Article type
Year
Abstract
Background:
Systematic reviews and meta-analysis are the standard methods to assess the association between prognostic markers and major events/conditions. However, the summary measures reported are not always explicitly presented and, therefore, different indirect methods of extracting estimates have been proposed.
Objectives:
To present two new, alternative methods for obtaining summary statistics to be included in a meta-analysis of prognostic studies based on simulating individual patient data, and to compare them with the already known, generalized least squares for trend estimation method and direct method.
Methods:
We have checked the performance of these methods using a between-study comparison, including 122 studies, and a within-study comparison, based on individual patient data from one of the studies.
Results:
The results obtained in this study show that generalized least squares for trend estimation method appears to overestimate the effect size when reported information is incomplete. For the within-study comparison, the closest approximation to the direct estimates was obtained using the approach based on simulating individual participant data.
Conclusions:
The proposed simulation methods are a good alternative when other well-known indirect methods cannot be used.
Patient or healthcare consumer involvement:
None
Systematic reviews and meta-analysis are the standard methods to assess the association between prognostic markers and major events/conditions. However, the summary measures reported are not always explicitly presented and, therefore, different indirect methods of extracting estimates have been proposed.
Objectives:
To present two new, alternative methods for obtaining summary statistics to be included in a meta-analysis of prognostic studies based on simulating individual patient data, and to compare them with the already known, generalized least squares for trend estimation method and direct method.
Methods:
We have checked the performance of these methods using a between-study comparison, including 122 studies, and a within-study comparison, based on individual patient data from one of the studies.
Results:
The results obtained in this study show that generalized least squares for trend estimation method appears to overestimate the effect size when reported information is incomplete. For the within-study comparison, the closest approximation to the direct estimates was obtained using the approach based on simulating individual participant data.
Conclusions:
The proposed simulation methods are a good alternative when other well-known indirect methods cannot be used.
Patient or healthcare consumer involvement:
None