Machine learning-guided intervention trials to predict treatment response at an individual patient level: an important second step following randomized clinical trials

2018 ◽  
Vol 25 (4) ◽  
pp. 701-702 ◽  
Author(s):  
Ives Cavalcante Passos ◽  
Benson Mwangi
Keyword(s):  
Machine Learning ◽  
Clinical Trials ◽  
Treatment Response ◽  
Randomized Clinical Trials ◽  
Second Step ◽  
Individual Patient Level ◽  
Patient Level ◽  
Intervention Trials ◽  
Predict Treatment Response

scholarly journals MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrea Delli Pizzi ◽  
Antonio Maria Chiarelli ◽  
Piero Chiacchiaretta ◽  
Martina d’Annibale ◽  
Pierpaolo Croce ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rectal Cancer ◽  
Treatment Response ◽  
Randomized Clinical Trials ◽  
Locally Advanced ◽  
Neoadjuvant Treatment ◽  
Learning Model ◽  
Partial Least Square ◽  
Machine Learning Model ◽  
Pre Treatment

AbstractNeoadjuvant chemo-radiotherapy (CRT) followed by total mesorectal excision (TME) represents the standard treatment for patients with locally advanced (≥ T3 or N+) rectal cancer (LARC). Approximately 15% of patients with LARC shows a complete response after CRT. The use of pre-treatment MRI as predictive biomarker could help to increase the chance of organ preservation by tailoring the neoadjuvant treatment. We present a novel machine learning model combining pre-treatment MRI-based clinical and radiomic features for the early prediction of treatment response in LARC patients. MRI scans (3.0 T, T2-weighted) of 72 patients with LARC were included. Two readers independently segmented each tumor. Radiomic features were extracted from both the “tumor core” (TC) and the “tumor border” (TB). Partial least square (PLS) regression was used as the multivariate, machine learning, algorithm of choice and leave-one-out nested cross-validation was used to optimize hyperparameters of the PLS. The MRI-Based “clinical-radiomic” machine learning model properly predicted the treatment response (AUC = 0.793, p = 5.6 × 10–5). Importantly, the prediction improved when combining MRI-based clinical features and radiomic features, the latter extracted from both TC and TB. Prospective validation studies in randomized clinical trials are warranted to better define the role of radiomics in the development of rectal cancer precision medicine.

Evidence of bias in randomized clinical trials of hepatitis C interferon therapies

2017 ◽  
Vol 14 (5) ◽  
pp. 483-488
Author(s):  
Fabio Tinè ◽  
Massimo Attanasio ◽  
Vito M R Muggeo ◽  
Ciprian M. Crainiceanu
Keyword(s):  
Clinical Trials ◽  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Randomized Clinical Trials ◽  
Experimental Treatment ◽  
Success Rates ◽  
Patient Level ◽  
Virus Rna ◽  
Experimental Therapies ◽  
Meta Regression

Introduction: Bias may occur in randomized clinical trials in favor of the new experimental treatment because of unblinded assessment of subjective endpoints or wish bias. Using results from published trials, we analyzed and compared the treatment effect of hepatitis C antiviral interferon therapies experimental or control. Methods: Meta-regression of trials enrolling naïve hepatitis C virus patients that underwent four therapies including interferon alone or plus ribavirin during past years. The outcome measure was the sustained response evaluated by transaminases and/or hepatitis C virus-RNA serum load. Data on the outcome across therapies were collected according to the assigned arm (experimental or control) and to other trial and patient-level characteristics. Results: The overall difference in efficacy between the same treatment labeled experimental or control had a mean of +11.9% (p < 0.0001). The unadjusted difference favored the experimental therapies of group IFN-1 (+6%) and group IFN-3 (+10%), while there was no difference for group IFN-2 because of success rates from large multinational trials. In a meta-regression model with trial-specific random effects including several trial and patient-level variables, treatment and arm type remained significant (p < 0.0001 and p = 0.0009 respectively) in addition to drug-schedule-related variables. Conclusion: Our study indicates the same treatment is more effective when labeled “experimental” compared to when labeled “control” in a setting of trials using an objective endpoint and even after adjusting for patient and study-level characteristics. We discuss several factors related to design and conduct of hepatitis C trials as potential explanations of the bias toward the experimental treatment.

scholarly journals Maintaining Integrity of Worldwide FINGER Clinical Trials During the COVID-19 Pandemic

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 68-68
Author(s):  
Susanne Roehr
Keyword(s):  
Clinical Trials ◽  
Study Design ◽  
World Wide ◽  
Randomized Clinical Trials ◽  
Statistical Design ◽  
Sensitivity Analyses ◽  
Intervention Delivery ◽  
Intention To Treat ◽  
Approaches To Study ◽  
Intervention Trials

Abstract The COVID-19 pandemic presents challenges to the conduct of randomized clinical trials of lifestyle interventions. World-Wide FINGERS international network convened a forum for researchers to discuss statistical design and analysis issues they faced during the pandemic. We report experiences of three trials that, at various stages of conduct, altered designs and analysis plans to navigate these issues. We provide recommendations for future trials to consider as they develop and launch behavioral intervention trials. The pandemic led researchers to change recruitment plans, interrupt timelines for assessments and intervention delivery, and move to remote intervention and assessments protocols. The necessity of these changes add emphasis to the importance, in study design and analysis, of intention to treat approaches, flexibility, within site stratification, interim power projections, and sensitivity analyses. Robust approaches to study design and analysis are critical to negotiate issues related to the intervention.

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.

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