Latent Subgroup Analysis of a Randomized Clinical Trial through a Semiparametric Accelerated Failure Time Mixture Model

Biometrics

Volumn 69, Issue 1, 2013, Pages 52-61

  Altstein, L ^a   Li, G ^b  

^a NOVARTIS INSTITUTES FOR BIOMEDICAL RESEARCH   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

All or none noncompliance; Buckley James estimator; Clinical trials; Competing risks; EM algorithm; Nonproportional hazards model; Treatment efficacy

Indexed keywords

ACCESS CONTROL; DERMATOLOGY; MEDICAL APPLICATIONS; RISK ASSESSMENT;

ALL-OR-NONE NONCOMPLIANCE; BUCKLEY-JAMES ESTIMATOR; CLINICAL TRIAL; COMPETING RISKS; EM ALGORITHMS; HAZARD MODELING; NONPROPORTIONAL; NONPROPORTIONAL HAZARD MODEL; RANDOMIZED CLINICAL TRIALS; TREATMENT EFFICACY;

ONCOLOGY;

ALGORITHM; MELANISM; PERFORMANCE ASSESSMENT; VARIANCE ANALYSIS;

ARTICLE; COMPUTER SIMULATION; DISEASE FREE SURVIVAL; HUMAN; LYMPH NODE DISSECTION; MELANOMA; METHODOLOGY; PATIENT COMPLIANCE; RANDOMIZED CONTROLLED TRIAL (TOPIC); STANDARD; STATISTICAL ANALYSIS; STATISTICAL MODEL;

COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; DISEASE-FREE SURVIVAL; HUMANS; LYMPH NODE EXCISION; MELANOMA; MODELS, STATISTICAL; PATIENT COMPLIANCE; RANDOMIZED CONTROLLED TRIALS AS TOPIC;

EID: 84875960306     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2012.01818.x     Document Type: Article

References (39)

