Meta-Analysis of Effects of Early Palliative Care on Health-Related Outcomes Among Advanced Cancer Patients

Background Early palliative care (PC) has received more attention for improving health-related outcomes for advanced cancer patients in recent years, but the results of previous studies are inconsistent. Objectives This study aimed to use meta-analysis and trial sequence analysis to evaluate the effect of early PC on health-related outcomes of advanced cancer patients. Methods All English publications were searched in PubMed, Web of Science, Embase, and the Cochrane Library from inception to March 2023, with a restriction that the study type was a randomized controlled trial. Results The results showed that early PC positively affected quality of life, satisfaction with care, and symptom burden reduction. However, early PC had no significant effect on anxiety or survival. Trial sequence analysis results showed that the effect of early PC on the quality of life was stable. Discussion This systematic review suggested that early PC could positively affect health-related outcomes for advanced cancer patients. Early PC can be used widely in clinical settings to improve health-related outcomes of advanced cancer. However, because of the trial sequence analysis results, further well-designed, clinical, randomized controlled trials with larger sample sizes are necessary to draw definitive conclusions.

Realist Approach to Qualitative Data Analysis

Background A realist approach has gained popularity in evaluation research, particularly in understanding causal explanations of how a program works (or not), the circumstances, and the observed outcomes. In qualitative inquiry, the approach has contributed to better theoretically based explanations regarding causal interactions. Objective The aim of this study was to discuss how we conducted a realist-informed data analysis to explore the causal interactions within qualitative data. Methods We demonstrated a four-step realist approach of retroductive theorizing in qualitative data analysis using a concrete example from our empirical research rooted in the critical realism philosophical stance. These steps include (a) category identification, (b) elaboration of context-mechanism-outcome configuration, (c) demi-regularities identification, and (d) generative mechanism refinement. Results The four-step qualitative realist data analysis underpins the causal interactions of important factors and reveals the underlying mechanisms. The steps produce comprehensive causal explanations that can be used by related parties—especially when making complex decisions that may affect wide communities. Discussion The core process of realist data analysis is retroductive theorizing. The four-step qualitative realist data analysis facilitates this theorizing by allowing the researcher to identify (a) patterns, (b) fluctuation of patterns, (c) mechanisms from collected data, and (d) to confirm proposed mechanisms.