Incomplete hierarchical data

Statistical Methods in Medical Research

Volumn 16, Issue 5, 2007, Pages 457-492

  Beunckens, Caroline ^a   Molenberghs, Geert ^a   Thijs, Herbert ^a   Verbeke, Geert ^b  

^a HASSELT UNIVERSITY   (Belgium)

^b UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ALPHA INTERFERON; PLACEBO;

CLINICAL TRIAL; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; DATA ANALYSIS; EXPERIMENT; HUMAN; INFORMATION PROCESSING; MULTICENTER STUDY; OUTCOME ASSESSMENT; RANDOMIZED CONTROLLED TRIAL; RESEARCH; RETINA MACULA AGE RELATED DEGENERATION; REVIEW; SENSITIVITY ANALYSIS; STATISTICAL ANALYSIS; VISUAL ACUITY;

HUMANS; LONGITUDINAL STUDIES; MACULAR DEGENERATION; MODELS, STATISTICAL; MULTICENTER STUDIES AS TOPIC; RANDOMIZED CONTROLLED TRIALS AS TOPIC;

EID: 35649011462     PISSN: 09622802     EISSN: None     Source Type: Journal    
DOI: 10.1177/0962280206075310     Document Type: Review

References (86)

1. Rubin DB. Inference and missing data. Biometrika 1976; 63: 581-92.
2. Little RJA, Rubin DB. Statistical analysis with missing data. Wiley, 1987.
3. Little RJA. Pattern-mixture models for multivariate incomplete data. Journal of the American Statistical Association 1993; 88: 125-34.
4. Little RJA. Informative dropout in longitudinal data analysis (discussion to Diggle PJ, Kenward MG). Applied Statistics 1994; 43: 78.
5. Shih WJ, Quan H. Testing for treatment differences with dropouts present in clinical trials - a composite approach. Statistics in Medicine 1997; 16: 1225-39.
6. Diggle PD, Kenward MG. Informative dropout in longitudinal data analysis (with discussion). Applied Statistics 1994; 43: 49-93.
7. Verbeke G, Molenberghs G. Linear mixed models for longitudinal data. Springer-Verlag, 2000.
8. Kenward MG, Molenberghs G. Likelihood based frequentist inference when data are missing at random. Statistical Science 1998; 12: 236-47.
9. Mallinckrodt CH, Clark WS, Carroll RJ, Molenberghs G. Assessing response profiles from incomplete longitudinal clinical trial data under regulatory considerations. Journal of Biopharmaceutical Statistics 2003; 13: 179-90.
10. Mallinckrodt CH, Sanger TM, Dube S, Debrota DJ, Molenberghs G, Carroll RJ, Zeigler Potter WM, Tollefson GD. Assessing and interpreting treatment effects in longitudinal clinical trials with missing data. Biological Psychiatry 2003; 53(8): 754-60.
11. Rubin DB. Multiple imputation for nonresponse in surveys. John Wiley & Sons, 1987.
12. Schafer JL. Analysis of incomplete multivariate data. Chapman & Hall, 1997.
13. Siddiqui O, Ali MW. A comparison of the random-effects pattern mixture model with last observation carried forward (LOCF) analysis in longitudinal clinical trials with dropouts. Journal of Biopharmaceutical Statistics 1998; 8: 545-63.
14. Molenberghs G, Thijs H, Jansen I, Beunckens C, Kenward MG, Mallinckrodt C, Carroll RJ. Analyzing incomplete longitudinal clinical trial data. Biostatistics 2004; 5: 445-64.
15. Beunckens C, Molenberghs G, Kenward MG. Tutorial: direct likelihood analysis versus simple forms of imputation for missing data in randomized clinical trials. Clinical Trials 2005; 2: 379-86.
16. Verbeke G, Molenberghs G. Linear Mixed Models in Practice: A SAS-Oriented Approach. Lecture Notes in Statistics. Volume 126. Springer-Verlag, 1997.
17. Buck SF. A method of estimation of missing values in multivariate data suitable for use with an electronic computer. Journal of the Royal Statistical Society, Series B 1960; 22: 302-6.
18. Liang K-Y, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika 1986; 73: 13-22.
19. 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.
20. Schafer JL. Multiple imputation in multivariate problems when the imputation and analysis models differ. Statistica Neerlandica 2003; 57: 19-35.
21. Agresti A. Categorical data analysis, second edition. John Wiley & Sons, 2002.
22. Molenberghs G, Verbeke G. Models for discrete longitudinal data. Springer-Verlag, 2005.
23. Diggle PJ, Heagerty P, Liang K-Y, Zeger SL. Analysis of longitudinal data. Oxford University Press, 2002.
24. Bahadur RR. A representation of the joint distribution of responses to n dichotomous items. In Solomon H ed. Studies in Item Analysis and Prediction. Stanford Mathematical Studies in the Social Sciences VI. Stanford University Press, 1961.
25. Ashford JR, Sowden RR. Multivariate probit analysis. Biometrics 1970; 26: 535-46.
26. Molenberghs G, Lesaffre E. Marginal modelling of correlated ordinal data using a multivariate Plackett distribution. Journal of the American Statistical Association 1994; 89: 633-44.
