Data analysis methods for assessing palliative care interventions in one-group pre–post studies. (15th November 2015)

• Record Type: Journal Article
• Authors: Ioroi, Takeshi; Kakuma, Tatsuyuki; Sakashita, Akihiro; Miki, Yuki; Ohtagaki, Kanako; Fujiwara, Yuka; Utsubo, Yuko; Nishimura, Yoshihiro; Hirai, Midori
• Is Part Of: SAGE open medicine. Volume 3(2015)
• Publication Date: 2015-11-15
• Subjects: Palliative care -- one-arm clinical trial -- quality of life; Medicine -- Periodicals; 610.5
• DOI: 10.1177/2050312115621313
• Languages: English
• ISSNs: 2050-3121

Abstract

Objectives: Studies of palliative care are often performed using single-arm pre–post study designs that lack causal inference. Thus, in this study, we propose a novel data analysis approach that incorporates risk factors from single-arm studies instead of using paired t-tests to assess intervention effects. Methods: Physical, psychological and social evaluations of eligible cancer inpatients were conducted by a hospital-based palliative care team. Quality of life was assessed at baseline and after 7 days of symptomatic treatment using the European Organization for Research and Treatment of Cancer QLQ-C15-PAL. Among 35 patients, 9 were discharged within 1 week and 26 were included in analyses. Structural equation models with observed measurements were applied to estimate direct and indirect intervention effects and simultaneously consider risk factors. Results: Parameters were estimated using full models that included associations among covariates and reduced models that excluded covariates with small effects. The total effect was calculated as the sum of intervention and covariate effects and was equal to the mean of the difference (0.513) between pre- and post-intervention quality of life (reduced model intervention effect, 14.749; 95% confidence intervals, −4.407 and 33.905; p = 0.131; covariate effect, −14.236; 95% confidence interval, −33.708 and 5.236; p = 0.152). Conclusion: Using the present analytical method for single-arm pre–post study designs, factors that modulate effects of interventions were modelled, and intervention and covariate effects were distinguished based on structural equation model.