Open AccessA new method for analysing transition to psychosis: Joint modelling of time‐to‐event outcome with time‐dependent predictors
Author(s) -
Yuen Hok Pan,
Mackin Andrew,
Nelson Barnaby
Publication year - 2018
Publication title -
international journal of methods in psychiatric research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.275
H-Index - 73
eISSN - 1557-0657
pISSN - 1049-8931
DOI - 10.1002/mpr.1588
Subject(s) - psychosis , anxiety , psychopathology , psychology , transition (genetics) , schizophrenia (object oriented programming) , outcome (game theory) , identification (biology) , clinical psychology , depression (economics) , psychiatry , mathematics , biochemistry , chemistry , botany , macroeconomics , mathematical economics , biology , economics , gene
Abstract An active area in psychosis research is the identification of predictors of transition to a psychotic state among those who are assessed as being at high risk of psychosis. Many of the potential predictors are time dependent in the sense that they may change over time and are measured at a number of assessment time points. Examples are various psychopathological measures such as negative symptoms, positive symptoms, depression, and anxiety. Most research in transition to psychosis has not made use of the dynamic nature of these measures, probably because suitable statistical methods and software have not been easily available. However, a relatively new statistical methodology is well suited to include such time‐dependent predictors in transition to psychosis analysis. This methodology is called joint modelling and has recently been incorporated in mainstream statistical software. This paper describes this methodology and demonstrates its usefulness using data from one of the pioneering studies on transition to psychosis.