- The stcurve command plots the survivor, failure, hazard, or cumulative hazard function after fitting many models for survival-time data. In Stata 18, stcurve has the following new features:
- After fitting a shared-frailty Cox model with stcox, you can now specify the expression _frailty = (numlist) in the at() option to adjust estimates of survivor and related functions for frailties set to the values in numlist.
- After fitting a Cox model for a multiple-record-per-subject interval-censored dataset using stintcox, you can specify the new atmeans option to evaluate the survivor or other function at time-specific means of the covariate.
- After fitting a Cox model for a multiple-record-per-subject interval-censored dataset using stintcox, you can specify the new atframe(frname) option to evaluate the survivor or other function at the values of the variables specified in the frname frame.
- After lasso cox or elasticnet cox, you can calculate predictions based on penalized coefficients by default, or you can calculate predictions based on postselection coefficients by specifying the postselection option.
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- After fitting a shared-frailty Cox model with stcox, predict now allows the atfrailty and atfrailty(varname|#) options when you predict the baseline survivor function, baseline cumulative-hazard function, or baseline hazard contributions. If you specify atfrailty, frailties are set to their estimated values when computing predictions. If you specify atfrailty(varname|#), frailties are instead set to the values in varname or #.
- The stintcox command, which fits Cox proportional hazards models for interval-censored data, now supports the vce(robust) option for estimating robust standard errors and the vce(cluster clustvar) option for estimating cluster—robust standard errors.