Display Settings:

Format

Send to:

Choose Destination
See comment in PubMed Commons below
Health Serv Outcomes Res Methodol. 2012 Jun;12(2-3):182-199. Epub 2012 Jun 5.

Joint modeling of longitudinal outcomes and survival using latent growth modeling approach in a mesothelioma trial.

Author information

  • 1Lilly Corporate Center, Eli Lilly and Company, Indianapolis, IN 46285 USA.

Abstract

Joint modeling of longitudinal and survival data can provide more efficient and less biased estimates of treatment effects through accounting for the associations between these two data types. Sponsors of oncology clinical trials routinely and increasingly include patient-reported outcome (PRO) instruments to evaluate the effect of treatment on symptoms, functioning, and quality of life. Known publications of these trials typically do not include jointly modeled analyses and results. We formulated several joint models based on a latent growth model for longitudinal PRO data and a Cox proportional hazards model for survival data. The longitudinal and survival components were linked through either a latent growth trajectory or shared random effects. We applied these models to data from a randomized phase III oncology clinical trial in mesothelioma. We compared the results derived under different model specifications and showed that the use of joint modeling may result in improved estimates of the overall treatment effect.

PMID:
22773919
[PubMed]
PMCID:
PMC3384782
Free PMC Article

Images from this publication.See all images (3)Free text

Fig. 1
Fig. 2
Fig. 3
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for PubMed Central
    Loading ...
    Write to the Help Desk