Sufficient Sample Sizes for Discrete-Time Survival Analysis Mixture Models

REFERENCES

• Jóźwiak , K. and Moerbeek , M. 2012a . Cost-effective designs for trials with discrete-time survival endpoints . Computational Statistics and Data Analysis , 56 ( 6 ) : 2086 – 2096 .
   Web of Science ®Google Scholar
• Jóźwiak , K. and Moerbeek , M. 2012b . Power analysis for trials with discrete-time survival endpoints . Journal of Educational and Behavioral Statistics. , 37 ( 5 ) : 630 – 654 .
   Web of Science ®Google Scholar
• Masyn , K. E. 2003 . Discrete-time survival analysis for single and recurrent events using latent variables , Unpublished doctoral dissertation, University of California at Los Angeles .
   Google Scholar
• Moerbeek , M. 2012 . Sample size issues for cluster randomized trials with discrete-time survival endpoints . Methodology , 8 ( 4 ) : 146 – 158 .
   Web of Science ®Google Scholar
• Muñoz , M. A and Acuña , J. D. 1999 . Sample size requirements of a mixture analysis method with applications in systematic biology . Journal of Theoretical Biology , 196 ( 2 ) : 263 – 265 .
   PubMed Web of Science ®Google Scholar
• Muthén , B. O. and Masyn , K. 2005 . Discrete-time survival mixture analysis . Journal of Educational and Behavioral Statistics , 30 ( 1 ) : 27 – 58 .
   Web of Science ®Google Scholar
• Muthén , B. O. 2004 . Handbook of quantitative methodology for the social sciences , Edited by: Kaplan , D. 345 – 368 . Newbury Park , CA : Sage Publications .
   Google Scholar
• Muthén , L. K. and Muthén , B. O. 2002 . How to use a Monte Carlo study to decide on sample size and determine power . Structural Equation Modeling , 9 ( 2 ) : 599 – 620 .
   Web of Science ®Google Scholar
• Muthén , L. K. and Muthén , B. O. 2010 . Mplus user's guide , sixth , Los Angeles : Muthén &Muthén . edition
   Google Scholar
• R Development Core Team . 2011 . R: A language and environment for statistical computing [Computer software manual] Vienna , , Austria
   Google Scholar
• Singer , J. D. and Willett , J. B. 1993 . It's about time: using discrete-time survival analysis to study duration and the timing of events . Journal of Educational and Behavior Statistics , 18 ( 2 ) : 155 – 195 .
   Web of Science ®Google Scholar
• Singer , J. D. and Willett , J. B. 2003 . Applied longitudinal data analysis. modeling change and event occurrence , Oxford : Oxford University Press .
   Google Scholar
• Torgerson , D. J. and Torgerson , C. J. 2008 . Designing randomised trials in health, education and the social sciences: An introduction , Basingstoke : Palgrave Macmillan .
   Google Scholar
• Vermunt , J. K. 1997 . Log-linear models for event histories , Thousand Oakes : Sage Publications .
   Google Scholar
• Vermunt , J. K. 2010 . Latent class modeling with covariates: Two improved three-step approaches . Political Analysis , 18 ( 4 ) : 450 – 469 .
   Web of Science ®Google Scholar
• Zhao , X. and Zhou , X. 2008 . Discrete-time survival models with long-term survivors . Statistics in Medicine , 27 ( 8 ) : 1261 – 1281 .
   PubMed Web of Science ®Google Scholar
• Zheng , J. and Frey , C. 2004 . Quantification of variability and uncertainty using mixture distributions: Evaluation of sample size, mixingweights, and separation between components . Risk Analysis , 24 ( 3 ) : 553 – 571 .
   PubMed Web of Science ®Google Scholar