Reporting of numerical and statistical differences in abstracts: improving but not optimal.
OBJECTIVE
The reporting of relative risk reductions (RRRs) or absolute risk reductions (ARRs) to quantify binary outcomes in trials engenders differing perceptions of therapeutic efficacy, and the merits of P values versus confidence intervals (CIs) are also controversial. We describe the manner in which numerical and statistical difference in treatment outcomes is presented in published abstracts.


METHODS
A descriptive study of abstracts published in 1986 and 1996 in 8 general medical and specialty journals.


METHODS
controlled, intervention trials with a binary primary or secondary outcome. Seven items were recorded: raw data (outcomes for each treatment arm), measure of relative difference (e.g., RRR), ARR, number needed to treat, P value, CI, and verbal statement of statistical significance. The prevalence of these items was compared between journals and across time.


RESULTS
Of 5,293 abstracts, 300 met the inclusion criteria. In 1986, 60% of abstracts did not provide both the raw data and a corresponding P value or CI, while 28% failed to do so in 1Dr. Hux is a Career Scientist of the Ontario Ministry of Health and receives salary support from the Institute for Clinical Evaluative Sciences in Ontario.996 ( P <.001; RRR of 53%; ARR of 32%; CI for ARR 21% to 43%). The variability between journals was highly significant ( P <.001). In 1986, 100% of abstracts lacked a measure of absolute difference while 88% of 1996 abstracts did so ( P <.001). In 1986, 98% of abstracts lacked a CI while 65% of 1996 abstracts did so ( P <.001).


CONCLUSIONS
The provision of quantitative outcome and statistical quantitative information has significantly increased between 1986 and 1996. However, further progress can be made to make abstracts more informative.
Dryver
Eric
E
Department of Medicine, University of Toronto, Ontario, Canada.
Hux
Janet E
JE
D000043:Abstracting and Indexing / Q000379:methods / Q000592:standards* / Q000706:statistics & numerical data*; D010506:Periodicals as Topic; D011786:Quality Control