Semiparametric estimation of treatment effect in a pretest-posttest study with missing data

Statistical Science

Volumn 20, Issue 3, 2005, Pages 261-301

  Davidian, Marie ^a   Tsiatis, Anastasios A ^a   Leon, Selene ^b   An, Hyonggin ^c   Little, Roderick ^d   Brumback, Babette A ^e   Brumback, Lyndia C ^f   Molenberghs, Geert ^g   Schafer, Joseph L ^h   Kang, Joseph D Y ^h  

^a NORTH CAROLINA STATE UNIVERSITY   (United States)

^b NOVARTIS PHARMA AG   (Switzerland)

^c UNIVERSITY OF IOWA   (United States)

^d UNIVERSITY OF MICHIGAN   (United States)

^e UNIVERSITY OF FLORIDA   (United States)

^f UNIVERSITY OF WASHINGTON   (United States)

^g HASSELT UNIVERSITY   (Belgium)

^h PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Analysis of covariance; Covariate adjustment; Influence function; Inverse probability weighting; Missing at random

Indexed keywords

EID: 26044449172     PISSN: 08834237     EISSN: None     Source Type: Journal    
DOI: 10.1214/088342305000000151     Document Type: Review

References (44)

1. BICKEL, P. J., KLAASSEN, C. A. J., RITOV, Y. and WELLNER, J. A. (1993). Efficient and Adaptive Estimation for Semiparametric Models. Johns Hopkins Univ. Press.
2. BROGAN, D. R. and KUTNER, M. H. (1980). Comparative analyses of pretest-posttest research designs. Amer. Statist. 34 229-232.
3. CASELLA, G. and BERGER, R. L. (2002). Statistical Inference, 2nd ed. Duxbury, Pacific Grove, CA.
4. CLEVELAND, W. S., GROSSE, E. and SHYU, W. M. (1993). Local regression models. In Statistical Models in S (J. M. Chambers and T. J. Hastie, eds.) 309-376. Wadsworth, Pacific Grove, CA.
5. CRAGER, M. R. (1987). Analysis of covariance in parallel-group clinical trials with pretreatment baseline. Biometrics 43 895-901.
6. FOLLMANN, D. A. (1991). The effect of screening on some pretest-posttest test variances. Biometrics 47 763-771.
7. HAMMER, S. M., KATZENSTEIN, D. A., HUGHES, M. D., GUNDAKER, H., SCHOOLEY, R. T., HAUBRICH, R. H., HENRY, W. K., LEDERMAN, M. M., PHAIR, J. P., NIU, M., HIRSCH, M. S. and MERIGAN, T. C., for The AIDS Clinical Trials Group Study 175 Study Team (1996). A trial comparing nucleoside monotherapy with combination therapy in HIV-infected adults with CD4 cell counts from 200 to 500 per cubic millimeter. New England J. Medicine 335 1081-1090.
8. HASTIE, T. J. and TIBSHIRANI, R. J. (1990). Generalized Additive Models. Chapman and Hall, London.
9. HORVITZ, D. G. and THOMPSON, D. J. (1952). A generalization of sampling without replacement from a finite universe. J. Amer. Statist. Assoc. 47 663-685.
10. KOCH, G. G., TANGEN, C. M., JUNG, J.-W. and AMARA, I. A. (1998). Issues for covariance analysis of dichotomous and ordered categorical data from randomized clinical trials and non-parametric strategies for addressing them. Statistics in Medicine 17 1863-1892.
11. LAIRD, N. (1983). Further comparative analyses of pretest-posttest research designs. Amer. Statist. 37 329-330.
12. LEON, S., TSIATIS, A. A. and DAVIDIAN, M. (2003). Semiparametric estimation of treatment effect in a pretest-posttest study. Biometrics 59 1046-1055.
13. LUENBERGER, D. G. (1969). Optimization by Vector Space Methods. Wiley, New York.
14. LUNCEFORD, J. K. and DAVIDIAN, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: A comparative study. Statistics in Medicine 23 2937-2960.
15. NEWEY, W. K. (1990). Semiparametric efficiency bounds. J. Applied Econometrics 5 99-135.
16. ROBINS, J. M. (1999). Robust estimation in sequentially ignorable missing data and causal inference models. In ASA Proc. Bayesian Statistical Science Section 6-10. Amer. Statist. Assoc., Alexandria, VA.
17. ROBINS, J. M., ROTNITZKY, A. and ZHAO, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. J. Amer. Statist. Assoc. 89 846-866.
18. RUBIN, D. B. (1976). Inference and missing data (with discussion). Biometrika 63 581-592.
