Optimal Methods for Reducing Proxy-Introduced Bias on Patient-Reported Outcome Measurements for Group-Level Analyses.

Abstract

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. 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. 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. 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.