Semiparametric Regression for Repeated Outcomes with Nonignorable Nonresponse

Journal of the American Statistical Association

Volumn 93, Issue 444, 1998, Pages 1321-1339

  Rotnitzky, Andrea ^a   Robins, James M ^a   Scharfstein, Daniel O ^b  

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^b JOHNS HOPKINS UNIVERSITY   (United States)

Author keywords

Curse of dimensionality; Estimating equations; Identification, Missing data; Semiparametric efficiency; Sensitivity analysis

Indexed keywords

EID: 0032264836     PISSN: 01621459     EISSN: 1537274X     Source Type: Journal    
DOI: 10.1080/01621459.1998.10473795     Document Type: Article

References (55)

1. Baker, S. G. (1995), “Marginal Regression for Repeated Binary Data With Outcome Subject to Nonignorable Nonresponse,” Biometrics, 51, 1042-1052.
2. Baker, S., and Laird, N. M. (1988), “Regression Analysis With Categorical Data Subject to Nonignorable Nonresponse,” Journal of the American Statistical Association, 83, 62-69.
3. Baker, S. G., Rosenberger, W. F., and DerSimonian, R. (1992), “Closed Form Estimate for Missing Counts in Two-Way Contingency Tables,” Statistics in Medicine, 11, 643-657.
4. Begun, J. M., Hall, W. J., Huang, W. M., and Wellner, J. A. (1983), “Information and Asymptotic Efficiency in Parametric-Nonparametric Models,” The Annals of Statistics, 11, 432-452.
5. Bickel, P. J., Klaassen, C. A. J., Ritov, Y., and Wellner, J. A. (1993), Efficient and Adaptive Inference in Semiparametric Models, Baltimore, MD: Johns Hopkins University Press.
6. Chamberlain, G. (1987), “Asymptotic Efficiency in Estimation With Conditional Moment Restrictions,” Journal of Econometrics, 34, 305-324.
7. Chambers, R. L., and Welsch, A. H. (1993), “Log-Linear Models for Survey Data With Non-Ignorable Non-Response,” Journal of the Royal Statistical Society, Ser. B, 55, 157-170.
8. Conaway, M. R. (1993), “'Non-Ignorable Non-Response Models for Time-Ordered Categorical Response Variable,” Applied Statistics, 42, 105-115.
9. Diggle, P., and Kenward, M. G. (1994), “Informative Dropout in Longitudinal Data Analysis” (with discussion), Applied Statistics, 43, 49-94.
10. Fischl, M. A., Parker, C. B., Pettinelli, C., Wulfsohn, M., Hirsch, M. S., Collier, A. C., Antoniskis, D., Ho, M., Richman, D. D., Fuchs, E., Merigan, S., Tsiatis, A., and the AIDS Clincial Trials Group (1990), “Randomized Controlled Trial of a reduced Daily Dose of Zidovudine in Patients With the Acquired Immunodeficiency Syndrome,” New England Journal of Medicine, 323, 1009-1019.
11. Fitzmaurice, G. M., Clifford, P., and Heath, A. F. (1996), “Logistic Regression Models for Binary Panel Data With Attrition,” Journal of the Royal Statistical Society, Ser. A, 159, 249-263.
12. Fitzmaurice, G. M., Laird, N. M., and Zahner, G. E. P. (1996), “Multivariate Logistic Models for Incomplete Binary Responses,” Journal of the American Statistical Association, 91, 99-108.
13. Fitzmaurice, G. M., Molenberghs, G., and Lipsitz, S. (1995), “Regression Models for Longitudinal Binary Responses With Informative DropOuts,” Journal of the Royal Statistical Society, Ser. B, 57, 691-704.
14. Glynn, J. J., Laird, N. M., and Rubin, D. B. (1986), “Selection Modelling Versus Mixture Modelling With Nonignorable Nonresponse,” in Drawing Inferences From Self-Selected Samples, ed. H. Wainer, New York: Springer-Verlag, pp. 115-142.
15. Hansen, L. P. (1982), “Large Sample Properties of Generalized Method of Moments Estimators,” Econometrica, 50, 1029-1054.
16. Hastie, T., and Tibshirani, R., (1990), Generalized Linear Models, London: Chapman and Hall.
17. Horvitz, D. G., and Thompson, D. J. (1952), “A Generalization of Sampling Without Replacement From a Finite Universe,” Journal of the American Statistical Association, 47, 663-685.
18. Hu, F. C., and Robins, J. (1992), “Estimation of the Causal Effect of a Time-Varying Exposure on the Marginal Mean of a Repeated Binary Outcome,” Ph.D. dissertation, Harvard School of Public Health, Dept, of Biostatistics.
19. Kalbfleisch, J. D., and Prentice, R. L., (1980), The Statistical Analysis of Failure Time Data, New York: Wiley.
20. Klein, J. P., and Moeschberger, M. L. (1988), “Bounds on Net Survival Probabilities for Dependent Competing Risks,” Biometrics, 44, 528-538.
21. Laird, N. (1988), “Missing Data in Longitudinal Studies,” Statistics in Medicine, 7, 305-315.
22. Liang, K. Y., and Zeger, S. L. (1986), “Longitudinal Data Analysis Using the Generalized Linear Model,” Biometrika, 73, 13-22.
23. Little, R. J. A. (1993), “Patten-Mixture Models for Multivariate Incomplete Data,” Journal of the American Statistical Association, 88, 125-134.
24. Little, R. J. A. (1994), “A Class of Pattem-Mixture Models for Normal Missing Data,” Biometrika, 81, 471-483.
25. Little, R. J. A. (1997), “Non-Response Models for the Analysis of NonMonotone Non-Ignorable Missing Data.” Statistics in Medicine, 16, 21-39.
