Abstract
A prognostic factor is any measure that is associated with the risk of future health outcomes in those with existing disease. Often, the prognostic ability of a factor is evaluated in multiple studies. However, meta‐analysis is difficult because primary studies often use different methods of measurement and/or different cut‐points to dichotomise continuous factors into ‘high’ and ‘low’ groups; selective reporting is also common. We illustrate how multivariate random effects meta‐analysis models can accommodate multiple prognostic effect estimates from the same study, relating to multiple cut‐points and/or methods of measurement. The models account for within‐study and between‐study correlations, which utilises more information and reduces the impact of unreported cut‐points and/or measurement methods in some studies. The applicability of the approach is improved with individual participant data and by assuming a functional relationship between prognostic effect and cut‐point to reduce the number of unknown parameters. The models provide important inferential results for each cut‐point and method of measurement, including the summary prognostic effect, the between‐study variance and a 95% prediction interval for the prognostic effect in new populations. Two applications are presented. The first reveals that, in a multivariate meta‐analysis using published results, the Apgar score is prognostic of neonatal mortality but effect sizes are smaller at most cut‐points than previously thought. In the second, a multivariate meta‐analysis of two methods of measurement provides weak evidence that microvessel density is prognostic of mortality in lung cancer, even when individual participant data are available so that a continuous prognostic trend is examined (rather than cut‐points). © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
Highlights
A prognostic factor is any measure that, among people with a given health condition, is associated with a subsequent clinical outcome [1,2]
We suggest approaches to meta-analysis of prognostic factor studies when faced with multiple cut-points and/or methods of measurement and missing results in some studies
We show how multivariate meta-analysis models can accommodate the correlation between such results [12] and allow summary meta-analysis results to be produced for each cut-point and method of measurement, thereby facilitating clinical interpretation
Summary
A prognostic factor is any measure that, among people with a given health condition, is associated with a subsequent clinical outcome [1,2]. Prognostic factors distinguish groups of people with a different average prognosis, and this allows them to be useful for clinical practice and health research They can help define disease at diagnosis, inform clinical and therapeutic decisions (either directly or as part of multivariable prognostic models), enhance the design and analysis of intervention trials and observational studies (as they are potential confounders) and may even identify targets for new interventions that aim to modify the course of a disease or health condition. De Azambuja et al [11] perform a meta-analysis of the prognostic ability of Ki-67 in patients with breast cancer and pool 38 unadjusted hazard ratios across studies; these related to 20 different cut-points and five different methods of measurement.
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