27. Lang JB, Agresti A. Simultaneously modelling joint and marginal distributions of multivariate categorical responses. Journal of the American Statistical Association 1994; 89: 625-32.
28. Dale JR. Global cross-ratio models for bivariate, discrete, ordered responses. Biometrics 1986; 42: 909-17.
29. Plackett RL. A class of bivariate distributions. Journal of the American Statistical Assocication 1965; 60: 516-22.
30. Molenberghs G, Lesaffre E. Marginal modelling of multivariate categorical data. Statistics in Medicine 1999; 18: 2237-55.
31. Glonek GFV, McCullagh P. Multivariate logistic models. Journal of the Royal Statistical Society, Series B 1995; 81: 477-82.
32. le Cessie S, van Houwelingen JC. Logistic regression for correlated binary data. Applied Statistics 1994; 43: 95-108.
33. Geys H, Molenberghs M, Lipsitz, SR. A note on the comparison of pseudo-likelihood and generalized estimating equations for marginal odds ratio models. Journal of the Statistical Computation and Simulation 1998; 62: 45-72.
34. Beunckens C, Sotto C, Molenberghs G. A simulation study comparing weighted estimating equations with multiple imputation based estimating equations. 2006. Submitted for publication.
35. Neuhaus JM. Statistical methods for longitudina, clustered designs with binary responses. Statistical Methods in Medical Research 1992; 1: 249-73.
36. Neuhaus JM, Kalbfleisch JD, Hauck WW. A comparison of cluster-specific and population-averaged approaches for analyzing correlated binary data. International Statistical Review 1991; 59: 25-35.
37. Stiratelli R, Laird N, Ware J. Random effects models for serial observations with dichotomous response. Biometrics 1984; 40: 961-72.
38. Breslow NE, Clayton DG. Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 1993; 88: 9-25.
39. Skellam JG. A probability distribution derived from the binomial distribution by regarding the probability of success as variable between the sets of trials. Journal of the Royal Statistical Society, Series B 1948; 10: 257-61.
40. Molenberghs G, Kenward MG, Lesaffre E. The analysis of longitudinal ordinal data with non-random dropout. Biometrika 1997; 84: 33-44.
41. Jansen I, Molenberghs G. A flexible marginal modeling strategy for non-monotone missing data. 2006. Submitted for Publication.
42. Baker SG, Laird NM. Regression analysis for categorical variables with outcome subject to non-ignorable non-response. Journal of the American Statistical Association, 1988; 83: 62-9.
43. Fay RE. Causal models for patterns of nonresponse. Journal of the American Statistical Association 1986; 81: 354-65.
44. Stasny EA. Estimating gross flows using panel data with nonresponse: an example from the Canadian Labour Force Survey. Journal of the American Statistical Association 1986; 81: 42-7.
45. Conaway MR. The analysis of repeated categorical measurements subject to nonignorable nonresponse. Journal of the American Statistical Association 1992; 87: 817-24.
46. Conaway MR. Non-ignorable non-response models for time-ordered categorical variables. Applied Statistics 1993; 42: 105-15.
47. Baker SG. Marginal regression for repeated binary data with outcome subject to non-ignorable non-response. Biometrics 1995; 51: 1042-52.
48. Baker SG, Rosenberger WF, DerSimonian R. Closed-form estimates for missing counts in two-way contingency tables. Statistics in Medicine 1992; 11: 643-57.
49. Jansen I, Hens N, Molenberghs G, Aerts M, Verbeke G, Kenward MG. The nature of sensitivity in monotone missing not at random models. Computational Statistics and Data Analysis 2006; 50: 830-58.
50. Molenberghs G, Verbeke G, Thijs H, Lesaffre E, Kenward MG. Mastitis in dairy cattle: local influence to assess sensitivity of the dropout process. Computational Statistics and Data Analysis 2001; 37: 93-113.
51. Verbeke G, Molenberghs G, Thijs H, Lesaffre E, Kenward MG. Sensitivity analysis for non-random dropout: a local influence approach. Biometrics 2001; 57: 43-50.
52. Thijs H, Molenberghs G, Verbeke G. The milk protein trial: influence analysis of the dropout process. Biometrical Journal 2000; 42: 617-46.
53. Cook RD. Assessment of local influence. Journal of the Royal Statistical Society, Series B 1986; 48: 133-69.
54. Van Steen K, Molenberghs G, Verbeke G, Thijs H. A local influence approach to sensitivity analysis of incomplete longitudina ordinal data. Statistical Modelling: An International Journal 2001; 1: 125-42.
55. Hens N, Aerts M, Molenberghs G, Thijs H, Verbeke G. Kernel weighted influence measures. Computational Statistics & Data Analysis 2005; 48: 467-87.
