Multivariate meta-analysis of prognostic factor studies with multiple cut-points and/or methods of measurement.
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).
Research Institute of Primary Care and Health Sciences, Keele University, Staffordshire, ST5 5BG, U.K.
Richard D
RD
Riley
School of Health and Population Sciences, Public Health Building, University of Birmingham, Edgbaston Birmingham, B15 2TT, U.K.
Eleni G
EG
Elia
School of MedicineD Floor Queen's Medical Centre, University of Nottingham, Nottingham, NG7 2UH, U.K.
Gemma
G
Malin
School of Health and Population Sciences, Public Health Building, University of Birmingham, Edgbaston Birmingham, B15 2TT, U.K.
Karla
K
Hemming
School of Health and Population Sciences, Public Health Building, University of Birmingham, Edgbaston Birmingham, B15 2TT, U.K.
Malcolm P
MP
Price
D001034:Apgar Score; D056808:Biostatistics; D001943:Breast Neoplasms / Q000378:metabolism / Q000401:mortality / Q000473:pathology; D005260:Female; D006801:Humans; D007223:Infant; D007226:Infant Mortality; D007231:Infant, Newborn; D019394:Ki-67 Antigen / Q000378:metabolism; D016014:Linear Models; D008175:Lung Neoplasms / Q000098:blood supply / Q000401:mortality; D055806:Microvessels / Q000473:pathology; D008954:Models, Biological; D015233:Models, Statistical; D015999:Multivariate Analysis; D017711:Nonlinear Dynamics; D011379:Prognosis
D019394:Ki-67 Antigen
cut-points; heterogeneity; multivariate meta-analysis; odds ratios and hazard ratios; prognostic factors