26. Little, R. J. A. (1995), “Modeling the Drop-Out Mechanism in Repeated Measures Studies,” Journal of the American Statistical Association, 90, 1112-1121.
27. Little, R. J., and Rubin, D. (1987), Statistical Analysis With Missing Data, New York: Wiley.
28. Moeschberger, M. L., and Klein, J. P. (1995), “Statistical Models for Dependent Competing Risks,” Lifetime Data Analysis, 1, 195-204.
29. Newey, W. K. (1990), “Semiparametric Efficiency Bounds,” Journal of Applied Econometrics, 5, 99-135.
30. Newey, W. K. (1992), “Efficient Estimation of Semiparametric Models via Moment Restrictions,” working paper, Massachusetts Institute of Technology, Dept, of Economics.
31. Newey, W. K., and McFadden, D. (1993), “Estimation in Large Samples,” in Handbook of Econometrics, Vol. 4, eds. D. McFadden and R. Engler, Amsterdam: North-Holland.
32. Nordheim, E. V. (1984), “Inference From Nonrandomly Missing Categorical Data: An Example From a Genetic Study on Turner's Syndrome,” Journal of the American Statistical Association, 7, 112-116.
33. Pierce, D. A. (1982), 'The Asymptotic Effect of Substituting Estimators for Parameters in Certain Types of Statistics,” The Annals of Statistics, 10, 475-478.
34. Robins, J. M. (1992), “Estimation of the Time-Dependent Accelerated Failure Time Model in the Presence of Confounding Factors,” Biometrika, 79, 321-334.
35. Robins, J. M. (1994), “Correcting for Non-Compliance in Randomized Trials Using Structural Nested Mean Models,” Communications in Statistics, 23, 2379-2412.
36. Robins, J. M. (1997), “Non-Response Models for the Analysis of Non Monotone Non-Ignorable Missing Data.” Statistics in Medicine, 16, 21-39
37. Robins, J. M. (1998a), “Marginal Structural Models vs. Structural Nested Models as Tools for Causal Inference,” in Statistical Models for Epidemiology, ed. E. Halloran, New York: Springer-Verlag, to appear.
38. Robins, J. M. (1998b), “Marginal Structural Models,” in Proceedings of the American Statistical Association Section on Bayesian Statistical Science, pp. 1-10.
39. Robins, J. M., Hsieh, F.-S., and Newey, W. (1995), “Semiparametric Efficient Estimation of a Conditional Density With Missing or Mismeasured Covariates,” Journal of the Royal Statistical Society, Ser. B, 57, 409-424.
40. Robins, J. M., and Ritov, Y. (1997), “Towards a Curse of Dimensionality Appropriate (CODA) Asymptotic Theory for Semiparametric Models,” Statistics in Medicine, 16, 285-319.
41. Robins, J. M., and Rotnitzky, A. (1995), “Semiparametric Efficiency in Multivariate Regression Models With Missing Data,” Journal of the American Statistical Association, 90, 122-129.
42. Robins, J. M., Rotnitzky, A., and Scharfstein, D. (1998), “Sensitivity Analysis for Selection Bias and Unmeasured Confounding in Missing Data and Causal Inference Models,” in Statistical Models for Epidemiology, ed. E. Halloran, New York: Springer-Verlag, to appear.
43. Robins, J. M., Rotnitzky, A., and Zhao, L. P. (1994), “Estimation of Regression Coefficients When a Regressor Is Not Always Observed,” Journal of the American Statistical Association, 89, 846-866.
44. Robins, J. M., Rotnitzky, A., and Zhao, L. P. (1995), “Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data,” Journal of the American Statistical Association, 90, 106-121.
45. Rothenberg, T. (1971), “Identification in Parametric Models,” Econometrica, 39, 577-591.
46. Rotnitzky, A., and Robins, J. M. (1996), “Semiparametric Efficiency in Regression Models for Repeated Measures and Non-Ignorable NonResponse,” technical report, Harvard School of Public Health.
47. Rotnitzky, A., and Robins, J. M. (1997), “Analysis of Semiparametric Regression Models With Non-Ignorable Non-Response,” Statistics in Medicine, 16, 81-102.
48. Rubin, D. B. (1976), “Inference and Missing Data,” Biometrika, 63, 581-592.
49. Schluchter, M. D. (1992), “Methods for the Analysis of Informatively Censored Longitudinal Data,” Statistics in Medicine, 11, 1861-1870.
50. Slud, E. V. and Rubinstein, L. V. (1983), “Dependent Censoring Competing Risks and Summary Survival Curves” Biometrika, 70, 643-649.
51. Van der Laan, M. J. (1993), “Efficient and Inefficient Estimation in Semiparametric Models,” Ph.D. dissertation, University of Utrecht, The Netherlands.
52. Wu, M. C., and Bailey, K. R. (1989), “Estimation and Comparison of Changes in the Presence of Informative Right Censoring: Conditional Linear Model,” Biometrics, 45, 939-955.
53. Wu, M. C., and Carroll, R. J. (1988), “Estimation and Comparison of Changes in the Presence of Informative Right Censoring by Modeling the Censoring Process,” Biometrics, 44, 175-188.
54. Zheng, M., and Klein, J. P. (1994), “A Self-Consistent Estimator of Marginal Survival Functions Based on Dependent Competing Risks and an Assumed Copula.” Communications in Statistics-Theory and Methods, 23, 2299-2311.
55. Zheng, M., and Klein, J. P. (1995) “Estimates of Marginal Survival for Dependent Competing Risks Based on an Assumed Copula.” Biometrika, 82, 127-138.