BJP-2000-BECH-421-8

Outcomes frank et al 1991 suggested using the term

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Unformatted text preview: ique. Outcomes Frank et al (1991) suggested using the term remission, rather than recovery, when defining response to drug therapy in the short-term treatment of depression. Partial remission after 4±6 weeks of treatment can be defined as at least a 50% reduction 422 compared with the baseline value for the HDRS±17 score, which corresponds to very much or much improved on the Clinical Global Impression Scale (CGI) (Guy, 1976). The CGI was used in all the USA trials, but only in a few of the non-USA trials. The primary outcome for USA and non-USA trials was defined as a binary variable on the HDRS±17; partial remission, that is at least 50% reduction compared with the baseline score on the HDRS±17 instrument. The secondary outcome in the USA trials was also a binary variable, defined as a much improved or very much improved on the CGI scale. Another secondary, but quantitative, outcome was the mean change in HDRS±17 scores from baseline to end-point. In this part of the analysis an HDRS subscale, the depression factor (including the six items of depressed mood, guilt, work and interests, retardation, psychic anxiety and general somatic), was also used (HDRS±6; Bech, 1989; O'Sullivan et al, 1997). The reasons for early treatment discontinuation were analysed as binary variables (adverse event, lack of efficacy or any reason). Meta-analytical methods Log odds ratio analysis for binary data We used the logarithm of the odds ratio method, which is based on a multiplicative model, that is the success rate (partial remission) in the treatment group is assumed to be a multiplicative function of that in the control group (Boissel et al, 1989). Due to the large number of statistical tests performed the level of statistical significance was set at a robust value Pˆ0.01 or less. A test for heterogeneity was also performed, and because this is an insensitive test, the level of statistical significance was set at a value of Pˆ0.10 or less. When heterogeneity was detected we analysed the data using a random effects model, which gives more conservative results, but can deal with a certain amount...
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This note was uploaded on 01/16/2013 for the course BMS 620 taught by Professor Panavalil during the Summer '12 term at Barry Univesity.

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