Instrumental variables and interactions in the causal analysis of a complex clinical trial

Statistics in Medicine

Volumn 26, Issue 7, 2007, Pages 1473-1496

  Bond, Simon J ^a   White, Ian R ^a   Walker, A Sarah ^b  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b MEDICAL RESEARCH COUNCIL   (United Kingdom)

Author keywords

Causality; Instrumental variables; Interaction; Longitudinal; Non compliance; Randomized control trial

Indexed keywords

NELFINAVIR; PLACEBO; VIRUS RNA;

ARTICLE; CAUSAL ANALYSIS; CHILD; CLINICAL RESEARCH; CLINICAL TRIAL; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; COVARIANCE; DRUG DOSE INCREASE; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS 1 INFECTION; LONGITUDINAL STUDY; MATHEMATICAL MODEL; METHODOLOGY; OUTCOME VARIABLE; PATIENT COMPLIANCE; RANDOMIZED CONTROLLED TRIAL; RECOMMENDED DRUG DOSE; SENSITIVITY ANALYSIS; STATISTICAL ANALYSIS; TREATMENT OUTCOME;

ANTI-HIV AGENTS; CHILD; DATA INTERPRETATION, STATISTICAL; FEMALE; HIV; HIV INFECTIONS; HUMANS; LONGITUDINAL STUDIES; MALE; MODELS, STATISTICAL; NELFINAVIR; PATIENT COMPLIANCE; RANDOMIZED CONTROLLED TRIALS; RNA, VIRAL;

EID: 33947102051     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.2644     Document Type: Article

References (35)

1. Sheiner LB, Rubin DB. Intention-to-treat analysis and the goals of clinical trials. Clinical Pharmacology and Therapeutics 1995; 57:6-15.
2. Mark SD, Robins JM. The analysis of randomized trials with compliance information-an application to the multiple risk factor intervention trial. Controlled Clinical Trials 1993; 14(2):79-97.
3. Bowden RJ, Turkington DA. Instrumental Variables. Cambridge University Press: Cambridge, 1984.
4. Rubin D. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 1974; 66:688-701.
5. Imbens GW, Rubin DB. Bayesian inference for causal; effects in randomized experiments with noncompliance. The Annals of Statistics 1997; 25:305-327.
6. Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. Journal of the American Statistical Association 1996; 91(434):444-472.
7. Hirano K, Imbens GW, Rubin DB, Zhou XH. Assessing the effect of an influenza vaccine in an encouragement design. Biostatistics 2000; 1(1):69-88.
8. Robins JM. Correcting for non-compliance in randomized trials using structural nested mean models. Communications in Statistics: Theory and Methods 1994; 23:2379-2412.
9. Sato T. A method for the analysis of repeated binary outcomes in randomized clinical trials with non-compliance. Statistics in Medicine 2001; 20:2761-2774.
10. Yau LHY, Little RJ. Inference for the complier-average causal effect from longitudinal data subject to noncompliance and missing data, with application to a job training assessment for the unemployed. Journal of the American Statistical Association 2001; 96(456):1232-1244.
11. Frangakis CE, Crookmeyer RS, Varadhan R, Safaeian M, Vlahov D, Strathdee SA. Methodology for evaluating a partially controlled longitudinal treatment using principal stratification, with application to a needle exchange program. Journal of the American Statistical Association 2004; 99(465):239-249.
12. Small DS, Ten Have TR, Joffe MM, Cheng J. Random effects logistic models for analysing efficacy of a longitudinal randomized treatment with non-adherence. Statistics in Medicine 2006; 25(12):1981-2007.
13. Nagelkerke N, Fidler V, Bernsen R, Borgdorff M. Estimating treatment effects in randomized clinical trials in the presence of non-compliance. Statistics in Medicine 2000; 19(14):1849-1864.
14. Bound J, Jaeger DA, Baker RM. Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. Journal of the American Statistical Association 1995; 90(430):443-450.
15. White H. Instrumental variables regression with independent observations. Econometrica 1982; 50(2):483-499.
16. Hsiao C. Analysis of Panel Data. Number 11 in Economic Society Monographs. Cambridge University Press: Cambridge, 1986.
17. Goetghebeur E, Lapp K. The effect of treatment compliance in a placebo-controlled trial: regression with unpaired data. Journal of the Royal Statistical Society, Series C 1997; 46:351-364.
18. Joffe MM, Brensinger C. Weighting in instrumental variables and G-estimation. Statistics in Medicine 2003; 22:1285-1303.
19. Small D. Inference and model selection for instrumental variables regression. Ph.D. Thesis, Stanford University, Department of Statistics, 2002.
20. Angrist JD, Imbens GW. Two-stage least sqaures estimation of average causal effects in models with variable treatment intensity. Journal of the American Statistical Association 1995; 90:431-442.
21. Paediatric European Network For Treatment of AIDS. Comparison of dual nucleoside-analogue reverse-transcriptase inhibitor regimes with and without nelfinavir in children with HIV-1 who have not previously been treated: the PENTA 5 randomised trial. Lancet 2002; 359:733-740.
22. White IR, Babiker AG, Walker AS, Darbyshire JH. Randomization-based methods for correcting for treatment changes: examples from the concorde trial. Statistics in Medicine 1999; 18:2617-2634.
23. Thiébaut R, Jacqmin-Gadda H, Babiker A, Commenges D, The CASCADE Collaboration. Joint modelling of bivariate longitudinal data with informative dropout and left-censoring, with application to the evolution of CD4+ cell count and HIV RNA viral load in response to treatment of HIV infection. Statistics in Medicine 2005; 24(1):65-82.
24. Jacqmin-Gadda H, Thiébaut R, Chêne G, Commenges D. Analysis of left-censored longitudinal data with application to viral load in HIV infection. Biostatistics 2000; 1:355-368.
25. Dempster AP, Laird NM, Rubin DB. Maximum likelihood estimation from incomplete data via the EM algorithm (with Discussion). Journal of the Royal Statistical Society, Series B 1977; 39(1):1-38.
26. Cleveland WS, Grosse E, Shyu WM. Local regression models. In Statistical Models in S (Chapter 8), Chambers JM, Hastie TJ (eds). Chapman & Hall: New York, 1992.
27. Pinheiro JC, Bates DM. Mixed-Effects Models in S and S-PLUS. Statistics in Computing. Springer: New York, 2000.
28. Wilkinson GN, Rogers CE. Symbolic description of factorial models for analysis of variance. Applied Statistics 1973; 22:392-399.
29. Ihaka R, Gentleman R. R: a language for data analysis and graphics. Journal of Computational and Graphical Statistics 1996; 5(3):299-314.
30. Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika 1986; 73:13-22.
31. Nagar AL. The bias and moment matrix of the general k-class estimators of the parameters in simultaneous equations. Econometrica 1959; 27(4):575-595.
32. Speigelhalter D, Thomas A, Best N, Lunn D. WinBUGS User Manual Version 1.4.1. MRC Biostatistics Unit: Cambridge, U.K., 2004.
33. Pocock SJ, Assman SE, Enos LE, Kasten LE. Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems. Statistics in Medicine 2002; 21:2917-2930.
34. White IR, Elbourne D. Assessing subgroup effects with binary data: can the use of different measures lead to different conclusions. Biomed Central Medical Research Methodology 2005; 5(1):15.
35. White IR. Uses and limitations of randomization-based efficacy estimators. Statistical Methods in Medical Research 2005; 14:327-347.