I'd like to announce finegray, a new package for Fine-Gray competing risks regression, now available from SSC.
To install:
finegray fits the Fine and Gray (1999) subdistribution hazard model for competing risks data. It estimates subdistribution hazard ratios (SHR) that quantify the effect of covariates on the cumulative incidence function in the presence of competing events.
The implementation uses a Mata forward-backward scan algorithm (Kawaguchi et al. 2021) that avoids data expansion entirely. It produces identical point estimates and log-likelihoods to stcrreg but runs much faster, especially on larger datasets. It is roughly 40x faster at N=500 and 650x faster at N=10,000 (see finegray help in Stata for more on this).
The package includes three commands:
- finegray: Fine-Gray subdistribution hazard regression
- finegray_predict: Post-estimation predictions (linear predictor, CIF, Schoenfeld residuals)
- finegray_phtest: Test proportional subdistribution hazards assumption
Features include full factor variable support (i., c., ##), stratified censoring distributions, clustered and model-based standard errors, left-truncated (delayed entry) data, CIF prediction at arbitrary time points, and compatibility with margins.
Quick example:
The package has been cross-validated against stcrreg, R's cmprsk::crr, and R's fastcmprsk::fastCrr, confirming agreement in coefficients, standard errors, log-likelihoods, and cumulative incidence functions.
Requires Stata 16 or later.
References:
Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. JASA 1999; 94(446): 496-509.
Kawaguchi ES, Shen JI, Suchard MA, Li G. Scalable algorithms for large competing risks data. Journal of Computational and Graphical Statistics 2021; 30(3): 685-693.
If you find any bugs or have any feature requests, please submit them at https://github.com/tpcopeland/Stata-Tools/issues.
Also, please feel free to offer feedback on other packages in my Stata-Tools GitHub. I plan on releasing some of the ones that are most useful on SSC in the near future when I feel I've adequately tested them. In particular, I feel the following packages might be of interest to many of you: tabtools, tvtools, codescan, consort, datamap, and eplot.
To install:
Code:
ssc install finegray
The implementation uses a Mata forward-backward scan algorithm (Kawaguchi et al. 2021) that avoids data expansion entirely. It produces identical point estimates and log-likelihoods to stcrreg but runs much faster, especially on larger datasets. It is roughly 40x faster at N=500 and 650x faster at N=10,000 (see finegray help in Stata for more on this).
The package includes three commands:
- finegray: Fine-Gray subdistribution hazard regression
- finegray_predict: Post-estimation predictions (linear predictor, CIF, Schoenfeld residuals)
- finegray_phtest: Test proportional subdistribution hazards assumption
Features include full factor variable support (i., c., ##), stratified censoring distributions, clustered and model-based standard errors, left-truncated (delayed entry) data, CIF prediction at arbitrary time points, and compatibility with margins.
Quick example:
Code:
webuse hypoxia, clear
gen byte status = failtype
stset dftime, failure(dfcens==1) id(stnum)
finegray ifp tumsize pelnode, compete(status) cause(1)
finegray_predict cif_hat, cif
finegray_phtest
Requires Stata 16 or later.
References:
Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. JASA 1999; 94(446): 496-509.
Kawaguchi ES, Shen JI, Suchard MA, Li G. Scalable algorithms for large competing risks data. Journal of Computational and Graphical Statistics 2021; 30(3): 685-693.
If you find any bugs or have any feature requests, please submit them at https://github.com/tpcopeland/Stata-Tools/issues.
Also, please feel free to offer feedback on other packages in my Stata-Tools GitHub. I plan on releasing some of the ones that are most useful on SSC in the near future when I feel I've adequately tested them. In particular, I feel the following packages might be of interest to many of you: tabtools, tvtools, codescan, consort, datamap, and eplot.
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