHOOL CHILD; PROBABILITY; PROCESS DEVELOPMENT; PROCESS MODEL; STATISTICAL ANALYSIS; STATISTICAL MODEL; SYSTEMATIC ERROR;

ALGORITHMS; CHILD; CHILD DEVELOPMENT; CHILD, PRESCHOOL; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HUMANS; INFANT; LONGITUDINAL STUDIES; MODELS, STATISTICAL; RESEARCH DESIGN; SOCIOECONOMIC FACTORS;

EID: 79954626453     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.4201     Document Type: Article

References (43)

1. Little RJA, Rubin DB. Statistical Analysis of Missing Data. Wiley: New York, 2002.
2. Rubin DB. Multiple Imputation for Nonresponse in Surveys. Wiley: New York, 1987.
3. Meng XL. Multiple-imputation inferences with uncongenial sources of input. Statistical Science 1994; 9:538-558.
4. Harel O, Zhou XH. Multiple imputation: review of theory, implementation, and software. Statistics in Medicine 2007; 26:3057-3077.
5. Liu M, Taylor JMG, Belin TR. Multiple imputation and posterior simulation for multivariate missing data in longitudinal studies. Biometrics 2000; 56:1157-1163.
6. Schafer JL, Yucel RM. Computational strategies for multivariate linear mixed-effects models with missing values. Journal of Computational and Graphical Statistics 2002; 11:437-457.
7. Ramsay JO, Silverman BW. Applied Functional Data Analysis. Springer: Berlin, 2005.
8. Guo W. Functional data analysis in longitudinal studies using smoothing splines. Statistical Methods in Medical Research 2004; 13:49-62.
9. Rice JA. Functional and longitudinal data analysis: perspectives on smoothing. Statistica Sinica 2004; 14:631-647.
10. Zhao X, Marron JS, Wells MT. The functional data analysis view of longitudinal data. Statistica Sinica 2004; 14:789-808.
11. Guo W. Functional mixed effect models. Biometrics 2002; 58:121-128.
12. Ruppert D, Wand MP, Carroll RJ. Semiparametric Regression. Cambridge University Press: Cambridge, 2003.
13. Longford NT. Random Coefficient Models. Oxford University Press: U.K., 1994.
14. Wahba G. Improper priors, spline smoothing and the problem of guarding against model errors in regression. Journal of the Royal Statistical Society, Series B 1978; 40:364-372.
15. Green PJ. Penalized likelihood for general semi-parametric regression models. International Statistical Review 1987; 55:245-260.
16. Zhang D, Lin X, Raz J, Sowers M. Semiparametric stochastic mixed models for longitudinal data. Journal of the American Statistical Association 1998; 93:710-719.
17. Brumback BA, Rice JA. Smoothing spline models for the analysis of nested and crossed samples of curves (with Discussion). Journal of the American Statistical Association 1998; 93:961-994.
18. Wang Y. Mixed effects smoothing spline analysis of variance. Journal of the Royal Statistical Society, Series B 1998; 60:159-174.
19. Wang Y. Smoothing spline models with correlated random errors. Journal of the American Statistical Association 1998; 93:341-348.
20. Verbyla P, Cullis BR, Kenward MG, Welham SJ. The analysis of designed experiments and longitudinal data by using smoothing splines (with Discussion). Applied Statistics 1999; 48:269-311.
21. Wand MP, Jones MC. Kernel Smoothing. Chapman and Hall: London, 1995.
22. Morris JS, Carroll RJ. Wavelet-Based functional mixed models. Journal of the Royal Statistical Society, Series B 2006; 68:179-199.
23. Antoniadis A, Spapatinas T. Estimation and inference in functional mixed-effects models. Computational Statistics and Data Analysis 2007; 51:4793-4813.
24. Gelfand AE, Smith AFM. Sampling-based approaches to calculate marginal densities. Journal of the American Statistical Association 1990; 85:398-409.
25. Chib S, Carlin BP. On MCMC sampling in hierarchical longitudinal models. Statistics and Computing 1999; 9:17-26.
26. Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences (with Discussion). Statistical Science 1992; 7:457-511.
27. Brooks-Gunn J, Duncan GJ, Maritato N. Poor families, poor outcomes: the well-being of children and youth. In Consequences of Growing up Poor, Duncan GJ, Brooks-Gunn J (eds). Russell Sage Foundation: New York, 1997; 1-17.
28. Case A, Lubtosky D, Paxson C. Economic status and health in childhood: the origins of the gradient. American Economic Review 2002; 92(5):1308-1334.
29. Demirtas H, Freels SA, Yucel RM. The plausibility of multivariate normality assumption when multiply imputing non-gaussian continuous outcomes: a simulation assessment. Journal of Statistical Computation and Simulation 2008; 78:2069-2084.
30. Little RJA, An H. Robust likelihood-based analysis of multivariate data with missing values. Statistica Sinica 2004; 14:949-968.
31. Collins LM, Schafer JL, Kam CM. A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods 2001; 6:330-351.
32. Davidian M, Giltinan DM. Nonlinear Models for Repeated Measurement Data. Chapman and Hall/CRC: London/Boca Raton, 1995.
33. Daniels MJ, Hogan JW. Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis. Chapman and Hall/CRC: London/Boca Raton, 2008.
34. He H, Huang LS. Double-smoothing for bias reduction in local linear regression. Journal of Statistical Planning and Inference 2009; 139:1056-1072.
35. Raghunathan TE. What do we do with missing data? Some options for analysis of incomplete data. Annual Review of Public Health 2004; 25:99-117.
36. Molenberghs G, Kenward MG. Missing Data in Clinical Studies. Wiley: West Sussex, 2007.
37. Schafer JL. Multiple imputation in multivariate problems when the imputation and analysis model differ. Statistica Neerlandica 2003; 57:19-35.
38. Horvitz DG, Thomospn DJ. A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association 1952; 47:663-685.
39. Robins JM, Rotnitzky A. Semiparametric efficiency in multivariate regression models with missing data. Journal of the American Statistical Association 1995; 90:122-129.
40. Robins JM, Rotnitzky A, Zhao LP. Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association 1995; 90:106-121.
41. Sharfstein 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.
42. Carpenter JR, Kenward MG, Vansteelandt S. A comparison of multiple imputation and double robust estimation for analyses with missing data. Journal of the Royal Statistical Society, Series A 2006; 169:571-584.
43. Qi L, Wang YF, He Y. A comparison of multiple imputation and fully augmented weighted estimators for Cox regression with missing covariate. Statistics in Medicine 2010; 29:2592-2604.