Abstract and Introduction
Abstract
Comparative effectiveness research (CER) allows evidence to be evaluated on the effectiveness, benefits, and detriments of management options, diagnostic tests, or ways to deliver health care. This process can be achieved in different ways, such as with well-designed randomized controlled trials or by meta-analyses. Several medical subspecialties are increasingly using CER, but CER remains underused by the neurosurgical community. Meta-analysis is a highly accurate method that permits results from multiple well-designed research studies to be quantitatively compared. Meta-analysis can be performed in many settings, such as the evaluation of treatment or of a diagnostic test or prognostic factor. Meta-analyses of randomized controlled treatment trials are well known, but there is a paucity of papers describing the ways to perform a meta-analysis of a diagnostic test. The aim of this paper is to improve neurosurgeons' familiarity with the meta-analysis of diagnostic test accuracy by describing and detailing each stage leading to publication.
Introduction
Comparative effectiveness research is defined by the Institute of Medicine as "the generation and synthesis of evidence that compares the benefit and harms of alternative methods to prevent, diagnose, treat and monitor a clinical condition or to improve delivery of care." The purpose of CER is "to assist consumers, clinicians, purchasers, and policy makers to make informed decisions that will improve health care at both the individual and population levels." This CER paradigm is not a novel concept as such, and began to take form in the 1850s through the writings of Claude Bernard, a French physiologist. Comparative effectiveness research will no doubt occupy an increasingly important place in clinical practice and health care policy in the future. Consequently, the neurosurgical community should be familiar with CER.
The number of diagnostic options has grown at an increasing rate over the past 2 decades in medicine, and likewise in neurosurgery. Currently, physicians have a large variety of diagnostic tests at their disposal for the same condition. Furthermore, they are faced with patients who require more and more guarantees, as well as the best diagnostic test associated with the fewest complications or side effects. Thus, it is a fundamental task for clinicians to gather data and to use that data to formulate an optimal care plan for their patients.
Meta-analysis represents a high level of evidencebased medicine by summarizing results of well-designed studies on the same topic, thereby achieving the best estimate of performance of a diagnostic device. Meta-analysis makes it possible to obtain more precise estimates when only small studies are available and can also determine the covariates that may influence results.
The process leading to publication of a meta-analysis should be transparent and reproducible. Unlike metaanalyses of randomized controlled treatment trials, the methodology of a meta-analysis of diagnostic test accuracy is less known and understood. Despite the existence for more than 10 years of more and more methodological work, many uncertainties remain, and there is no consensus indicating the best statistical method to synthesize results from studies of diagnostic tests.
The aim of this paper is to improve neurosurgeons' familiarity with the meta-analysis of diagnostic test accuracy by describing and detailing each stage leading to the publication of such a meta-analysis.