Migraine in Children: Preventive Pharmacologic Treatments
This report is based on research conducted by the Minnesota Evidence-based Practice Center (EPC) under contract to the Agency for Healthcare Research and Quality (AHRQ), Rockville, MD (Contract No. 290-07-10064-I). The findings and conclusions in this document are those of the authors, who are responsible for its contents; the findings and conclusions do not necessarily represent the views of AHRQ. Therefore, no statement in this report should be construed as an official position of AHRQ or of the U.S. Department of Health and Human Services. This document is in the public domain and may be used and reprinted without special permission. Citation of the source is appreciated. Suggested citation: Shamliyan TA, Kane RL, Ramakrishnan R, Taylor FR. Migraine in Children: Preventive Pharmacologic Treatments. Comparative Effectiveness Review No. 108. (Prepared by the University of Minnesota Evidence-based Practice Center under Contract No. 290-2007-10064-I.) AHRQ Publication No. 13-EHC065-EF. Rockville, MD: Agency for Healthcare Research and Quality; June 2013. www.effectivehealthcare.ahrq.gov/reports/final.cfm.
Steps to reproduce
We abstracted minimum datasets to reproduce the results presented by the authors. For categorical variables we abstracted the number of events among treatment groups to calculate rates, relative risk, and absolute risk differences (ARDs). Means and standard deviations of continuous variables were abstracted to calculate mean differences with a 95% confidence interval (CI). For RCTs in the quantitative analysis set, we abstracted the number randomized to each treatment group as the denominator to calculate estimates by applying intention-to-treat principles. We abstracted the time when the outcomes were assessed as weeks from randomization and time of followup after treatments. Using Meta-Analyst and STATA® software, we calculated the relative risk and absolute risk difference from the abstracted events and the mean differences in continuous variables from the reported means and standard deviations. We evaluated statistical significance at a 95% confidence level.