Laurent Eckert1, Bruno Falissard. 1. Inserm, U669, Paris, and Univ Paris-sud 11, Paris, France. laurent.eckert@gmail.com
Abstract
BACKGROUND: In the absence of well-powered, randomised, direct-comparison trials, indirect comparisons are the only option for comparing treatment strategies. Several methodologies have been developed and each has sparked criticism. Using direct comparisons of escitalopram versus venlafaxine extended release (XR), we explore the differences between the two compounds through indirect comparisons. METHODS: The CENTRAL, Medline and Embase databases were interrogated, focusing on randomized placebo-controlled clinical trials involving adult patients treated for major depressive disorder in the acute phase. Corresponding authors were contacted to reduce missing data. Effect sizes were derived from each study's primary outcome. For indirect comparisons, a global effect size was computed through meta-regression. For direct comparisons, the studies were considered separately due to missing data. Non-inferiority assessments were employed. The conclusion of the meta-regression was then compared with the conclusions made in direct comparison trials. RESULTS: Ten placebo-controlled studies--six assessing escitalopram and four assessing venlafaxine XR--and two direct comparison studies were retrieved. Escitalopram was found to be non-inferior to venlafaxine XR in both indirect and direct comparisons with results of mean -0.02 (unilateral 95% confidence interval [CI] -0.16 to infinity) and 0.23 (95% CI -0.01 to infinity), respectively. Results obtained by both indirect and direct comparisons were similar. Investigating the influence of age, gender repartition and severity at baseline suggests that results are consistent. Results were also considered robust against publication bias. CONCLUSIONS: This empirical finding suggests that escitalopram is non-inferior to venlafaxine XR. This reinforces the evidence found in direct comparisons trials. Indirect comparisons through meta-regression may be suitable to support decision-making. To fully assess its potential, further evaluation of this methodology, using other examples, is needed.
BACKGROUND: In the absence of well-powered, randomised, direct-comparison trials, indirect comparisons are the only option for comparing treatment strategies. Several methodologies have been developed and each has sparked criticism. Using direct comparisons of escitalopram versus venlafaxine extended release (XR), we explore the differences between the two compounds through indirect comparisons. METHODS: The CENTRAL, Medline and Embase databases were interrogated, focusing on randomized placebo-controlled clinical trials involving adult patients treated for major depressive disorder in the acute phase. Corresponding authors were contacted to reduce missing data. Effect sizes were derived from each study's primary outcome. For indirect comparisons, a global effect size was computed through meta-regression. For direct comparisons, the studies were considered separately due to missing data. Non-inferiority assessments were employed. The conclusion of the meta-regression was then compared with the conclusions made in direct comparison trials. RESULTS: Ten placebo-controlled studies--six assessing escitalopram and four assessing venlafaxine XR--and two direct comparison studies were retrieved. Escitalopram was found to be non-inferior to venlafaxine XR in both indirect and direct comparisons with results of mean -0.02 (unilateral 95% confidence interval [CI] -0.16 to infinity) and 0.23 (95% CI -0.01 to infinity), respectively. Results obtained by both indirect and direct comparisons were similar. Investigating the influence of age, gender repartition and severity at baseline suggests that results are consistent. Results were also considered robust against publication bias. CONCLUSIONS: This empirical finding suggests that escitalopram is non-inferior to venlafaxine XR. This reinforces the evidence found in direct comparisons trials. Indirect comparisons through meta-regression may be suitable to support decision-making. To fully assess its potential, further evaluation of this methodology, using other examples, is needed.
Authors: James E Signorovitch; Eric Q Wu; Andrew P Yu; Charles M Gerrits; Evan Kantor; Yanjun Bao; Shiraz R Gupta; Parvez M Mulani Journal: Pharmacoeconomics Date: 2010 Impact factor: 4.981