Statistical issues in survival analysis (Part XVIV)

Statistical issues in survival analysis (Part XVIV) December 6, 2023 In article that appeared in Biostatistics, Wu et al describe a joint modeling approach of longitduinal data like quality of life and survival data on a retrospective time scale and handling of informative censoring issues. These have been handled on more of a prospective time issue, but much less retrospectively. One of the biggest issues has been handling dropouts in a joint modeling on a retrospective time scale. They have proposed a semiparametric modeling that jointly models longitudinal QOL data, death time, and informative censoring time. It has used a semiparametric mixed effect submodel for the longitudinal QOL data and a competing risk survival submodel with piecewise hazard for time to death and dropout time. The mixed effect submodel used splines to allow for potential non-linearity. Their model incorporated a frailty term in the hazard function for the competing risks model. Even the frailty component was shown to have separate coefficients for each piecewise exponential. They then wrote out this piece as a Cox model with the baseline hazard rate represented as two separate pieces for the competing risks and another Cox model with _____ and then combine these two versions as the final longitudinal model. They then used regression splines with equally spaced knots at quantiles to estimate the parameters.

They had to calculate the log-likelhood across 6 groups based on the delta that the particpant was in or rather based on their survival status. They then had 6 log-likelihood functions to estimate which could create computational complexity.

Start at section 3.2 Written by, Usha Govindarajulu Keywords: survival, longitudinal, dropout, retrospective, References Quran Wu, Michael Daniels, Areej El-Jawahri, Marie Bakitas, Zhigang Li, Joint modeling in presence of informative censoring on the retrospective time scale with application to palliative care research, Biostatistics, 2023;, kxad028, https://doi.org/10.1093/biostatistics/kxad028