A new method for analysing transition to psychosis: Joint modelling of time‐to‐event outcome with time‐dependent predictors

Yuen, Hok Pan; Mackinnon, Andrew; Nelson, Barnaby

doi:10.1002/mpr.1588

Cited by 22 publications

(18 citation statements)

References 45 publications

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“…Further, more advanced statistical methods should be considered to enhance predictive accuracy in these models, such as the least absolute shrinkage and selection operator(LASSO), ridge regression or elastic net techniques. Finally, since prediction algorithms typically use only baseline predictors, dynamic prediction, which involves longitudinal data collection of the predictors at subsequent assessment(s), is a potential option 44,45 .…”

Section: Current Issues With Predictionmentioning

confidence: 99%

Progression from being at-risk to psychosis: next steps

et al. 2020

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Over the past 20 years there has been a great deal of research into those considered to be at risk for developing psychosis. Much has been learned and studies have been encouraging. The aim of this paper is to offer an update of the current status of research on risk for psychosis, and what the next steps might be in examining the progression from CHR to psychosis. Advances have been made in accurate prediction, yet there are some methodological issues in ascertainment, diagnosis, the use of data-driven selection methods and lack of external validation. Although there have been several high-quality treatment trials the heterogeneity of this clinical high-risk population has to be addressed so that their treatment needs can be properly met. Recommendations for the future include more collaborative research programmes, and ensuring they are accessible and harmonized with respect to criteria and outcomes so that the field can continue to move forward with the development of large collaborative consortiums as well as increased funding for multisite projects.

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Section: Current Issues With Predictionmentioning

confidence: 99%

Progression from being at-risk to psychosis: next steps

et al. 2020

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“…Although there is increasing recognition that psychopathological symptoms change dynamically rather than gradually before disease onset and that this could be exploited to improve the prediction of full-threshold disorders, 22 non-linear modelling of change in symptoms did not lead to improved prediction in this study. One possible explanation is that our effective sample sizealthough slightly larger than in previous studies [25][26][27] was not large enough and the signal to noise ratio was too small to reliably estimate such complex relationships and therefore a simpler model was preferred.…”

Section: Discussionmentioning

confidence: 88%

“…22 Joint modelling methods 24 are a relatively recent statistical innovation that allow flexibly relating a longitudinal process (e.g., change in CHR-P symptoms over time) to a time-to-event outcome (e.g., time to transition to psychosis) and thus can dynamically update predictions over time. So far, only three studies have applied joint models to predict transition to psychosis, [25][26][27] with two of them 25,26 having largely overlapping samples.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Dynamic Risk Prediction Model to Forecast Psychosis Onset in Patients at Clinical High Risk

Studerus

Beck

Fusar‐Poli

et al. 2019

Schizophrenia Bulletin

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The prediction of outcomes in patients at Clinical High Risk for Psychosis (CHR-P) almost exclusively relies on static data obtained at a single snapshot in time (ie, baseline data). Although the CHR-P symptoms are intrinsically evolving over time, available prediction models cannot be dynamically updated to reflect these changes. Hence, the aim of this study was to develop and internally validate a dynamic risk prediction model (joint model) and to implement this model in a user-friendly online risk calculator. Furthermore, we aimed to explore the prognostic performance of extended dynamic risk prediction models and to compare static with dynamic prediction. One hundred ninety-six CHR-P patients were recruited as part of the “Basel Früherkennung von Psychosen” (FePsy) study. Psychopathology and transition to psychosis was assessed at regular intervals for up to 5 years using the Brief Psychiatric Rating Scale-Expanded (BPRS-E). Various specifications of joint models were compared with regard to their cross-validated prognostic performance. We developed and internally validated a joint model that predicts psychosis onset from BPRS-E disorganization and years of education at baseline and BPRS-E positive symptoms during the follow-up with good prognostic performance. The model was implemented as online risk calculator (http://www.fepsy.ch/DPRP/). The use of extended joint models slightly increased the prognostic accuracy compared to basic joint models, and dynamic models showed a higher prognostic accuracy than static models. Our results confirm that extended joint modeling could improve the prediction of psychosis in CHR-P patients. We implemented the first online risk calculator that can dynamically update psychosis risk prediction.

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“…This type of prediction research may more accurately model the highly dynamic nature of psychopathology and system change, as well as have treatment implications, such as introducing a means of identifying critical periods of risk for mental state deterioration. Empirical work adopting this dynamic approach has already shown significant promise [14][15][16][17] and may be particularly relevant to individual-level, 'precision' medicine/prediction. 14…”

Section: Prediction Researchmentioning

confidence: 99%

Detail, dynamics and depth: useful correctives for some current research trends

2018

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SummarySeveral research trends in contemporary psychiatry would benefit from greater emphasis on detailed assessment, modelling dynamic change, and micro-level analysis. This may assist with clarifying nosological and pathoaetiological issues. We make this case by referring to three areas: psychopathology and nosology; prediction research; and 'big N' data sets. Declaration of interestNone. In this editorial, we argue that a number of tools used in contemporary psychiatric research, particularly in psychopathological assessment, are too blunt. The field would benefit from a greater emphasis on sharper, more detailed assessment and analysis; where there is great surface area, further depth could be introduced; where the 'macro' abounds, let us not forget the value of the 'micro'. This would enrich, balance and provide a corrective to several research trends, and possibly clarify nosological and pathoaetiological issues. We make this case by referring to three areas: psychopathology and nosology, prediction research and 'big N' data-sets. Copyright and usage Psychopathology and nosologyMuch recent work has questioned traditional diagnostic categories 1 and moved to identify domains of psychopathology and dimensions/continua that cut across diagnostic boundaries.2 This can be seen in the checklist-or atomistic-type approach to measuring signs and symptoms, 3 which have found significant overlap in psychotic and other symptoms across disorders (e.g. between schizophrenia and bipolar disorder 4 and between psychotic disorders and borderline personality disorder 5 ), as well as significant presence of 'symptoms' in general community (nonpatient) samples.2 The National Institute of Mental Health Research Domain Criteria signal a frustration with the DSM phenotypebased classification system and represent an attempt to base psychiatric nosology on neuroscience and behavioural science instead of descriptive phenomenology.6 These approaches promote the idea that phenotypes are far more overlapping and indistinct than traditionally thought, and that psychopathological domains can be measured quickly and simply in order to map on to the 'real' underlying pathology of neurobiological dysfunction. Just as an individual can be genotyped by running a DNA sequence with biological assays, their phenotype can be characterised through efficient (ideally self-report) measures that operationalise the array of psychiatric symptoms and signs.However, part of the frustration with phenotype-based classification and the perceived roadblock that it has introduced to research progress may be attributable not to phenotype-based classification per se, but rather to the oversimplified and broad nature of contemporary psychopathological description present in DSM-III onwards and many of the instruments used to measure psychopathology in research studies.7 What is required is perhaps not so much a discarding or downgrading of the centrality of phenotypes, but rather a resuscitation and refinement of psychopathological understanding and assessment...

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A new method for analysing transition to psychosis: Joint modelling of time‐to‐event outcome with time‐dependent predictors

Cited by 22 publications

References 45 publications

Progression from being at-risk to psychosis: next steps

Progression from being at-risk to psychosis: next steps

Development and Validation of a Dynamic Risk Prediction Model to Forecast Psychosis Onset in Patients at Clinical High Risk

Detail, dynamics and depth: useful correctives for some current research trends

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