Interval censored Cox proportional hazards model in R Given interval censored survival times, how do I perform an interval censored Cox PH model in R? An rseek search turns up the package intcox, which no longer exists in the R repository. I'm almost positive the coxph function in the survival package cannot handle interval censored survival data. 
Also, I don't want to impute the data and then use the coxph function. This method underestimates the standard errors of the coefficients because you are ignoring the uncertainty of the interval censoring.
 A: As stated above, you can use the survreg function. A note though: this is not strictly a Cox PH model, but rather location-scale models. Using the default log-transformation, this is the aft model. In the case of the exponential distribution, the proportional hazards and aft model are equivalent, so if distribution is set to exponential, this is a proportional hazards model with an exponential baseline. Likewise, if a baseline Weibull distribution aft model is used, the parameter estimates are just a linear transformation of those used in the proportional hazards model
 with Weibull baseline distribution. But in general, survreg does not fit a Cox PH model. 
If a semi-parametric model is desired, as found implemented in intcox, a word of caution: there are several issues with the current version of intcox (algorithm typically prematurely terminates significantly far from the MLE, fails outright with uncensored observations, no standard errors automatically presented). 
A new alternative that you could use is the package "icenReg". 
Admission of bias: this is the author of icenReg.
A: To do interval censored analysis in R, you must create a Surv object, and then use survfit(). If you have more than a variable, the intcox package solves the problem.
