Multivariate analysis of the population representativeness of related clinical studies

Zhe He; Patrick Ryan; Julia Hoxha; Shuang Wang; Simona Carini; Ida Sim; Chunhua Weng

doi:10.1016/j.jbi.2016.01.007

Multivariate analysis of the population representativeness of related clinical studies

J Biomed Inform. 2016 Apr:60:66-76. doi: 10.1016/j.jbi.2016.01.007. Epub 2016 Jan 25.

Authors

Zhe He¹, Patrick Ryan², Julia Hoxha³, Shuang Wang⁴, Simona Carini⁵, Ida Sim⁵, Chunhua Weng⁶

Affiliations

¹ Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. Electronic address: zh2132@columbia.edu.
² Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA; Janssen Research and Development, Titusville, NJ 08560, USA; Observational Health Data Sciences and Informatics, New York, NY 10032, USA.
³ Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.
⁴ Department of Biostatistics, Columbia University, New York, NY 10032, USA.
⁵ Department of Medicine, University of California, San Francisco, CA 94143, USA.
⁶ Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA; Observational Health Data Sciences and Informatics, New York, NY 10032, USA.

Abstract

Objective: To develop a multivariate method for quantifying the population representativeness across related clinical studies and a computational method for identifying and characterizing underrepresented subgroups in clinical studies.

Methods: We extended a published metric named Generalizability Index for Study Traits (GIST) to include multiple study traits for quantifying the population representativeness of a set of related studies by assuming the independence and equal importance among all study traits. On this basis, we compared the effectiveness of GIST and multivariate GIST (mGIST) qualitatively. We further developed an algorithm called "Multivariate Underrepresented Subgroup Identification" (MAGIC) for constructing optimal combinations of distinct value intervals of multiple traits to define underrepresented subgroups in a set of related studies. Using Type 2 diabetes mellitus (T2DM) as an example, we identified and extracted frequently used quantitative eligibility criteria variables in a set of clinical studies. We profiled the T2DM target population using the National Health and Nutrition Examination Survey (NHANES) data.

Results: According to the mGIST scores for four example variables, i.e., age, HbA1c, BMI, and gender, the included observational T2DM studies had superior population representativeness than the interventional T2DM studies. For the interventional T2DM studies, Phase I trials had better population representativeness than Phase III trials. People at least 65years old with HbA1c value between 5.7% and 7.2% were particularly underrepresented in the included T2DM trials. These results confirmed well-known knowledge and demonstrated the effectiveness of our methods in population representativeness assessment.

Conclusions: mGIST is effective at quantifying population representativeness of related clinical studies using multiple numeric study traits. MAGIC identifies underrepresented subgroups in clinical studies. Both data-driven methods can be used to improve the transparency of design bias in participation selection at the research community level.

Keywords: Clinical trial; Knowledge representation; Selection bias.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Algorithms*
Biomedical Research / standards*
Clinical Trials as Topic
Databases, Factual
Demography / methods*
Diabetes Mellitus, Type 2
Humans
Medical Informatics Computing
Multivariate Analysis
Nutrition Surveys
Observational Studies as Topic
Patient Selection
Selection Bias*

Abstract

Publication types

MeSH terms

Grants and funding