Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modeling working group

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...

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Veröffentlicht in:	Statistics in medicine 2015-06, Vol.34 (14), p.2181-2195
Hauptverfasser:	Lawrence Gould, A., Boye, Mark Ernest, Crowther, Michael J., Ibrahim, Joseph G., Quartey, George, Micallef, Sandrine, Bois, Frederic Y.
Format:	Artikel
Sprache:	eng
Schlagworte:	Anti-HIV Agents - pharmacology applications Bayes Theorem Bayesian analysis Biomarkers, Pharmacological - blood CD4 Lymphocyte Count Clinical trials Clinical Trials as Topic - methods Clinical Trials as Topic - statistics & numerical data Drug Design Epidemiologic Research Design Graft Rejection - immunology Graft Rejection - prevention & control HIV Infections - drug therapy HIV Infections - immunology HIV Infections - virology Humans Intervention Kidney Transplantation - adverse effects Life Sciences Longitudinal Studies Medical statistics Models, Statistical Proportional Hazards Models Quality of Life random effects Renal Insufficiency, Chronic - surgery Software Survival Analysis time-dependent Toxicology Viral Load
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