ABSTRACT

Objectives

The effect of imputing missing data followed by propensity score analysis on the incremental cost-effectiveness ratio (ICER) in a cost-effectiveness analysis is unknown. The objective was to compare alternative approaches in grouping data following imputation and prior to calculating propensity scores for use in economic evaluation.

Methods

Patient-level data from an observational study of 573 children with Crohn’s disease were used in a microsimulation model to determine the incremental cost of early anti-tumor necrosis factor-α treatment compared to standard care per remission week gained. Multiple imputation of a missing covariate followed by propensity score matching to create comparator groups was approached in two ways. The Within approach calculated propensity scores on each imputed dataset separately, while the Across method averaged propensity scores to create one matched population resulting in multiple sets of health state transition probabilities.

Results

The incremental cost per remission week gained ranged from CAD$2,236 to CAD$12,464 (mean CAD$4,266) with Within datasets and was CAD$4,679 per remission week gained with the Across dataset.

Conclusion

Imputation of missing patient-level data and propensity score analysis increases methodological uncertainty in cost-effectiveness analysis. The present study indicated that the Across approach may be less cumbersome, and slightly reduce bias and variance.

中文翻译：

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在克罗恩病的成本效益分析中估算缺失的患者水平数据和倾向评分匹配

摘要

目标

在成本效益分析中，估算缺失数据和倾向得分分析对增量成本效益比 (ICER) 的影响尚不清楚。目的是比较在插补后和计算用于经济评估的倾向得分之前对数据进行分组的替代方法。

方法

一项针对 573 名克罗恩病儿童的观察性研究的患者水平数据用于微观模拟模型，以确定与标准治疗相比，早期抗肿瘤坏死因子-α 治疗每获得缓解周所增加的成本。以两种方式处理缺失协变量的多重插补，然后进行倾向得分匹配以创建比较组。Within 方法分别计算每个估算数据集的倾向得分，而 Across 方法平均倾向得分以创建一个匹配的人群，从而产生多组健康状态转换概率。

结果

使用 Within 数据集时，每个缓解周获得的增量成本从 2,236 加元到 12,464 加元（平均 4,266 加元）不等，使用 Across 数据集获得的每个缓解周增加成本为 4,679 加元。

结论

缺失的患者水平数据和倾向评分分析的插补增加了成本效益分析中的方法学不确定性。本研究表明，Across 方法可能不那么繁琐，并略微减少偏差和方差。