title: Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modeling working group.
abstract: Explicitly modeling underlying relationships between a survival endpoint and processes that generate longitudinal measured or reported outcomes potentially could improve the efficiency of clinical trials and provide greater insight into the various dimensions of the clinical effect of interventions included in the trials. Various strategies have been proposed for using longitudinal findings to elucidate intervention effects on clinical outcomes such as survival. The application of specifically Bayesian approaches for constructing models that address longitudinal and survival outcomes explicitly has been recently addressed in the literature. We review currently available methods for carrying out joint analyses, including issues of implementation and interpretation, identify software tools that can be used to carry out the necessary calculations, and review applications of the methodology.
authors.affiliation: Merck Research Laboratories, 351 North Sumneytown Pike, North Wales, PA 19454, U.S.A.
authors.forename: A
authors.initials: A
authors.lastname: Lawrence Gould
authors.affiliation: Eli Lilly, 893 S. Delaware Street, Indianapolis, IN 46285, U.S.A.
authors.forename: Mark Ernest
authors.initials: ME
authors.lastname: Boye
authors.affiliation: Department of Health Sciences, University of Leicester, Adrian Building, University Road, Leicester LE1 7RH, U.K.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Crowther
authors.affiliation: Department of Statistics and Operations Research, University of North Carolina, 318 Hanes Hall Chapel Hill, NC 27599, U.S.A.
authors.forename: Joseph G
authors.initials: JG
authors.lastname: Ibrahim
authors.affiliation: Genentech, South San Francisco, CA, U.S.A.
authors.forename: George
authors.initials: G
authors.lastname: Quartey
authors.affiliation: Sanofi-Aventis, Paris, France.
authors.forename: Sandrine
authors.initials: S
authors.lastname: Micallef
authors.affiliation: Université de Technologie de Compiègne, Centre de Recherche de Royallieu, 60205 Compiègne Cedex, France.|INERIS/CRD/VIVA/METO, Verneuil en Halatte, France.
authors.forename: Frederic Y
authors.initials: FY
authors.lastname: Bois
mesh_terms: D019380:Anti-HIV Agents / Q000494:pharmacology; D001499:Bayes Theorem; D054316:Biomarkers, Pharmacological / Q000097:blood; D018791:CD4 Lymphocyte Count; D002986:Clinical Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data; D015195:Drug Design; D015340:Epidemiologic Research Design; D006084:Graft Rejection / Q000276:immunology / Q000517:prevention & control; D015658:HIV Infections / Q000188:drug therapy / Q000276:immunology / Q000821:virology; D006801:Humans; D016030:Kidney Transplantation / Q000009:adverse effects; D008137:Longitudinal Studies; D015233:Models, Statistical; D016016:Proportional Hazards Models; D011788:Quality of Life; D051436:Renal Insufficiency, Chronic / Q000601:surgery; D012984:Software; D016019:Survival Analysis; D019562:Viral Load
chemical_list: D019380:Anti-HIV Agents; D054316:Biomarkers, Pharmacological
keywords: applications; random effects; software; time-dependent