Quality of abstracts of pilot trials in heart failure : a protocol for a systematic survey
CITATION: Isiguzo, G., et al. 2017. Quality of abstracts of pilot trials in heart failure : a protocol for a systematic survey. Contemporary Clinical Trials Communications, 8:258-263, doi:10.1016/j.conctc.2017.11.004.
The original publication is available at https://www.sciencedirect.com
Introduction: Pilot trials are initial small-scale studies done to inform the design of larger trials. Their findings like other studies are usually disseminated as peer-reviewed journal articles. Abstracts are used to introduce the contents to readers, and give a general idea about the full reports and sometimes are the only source of information available to readers. Despite their importance, the contents of abstracts of trial reports are usually not informative enough and lack the essential details. Methods and analysis: This is a protocol for a planned systematic survey with a primary aim of analyzing the reporting quality measured as the completeness of the reporting of pilot trial abstracts in heart failure. The secondary aim will be to explore factors associated with better reporting quality. Abstracts of heart failure pilot trials in humans (journal and conference abstracts) published in the English language from 1 January 1990 to 30 November 2016 will be assessed to determine the reporting quality, based on the CONSORT 2010 statement extension to randomized pilot and feasibility trials. All non-pilot/feasibility trials and non-human pilot trials will be excluded. We will search Medline (PUBMED), Cochrane controlled trials register, Scopus and African wide information databases for pilot trials in heart failure. Title and abstracts of identified studies will be screened for inclusion and data extracted independently by two reviewers in duplicate without using the full text. Reported and unreported items on the abstracts will be presented as frequencies and percentages, a descriptive analysis will be used to interpret the reporting quality and regression analysis used for characteristics associated with greater statistical reporting at 95% confidence interval.