Determinants of patient recruitment in a multicenter clinical trials group: trends, seasonality and the effect of large studies.
BACKGROUND
We examined whether quarterly patient enrollment in a large multicenter clinical trials group could be modeled in terms of predictors including time parameters (such as long-term trends and seasonality), the effect of large trials and the number of new studies launched each quarter. We used the database of all clinical studies launched by the AIDS Clinical Trials Group (ACTG) between October 1986 and November 1999. Analyses were performed in two datasets: one included all studies and substudies (n = 475, total enrollment 69,992 patients) and the other included only main studies (n = 352, total enrollment 57,563 patients).


RESULTS
Enrollment differed across different months of the year with peaks in spring and late fall. Enrollment accelerated over time (+27 patients per quarter for all studies and +16 patients per quarter for the main studies, p < 0.001) and was affected by the performance of large studies with target sample size > 1,000 (p < 0.001). These relationships remained significant in multivariate autoregressive modeling. A time series based on enrollment during the first 32 quarters could forecast adequately the remaining 21 quarters.


CONCLUSIONS
The fate and popularity of large trials may determine the overall recruitment of multicenter groups. Modeling of enrollment rates may be used to comprehend long-term patterns and to perform future strategic planning.
Haidich
A B
AB
Clinical Trials and Evidence-Based Medicine Unit, Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece. me00409@cc.uoi.gr
Ioannidis
J P
JP
D002986:Clinical Trials as Topic / Q000639:trends*; D015981:Epidemiologic Factors; D005069:Evaluation Studies as Topic; D006801:Humans; D015233:Models, Statistical; D015337:Multicenter Studies as Topic / Q000639:trends*; D015999:Multivariate Analysis; D018579:Patient Selection; D011237:Predictive Value of Tests; D012044:Regression Analysis; D012621:Seasons; D013995:Time