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Table 4 Considerations when undertaking systematic reviews of prognostic factors

From: Methodological issues and recommendations for systematic reviews of prognostic studies: an example from cardiovascular disease

Considerations Description
Primary study identification Studies are not necessarily ‘badged’ as prognostic/predictive and a variety of terms are inconsistently used (e.g. risk, association, relationship etc.)
Using prognostic filters substantially reduces the volume of search hits, but it is likely that relevant studies will be missed
Study selection Selection criteria are not consistently reported. This may be particularly important in terms of specifying study design (retrospective/prospective)
Hierarchy of studies Where large numbers of (poor quality) primary studies are identified, a step-wise approach to inclusion may be feasible: (i) inclusion only of studies reporting a prognostic model/ results adjusted for other prognostic factors, (ii) inclusion of prospective studies reporting on a single prognostic factor and (iii) inclusion of all studies reporting on a single prognostic factor
Definition of prognostic factor If identifying a potential prognostic factor is dependent on a diagnostic test, then diagnostic accuracy aspects of one or more tests may need to be assessed in a separate exercise (the QUADAS tool [19] may be appropriate for this)
Consider whether it is clinically appropriate to dichotomise prognostic factor or whether it should be used as a continuous variable (particularly in a model)
Quality assessment The QUIPS tool [22] should be used to inform quality assessment rather than tools relating to specific study design; further tailoring may be necessary depending on topic specific issues
Analysis Meta-analysis should only be undertaken after extensive consideration of clinical and methodological heterogeneity
Data for meta-analysis can potentially be maximised by converting outcome statistics, which may also allow exploration of sensitivity of results to use of statistics
Meta-analysis results should be made specific to particular threshold values or ideally for the factor left on its continuous scale
Adjusted results should be presented where possible
Time-to-event analyses should be considered when accounting for different lengths of follow-up
Small study effects (potential publication bias) should be examined in those meta-analyses containing ten or more studies
Models based on individual patient data should be considered