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Wednesday May 11th 2011, 16.00 - 17.00, Room G107, Alan Turing Building, University of Manchester. Tea and coffee at 15.30 on the Atrium Bridge Richard Riley, School of Mathematics,University
of Birmingham Prognostic factors are associated with the risk of a subsequent outcome in people with a given disease or health condition. They have a broad array of uses in clinical practice and healthcare research. For example, prognostic factors help define disease at diagnosis; they inform treatment strategies for individual patients; they may be causal for disease outcome; and they inform randomisation strategies in clinical trials. Research to identify factors that are truly prognostic is abundant in the medical literature, and meta-analysis is needed to combine multiple prognostic factor studies and produce evidence-based prognostic factor results. Meta-analysis using individual participant data (IPD), where the raw data are synthesised from multiple studies, has been championed as the gold-standard for synthesising prognostic factor studies. In this talk I will examine the feasibility and conduct of this approach, using a systematic review of currently published IPD meta-analyses of prognostic factors studies. I will show that availability of IPD offers many advantages, such as checking modelling assumptions; analysing variables on their continuous scale with the possibility to assess non-linear relationships; and obtaining results adjusted for other variables. However, researchers also faced many challenges, such as large cost and time required to obtain and clean IPD; unavailable IPD for some studies; different sets of prognostic factors in each study; and variability in study methods of measurement. I will conclude with suggestions for where improvements are needed in current IPD meta-analyses; in particular, continuous variables are often categorised without reason; publication bias and availability bias are rarely examined; and reporting standards are often sub-standard. |
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