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Multiple imputation for handling missing outcome data when estimating the relative risk

机译：估算相对风险时用于处理缺失结果数据的多重估算

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Background Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk. Standard methods for imputing incomplete binary outcomes involve logistic regression or an assumption of multivariate normality, whereas relative risks are typically estimated using log binomial models. It is unclear whether misspecification of the imputation model in this setting could lead to biased parameter estimates. Methods Using simulated data, we evaluated the performance of multiple imputation for handling missing data prior to estimating adjusted relative risks from a correctly specified multivariable log binomial model. We considered an arbitrary pattern of missing data in both outcome and exposure variables, with missing data induced under missing at random mechanisms. Focusing on standard model-based methods of multiple imputation, missing data were imputed using multivariate normal imputation or fully conditional specification with a logistic imputation model for the outcome. Results Multivariate normal imputation performed poorly in the simulation study, consistently producing estimates of the relative risk that were biased towards the null. Despite outperforming multivariate normal imputation, fully conditional specification also produced somewhat biased estimates, with greater bias observed for higher outcome prevalences and larger relative risks. Deleting imputed outcomes from analysis datasets did not improve the performance of fully conditional specification. Conclusions Both multivariate normal imputation and fully conditional specification produced biased estimates of the relative risk, presumably since both use a misspecified imputation model. Based on simulation results, we recommend researchers use fully conditional specification rather than multivariate normal imputation and retain imputed outcomes in the analysis when estimating relative risks. However fully conditional specification is not without its shortcomings, and so further research is needed to identify optimal approaches for relative risk estimation within the multiple imputation framework.
机译：背景技术多重插补是一种在医学研究中处理丢失数据的流行方法，但人们对其估计相对风险的适用性知之甚少。推算不完全二进制结果的标准方法包括逻辑回归或多元正态性假设，而相对风险通常使用对数二项式模型估算。目前尚不清楚在这种情况下归因模型的误定是否会导致参数估计有偏差。方法使用模拟数据，在从正确指定的多变量对数二项式模型估计调整后的相对风险之前，我们评估了多重插补处理缺失数据的性能。我们考虑了结果变量和暴露变量中缺失数据的任意模式，在随机机制缺失的情况下诱发了缺失数据。着眼于基于标准模型的多重插补方法，使用多元正态插补或完全条件规范对结果进行逻辑插补模型插补缺失数据。结果在模拟研究中，多元正态插补的效果不佳，始终如一地产生偏倚于零值的相对风险估计值。尽管多变量正态插值的表现优于全条件归因，但完全有条件的规范也产生了一些有偏差的估计，对于较高的结局患病率和较大的相对风险，观察到更大的偏差。从分析数据集中删除估算结果并不能提高完全条件指定的性能。结论多元正态插补和全条件规范都产生了相对风险的偏差估计，大概是因为两者都使用了错误指定的插补模型。根据模拟结果，我们建议研究人员使用完全条件指定而不是多元正态推算，并在估算相对风险时保留推算结果在分析中。但是，全条件规范并非没有缺点，因此需要进一步的研究来确定在多重插补框架内进行相对风险估计的最佳方法。

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