PSHREG: A SAS macro for proportional and nonproportional subdistribution hazards regression

Computer Methods and Programs in Biomedicine

Volumn 118, Issue 2, 2015, Pages 218-233

  Kohl, Maria ^a,b   Plischke, Max ^a   Leffondré, Karen ^c   Heinze, Georg ^a  

^a MEDICAL UNIVERSITY OF VIENNA   (Austria)

^b UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

^c UNIVERSITÉ DE BORDEAUX   (France)

Author keywords

Competing risks; Cumulative incidence; Regression analysis; SAS software; Subdistribution hazard ratio; Survival analysis

Indexed keywords

HAZARDS; MAXIMUM LIKELIHOOD ESTIMATION; REGRESSION ANALYSIS; RISK ASSESSMENT;

CHRONIC KIDNEY DISEASE; COMPETING RISKS; COUNTING PROCESS; CUMULATIVE INCIDENCE; HAZARD RATIO; MAXIMUM LIKELIHOOD ESTIMATE; NONPROPORTIONAL; SURVIVAL ANALYSIS;

MACROS;

ADULT; ARTICLE; CHRONIC KIDNEY DISEASE; COMPUTER PROGRAM; CORRELATION ANALYSIS; HAZARD RATIO; HUMAN; MAJOR CLINICAL STUDY; MAXIMUM LIKELIHOOD METHOD; NONPROPORTIONAL SUBDISTRIBUTION HAZARD REGRESSION; PREDICTION; PROPORTIONAL HAZARDS MODEL; PROPORTIONAL SUBDISTRIBUTION HAZARD REGRESSION; REGRESSION ANALYSIS; RISK FACTOR; STANDARDIZED INCIDENCE RATIO; SURVIVAL TIME; RISK ASSESSMENT; STATISTICAL MODEL; THEORETICAL MODEL;

HUMANS; LIKELIHOOD FUNCTIONS; MODELS, THEORETICAL; PROPORTIONAL HAZARDS MODELS; RISK ASSESSMENT;

EID: 84922476780     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2014.11.009     Document Type: Article

References (25)

1. Bakoyannis G., Touloumi G. Practical methods for competing risk data: a review. Stat. Methods Med. Res. 2012, 21:257-272. 10.1177/0962280210394479.
2. Kalbfleisch J., Prentice R. The Statistical Analysis of Failure Time Data 1980, John Wiley & Sons, New York.
3. Rosthoj S., Andersen P.K., Abildstrom S. SAS macros for estimation of the cumulative incidence functions based on a cox regression model for competing risks survival data. Comput. Methods Progr. Biomed. 2004, 74:69-75. 10.1016/S0169-2607(03)00069-5.
4. Fine J., Gray R. A proportional hazards model for the subdistribution of a competing risk. J. Am. Stat. Assoc. 1999, 94:496-509. 10.1080/01621459.1999.10474144.
5. Wolbers M., Koller M.T., Stel V.S., Schaer B., Jager K.J., Leffondré K., Heinze G. Competing risks analyses: objectives and approaches. Eur. Heart J. 2014, 35:2936-2941. 10.1093/eurheartj/ehu131.
6. Andersen P.K., Geskus R., de Witte T., Putter H. Competing risks in epidemiology: possibilities and pitfalls. Int. J. Epidemiol. 2012, 41:861-870. 10.1093/ije/dyr213.
7. Gray B. cmprsk: Subdistribution analysis of competing risks. R package version 2.2-7 2014, (accessed 24.06.14). http://cran.r-project.org/web/packages/cmprsk/index.html.
8. StataCorp Stata statistical software: Release 12 2011, StataCorp LP, College Station, TX.
9. Waltoft B.L. A SAS-macro for estimation of the cumulative incidence using Poisson regression. Comput. Methods Progr. Biomed. 2009, 93:140-147. 10.1016/j.cmpb.2008.08.004.
10. Zhang X., Zhang M.-J. SAS macros for estimation of direct adjusted cumulative incidence curves under proportional subdistribution hazards models. Comput. Methods Progr. Biomed. 2011, 101:87-93. 10.1016/j.cmpb.2010.07.005.
11. Geskus R. Cause-specific cumulative incidence estimation and the fine and gray model under both left truncation and right censoring. Biometrics 2011, 67:39-49. 10.1111/j.1541-0420.2010.01420.x.
12. Firth D. Bias reduction of maximum likelihood estimates. Biometrika 1993, 80:27-38. 10.1093/biomet/80.1.27.
13. Heinze G., Schemper M. A solution to the problem of monotone likelihood in Cox regression. Biometrics 2001, 57:114-119. 10.1111/j.0006-341X.2001.00114.x.
14. Heinze G., Dunkler D. Avoiding infinite estimates of time-dependent effects in small-sample survival studies. Stat. Med. 2008, 27:6455-6469. 10.1002/sim.3418.
15. Plischke M., Kohl M., Shayganfar S., Handisurya A., Heinze G., et al. Urine osmolarity and risk of dialysis initiation in a chronic kidney disease cohort: a possible titration target?. PLoS ONE 2012, 9(3.). 10.1371/journal.pone.0093226.
16. Lin D., Wei L. The robust inference for the Cox proportional hazards model. J. Am. Stat. Assoc. 1989, 84:1074-1078. 10.1080/01621459.1989.10478874.
17. Therneau T.M., Grambsch P.M. Modeling Survival Data: Extending the Cox Model 2000, Springer, New York.
18. Cox D.R., Oakes D. Analysis of Survival Data 1984, Chapman & Hall, London.
19. Sauerbrei W., Royston P., Binder H. Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Stat. Med. 2007, 26:5512-5528. 10.1002/sim.3148.
20. Lin I., Chang W., Liao Y. Shrinkage methods enhanced the accuracy of parameter estimation using Cox models with small number of events. J. Clin. Epidemiol. 2013, 66:743-751. 10.1016/j.jclinepi.2013.02.002.
21. Durrleman S., Simon R. Flexible regression models with cubic splines. Statist. Med. 1989, 8:551-561. 10.1002/sim.4780080504.
22. Harrell F. SAS macros and data step programs useful in survival analysis and logistic regression 1986, (accessed 07.10.14). http://biostat.mc.vanderbilt.edu/wiki/pub/Main/SasMacros/survrisk.txt.
23. Royston P., Altman D. Regression using fractional polynomials of continuous covariates: Parsimonious parametric modelling. Appl. Stat. 1994, 43:429-467.
24. Rostgaard K. Methods for stratification of person-time and events: a prerequisite for Poisson regression and SIR estimation. Epidemiol. Perspect. Innov. 2008, 5. 10.1186/1742-5573-5-7.
25. Beyersmann J., Schumacher M. Time-dependent covariates in the proportional subdistribution hazards model for competing risks. Biostatistics 2008, 9:765-776. 10.1093/biostatistics/kxn009.