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Covariate Selection in High-Dimensional Propensity Score Analyses of Treatment Effects in Small Samples

機(jī)譯：高維度傾向得分分析中小樣本治療效果的協(xié)變量選擇

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To reduce bias by residual confounding in nonrandomized database studies, the high-dimensional propensity score (hd-PS) algorithm selects and adjusts for previously unmeasured confounders. The authors evaluated whether hd-PS maintains its capabilities in small cohorts that have few exposed patients or few outcome events. In 4 North American pharmacoepidemiologic cohort studies between 1995 and 2005, the authors repeatedly sampled the data to yield increasingly smaller cohorts. They identified potential confounders in each sample and estimated both an hd-PS that included 0–500 covariates and treatment effects adjusted by decile of hd-PS. For sensitivity analyses, they altered the variable selection process to use zero-cell correction and, separately, to use only the variables’ exposure association. With >50 exposed patients with an outcome event, hd-PS-adjusted point estimates in the small cohorts were similar to the full-cohort values. With 25–50 exposed events, both sensitivity analyses yielded estimates closer to those obtained in the full data set. Point estimates generally did not change as compared with the full data set when selecting >300 covariates for the hd-PS. In these data, using zero-cell correction or exposure-based covariate selection allowed hd-PS to function robustly with few events. hd-PS is a flexible analytical tool for nonrandomized research across a range of study sizes and event frequencies.
機(jī)譯：為了減少非隨機(jī)數(shù)據(jù)庫研究中殘留混雜造成的偏見，高維傾向評(píng)分（hd-PS）算法選擇并調(diào)整了先前無法測量的混雜因素。作者評(píng)估了hd-PS是否在很少有暴露患者或很少發(fā)生預(yù)后事件的小型隊(duì)列中保持其功能。在1995年至2005年間進(jìn)行的4項(xiàng)北美藥物流行病學(xué)隊(duì)列研究中，作者反復(fù)采樣數(shù)據(jù)以得出越來越小的隊(duì)列。他們?cè)诿總€(gè)樣本中確定了潛在的混雜因素，并估計(jì)了包含0-500個(gè)協(xié)變量的hd-PS以及通過hd-PS的十分位數(shù)調(diào)整的治療效果。為了進(jìn)行敏感性分析，他們更改了變量選擇過程，以使用零像元校正，并分別使用變量的暴露關(guān)聯(lián)。對(duì)于> 50名暴露于預(yù)后事件的患者，小隊(duì)列中的hd-PS調(diào)整后的點(diǎn)估計(jì)與全隊(duì)列值相似。對(duì)于25–50個(gè)暴露事件，兩種敏感性分析得出的估計(jì)值都接近完整數(shù)據(jù)集中的估計(jì)值。當(dāng)為hd-PS選擇> 300個(gè)協(xié)變量時(shí)，點(diǎn)估計(jì)與完整數(shù)據(jù)集相比通常不會(huì)發(fā)生變化。在這些數(shù)據(jù)中，使用零像元校正或基于曝光的協(xié)變量選擇使hd-PS能夠在很少事件的情況下穩(wěn)定運(yùn)行。 hd-PS是一種靈活的分析工具，適用于各種研究規(guī)模和事件頻率的非隨機(jī)研究。

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