№ files_lp_4_process_3_113801
Academic research text discussing statistical modelling of longitudinal PSA biomarker trajectories and survival outcomes in prostate cancer patients using joint modelling methods within clinical study data.
Year: Not specified
Subject area: Prostate cancer prognosis and biostatistical modelling
Type of document: Scientific research article abstract and introduction
Research field: Oncology; Biostatistics; Medical statistics
Study design: Retrospective study
Sample size: 300 prostate cancer patients
Biomarker studied: Prostate-specific antigen (PSA)
Statistical methods: Joint modelling of longitudinal and survival data; Cox proportional-hazards model; linear mixed models; Kaplan–Meier survival analysis
Key clinical variable: Gleason score
Main outcome: Overall survival and PSA trajectory relationship
Data characteristics: Longitudinal biomarker measurements and time-to-event data with informative dropout
Purpose of analysis: Modelling the dynamic association between PSA severity trajectories and survival outcomes
Keywords: Prognosis of prostate cancer; trajectories measurements; simultaneous analysis; longitudinal and time-to-event data; informative dropout
Price: 8 / 10 USD
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