1. Abadie, A. (2002). Bootstrap tests for distributional treatment effects in instrumental variable models. Journal of the American Statistical Association 97, 284-292.
2. Altstein, L., Li, G., and Elashoff, R. (2011). A method to estimate treatment efficacy among latent subgroups of a randomized clinical trial. Statistics in Medicine 30, 709-717.
3. Angrist, J., Imbens, G., and Rubin, D. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association 91, 444-455.
4. Baker, S. (2000). Analyzing a randomized cancer prevention trial with a missing binary outcome, an auxiliary variable, and all-or-none compliance. Journal of the American Statistical Association 95, 43-50.
5. Buckley, J. and James, I. (1979). Linear regression with censored data. Biometrika 66, 429-436.
6. Cuzick, J., Edwards, R., and Segnan, N. (1997). Adjusting for non-compliance and contamination in randomized clinical trials. Statistics in Medicine 16, 1017-1029.
7. Cuzick, J., Sasieni, P., Myles, J., and Tyrer, J. (2007). Estimating the effect of treatment in a proportional hazards model in the presence of non-compliance and contamination. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 69, 565-588.
8. Dempster, A., Laird, N., and Rubin, D. (1997). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 39, 1-38.
9. Elashoff, R., Li, G., and Zhou, Y. (2012). Nonparametric inference for assessing treatment efficacy in randomized clinical trials with a time-to-event outcome and all-or-none compliance. Biometrika 99, 393-404.
10. Follmann, D. (2000). On the effect of treatment among would-be treatment compliers: An analysis of the multiple risk factor intervention trial. Journal of the American Statistical Association 95, 1101-1109.
11. Frangakis, C. and Rubin, D. (1999). Addressing complications of intention-to-treat analysis in the combined presence of all-or-none treatment-noncompliance and subsequent missing out comes. Biometrika 86, 365-379.
12. Gasser, T. and Müller, H. (1979). Kernel estimation of regression functions. In Proceedings of a Workshop on Smoothing Techniques for Curve Estimation, T. Gasser and M. Rosenblatt (eds), 23-68. Berlin: Springer-Verlag.
13. Gipponi, M., Solari, N., Di Somma, F., Bertoglio, S., and Cafiero, F. (2004). New fields of application of the sentinel lymph node biopsy in the pathologic staging of solid neoplasms: Review of literature and surgical perspectives. Journal of Surgical Oncology 85, 171-179.
14. Goetghebeur, E. and Molenberghs, G. (1996). Causal inference in a placebo-controlled clinical trial with binary outcome and ordered compliance. Journal of the American Statistical Association 91, 928-934.
15. Hess, K., Serachitopol, D., and Brown, B. (1999). Hazard function estimators: A simulation study. Statistics in Medicine 18, 3075-3088.
16. Imbens, G. and Rubin, D. (1997a). Bayesian inference for causal effects in randomized experiments with noncompliance. Annals of Statistics 25, 305-327.
17. Imbens, G. and Rubin, D. (1997b). Estimating outcome distributions for compliers in instrumental variables models. The Review of Economic Studies 64, 555-574.
18. Jin, Z., Lin, D., and Ying, Z. (2006). On least-squares regression with censored data. Biometrika 93, 147-161.
19. Klein, J. and Moeschberger, M. (2003). Survival Analysis: Techniques for Censored and Truncated Data, 2nd edition. New York: Springer-Verlag.
20. Komarek, A., Lesaffre, E., and Hilton, J. (2005). Accelerated failure time model for arbitrarily censored data with smoothed error distribution. Journal of Computational and Graphical Statistics 14, 726-745.
21. Lagakos, S. (2006). The challenge of subgroup analyses-reporting without distorting. The New England Journal of Medicine 354, 1667-1669.
22. Lai, T. and Ying, Z. (1991). Large sample theory of a modified Buckley-James estimator for regression analysis with censored data. The Annals of Statistics 19, 1370-1402.
23. Little, R. and Yau, L. (1998). Statistical techniques for analyzing data from prevention trials: Treatment of no-shows using Rubin's causal model. Psychological Methods 3, 147-159.
24. Loeys, T. and Goetghebeur, E. (2003). A causal proportional hazards estimator for the effect of treatment actually received in a randomized trial with all-or-nothing compliance. Biometrics 59, 100-105.
25. Louis, T. (1981). Nonparametric analysis of an accelerated failure time model. Biometrika 68, 381-390.
26. Mealli, F., Imbens, G., Ferro, S., and Biggeri, A. (2004). Analyzing a randomized trial on breast self-examination with noncompliance and missing outcomes. Biostatistics 5, 207-222.
27. Morton, D., Thompson, J., Cochran, A., Mozzillo, N., Elashoff, R., Essner, R., Nieweg, O., Roses, D., Hoekstra, H., Karakousis, C., Reintgen, D., Coventry, B., Glass, E., Wang, H.; and the Multi center Selective Lymphadenectomy Trial Group, (2006). Sentinel-node biopsy or nodal observation in melanoma. New England Journal of Medicine 355, 1307-1317.
28. Morton, D., Thompson, J., Essner, R., Elashoff, R., Stern, S., Nieweg, O., Roses, D., Karakousis, C., Mozzillo, N., Reingten, D., Wang, H., Glass, E., Cochran, A.; and the Multicenter Selective Lymphadenectomy Trial Group, (1999). Validation of the accuracy of intraoperative lymphatic mapping and sentinel lymphadenectomy for early-stage melanoma: A multicenter trial. Annals of Surgery 230, 453-463.
29. Müller, H. and Wang, J. L. (1990). Locally adaptive hazard smoothing. Probability Theory and Related Fields 85, 523-538.
30. O'Malley, A. and Normand, S. (2004). Likelihood methods for treatment noncompliance and subsequent nonresponse in randomized trials. Biometrics 61, 325-334.
31. Peng, Y., Little, R. A., and Raghunathan, T. (2004). An extended general location model for causal inferences from data subject to noncompliance and missing values. Biometrics 60, 598-607.
32. Ritov, Y. (1990). Estimation in a linear regression model with censored data. The Annals of Statistics 18, 303-328.
33. Robins, J. and Tsiatis, A. (1991). Correcting for noncompliance in randomized trials using rank preserving structural failure time models. Communications in Statistics A 20, 2609-2631.
34. Sommer, A. and Zeger, S. (1991). On estimating efficacy from clinical trials. Statistics in Medicine 10, 45-52.
35. Stare, J., Harrell, F., and Heinzl, H. (2001). An S-plus program to fit linear regression models to censored data using the Buckley-James method. Computer Methods and Programs :in Biomedicine 64, 45-52.
36. Tsiatis, A. (1990). Estimating regression parameters using linear rank tests for censored data. Annals of Statistics 18, 354-372.
37. Wang, R., Lagakos, S., Ware, J., Hunter, D., and Drazen, J. (2007). Statistics in medicine-reporting of subgroup analyses in clinical trials. The New England Journal of Medicine 357, 2189-2194.
38. Wei, L. (1992). The accelerated failure time model: A useful alternative to the Cox regression model in survival analysis. Statistics in Medicine 11, 1871-1879.
39. Zeng, D. and Lin, D. (2007). Efficient estimation for the accelerated failure time model. Journal of the American Statistical Association 102(480), 1387-1396.