Reduction of sampling bias of odds ratios for vertebral fractures using propensity scores
Reduction of sampling bias of odds ratios for vertebral fractures using propensity scores
复制标题
使用倾向评分减少椎体骨折比值比的抽样偏差
作者：
Ying Lu;H. Jin;H. Jin;M.;C. Glüer
一种新引入的诊断技术对骨折风险的预测能力的评估常常受到巨大成本和前瞻性研究所需的长时间的限制。预测能力的初步估计通常依赖于横断面病例对照研究，其中比较正常和骨折受试者的骨测量值。将测量到的区分能力作为预测能力的估计。由于可能存在样本选择偏差，研究参与者可能具有不同的骨密度（BMD）值，骨折患者的骨折严重程度可能不同。以比值比表示的测量歧视性能力的相同诊断技术，在不同患者和对照人群的研究中会有所不同。方法在本文中，我们提出了一种加权逻辑回归方法来调整比值比，以减少抽样偏差的影响。权重由年龄、畸形严重程度、骨密度及其相互作用得出，采用倾向评分理论和参考人群数据。结果骨质疏松症和超声研究（OPUS）数据的模拟示例表明，该程序可以有效地减少由抽样差异引入的优势比的估计偏差，例如脊柱和髋关节的双x线吸收仪（DXA）扫描以及各种定量超声技术。所得的估计比值比偏差较小，相应的95%置信区间包含来自总体数据的真实比值比。结论：基于倾向得分和加权逻辑回归的统计校正程序可以有效地减少抽样偏倚对横断面病例对照研究计算的优势比的影响。对于一项新的诊断技术，髋关节骨密度和畸形严重程度信息是必要的，并且可能足以得出调整测量的标准化优势比所需的倾向评分。
IntroductionAssessment of the predictive power of a newly introduced diagnostic technique with regard to fracture risk is frequently limited by the enormous costs and long time periods required for prospective studies. A preliminary estimate of predictive power usually relies on cross-sectional case-control studies in which bone measurements of normal and fractured subjects are compared. The measured discriminatory power is taken as an estimate of predictive power. Because of possible sample selection bias, study participants may have different bone mineral density (BMD) values, and fractured patients may have fractures of different severity levels. The same diagnostic techniques for the measured discriminatory power, expressed as odds ratios, will differ among studies with different patient and control populations.MethodsIn this paper, we propose a weighted logistic regression approach to adjust the odds ratio in order to reduce the effect of sampling bias. The weight is derived from age, deformity severity, BMD, and the interactions of these, using the propensity score theory and reference population data.ResultsSimulation examples using data from the Osteoporosis and Ultrasound Study (OPUS) demonstrate that such a procedure can effectively reduce the estimation bias of odds ratios introduced by sampling differences, such as for dual x-ray absorptiometry (DXA) scans of the spine and hip as well as various quantitative ultrasound techniques. The derived estimated odds ratios are substantially less biased, and the corresponding 95% confidence intervals contain the true odds ratios from the population data.ConclusionsWe conclude that a statistical correction procedure based on propensity scores and weighted logistic regression can effectively reduce the effect of sampling bias on the odds ratios calculated from cross-sectional case-control studies. For a new diagnostic technique, hip BMD and deformity severity information are necessary and likely sufficient to derive the propensity scores required to adjust the measured standardized odds ratios.