Table 1 shows the mean (± standard deviation, SD) year of publication, reported effect size, baseline value, and study size for each of the four investigated medical therapies.
For Pravastatin other variables did not add more than 0.68percentt when entered in the model, for Timolol "baseline value" added 3.93 and "study size" 2.48percentt.
Our study was limited in that we did not explore if parameters other than baseline value, treatment group and study size contribute to the decrease of the reported effect size over time.
The relationship between baseline and change in endpoints, and study size versus year of publication, was assessed using linear regression.
For this, we performed a multiple regression analysis with reported effect size as the outcome variable and publication year, baseline, study size, and treatment group as possible predictors.
The primary outcome variable (reported effect size) and the secondary outcome variables (baseline, study size and treatment group) were regressed against the publication year.
An example bubble plot was produced to display the evidence-map data and visualize research gaps according to four parameters: comparison types, population baseline health status, outcome groups, and study sample size.
It was not associated with age, sex, or race, but with year of study initiation, pain baseline, and effect size in active drug groups in both diseases.
The beneficial effects of allowing 0 –90 day comorbidity capture were to enhance the study size and to expand the breadth of baseline health status in the subject pool.
Total study size = no. of patients with valid baseline and follow-up SF-6D score and follow-up global change score.
