Evidence synthesis for prognosis and prediction: application, methodology and use of individual participant data

Ensor, Joie (2017). Evidence synthesis for prognosis and prediction: application, methodology and use of individual participant data. University of Birmingham. Ph.D.

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Abstract

Prognosis research summarises, explains and predicts future outcomes in patients with a particular condition. This thesis investigates the application and development of evidence synthesis methods for prognosis research, with particular attention given to improving individualised predictions from prognostic models developed and/or validated using metaanalysis techniques.

A review of existing prognostic models for recurrence of venous thromboembolism highlighted several methodological and reporting issues. This motivated the development of a new model to address previous shortcomings, in particular by explicitly modelling and reporting the baseline hazard to enable individualised risk predictions over time. The new model was developed using individual participant data from several studies, using a novel internal-external cross-validation approach. This highlighted the potential for between-study heterogeneity in model performance, and motivated the investigation of recalibration methods to substantially improve consistency in model performance across populations.

Finally, a new multiple imputation method was developed to investigate the impact of missing threshold information in meta-analysis of prognostic test accuracy. Computer code was developed to implement the method, and applied examples indicated missing thresholds could have a potentially large impact on conclusions. A simulation study indicated that the new method generally improves on the current standard, in terms of bias, precision and coverage.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):