Crown William, Chang Jessica, Olson Melvin, Kahler Kristijan, Swindle Jason, Buzinec Paul, Shah Nilay, Borah Bijan
Optum Labs, One Main Street, 10th Floor, Cambridge, MA 02142, USA.
Global Head HEOR Excellence, Novartis Pharma AG, 4056, Basel, Switzerland.
J Comp Eff Res. 2015 Sep;4(5):455-63. doi: 10.2217/cer.15.23. Epub 2015 Oct 5.
Missing data, particularly missing variables, can create serious analytic challenges in observational comparative effectiveness research studies. Statistical linkage of datasets is a potential method for incorporating missing variables. Prior studies have focused upon the bias introduced by imperfect linkage.
This analysis uses a case study of hepatitis C patients to estimate the net effect of statistical linkage on bias, also accounting for the potential reduction in missing variable bias.
The results show that statistical linkage can reduce bias while also enabling parameter estimates to be obtained for the formerly missing variables.
The usefulness of statistical linkage will vary depending upon the strength of the correlations of the missing variables with the treatment variable, as well as the outcome variable of interest.
在观察性比较效果研究中，缺失数据，尤其是缺失变量，会带来严重的分析挑战。数据集的统计链接是纳入缺失变量的一种潜在方法。先前的研究集中在不完美链接所引入的偏差上。
本分析以丙型肝炎患者为例，估计统计链接对偏差的净效应，同时考虑缺失变量偏差的潜在减少。
结果表明，统计链接可以减少偏差，同时还能为先前缺失的变量获得参数估计值。
统计链接的有用性将因缺失变量与治疗变量以及感兴趣的结果变量之间相关性的强度而异。
