TY - JOUR AU - Khan, Shahedul A. AU - Khosa, Saima K. PY - 2016 DA - 2016/11/29 TI - Generalized log-logistic proportional hazard model with applications in survival analysis JO - Journal of Statistical Distributions and Applications SP - 16 VL - 3 IS - 1 AB - Proportional hazard (PH) models can be formulated with or without assuming a probability distribution for survival times. The former assumption leads to parametric models, whereas the latter leads to the semi-parametric Cox model which is by far the most popular in survival analysis. However, a parametric model may lead to more efficient estimates than the Cox model under certain conditions. Only a few parametric models are closed under the PH assumption, the most common of which is the Weibull that accommodates only monotone hazard functions. We propose a generalization of the log-logistic distribution that belongs to the PH family. It has properties similar to those of log-logistic, and approaches the Weibull in the limit. These features enable it to handle both monotone and nonmonotone hazard functions. Application to four data sets and a simulation study revealed that the model could potentially be very useful in adequately describing different types of time-to-event data. SN - 2195-5832 UR - https://doi.org/10.1186/s40488-016-0054-z DO - 10.1186/s40488-016-0054-z ID - Khan2016 ER -