Background:Caregivers, or proxies, often complete patient-reported outcomes (PROs) on behalf of patients; yet, research has demonstrated proxies rate patient outcomes worse than patients rate their own outcomes. To improve interpretability of PROs in group-level analyses, our study aimed to identify optimal approaches for reducing proxy-introduced bias in the analysis of PROs.Methods:Data were simulated based on 200 patients with stroke and their proxies who both completed 9 PROMIS domains as part of a cross-sectional study. The sample size was varied as 50, 100, 200, and 500, and the proportion of patients with proxy-respondents was varied as 10%, 20%, and 50%. Six methods for handling proxy-completions were investigated: (1) complete case analysis; (2) proxy substitution; (3) Method 2 plus proxy adjustment; (4) Method 3 including inverse-probability of treatment weighting; (5) multiple imputation; (6) linear equating. These methods were evaluated by comparing average bias in PROMIS T-scores (estimated versus observed patient-only responses), as well as by comparing estimated regression coefficients to models using patient-only responses.Results:Overall mean T-score differences ranged from 0 to 1.75. The range of mean differences varied by the 6 methods with methods 1 and 5 providing estimates closest to the observed mean. In regression models, all but inverse-probability of treatment weighting resulted in low bias when proxy-completions were 10% to 20%. With 50% proxy-completions, method 5 resulted in less accurate estimations while methods 1 to 3 provided less proxy-introduced bias. Bias remained low across domain and varying sample sizes but increased with larger percentages of proxy-respondents.Conclusions:Our study found modest proxy-introduced bias when estimating PRO scores or regression estimates across multiple domains of health. This bias remained low, even when sample size was 50 and there were large proportions of proxy-completions. While many of these methods can be chosen for including proxies in stroke PRO research with <20% proxy-respondents, proxy substitution with adjustment resulted in low bias with 50% proxy-respondents.

中文翻译：

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减少对患者报告结果测量的代理引入偏差的最佳方法，用于组级分析

背景：护理人员或代理人通常代表患者完成患者报告结果 (PRO)；然而，研究表明，代理人对患者结果的评价比患者对自己结果的评价更差。为了提高 PRO 在组级分析中的可解释性，我们的研究旨在确定减少 PRO 分析中代理引入偏差的最佳方法。 方法：基于 200 名中风患者及其代理模拟数据，他们都完成了 9 个 PROMIS 域作为横断面研究的一部分。样本大小分别为 50、100、200 和 500，代理响应患者的比例为 10%、20% 和 50%。研究了处理代理完成的六种方法：（1）完整的案例分析；(2) 代理替代；(3) 方法二加代理调整；(4) 方法3包括处理加权的逆概率；(5) 多重插补；(6) 线性等式。这些方法是通过比较 PROMIS 中的平均偏差来评估的T分数（估计与观察到的仅患者反应），以及将估计的回归系数与使用仅患者反应的模型进行比较。 结果：总体平均T- 分数差异范围从 0 到 1.75。平均差异的范围因 6 种方法而异，方法 1 和方法 5 提供最接近观察到的平均值的估计值。在回归模型中，当代理完成率为 10% 到 20% 时，除处理权重的逆概率外，其他所有因素都会导致低偏差。对于 50% 的代理完成，方法 5 导致不太准确的估计，而方法 1 到 3 提供较少的代理引入偏差。跨领域和不同样本量的偏差仍然很低，但随着代理受访者百分比的增加而增加。结论：我们的研究发现，在跨多个健康领域估计 PRO 分数或回归估计时，存在适度的代理引入偏差。即使样本量为 50 并且代理完成的比例很大，这种偏差仍然很低。