Chapter 3. The Challenges of Conducting Clinical Trials in Diseases with Small Target Populations

2014 ◽  
pp. 53-79
Author(s):  
Steven Arkin
Keyword(s):  
Clinical Trials ◽  
Small Target ◽  
Target Populations

scholarly journals Development of a Vaccine against Human Cytomegalovirus: Advances, Barriers, and Implications for the Clinical Practice

Vaccines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 551
Author(s):  
Sara Scarpini ◽  
Francesca Morigi ◽  
Ludovica Betti ◽  
Arianna Dondi ◽  
Carlotta Biagi ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Human Cytomegalovirus ◽  
Congenital Infection ◽  
Health Concern ◽  
Live Vaccines ◽  
Target Populations ◽  
Immune Correlates ◽  
Common Causes ◽  
The Status ◽  
Life Threatening

Human cytomegalovirus (hCMV) is one of the most common causes of congenital infection in the post-rubella era, representing a major public health concern. Although most cases are asymptomatic in the neonatal period, congenital CMV (cCMV) disease can result in permanent impairment of cognitive development and represents the leading cause of non-genetic sensorineural hearing loss. Moreover, even if hCMV mostly causes asymptomatic or pauci-symptomatic infections in immunocompetent hosts, it may lead to severe and life-threatening disease in immunocompromised patients. Since immunity reduces the severity of disease, in the last years, the development of an effective and safe hCMV vaccine has been of great interest to pharmacologic researchers. Both hCMV live vaccines—e.g., live-attenuated, chimeric, viral-based—and non-living ones—subunit, RNA-based, virus-like particles, plasmid-based DNA—have been investigated. Encouraging data are emerging from clinical trials, but a hCMV vaccine has not been licensed yet. Major difficulties in the development of a satisfactory vaccine include hCMV’s capacity to evade the immune response, unclear immune correlates for protection, low number of available animal models, and insufficient general awareness. Moreover, there is a need to determine which may be the best target populations for vaccine administration. The aim of the present paper is to examine the status of hCMV vaccines undergoing clinical trials and understand barriers limiting their development.

scholarly journals A Distribution-based Method for Assessing The Differences between Clinical Trial Target Populations and Patient Populations in Electronic Health Records

2014 ◽  
Vol 05 (02) ◽  
pp. 463-479 ◽  
Author(s):  
P. Ryan ◽  
Y. Zhang ◽  
F. Liu ◽  
J. Gao ◽  
J.T. Bigger ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Electronic Health Records ◽  
Real World ◽  
World Population ◽  
Health Records ◽  
Target Populations ◽  
Electronic Health

SummaryObjective: 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

scholarly journals Quantifying representativeness in randomized clinical trials using machine learning fairness metrics

JAMIA Open ◽  
2021 ◽  
Vol 4 (3) ◽  
Author(s):  
Miao Qi ◽  
Owen Cahan ◽  
Morgan A Foreman ◽  
Daniel M Gruen ◽  
Amar K Das ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Trials ◽  
Randomized Clinical Trials ◽  
Smoking Status ◽  
Binary Classification ◽  
Interactive Visualization ◽  
Community Based ◽  
Target Populations ◽  
Health And Nutrition

Abstract Objective We help identify subpopulations underrepresented in randomized clinical trials (RCTs) cohorts with respect to national, community-based or health system target populations by formulating population representativeness of RCTs as a machine learning (ML) fairness problem, deriving new representation metrics, and deploying them in easy-to-understand interactive visualization tools. Materials and Methods We represent RCT cohort enrollment as random binary classification fairness problems, and then show how ML fairness metrics based on enrollment fraction can be efficiently calculated using easily computed rates of subpopulations in RCT cohorts and target populations. We propose standardized versions of these metrics and deploy them in an interactive tool to analyze 3 RCTs with respect to type 2 diabetes and hypertension target populations in the National Health and Nutrition Examination Survey. Results We demonstrate how the proposed metrics and associated statistics enable users to rapidly examine representativeness of all subpopulations in the RCT defined by a set of categorical traits (eg, gender, race, ethnicity, smoking status, and blood pressure) with respect to target populations. Discussion The normalized metrics provide an intuitive standardized scale for evaluating representation across subgroups, which may have vastly different enrollment fractions and rates in RCT study cohorts. The metrics are beneficial complements to other approaches (eg, enrollment fractions) used to identify generalizability and health equity of RCTs. Conclusion By quantifying the gaps between RCT and target populations, the proposed methods can support generalizability evaluation of existing RCT cohorts. The interactive visualization tool can be readily applied to identified underrepresented subgroups with respect to any desired source or target populations.

P51 Interaction of epidemic and individual heterogeneity on target populations of clinical trials

1997 ◽  
Vol 18 (3) ◽  
pp. S141
Author(s):  
John A. Ioannidis ◽  
Joseph C. Cappelleri ◽  
Christopher H. Schmid ◽  
Joseph Lau
Keyword(s):  
Clinical Trials ◽  
Individual Heterogeneity ◽  
Target Populations

scholarly journals Quantifying representativeness in randomized clinical trials using machine learning fairness metrics

2021 ◽  
Author(s):  
Miao Qi ◽  
Owen Cahan ◽  
Morgan A. Foreman ◽  
Daniel M. Gruen ◽  
Amar K. Das ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Trials ◽  
Randomized Clinical Trials ◽  
Binary Classification ◽  
Community Based ◽  
Target Populations ◽  
Health And Nutrition ◽  
Visualization Tools ◽  
Race Ethnicity

Objective We formulate population representativeness of randomized clinical trials (RCTs) as a machine learning (ML) fairness problem, derive new representation metrics, and deploy them in visualization tools which help users identify subpopulations that are underrepresented in RCT cohorts with respect to national, community-based or health system target populations. Materials and Methods We represent RCT cohort enrollment as random binary classification fairness problems, and then show how ML fairness metrics based on enrollment fraction can be efficiently calculated using easily computed rates of subpopulations in RCT cohorts and target populations. We propose standardized versions of these metrics and deploy them in an interactive tool to analyze three RCTs with respect to type-2 diabetes and hypertension target populations in the National Health and Nutrition Examination Survey (NHANES). Results We demonstrate how the proposed metrics and associated statistics enable users to rapidly examine representativeness of all subpopulations in the RCT defined by a set of categorical traits (e.g., sex, race, ethnicity, smoker status, and blood pressure) with respect to target populations. Discussion The normalized metrics provide an intuitive standardized scale for evaluating representation across subgroups, which may have vastly different enrollment fractions and rates in RCT study cohorts. The metrics are beneficial complements to other approaches (e.g., enrollment fractions and GIST) used to identify generalizability and health equity of RCTs. Conclusion By quantifying the gaps between RCT and target populations, the proposed methods can support generalizability evaluation of existing RCT cohorts, enrollment target decisions for new RCTs, and monitoring of RCT recruitment, ultimately contributing to more equitable public health outcomes.

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