56. Kenward MG, Molenberghs G, Goetghebeur E. Sensitivity analysis for incomplete categorical data. Statistical Modelling 2001; 1: 31-48.
57. Thijs H, Molenberghs G, Michiels B, Verbeke G, Curran D. Strategies to fit pattern-mixture models. Biostatistics 2002; 3: 245-65.
58. Michiels B, Molenberghs G, Bijnens L, Vangeneugden T, Thijs H. Selection models and pattern-mixture models to analyze longitudinal quality of life data subject to dropout. Statistics in Medicine 2002; 21: 1023-41.
59. Laird NM. (discussion to Diggle PJ, Kenward MG) Informative dropout in longitudinal data analysis. Applied Statistics 1994; 43: 84.
60. Rubin DB. Informative dropout in longitudinal data analysis (discussion to Diggle PJ, Kenward MG). Applied Statistics 1994; 43: 80-2.
61. Cook RD, Weisberg S. Residuals and influence in regression. Chapman & Hall, 1982.
62. Chatterjee S, Hadi AS. Sensitivity analysis in linear regression. John Wiley & Sons, 1988.
63. Cook RD. Detection of influential observations in linear regression. Technometrics 1977; 19: 15-18.
64. Cook RD. Influential observations in linear regression. Journal of the American Statistical Association 1979; 74: 169-74.
65. Beckman RJ, Nachtsheim CJ, Cook RD. Diagnostics for mixed-model analysis of variance. Technometrics 1987; 29: 413-26.
66. Lesaffre E, Verbeke G. Local influence in linear mixed models. Biometrics 1998; 54: 570-82.
67. Little RJA. Modeling the drop-out mechanism in repeated measures studies. Journal of the American Statistical Association 1995; 90: 1112-21.
68. Rubin DB. Formalizing subjective notions about the effect of nonrespondents in sample surveys. Journal of the American Statistical Association 1977; 72: 538-43.
69. Glynn RJ, Laird NM, Rubin DB. Selection m,odelling versus mixture modelling with nonignorable nonresponse. In Wainer, H ed. Drawing Inferences from Self-Selected Samples. Springer Verlag 1986; 115-42.
70. Hogan JW, Laird NM. Mixture models for the joint distribution of repeated measures and event times. Statistics in Medicine 1997; 16: 239-58.
71. Verbeke G, Lesaffre E, Spiessens B. The practical use of different strategies to handle dropout in longitudinal studies. Drug Information Journal 2001; 35: 419-34.
72. Molenberghs G, Michiels B, Lipsitz SR. Selection models and pattern-mixture models for incomplete categorical data with covariates. Biometrics 1999; 55: 978-83.
73. Michiels B, Molenberghs G, Lipsitz SR. A pattern-mixture odds ratio model for incomplete categorical data. Communications in Statistics: Theory and Methods 1999; 28: 2843-69.
74. Cohen J, Cohen P. Applied multiple regression/correlation analysis for the behavioral sciences, second edition. Erlbaum, 1983.
75. Muthén B, Kaplan D, Hollis M. On structural equation modeling with data that are not missing completely at random. Psychometrika 1987; 52: 431-62.
76. Allison PD. Estimation of linear models with incomplete data. Sociology Methodology 1987; 71-103.
77. McArdle JJ, Hamagami F. Modeling incomplete longitudinal and crosssectional data using latent growth structural models. Experimental Aging Research 1992; 18: 145-66.
78. Little RJA, Wang Y. Pattern-mixture models for multivariate incomplete data with covariates. Biometrics 1996; 52: 98-111.
79. Hedeker D, Gibbons RD. Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychological Methods 1997; 2: 64-78.
80. Ekholm A, Skinner C. The muscatine children's obesity data reanalysed using pattern mixture models. Applied Statistics 1998; 47: 251-63.
81. Molenberghs G, Michiels B, Kenward MG. Pseudo-likelihood for combined selection and pattern-mixture models for missing data problems. Biometrical Journal 1998; 40: 557-72.
82. Park T, Lee S-L. Simple pattern-mixture models for longitudinal data with missing observations: analysis of urinary incontinence data. Statistics in Medicine 1999; 18: 2933-41.
83. Molenberghs G, Michiels B, Kenward MG, Diggle PJ. Missing data mechanisms and pattern-mixture models. Statistica Neerlandica 1998; 52: 153-61.
84. Sheiner LB, Beal SL, Dunne A. Analysis of nonrandomly censored ordered categorical longitudinal data from analgesic trials. Journal of the American Statistical Association 1997; 92: 1235-44.
85. Verbeke, Lesaffre, Spiessens B. (2001) The practical use of different strategies to handle dropout in longitudinal studies. Drug Information Journal 35: 419-34.
86. Rotnitzky A, Cox DR, Bottai M, Robins J. Likelihood-based inference with singular information matrix. Bernouilli 2000; 6: 243-84.