Review of analytical methods for prospective cohort studies using time to event data: single studies and implications for meta-analysis.
Prospective cohort studies are extremely important in epidemiological research as they give direct information on the sequence of events, which can be used to demonstrate causality. They also have the advantage that many diseases can be studied simultaneously. However, they are usually very time consuming and expensive to run. In addition, practitioners of evidence-based medicine prefer to make decisions based on several studies rather than a single study, hence the need for meta-analysis. The use of meta-analyses in order to synthesize the evidence from randomized controlled trials is extremely popular in medicine and is also being utilized increasingly in epidemiology. The statistical methodology for meta-analyses of epidemiological studies is a long way behind in terms of the advances made in the methodology for randomized controlled trials. Numerous methodological issues, particularly in respect to dealing with biases inherent in these types of studies, have made the results of meta-analyses of epidemiological studies that use summary data open to criticism. This review mainly concentrates on analytical methods for prospective cohort studies that have survival outcomes. In addition, the implications for meta-analysis assuming that the analyst has access to individual participant data are also discussed. The approaches are described with respect to underlying theory and assumptions. It is hoped that this review will promote the use of these approaches in meta-analyses conducted in epidemiology as well as providing some directions for future research.
Bennett
Derrick A
DA
The Clinical Trials Research Unit, Faculty of Medicine and Health Sciences, University of Auckland, Auckland, New Zealand. d.bennett@ctru.auckland.ac.nz
D015331:Cohort Studies; D016021:Epidemiologic Studies; D006801:Humans; D016015:Logistic Models; D015201:Meta-Analysis as Topic; D016016:Proportional Hazards Models; D011446:Prospective Studies