Current practice in analysing and reporting binary outcome data—a review of randomised controlled trial reports

Background

Randomised controlled trials (RCTs) need to be reported so that their results can be unambiguously and robustly interpreted. Binary outcomes yield unique challenges, as different analytical approaches may produce relative, absolute, or no treatment effects, and results may be particularly sensitive to the assumptions made about missing data. This review of recently published RCTs aimed to identify the methods used to analyse binary primary outcomes, how missing data were handled, and how the results were reported.

Methods

Systematic review of reports of RCTs published in January 2019 that included a binary primary outcome measure. We identified potentially eligible English language papers on PubMed, without restricting by journal or medical research area. Papers reporting the results from individually randomised, parallel-group RCTs were included.

Results

Two hundred reports of RCTs were included in this review. We found that 64% of the 200 reports used a chi-squared-style test as their primary analytical method. Fifty-five per cent (95% confidence interval 48% to 62%) reported at least one treatment effect measure, and 38% presented only a p value without any treatment effect measure. Missing data were not always adequately described and were most commonly handled using available case analysis (69%) in the 140 studies that reported missing data. Imputation and best/worst-case scenarios were used in 21% of studies. Twelve per cent of articles reported an appropriate sensitivity analysis for missing data.

Conclusions

The statistical analysis and reporting of treatment effects in reports of randomised trials with a binary primary endpoint requires substantial improvement. Only around half of the studied reports presented a treatment effect measure, hindering the understanding and dissemination of the findings. We also found that published trials often did not clearly describe missing data or sensitivity analyses for these missing data. Practice for secondary endpoints or observational studies may differ.