Sohani Zahra N, Meyre David, de Souza Russell J, Joseph Philip G, Gandhi Mandark, Dennis Brittany B, Norman Geoff, Anand Sonia S
Population Genomics Program, Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada.
Chanchlani Research Centre, McMaster University, Hamilton, ON, Canada.
BMC Genet. 2015 May 15;16:50. doi: 10.1186/s12863-015-0211-2.
Advances in genomics technology have led to a dramatic increase in the number of published genetic association studies. Systematic reviews and meta-analyses are a common method of synthesizing findings and providing reliable estimates of the effect of a genetic variant on a trait of interest. However, summary estimates are subject to bias due to the varying methodological quality of individual studies. We embarked on an effort to develop and evaluate a tool that assesses the quality of published genetic association studies. Performance characteristics (i.e. validity, reliability, and item discrimination) were evaluated using a sample of thirty studies randomly selected from a previously conducted systematic review.
The tool demonstrates excellent psychometric properties and generates a quality score for each study with corresponding ratings of 'low', 'moderate', or 'high' quality. We applied our tool to a published systematic review to exclude studies of low quality, and found a decrease in heterogeneity and an increase in precision of summary estimates.
This tool can be used in systematic reviews to inform the selection of studies for inclusion, to conduct sensitivity analyses, and to perform meta-regressions.
基因组学技术的进步导致已发表的基因关联研究数量急剧增加。系统评价和荟萃分析是综合研究结果并对基因变异对感兴趣性状的影响提供可靠估计的常用方法。然而，由于个别研究的方法学质量参差不齐，汇总估计容易出现偏差。我们着手开发和评估一种工具，用于评估已发表的基因关联研究的质量。使用从先前进行的系统评价中随机选择的30项研究样本评估性能特征（即效度、信度和项目区分度）。
该工具显示出优异的心理测量特性，并为每项研究生成一个质量分数，以及相应的“低”、“中”或“高”质量评级。我们将我们的工具应用于一篇已发表的系统评价，以排除低质量的研究，发现异质性降低，汇总估计的精度提高。
该工具可用于系统评价，以指导纳入研究的选择、进行敏感性分析和进行meta回归。
