Meta-analysis of risk prediction studies
Abstract
This thesis identifies and demonstrates the methodological challenges of meta-analysing risk prediction models using either aggregate data or individual patient data (IPD). \n \nFirstly, a systematic review of published breast cancer models is performed, to summarise their content and performance using aggregate data. It is found that models were not available for comparison. To address this issue, a systematic review is performed to examine articles that develop and/or validate a risk prediction model using IPD from multiple studies. This identifies that most articles only use the IPD for model development, and thus ignore external validation, and also ignore clustering of patients within studies. In response to these issues, IPD is obtained from an article which uses parathyroid hormone (PTH) assay (a continuous variable) to predict postoperative hypocalcaemia after thyroidectomy. It is shown that ignoring clustering is inappropriate, as it ignores potential between-study heterogeneity in discrimination and calibration performance. \n \nThis dataset was also used to evaluate an imputation method for dealing with missing thresholds when IPD are unavailable, and the simulation results indicate the approach performs well, though further research is required. \n \nThis thesis therefore makes a positive contribution towards meta-analysis of risk prediction models to improve clinical practice. \n \n
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Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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