A causal proportional hazards estimator for the effect of treatment actually received in a randomized trial with all-or-nothing compliance

Biometrics

Volumn 59, Issue 1, 2003, Pages 100-105

  Loeys, T ^a,b   Goetghebeur, E ^b  

^a Merck Sharp and Dohme   (Belgium)

^b GHENT UNIVERSITY   (Belgium)

Author keywords

Causal inference; Compliance; Proportional hazards; Randomized trial; Structural model; Survival

Indexed keywords

HAZARDS; HYSTERESIS;

ALL OR NOTHINGS; CAUSAL INFERENCES; COMPLIANCE; FAILURE TIME; PROPORTIONAL HAZARD MODELS; PROPORTIONAL HAZARDS; RANDOMIZED TRIAL; STRUCTURAL MODELING; SURVIVAL; SURVIVAL DATA;

COMPLIANCE CONTROL;

BIOMETRY; MEDICINE; SURVIVAL;

ARTICLE; BIOSTATISTICS; CLINICAL TRIAL; COLORECTAL CANCER; HUMAN; LIVER METASTASIS; MAJOR CLINICAL STUDY; MULTIMODALITY CANCER THERAPY; NONBIOLOGICAL MODEL; OUTCOMES RESEARCH; PHASE 3 CLINICAL TRIAL; STATISTICAL MODEL; SURVIVAL RATE; TREATMENT OUTCOME;

TURDUS;

EID: 0037367588     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/1541-0420.00012     Document Type: Article

References (17)

1. Angrist, J. D., Imbens, G. W., and Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association 91, 444-1455.
2. Barlow, R. E., Bartholomew, D. J., Bremner, J. M., and Brunk, H. D. (1972). Statistical Inference under Order Restrictions. New York: Wiley.
3. Breslow, N. E. (1974). Covariance analysis of censored survival data. Biometrics 30, 89-199.
4. Efron, B. and Tibshirani, R. J. (1993). An Introduction to the Bootstrap. New York: Chapman & Hall.
5. Fleming, T. R. and Lin, D. Y. (2000). Survival analysis in clinical trials: past developments and future directions. Biometrics 56, 971-983.
6. Follmann, D. A. (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.
7. 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.
8. Hernan, M. A., Brumback, B., and Robins, J. M. (2001). Marginal structural models to estimate the joint causal effect of nonrandomized treatments. Journal of the American Statistical Association 96, 440-448.
9. Joffe, M. M. (2001). Administrative and artificial censoring in censored regression models. Statistics in Medicine 20, 2287-2301.
10. Kemeny, M. M., Adak, S., Gray, B., Macdonald, J. S., Smith, T., Lipsitz, S., Sigurdson, E. R., O'Dwyer, P. J., and Benson, A. B. (2002). Combined-modality treatment for resectable metastatic colorectal carcinoma to the liver: Surgical resection of hepatic metastases in combination with continuous infusion of chemotherapy - An intergroup study. Journal of Clinical Oncology 20, 1499-1505.
11. Kemeny, N., Huan, Y., Cohen, A. M., Shi, W., Conti, J. A., Brennan, M. F., Bertino, J. R., Turnbull, A. D. M., Sullivan, D., Stockman, J., Blumgart, L. H., and Fong, Y. (1999). Hepatic arterial infusion of chemotherapy after resection of hepatic metastases from colorectal cancer. The New England Journal of Medicine 341, 2039-2048.
12. Pearl, J. (2001). Causal inference in the health sciences: A conceptual introduction. Health Services and Outcomes Research Methodology 2(3), 189-220.
13. Robins, J. M. (1993). Analytic methods for estimating HIV treatment and cofactor effects. In Methodological Issues in Aids Behavioral Research, D. G. Ostrow and R. Kessler RC (eds), 213-290. New York: Plenum.
14. Robins, J. M. (1999). Marginal structural models versus structural nested models as tools for causal inference. In Statistical Models in Epidemiology: The Environment and Clinical Trials, M. E. Halloran and D. Berry (eds). New York: Springer-Verlag.
15. Robins, J. M. and Tsiatis, A. A. (1991). Correcting for noncompliance in randomized trials using rank preserving structural failure time models. Communications in Statistics A 20(8), 2609-2631.
16. Rubin, D. (1998). More powerful randomisation-based p-values in double-blind trials with noncompliance. Statistics in Medicine 17, 371-385.
17. Tukey, J. W. (1958). Bias and confidence in not-quite large samples. Annals of Mathematical Statistics 29, 614.