Statistical inference and effect measures in abstracts of major HIV and AIDS journals, 1987–2022: A systematic review - Andreas Stang; Henning Schäfer; Ahmad Idrissi-Yaghir; Christoph M. Friedrich; Matthew P. Fox (Global Epidemiology)
Objectives: With the emergence of HIV/AIDS journals, the development of the reporting of statistical inference and effect measures in published abstracts can be examined from the beginning in a new field. The aim of this study was to describe time trends of statistical inference and effect measure reporting of major HIV/AIDS journals Methods: We included 10 major HIV/AIDS journals and analyzed all available PubMed entries for the period 1987 through 2022. We applied rule-based text mining and machine learning methodology to detect the presence of confidence intervals, numerical p-values or comparisons of p-values with thresholds, language describing statistical significance, and effect measures for dichotomous outcomes Results: Among 41,730 PubMed entries from the major HIV/AIDS journals, 31,665 contained an abstract. In the early years, most abstracts reporting statistical inference contained only significance terminology without confidence intervals and p-values. From 1988 to 2005, each year 30 % of all abstracts contained p-values without confidence intervals. Thereafter, this reporting style continued to decline. The reporting of confidence intervals increased steadily from 1988 (11 %) to 2022 (56 %). Of the 17 % of abstracts in 2017–2022 that included any effect measure, half reported odds ratios (51 %), followed by hazard ratios (28 %) and risk ratios (16 %). Difference measures and number needed to treat or harm were very uncommon Conclusions: Within the HIV/AIDS literature, there has been widespread use of confidence intervals. Most of the journals that we reviewed had a decrease in reporting only statistical significance without confidence intervals over time