Schwartz Carolyn E, Sajobi Tolulope T, Verdam Mathilde G E, Sebille Veronique, Lix Lisa M, Guilleux Alice, Sprangers Mirjam A G
DeltaQuest Foundation, Inc, Concord, MA, USA,
Qual Life Res. 2015 Mar;24(3):521-8. doi: 10.1007/s11136-014-0746-0. Epub 2014 Jul 10.
Missing data due to attrition or item non-response can result in biased estimates and loss of power in longitudinal quality-of-life (QOL) research. The impact of missing data on response shift (RS) detection is relatively unknown. This overview article synthesizes the findings of three methods tested in this special section regarding the impact of missing data patterns on RS detection in incomplete longitudinal data.
The RS detection methods investigated include: (1) Relative importance analysis to detect reprioritization RS in stroke caregivers; (2) Oort's structural equation modeling (SEM) to detect recalibration, reprioritization, and reconceptualization RS in cancer patients; and (3) Rasch-based item-response theory-based (IRT) models as compared to SEM models to detect recalibration and reprioritization RS in hospitalized chronic disease patients. Each method dealt with missing data differently, either with imputation (1), attrition-based multi-group analysis (2), or probabilistic analysis that is robust to missingness due to the specific objectivity property (3).
Relative importance analyses were sensitive to the type and amount of missing data and imputation method, with multiple imputation showing the largest RS effects. The attrition-based multi-group SEM revealed differential effects of both the changes in health-related QOL and the occurrence of response shift by attrition stratum, and enabled a more complete interpretation of findings. The IRT RS algorithm found evidence of small recalibration and reprioritization effects in General Health, whereas SEM mostly evidenced small recalibration effects. These differences may be due to differences between the two methods in handling of missing data.
Missing data imputation techniques result in different conclusions about the presence of reprioritization RS using the relative importance method, while the attrition-based SEM approach highlighted different recalibration and reprioritization RS effects by attrition group. The IRT analyses detected more recalibration and reprioritization RS effects than SEM, presumably due to IRT's robustness to missing data. Future research should apply simulation techniques in order to make conclusive statements about the impacts of missing data according to the type and amount of RS.
在纵向生活质量（QOL）研究中，因人员流失或项目无应答导致的数据缺失会导致估计偏差和效能损失。数据缺失对反应转移（RS）检测的影响相对未知。这篇综述文章综合了在本特刊中测试的三种方法的研究结果，这些方法涉及数据缺失模式对不完整纵向数据中RS检测的影响。
所研究的RS检测方法包括：（1）用于检测中风护理人员中重新排序RS的相对重要性分析；（2）用于检测癌症患者中重新校准、重新排序和重新概念化RS的奥尔特结构方程模型（SEM）；（3）与SEM模型相比，基于拉施克项目反应理论（IRT）的模型，用于检测住院慢性病患者中的重新校准和重新排序RS。每种方法处理数据缺失的方式不同，分别采用插补法（1）、基于损耗的多组分析（2）或因特定客观性属性对缺失具有稳健性的概率分析（3）。
相对重要性分析对数据缺失的类型和数量以及插补方法敏感，多重插补显示出最大的RS效应。基于损耗的多组SEM揭示了健康相关QOL变化和按损耗分层的反应转移发生的不同效应，并能对研究结果进行更完整的解释。IRT RS算法在总体健康方面发现了小的重新校准和重新排序效应的证据，而SEM大多证明了小的重新校准效应。这些差异可能是由于两种方法在处理数据缺失方面的不同。
数据缺失插补技术使用相对重要性方法对重新排序RS的存在得出不同结论，而基于损耗的SEM方法突出了按损耗组划分的不同重新校准和重新排序RS效应。IRT分析比SEM检测到更多的重新校准和重新排序RS效应，推测是由于IRT对数据缺失具有稳健性。未来的研究应应用模拟技术，以便根据RS的类型和数量对数据缺失的影响做出确定性陈述。
