Commentary on Raghuraman et al. (2021). On the Long-Term Efficacy and Effectiveness of Narrative Exposure Therapy

In the past years, several systematic reviews and meta-analyses have been published assessing the effectiveness of narrative exposure therapy [NET; (Lely et al., 2019; Raghuraman et al., 2021; Siehl et al., 2021; Wei and Chen, 2021)]. The meta-analyses had different aims and came to different conclusions about the effectiveness of NET. Lely et al. (2019) and Wei and Chen (2021) focused on between-treatment effects, post intervention, comparing NET with active and non-active control- treatment-conditions. In addition to comparing between-treatment effects Raghuraman et al. (2021) and Siehl et al. (2021) assessed also the temporal stability of the effects. The latter two studies investigated the reduction of symptom severity of posttraumatic stress disorder (PTSD) and the percentage of PTSD diagnoses over several follow-up periods. Raghuraman et al. (2021) indicated a medium standardized mean difference (SMD) in favor of NET in comparison to active and inactive control groups in the long-term and no benefit regarding PTSD diagnoses. The authors cautioned against using the existing evidence to inform policies and guidelines. In contrast, Siehl et al. (2021) found a large SMD in favor of NET compared to active or inactive control groups in the long-term. They reported an improvement of effectiveness over time when analyzing active control groups and concluded that NET is an effective treatment approach in post-conflict settings and refugee populations, highlighting the high external validity of the trials. Acknowledging the significant efforts of both author groups to select, code, and analyze the existing evidence, we aim to clarify potential underlying reasons for the differences between the two meta-analyses. The purpose of this commentary is two-fold: (a) discuss more generally ways to assess the quality of a treatment, such as NET, that is used in a broad range of contexts and (b) more specific differences between the two meta-analyses in (1) selecting and analyzing strategies, and (2) potential coding errors.

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