Clinical Gerontopsychology, Humboldt Universität zu Berlin, Berlin, Germany.
Psychother Psychosom. 2015;84(3):159-66. doi: 10.1159/000376583. Epub 2015 Mar 28.
Developing and evaluating interventions for patients with age-associated disorders is a rising field in psychotherapy research. Its methodological challenges include the high between-subject variability and the wealth of influencing factors associated with longer lifetime. Latent change score modeling (LCSM), a technique based on structural equation modeling, may be well suited to analyzing longitudinal data sets obtained in clinical trials. Here, we used LCSM to evaluate the antidepressant effect of a combined cognitive behavioral/cognitive rehabilitation (CB/CR) intervention in Alzheimer's disease (AD).
LCSM was applied to predict the change in depressive symptoms from baseline as an outcome of the CORDIAL study, a randomized controlled trial involving 201 patients with mild AD. The participants underwent either the CORDIAL CB/CR program or standard treatment. Using LCSM, the model best predicting changes in Geriatric Depression Scale scores was determined based on this data set.
The best fit was achieved by a model predicting a decline in depressive symptoms between before and after testing. Assignment to the intervention group as well as female gender revealed significant effects in model fit indices, which remained stable at 6- and 12-month follow-up examinations. The pre-post effect was pronounced for patients with clinically relevant depressive symptoms at baseline.
LCSM confirmed the antidepressant effect of the CORDIAL therapy program, which was limited to women. The effect was pronounced in patients with clinically relevant depressive symptoms at baseline. Methodologically, LCSM appears well suited to analyzing longitudinal data from clinical trials in aged populations, by accounting for the high between-subject variability and providing information on the differential indication of the probed intervention.
针对与年龄相关的障碍患者的干预措施的开发和评估是心理治疗研究中一个兴起的领域。其方法学挑战包括个体间的高度变异性和与更长寿命相关的大量影响因素。基于结构方程模型的潜在变化评分模型（LCSM）可能非常适合分析临床试验中获得的纵向数据集。在这里，我们使用 LCSM 来评估认知行为/认知康复（CB/CR）联合干预对阿尔茨海默病（AD）的抗抑郁作用。
LCSM 被应用于预测 CORDIAL 研究中从基线开始的抑郁症状变化，这是一项涉及 201 例轻度 AD 患者的随机对照试验。参与者接受了 CORDIAL CB/CR 计划或标准治疗。使用 LCSM，根据该数据集确定了预测老年抑郁量表评分变化的最佳模型。
预测测试前后抑郁症状下降的模型拟合度最佳。分配到干预组以及女性性别在模型拟合指标中显示出显著的效果，在 6 个月和 12 个月的随访检查中仍然稳定。对于基线时具有临床相关抑郁症状的患者，预-后效果明显。
LCSM 证实了 CORDIAL 治疗计划的抗抑郁作用，该作用仅限于女性。对于基线时具有临床相关抑郁症状的患者，效果明显。从方法学上讲，LCSM 似乎非常适合分析老年人临床试验的纵向数据，通过考虑个体间的高度变异性并提供关于所探测干预措施的差异指示的信息。
