OBJECTIVE: Research based on pooling data from clinical trials suggests that it is possible to extrapolate from a CGI-S to a PANSS severity score and from the CGI-I to a PANSS percentage change score. This research has not been replicated nor examined in individual trials. This study aims to examine the feasibility of extrapolation from the CGI to the PANSS across and within 4 large clinical trials of antipsychotic medication. METHODS: Equipercentile linking is used to examine extrapolation (a) from CGI-S to PANSS severity ratings and (b) from CGI-I to PANSS percentage change (n=2698). Linking is conducted at baseline and after 2, 4, 6 and 8 weeks of treatment from ITT clinical trial participants with schizophrenia. RESULTS: Across weeks 2, 4, 6 and 8, being considered 'not ill' according to the CGI-S corresponded to PANSS scores of 31-2. The relationship between the CGI-S and the PANSS followed an increasing trend, such that 'very mild' corresponded with 41-7, 'mild' corresponded with 55-62, 'moderate' corresponded with 71-7, 'marked' corresponded with 88-94, 'severe' corresponded with 105-110, and 'extremely severe' corresponded with 126-134. The relationship between CGI-I ratings and percentage change followed a linear trend, such that 'very much improved' corresponded to PANSS percentage change scores from 79 to 75, 'much improved' corresponded with 45 to 49, 'minimally improved' corresponded with 21 to 23, 'unchanged' corresponded with 2 to 3, 'minimally worse' corresponded with -15 to -20, 'much worse' corresponded with -44 to -51. Generally, within the trials the cut-off ranges identified overlapped within around 10 points of those found in the pooled analysis. CONCLUSIONS: Despite trial heterogeneity, the results support the extrapolation from the CGI-I to PANSS percentage change. Extrapolation of the CGI-S to the PANSS is observed, except in the case of severe symptomatology which is rare. Collectively, the results support the extrapolation between the PANSS and CGI.
OBJECTIVE: Research based on pooling data from clinical trials suggests that it is possible to extrapolate from a CGI-S to a PANSS severity score and from the CGI-I to a PANSS percentage change score. This research has not been replicated nor examined in individual trials. This study aims to examine the feasibility of extrapolation from the CGI to the PANSS across and within 4 large clinical trials of antipsychotic medication. METHODS: Equipercentile linking is used to examine extrapolation (a) from CGI-S to PANSS severity ratings and (b) from CGI-I to PANSS percentage change (n=2698). Linking is conducted at baseline and after 2, 4, 6 and 8 weeks of treatment from ITT clinical trial participants with schizophrenia. RESULTS: Across weeks 2, 4, 6 and 8, being considered 'not ill' according to the CGI-S corresponded to PANSS scores of 31-2. The relationship between the CGI-S and the PANSS followed an increasing trend, such that 'very mild' corresponded with 41-7, 'mild' corresponded with 55-62, 'moderate' corresponded with 71-7, 'marked' corresponded with 88-94, 'severe' corresponded with 105-110, and 'extremely severe' corresponded with 126-134. The relationship between CGI-I ratings and percentage change followed a linear trend, such that 'very much improved' corresponded to PANSS percentage change scores from 79 to 75, 'much improved' corresponded with 45 to 49, 'minimally improved' corresponded with 21 to 23, 'unchanged' corresponded with 2 to 3, 'minimally worse' corresponded with -15 to -20, 'much worse' corresponded with -44 to -51. Generally, within the trials the cut-off ranges identified overlapped within around 10 points of those found in the pooled analysis. CONCLUSIONS: Despite trial heterogeneity, the results support the extrapolation from the CGI-I to PANSS percentage change. Extrapolation of the CGI-S to the PANSS is observed, except in the case of severe symptomatology which is rare. Collectively, the results support the extrapolation between the PANSS and CGI.
Authors: Rebecca Schennach; Hans-Jürgen Möller; Michael Obermeier; Florian Seemüller; Markus Jäger; Max Schmauss; Gerd Laux; Herbert Pfeiffer; Dieter Naber; Lutz G Schmidt; Wolfgang Gaebel; Joachim Klosterkötter; Isabella Heuser; Wolfgang Maier; Matthias R Lemke; Eckart Rüther; Stefan Klingberg; Markus Gastpar; Richard Musil; Ilja Spellmann; Michael Riedel Journal: Int J Methods Psychiatr Res Date: 2015-07-14 Impact factor: 4.035
Authors: Todd S Woodward; Kwanghee Jung; Geoffrey N Smith; Heungsun Hwang; Alasdair M Barr; Ric M Procyshyn; Sean W Flynn; Mark van der Gaag; William G Honer Journal: Eur Arch Psychiatry Clin Neurosci Date: 2013-10-15 Impact factor: 5.270
Authors: Michael H Allen; David G Daniel; Dennis A Revicki; Carla M Canuso; Ibrahim Turkoz; Dong-Jing Fu; Larry Alphs; K Jack Ishak; John J Bartko; Jean-Pierre Lindenmayer Journal: Innov Clin Neurosci Date: 2012-01
Authors: Alka Sabharwal; Gurprit Grover; Sakshi Kaushik; K E Sadanandan Unni Journal: Int J Methods Psychiatr Res Date: 2019-01-16 Impact factor: 4.035
Authors: Shaunna L Clark; Renan P Souza; Daniel E Adkins; Karolina Aberg; József Bukszár; Joseph L McClay; Patrick F Sullivan; Edwin J C G van den Oord Journal: Pharmacogenet Genomics Date: 2013-02 Impact factor: 2.089