Generating survival times to simulate Cox proportional hazards models with time-varying covariates

Statistics in Medicine

Volumn 31, Issue 29, 2012, Pages 3946-3958

  Austin, Peter C ^a,b  

^a INSTITUTE FOR CLINICAL EVALUATIVE SCIENCES   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

Author keywords

Exponential distribution; Gompertz distribution; Power and sample size calculation; Proportional hazards model; Simulations; Survival analysis; Time dependent covariate; Time varying covariates; Weibull distribution

Indexed keywords

ACUTE HEART INFARCTION; ANALYSIS OF COVARIANCE; ARTICLE; CHRONIC DISEASE; FOLLOW UP; HAZARD RATIO; HUMAN; LINEAR SYSTEM; MONTE CARLO METHOD; ORGAN TRANSPLANTATION; PATIENT ATTITUDE; PREDICTION; PROPORTIONAL HAZARDS MODEL; RADIATION DOSE; RADIATION EXPOSURE; STATISTICAL DISTRIBUTION; STATISTICAL MODEL; STATISTICAL SIGNIFICANCE; SURVIVAL TIME; TIME; TREATMENT OUTCOME;

HUMANS; MONTE CARLO METHOD; MYOCARDIAL INFARCTION; PROPORTIONAL HAZARDS MODELS; RISK ASSESSMENT; RISK FACTORS; SURVIVAL ANALYSIS; TIME FACTORS;

EID: 84870054871     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.5452     Document Type: Article

References (14)

1. Leemis LM. Variate generation for accelerated life and proportional hazards models. Operations Research 1987; 35:892-894.
2. Bender R, Augustin T, Blettner M. Generating survival times to simulate Cox proportional hazards models. Statistics in Medicine 2005; 24(11):1713-1723.
3. Cox D. Regression models and life tables (with discussion). Journal of the Royal Statistical Society - Series B 1972; 34:187-220.
4. Therneau TM, Grambsch PM. Modeling Survival Data: Extending the Cox Model. Springer-Verlag: New York, 2000.
5. Suissa S. Immortal time bias in pharmacoepidemiology. American Journal of Epidemiology 2007; 167(4):492-499.
6. Beyersmann J, Wolkewitz M, Schumacher M. The impact of time-dependent bias in proportional hazards modelling. Statistics in Medicine 2008; 27:6439-6454.
7. Beyersmann J, Gastmeier P, Wolkewitz M, Schumacher M. An easy mathematical proof showed that time-dependent bias inevitably leads to biased effect estimation. Journal of Clinical Epidemiology 2008; 61:1216-1221.
8. Austin PC, Mamdani MM, van Walraven C, Tu JV. Quantifying the impact of survivor treatment bias in observational studies. Journal of Evaluation in Clinical Practice 2006; 12(6):601-612.
9. Tu JV, Austin P, Naylor CD. Temporal changes in the outcomes of acute myocardial infarction in Ontario, 1992-96. Canadian Medical Association Journal 1999; 161(10):1257-1261.
10. Tu JV, Austin PC, Walld R, Roos L, Agras J, McDonald KM. Development and validation of the Ontario acute myocardial infarction mortality prediction rules. Journal of the American College of Cardiology 2001; 37(4):992-997.
11. Mackenzie T, Abrahamowicz M. Marginal and hazard ratio specific random data generation: applications to semi-parametric bootstrapping. Statistics and Computing 2002; 12:245-252.
12. Beyersmann J, Latouche A, Buchholz A, Schumacher M. Simulating competing risks data in survival analysis. Statistics in Medicine 2009; 28(6):956-971.
13. Leemis LM, Shih LH, Reynertson K. Variate generation for accelerated life and proportional hazards models with time dependent covariates. Statistics and Probability Letters 1990; 10:335-339.
14. Sylvestre M-P, Abrahamowicz M. Comparison of algorithms to generate event time conditional on time-dependent covariates. Statistics in Medicine 2008; 27:2618-2636.