New statistical software for the proportional hazards model with current status data

Currently, there are some statistical software packages such as intcox, survBayes, and BITE that are designed to analyze current status data (in which failure times are known to be either left- or right-censored) based on the proportional hazards model. They, however, either fail to directly provide standard errors for the estimated regression parameters or require frailty terms. As a result, practitioners often analyze their data using packages for right-censored data. By so doing, they mistreat left-censored observations as exact. This paper describes our newly developed statistical software for the proportional hazards model with current status data. The software is implemented in the R and C languages and consists of the following two simple steps: (a) find MLEs of the regression parameter and the cumulative hazard function; (b) compute the variance–covariance matrix of the regression parameter estimator by using the generalized missing information principle (GMIP) developed by Kim [Kim, J.S., 2003b. Efficient estimation for the proportional hazards model with left-truncated and Case 1 interval-censored data. Statista Sinica 13 (2), 519–537]. Our simulation study results show that our method works well in terms of bias, standard error, and power. By treating current status data as right-censored data, we also show the discrepancy in terms of bias, standard error, and power. Real examples are provided to illustrate the use of the software. This method can be extended to both general interval-censored data and truncated and interval-censored data.