Semiparametric joint modeling to estimate the treatment effect on a longitudinal surrogate with application to chronic kidney disease trials.

ABSTRACT: In clinical trials where long follow-up is required to measure the primary outcome of interest, there is substantial interest in using an accepted surrogate outcome that can be measured earlier in time or with less cost to estimate a treatment effect. For example, in clinical trials of chronic kidney disease, the effect of a treatment is often demonstrated on a longitudinal surrogate, the change of the longitudinal outcome (glomerular filtration rate, GFR) per year or GFR slope. However, estimating the effect of a treatment on the GFR slope is complicated by the fact that GFR measurement can be terminated by the occurrence of a terminal event, such as death or kidney failure. Thus, to estimate this effect, one must consider both the longitudinal GFR trajectory and the terminal event proces

SUBMITTER: Wang X 

PROVIDER: S-EPMC12320702 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature