Sudden Gains

Tang, Tuo-Xian

doi:10.1002/9781118625392.wbecp378

Cited by 1 publication

(3 citation statements)

References 15 publications

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“…Tang and DeRubeis [1] originally defined a 7 point cutoff on the BDI for the first criterion based on frequency distribution plots of session to session change scores on the BDI in clinical trials. The authors reported that 7 BDI points approximately reflected one standard deviation in clinical samples [9]. Stiles et al [8] noted that 7 BDI points was close to the reliable change value reported in Barkham et al [14] and therefore used the RCI formula to define a cutoff for a new measure.…”

Section: Defining a Cutoff For The First Criterionmentioning

confidence: 82%

“…Originally an independent t test was proposed to compare the size of the sudden gain with symptom fluctuation before and after the gain. This method was controversial given the assumption of independence of the measurements before and after the gain is not met [3,9]. Consequently the wording of this criterion was updated by Tang and colleagues [5,9], though the calculations remained the same: The difference between the mean scores of the three measurements before the gain (M pre ), and the three measurements after the gain (M post ), must be greater than the pooled standard deviation of these two groups multiplied by a critical value of 2.776 (i.e.…”

Section: Identification Of Sudden Gainsmentioning

confidence: 99%

“…Secondly, where values are missing in the period around the potential sudden gain, two approaches have been described to evaluate the stability of the change. Following the updated version of the third criterion by Tang and colleagues [5,9] some studies have used a critical value of 2.776 across all session to session intervals to check the stability [13]. An alternative approach adjusts the critical values used in criterion 3 (see Eq 1) based on the data that were available in the period around the potential sudden gain [11]: Where no data are missing t (4;97.5%) > 2.776; where one datapoint is missing either before or after the gain t (3;97.5%) > 3.182; and where one datapoint is missing both before and after the gain t (2;97.5%) > 4.303.…”

Section: Missing Datamentioning

confidence: 99%

See 2 more Smart Citations

suddengains: An R package to identify sudden gains in longitudinal data

et al. 2020

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Sudden gains are large and stable improvements in an outcome variable between consecutive measurements, for example during a psychological intervention with multiple assessments. Researching these occurrences could help understand individual change processes in longitudinal data. Three criteria are generally used to identify sudden gains in psychological interventions. However, applying these criteria can be time consuming and prone to errors if not fully automated. Adaptations to these criteria and methodological decisions such as how multiple gains are handled vary across studies and are reported with different levels of detail. These problems limit the comparability of individual studies and make it hard to understand or replicate the exact methods used. The R package suddengains provides a set of tools to facilitate sudden gains research. This article illustrates how to use the package to identify sudden gains or sudden losses and how to extract descriptive statistics as well as exportable data files for further analysis. It also outlines how these analyses can be customised to apply adaptations of the standard criteria. The suddengains package therefore offers significant scope to improve the efficiency, reporting, and reproducibility of sudden gains research.

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