IRCCS Istituto Ortopedico Galeazzi, Milan, Italy; University of Milano-Bicocca, Milan, Italy.
Datareg, Cinisello Balsamo, Italy; Politecnico of Milan, Milan, Italy.
Comput Methods Programs Biomed. 2019 Nov;181:104837. doi: 10.1016/j.cmpb.2019.01.009. Epub 2019 Jan 17.
Collecting Patient-Reported Outcomes (PROs) is an important way to get first-hand information by patients on the outcome of treatments and surgical procedure they have undergone, and hence about the quality of the care provided. However, the quality of PRO data cannot be given for granted and cannot be traced back to the dimensions of timeliness and completeness only. While the reliability of these data can be guaranteed by adopting standard and validated questionnaires that are used across different health care facilities all over the world, these facilities must take responsibility to assess, monitor and ensure the validity of PROs that are collected from their patients. Validity is affected by biases that are hidden in the data collected. This contribution is then aimed at measuring bias in PRO data, for the impact that these data can have on clinical research and post-marketing surveillance.
We considered the main biases that can affect PRO validity: Response bias, in terms of Acquiescence bias and Fatigue bias; and Non-Response bias. To assess Acquiescence bias, phone interviews and online surveys were compared, adjusted by age. To assess Fatigue bias, we proposed a specific item about session length and compared PROs scores stratifying according to the responses to this item. We also calculated the intra-patient agreement by conceiving an intra-interview test-retest. To assess Non-Response bias, we considered patients who participated after the saturation of the response-rate curve as proxy of potential non respondents and compared the outcomes in these two strata. All methods encompassed common statistical techniques and are cost-effective at any facility collecting PRO data.
Acquiescence bias resulted in significantly different scores between patients reached by either phone or email. In regard to Fatigue bias, stratification by perceived fatigue resulted in contrasting results. A relevant difference was found in intra-patient agreement and an increasing difference in average scores as a function of interview length (or completion time). In regard to Non-Response bias, we found non-significant differences both in scores and variance.
In this paper, we present a set of cost-effective techniques to assess the validity of retrospective PROs data and share some lessons learnt from their application at a large teaching hospital specialized in musculoskeletal disorders that collects PRO data in the follow-up phase of surgery performed therein. The main finding suggests that response bias can affect the PRO validity. Further research on the effectiveness of simple and cost-effective solutions is necessary to mitigate these biases and improve the validity of PRO data.
收集患者报告的结局(PROs)是一种重要的方式,可以直接从患者那里获得关于他们所接受的治疗和手术结果以及所提供的护理质量的信息。然而,PRO 数据的质量不能被视为理所当然,也不能仅仅追溯到及时性和完整性维度。虽然这些数据的可靠性可以通过采用在全球各地的不同医疗保健机构中使用的标准和经过验证的问卷来保证,但这些机构必须负责评估、监测和确保从患者那里收集的 PRO 的有效性。有效性受到数据收集过程中隐藏的偏差的影响。因此,本研究旨在测量 PRO 数据中的偏差,因为这些数据可能会对临床研究和上市后监测产生影响。
我们考虑了可能影响 PRO 有效性的主要偏差:响应偏差,包括默认偏差和疲劳偏差;以及非响应偏差。为了评估默认偏差,我们比较了电话访谈和在线调查,并根据年龄进行了调整。为了评估疲劳偏差,我们提出了一个关于会议长度的特定项目,并根据对该项目的回答对 PRO 评分进行分层比较。我们还通过构思访谈内的测试-重测来计算患者内的一致性。为了评估非响应偏差,我们将参与到响应率曲线饱和后的患者视为潜在的非响应者,并将这两个分层的结果进行比较。所有方法都包含常见的统计技术,并且在任何收集 PRO 数据的机构都具有成本效益。
默认偏差导致通过电话或电子邮件联系的患者的评分存在显著差异。关于疲劳偏差,根据感知疲劳进行分层会导致结果不一致。在患者内的一致性方面存在显著差异,并且随着访谈长度(或完成时间)的增加,平均评分的差异也在增加。关于非响应偏差,我们发现评分和方差都没有显著差异。
在本文中,我们提出了一组具有成本效益的技术,用于评估回顾性 PRO 数据的有效性,并分享了在一家专门治疗肌肉骨骼疾病的大型教学医院应用这些技术的经验教训,该医院在手术的随访阶段收集 PRO 数据。主要发现表明,响应偏差会影响 PRO 的有效性。需要进一步研究简单且具有成本效益的解决方案的有效性,以减轻这些偏差并提高 PRO 数据的有效性。