Summary
Objective: To improve the transparency of clinical trial generalizability and to illustrate
the method using Type 2 diabetes as an example.
Methods: Our data included 1,761 diabetes clinical trials and the electronic health records
(EHR) of 26,120 patients with Type 2 diabetes who visited Columbia University Medical
Center of New-York Presbyterian Hospital. The two populations were compared using
the Generalizability Index for Study Traits (GIST) on the earliest diagnosis age and
the mean hemoglobin A1c (HbA1c ) values.
Results: Greater than 70% of Type 2 diabetes studies allow patients with HbA1c measures between 7 and 10.5, but less than 40% of studies allow HbA1c <7 and fewer than 45% of studies allow HbA1c >10.5. In the real-world population, only 38% of patients had HbA1c between 7 and 10.5, with 12% having values above the range and 52% having HbA1c <7. The GIST for HbA1c was 0.51. Most studies adopted broad age value ranges, with the most common restrictions
excluding patients >80 or <18 years. Most of the real-world population fell within
this range, but 2% of patients were <18 at time of first diagnosis and 8% were >80.
The GIST for age was 0.75. Conclusions: We contribute a scalable method to profile and compare aggregated clinical trial
target populations with EHR patient populations. We demonstrate that Type 2 diabetes
studies are more generalizable with regard to age than they are with regard to HbA1c . We found that the generalizability of age increased from Phase 1 to Phase 3 while
the generalizability of HbA1c decreased during those same phases. This method can generalize to other medical conditions
and other continuous or binary variables. We envision the potential use of EHR data
for examining the generaliz-ability of clinical trials and for defining population-representative
clinical trial eligibility criteria.
Citation: Weng C, Li Y, Ryan P, Zhang Y, Liu F, Gao J, Bigger JT, Hripcsak G. A distribution-based
method for assessing the differences between clinical trial target populations and
patient populations in electronic health records. Appl Clin Inf 2014; 5: 463–479 http://dx.doi.org/10.4338/ACI-2013-12-RA-0105
Keywords Clinical trials - selection bias - comparative effectiveness research - electronic
health records - clinical research informatics - meta-analysis
