Statistical power in parallel group point exposure studies with time-to-event outcomes: An empirical comparison of the performance of randomized controlled trials and the inverse probability of treatment weighting (IPTW) approach

BMC Medical Research Methodology

Volumn 15, Issue 1, 2015, Pages

  Austin, Peter C ^a,b,c   Schuster, Tibor ^d,e,f   Platt, Robert W ^e,g  

^a INSTITUTE FOR CLINICAL EVALUATIVE SCIENCES   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

^c Canada   (Canada)

^d ROYAL CHILDREN'S HOSPITAL   (Australia)

^e MCGILL UNIVERSITY   (Canada)

^f UNIVERSITY OF MELBOURNE   (Australia)

^g MCGILL UNIVERSITY   (Canada)

Author keywords

Causal inference; Inverse probability of treatment weighting; Monte Carlo simulations; Observational study; Propensity score; Randomized controlled trial; Survival analysis

Indexed keywords

COMPARATIVE STUDY; HUMAN; METHODOLOGY; MONTE CARLO METHOD; OBSERVATIONAL STUDY; PROCEDURES; PROPENSITY SCORE; PROPORTIONAL HAZARDS MODEL; RANDOMIZED CONTROLLED TRIAL (TOPIC); STATISTICAL ANALYSIS; SURVIVAL ANALYSIS;

DATA INTERPRETATION, STATISTICAL; HUMANS; MONTE CARLO METHOD; OBSERVATIONAL STUDIES AS TOPIC; PROPENSITY SCORE; PROPORTIONAL HAZARDS MODELS; RANDOMIZED CONTROLLED TRIALS AS TOPIC; RESEARCH DESIGN; SURVIVAL ANALYSIS;

EID: 84944754060     PISSN: None     EISSN: 14712288     Source Type: Journal    
DOI: 10.1186/s12874-015-0081-3     Document Type: Article

References (29)

1. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70:41-55.
2. Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc. 1984;79:516-24.
3. Austin PC. An introduction to propensity-score methods for reducing the effects of confounding in observational studies. Multivar Behav Res. 2011;46:399-424. doi: 10.1080/00273171.2011.568786.
4. Rosenbaum PR. Model-based direct adjustment. J Am Stat Assoc. 1987;82:387-94.
5. Austin PC. A tutorial and case study in propensity score analysis: an application to estimating the effect of in-hospital smoking cessation counseling on mortality. Multivar Behav Res. 2011;46:119-51. doi: 10.1080/00273171.2011.540480.
6. Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med. 2015. doi: 10.1002/sim.6607.
7. Hernan MA, Robins JM. Estimating causal effects from epidemiological data. J Epidemiol Community Health. 2006;60:578-86. doi: 10.1136/jech.2004.029496.
8. Greenland S. Interpretation and choice of effect measures in epidemiologic analyses. Am J Epidemiol. 1987;125(5):761-8.
9. Greenland S, Robins JM, Pearl J. Confounding and collapsibility in causal inference. Stat Sci. 1999;14:29-46.
10. Rosner B. Fundamentals of Biostatistics. Belmont, CA: Duxbury Press; 1995.
11. Schoenfeld DA. Sample-size formula for the proportional-hazards regression model. Biometrics. 1983;39(2):499-503.
12. Breslow NE, Day NE. Statistical methods in cancer research. Volume II - The design and analysis of cohort studies. IARC Sci Publ. 1987;82:1-406.
13. Austin PC, Manca A, Zwarenstein M, Juurlink DN, Stanbrook MB. A substantial and confusing variation exists in handling of baseline covariates in randomized controlled trials: a review of trials published in leading medical journals. J Clin Epidemiol. 2010;63(2):142-53. doi: 10.1016/j.jclinepi.2009.06.002.
14. Austin PC. The performance of different propensity score methods for estimating marginal hazard ratios. Stastisics in Medicine. 2012. doi: 10.1002/sim.5705.
15. Austin PC, Steyerberg EW. Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable. BMC Med Res Methodol. 2012;12:82. doi: 10.1186/1471-2288-12-82.
16. Bender R, Augustin T, Blettner M. Generating survival times to simulate Cox proportional hazards models. Stat Med. 2005;24(11):1713-23.
17. Ko DT, Mamdani M, Alter DA. Lipid-lowering therapy with statins in high-risk elderly patients: the treatment-risk paradox. JAMA. 2004;291:1864-70.
18. Austin PC. A data-generation process for data with specified risk differences or numbers needed to treat. Communications in Statistics - Simulation and Computation. 2010;39:563-77. doi: 10.1080/03610910903528301.
19. Austin PC, Stafford J. The performance of two data-generation processes for data with specified marginal treatment odds ratios. Communications in Statistics - Simulation and Computation. 2008;37:1039-51.
20. Austin PC, Grootendorst P, Anderson GM. A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study. Stat Med. 2007;26(4):734-53.
21. Lefebvre G, Delaney JA, Platt RW. Impact of mis-specification of the treatment model on estimates from a marginal structural model. Stastisics in Medicine. 2008;27(18):3629-42.
22. Lunceford JK, Davidian M. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Stat Med. 2004;23(19):2937-60.
23. Cole SR, Hernan MA. Adjusted survival curves with inverse probability weights. Comput Methods Programs Biomed. 2004;75:45-9.
24. Cole SR, Hernan MA. Constructing inverse probability weights for marginal structural models. Am J Epidemiol. 2008;168(6):656-64. doi: 10.1093/aje/kwn164.
25. Robins JM, Rotnitzky A, Zhao LP. Estimation of regression coefficients when some regressors are not always observed. J Am Stat Assoc. 1994;89(427):846-66.
26. Joffe MM, Ten Have TR, Feldman HI, Kimmel SE. Model selection, confounder control, and marginal structural models: Review and new applications. Am Stat. 2004;58:272-9.
27. Lin DY, Wei LJ. The robust inference for the proportional hazards model. J Am Stat Assoc. 1989;84(408):1074-8. doi: 10.1080/01621459.1989.10478874.
28. Sturmer T, Joshi M, Glynn RJ, Avorn J, Rothman KJ, Schneeweiss S. A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods. J Clin Epidemiol. 2006;59(5):437-47.
29. Austin PC, Mamdani MM, Stukel TA, Anderson GM, Tu JV. The use of the propensity score for estimating treatment effects: administrative versus clinical data. Stat Med. 2005;24(10):1563-78.