Further developments in summarising and meta-analysing single-case data: An illustration with neurobehavioural interventions in acquired brain injury

Extrapolating baseline trend in single-case data: Problems and tentative solutions

“…This immediate effect represents a comparison, for the first intervention-phase measurement occasion, between the extrapolated baseline trend and the fitted intervention-phase trend. Second, an Table 2 Values for the different versions of the mean phase difference (MPD) as applied to the multiple-baseline data from Ciullo et al (2015) Tier Baseline Trend Slope MPD 2013 (Manolov & Solanas, 2013); MPD 2015 (Manolov & Rochat, 2015); MPD TS: using the Theil-Sen estimate of the intercept. MASE, mean absolute scaled error; n A , number of baseline measurements; n B , number of intervention-phase measurements;n B , number of intervention-phase measurement occasions to which to extrapolate the baseline trend, when extrapolation is limited;…”

“…As part of this discussion, we propose tentative solutions to the issues identified. Moreover, we specifically aim to improve one analytical procedure, which extrapolates baseline trend and compares this extrapolation to the actual intervention-phase data: the mean phase difference (MPD; Manolov & Solanas, 2013; see also the modification and extension in Manolov & Rochat, 2015).…”

Efficacy of metacognitive therapy in improving mental health: A meta‐analysis of single‐case studies

“…(), when inverse variance weighting is impossible. The meta‐analysis using the percentage change index was performed via the R code from Manolov and Rochat ().…”

“…The meta-analysis of these one-per-study average percentage change index values was performed using the number of measurements available in the study as a weight, a reasonable approach according to Kratochwill et al (2010), when inverse variance weighting is impossible. The meta-analysis using the percentage change index was performed via the R code from Manolov and Rochat (2015).…”

MultiSCED: A tool for (meta-)analyzing single-case experimental data with multilevel modeling

“…Specifically for R, a challenge for the future is to combine the individual pieces of software that already exist into one comprehensible tool or package. Merging Multi-SCED with the R code provided by Manolov and Moeyaert (2017), the functionality from the SCDA plug-in for R commander (Bulté & Onghena, 2013), the SSDforR package (Auerbach & Schudrich, 2013), the scdhlm package (Pustejovsky, 2016) and other existing Shiny applications like the application at https://manolov.shinyapps.io/ SeveralAB (Manolov & Rochat, 2015;Manolov & Solanas, 2018), and streamlining the end result might be very useful to bring applied researchers to R for analyzing their SCED data. For an overview of tools in R and other software options, we refer to the list of single-case data analysis software tools at https://osf.io/sdv4m/.…”