Factor analysis, causal indicators and quality of life
Factor analysis, causal indicators and quality of life
复制标题
因素分析、因果指标和生活质量
作者：
P. Fayers;D. Hand
探索性因素分析 (EFA) 仍然是证明新仪器结构有效性的标准和最广泛使用的方法之一。然而，全民教育模型做出的假设可能不适用于所有生活质量 (QOL) 工具，因此全民教育的结果可能会产生误导。特别是，全民教育假设生活质量（以及任何假定的子量表或“因素”）的基本结构可以被视为由这些因素或子量表中的项目所反映。然而，生活质量工具经常包含疾病、症状或治疗副作用等“因果指标”。这些项目可能会导致经历这些项目的患者生活质量下降，但不一定适用相反的关系：并非所有生活质量较差的患者都需要经历相同的症状。因此，症状项目的高水平可能意味着患者的生活质量可能较差，但生活质量的低水平不一定意味着患者可能患有该症状。这与常见的全民教育模型相反，在该模型中隐含地假设生活质量和任何分量表的变化“导致”或可能通过其所有组成项目的相应变化反映出来；因此，全民教育中的项目被称为“效果指标”。此外，与疾病相关的症状群或治疗引起的副作用可能会导致不同的研究发现不同的项目组高度相关；例如，一项涉及接受手术和化疗的肺癌患者的研究可能会发现一组高度相关的症状，而接受激素治疗的前列腺癌患者可能会具有非常不同的症状相关结构。由于全民教育基于分析相关矩阵并假设所有项目都是效果指标，因此它将提取代表疾病或治疗后果的因素。根据治疗模式或疾病类型和阶段的不同，这些因素可能在不同的患者亚组之间有所不同。这些因素几乎不包含有关项目与任何潜在 QOL 结构之间关系的信息。对于那些包含因果指标的 QOL 工具来说，因子分析作为量表验证的方法在很大程度上是无关紧要的，并且只能与效应指标的项目一起使用。
Exploratory factor analysis (EFA) remains one of the standard and most widely used methods for demonstrating construct validity of new instruments. However, the model for EFA makes assumptions which may not be applicable to all quality of life (QOL) instruments, and as a consequence the results from EFA may be misleading. In particular, EFA assumes that the underlying construct of QOL (and any postulated subscales or ‘factors’) may be regarded as being reflected by the items in those factors or subscales. QOL instruments, however, frequently contain items such as diseases, symptoms or treatment side effects, which are ‘causal indicators.’ These items may cause reduction in QOL for those patients experiencing them, but the reverse relationship need not apply: not all patients with a poor QOL need be experiencing the same set of symptoms. Thus a high level of a symptom item may imply that a patient's QOL is likely to be poor, but a poor level of QOL need not imply that the patient probably suffers from that symptom. This is the reverse of the common EFA model, in which it is implicitly assumed that changes in QOL and any subscales ‘cause’ or are likely to be reflected by corresponding changes in all their constituent items; thus the items in EFA are called ‘effect indicators.’ Furthermore, disease-related clusters of symptoms, or treatment-induced side-effects, may result in different studies finding different sets of items being highly correlated; for example, a study involving lung cancer patients receiving surgery and chemotherapy might find one set of highly correlated symptoms, whilst prostate cancer patients receiving hormone therapy would have a very different symptom correlation structure. Since EFA is based upon analyzing the correlation matrix and assuming all items to be effect indicators, it will extract factors representing consequences of the disease or treatment. These factors are likely to vary between different patient subgroups, according to the mode of treatment or the disease type and stage. Such factors contain little information about the relationship between the items and any underlying QOL constructs. Factor analysis is largely irrelevant as a method of scale validation for those QOL instruments that contain causal indicators, and should only be used with items which are effect indicators.
影响因子：
22.4
作者：
MACCALLUM, RC;BROWNE, MW
通讯作者：
BROWNE, MW
影响因子：
45.3
作者：
CELLA, DF;TULSKY, DS;HARRIS, J
通讯作者：
HARRIS, J
影响因子：
22.4
作者：
MACCALLUM, RC;WEGENER, DT;FABRIGAR, LR
通讯作者：
FABRIGAR, LR