PMID- 26820188
OWN - NLM
STAT- MEDLINE
DCOM- 20170104
LR  - 20240325
IS  - 1532-0480 (Electronic)
IS  - 1532-0464 (Print)
IS  - 1532-0464 (Linking)
VI  - 60
DP  - 2016 Apr
TI  - Multivariate analysis of the population representativeness of related clinical 
      studies.
PG  - 66-76
LID - S1532-0464(16)00008-3 [pii]
LID - 10.1016/j.jbi.2016.01.007 [doi]
AB  - 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.
CI  - Copyright (c) 2016 Elsevier Inc. All rights reserved.
FAU - He, Zhe
AU  - He Z
AD  - Department of Biomedical Informatics, Columbia University, New York, NY 10032, 
      USA. Electronic address: zh2132@columbia.edu.
FAU - Ryan, Patrick
AU  - Ryan P
AD  - 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.
FAU - Hoxha, Julia
AU  - Hoxha J
AD  - Department of Biomedical Informatics, Columbia University, New York, NY 10032, 
      USA.
FAU - Wang, Shuang
AU  - Wang S
AD  - Department of Biostatistics, Columbia University, New York, NY 10032, USA.
FAU - Carini, Simona
AU  - Carini S
AD  - Department of Medicine, University of California, San Francisco, CA 94143, USA.
FAU - Sim, Ida
AU  - Sim I
AD  - Department of Medicine, University of California, San Francisco, CA 94143, USA.
FAU - Weng, Chunhua
AU  - Weng C
AD  - Department of Biomedical Informatics, Columbia University, New York, NY 10032, 
      USA; Observational Health Data Sciences and Informatics, New York, NY 10032, USA.
LA  - eng
GR  - R01 LM009886/LM/NLM NIH HHS/United States
GR  - UL1 TR000040/TR/NCATS NIH HHS/United States
GR  - R01LM009886/LM/NLM NIH HHS/United States
GR  - UL1TR000040/TR/NCATS NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20160125
PL  - United States
TA  - J Biomed Inform
JT  - Journal of biomedical informatics
JID - 100970413
SB  - IM
MH  - *Algorithms
MH  - Biomedical Research/*standards
MH  - Clinical Trials as Topic
MH  - Databases, Factual
MH  - Demography/*methods
MH  - Diabetes Mellitus, Type 2
MH  - Humans
MH  - Medical Informatics Computing
MH  - Multivariate Analysis
MH  - Nutrition Surveys
MH  - Observational Studies as Topic
MH  - Patient Selection
MH  - *Selection Bias
PMC - PMC4837055
MID - NIHMS754593
OTO - NOTNLM
OT  - Clinical trial
OT  - Knowledge representation
OT  - Selection bias
COIS- COMPETING INTERESTS None.
EDAT- 2016/01/29 06:00
MHDA- 2017/01/05 06:00
PMCR- 2017/04/01
CRDT- 2016/01/29 06:00
PHST- 2015/07/11 00:00 [received]
PHST- 2016/01/15 00:00 [revised]
PHST- 2016/01/19 00:00 [accepted]
PHST- 2016/01/29 06:00 [entrez]
PHST- 2016/01/29 06:00 [pubmed]
PHST- 2017/01/05 06:00 [medline]
PHST- 2017/04/01 00:00 [pmc-release]
AID - S1532-0464(16)00008-3 [pii]
AID - 10.1016/j.jbi.2016.01.007 [doi]
PST - ppublish
SO  - J Biomed Inform. 2016 Apr;60:66-76. doi: 10.1016/j.jbi.2016.01.007. Epub 2016 Jan 
      25.