19. SCHARFSTEIN, D. O., ROTNITZKY, A. and ROBINS, J. M. (1999). Rejoinder to "Adjusting for nonignorable drop-out using semiparametric nonresponse models." J. Amer. Statist. Assoc. 94 1135-1146.
20. SINGER, J. M. and ANDRADE, D. F. (1997). Regression models for the analysis of pretest/posttest data. Biometrics 53 729-735.
21. STANEK, E. J., III (1988). Choosing a pretest-posttest analysis. Amer. Statist. 42 178-183.
22. STEIN, R. A. (1989). Adjusting treatment effects for baseline and other predictor variables. In ASA Proc. Biopharmaceutical Section 274-280. Amer. Statist. Assoc., Alexandria, VA.
23. VAN DER LAAN, M. J. and ROBINS, J. M. (2003). Unified Methods for Censored Longitudinal Data and Causality. Springer, New York.
24. YANG, L. and TSIATIS, A. A. (2001). Efficiency study of estimators for a treatment effect in a pretest-posttest trial. Amer. Statist. 55 314-321.
25. ALLISON, P. D. (2002). Missing Data. Sage, Thousand Oaks, CA.
26. CHENG, P. E. (1994). Nonparametric estimation of mean functionals with data missing at random. J. Amer. Statist. Assoc. 89 81-87.
27. COLLINS, L. M., SCHAFER, J. L. and KAM, C.-M. (2001). A comparison of inclusive and restrictive strategies in modern missing-data procedures. Psychological Methods 6 330-351.
28. EILERS, P. H. C. and MARX, B. D. (1996). Flexible smoothing with B-splines and penalties (with discussion). Statist. Sci. 11 89-121.
29. FRANGAKIS, C. E. and RUBIN, D. B. (1999). Addressing complications of intention-to-treat analysis in the combined presence of all-or-none treatment-noncompliance and subsequent missing outcomes. Biometrika 86 365-379.
30. GRAHAM, J. W. and SCHAFER, J. L. (1999). On the performance of multiple imputation for multivariate data with small sample size. In Statistical Strategies for Small Sample Research (R. Hoyle, ed.) 1-29. Sage, Thousand Oaks, CA.
31. LEHMANN, E. L. and CASELLA, G. (1998). Theory of Point Estimation, 2nd ed. Springer, New York.
32. LITTLE, R. J. A. and AN, H. (2004). Robust likelihood-based analysis of multivariate data with missing values. Statist. Sinica 14 949-968.
33. LITTLE, R. J. A. and RUBIN, D. B. (2002). Statistical Analysis with Missing Data, 2nd ed. Wiley, New York.
34. LORD, F. M. (1967). A paradox in the interpretation of group comparisons. Psychological Bulletin 68 304-305.
35. MALLINCKRODT, C. H., CLARK, W. S., CARROLL, R. J. and MOLENBERGHS, G. (2003). Assessing response profiles from incomplete longitudinal clinical trial data under regulatory considerations. J. Biopharmaceutical Statistics 13 179-190.
36. MOLENBERGHS, G., THIJS, H., JANSEN, I., BEUNCKENS, C., KENWARD, M. G., MALLINCKRODT, C. and CARROLL, R. J. (2004). Analyzing incomplete longitudinal clinical trial data. Biostatistics 5 445-464.
37. RAGHUNATHAN, T. E., LEPKOWSKI, J. M., VAN HOEWYK, J. and SOLENBERGER, P. (2001). A multivariate technique for multiply imputing missing values using a sequence of regression models. Survey Methodology 27 85-95.
38. ROBINS, J. M. (1994). Correcting for non-compliance in randomized trials using structural nested mean models. Comm. Statist. Theory Methods 23 2379-2412.
39. RUBIN, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley, New York.
40. SCHAFER, J. L. (1997). Analysis of Incomplete Multivariate Data. Chapman and Hall, London.
41. SHAO, J. and ZHONG, B. (2003). Last observation carry-forward and last observation analysis. Statistics in Medicine 22 2429-2441.
42. STONE, R. (1993). The assumptions on which causal inferences rest. J. Roy. Statist. Soc. Ser. B 55 455-466.
43. VAN BUUREN, S. and OUDSHOORN, C. G. M. (1999). Flexible multivariate imputation by MICE. Leiden: TNO Preventie en Gezondheid, TNO/VGZ/PG 99.054. For associated software see www.multiple-imputation.com.
44. VERBEKE, G. and MOLENBERGHS, G. (2000). Linear Mixed Models for Longitudinal Data. Springer, New York.