Assessing heterogeneity of treatment effect in a clinical trial with the proportional interactions model

2013 ◽  
Vol 32 (28) ◽  
pp. 4906-4923 ◽  
Author(s):  
Stephanie A. Kovalchik ◽  
Ravi Varadhan ◽  
Carlos O. Weiss
Keyword(s):  
Clinical Trial ◽  
Treatment Effect ◽  
Heterogeneity Of Treatment Effect

scholarly journals Explainable AI enables clinical trial patient selection to retrospectively improve treatment effects in schizophrenia

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Monika S. Mellem ◽  
Matt Kollada ◽  
Jane Tiller ◽  
Thomas Lauritzen
Keyword(s):  
Clinical Trial ◽  
Patient Selection ◽  
Treatment Effect ◽  
Controlled Clinical Trial ◽  
Learning Approaches ◽  
Full Sample ◽  
Cohen’S D ◽  
Explainable Ai ◽  
Cohen's D ◽  
Analytical Approaches

Abstract Background Heterogeneity among patients’ responses to treatment is prevalent in psychiatric disorders. Personalized medicine approaches—which involve parsing patients into subgroups better indicated for a particular treatment—could therefore improve patient outcomes and serve as a powerful tool in patient selection within clinical trials. Machine learning approaches can identify patient subgroups but are often not “explainable” due to the use of complex algorithms that do not mirror clinicians’ natural decision-making processes. Methods Here we combine two analytical approaches—Personalized Advantage Index and Bayesian Rule Lists—to identify paliperidone-indicated schizophrenia patients in a way that emphasizes model explainability. We apply these approaches retrospectively to randomized, placebo-controlled clinical trial data to identify a paliperidone-indicated subgroup of schizophrenia patients who demonstrate a larger treatment effect (outcome on treatment superior than on placebo) than that of the full randomized sample as assessed with Cohen’s d. For this study, the outcome corresponded to a reduction in the Positive and Negative Syndrome Scale (PANSS) total score which measures positive (e.g., hallucinations, delusions), negative (e.g., blunted affect, emotional withdrawal), and general psychopathological (e.g., disturbance of volition, uncooperativeness) symptoms in schizophrenia. Results Using our combined explainable AI approach to identify a subgroup more responsive to paliperidone than placebo, the treatment effect increased significantly over that of the full sample (p < 0.0001 for a one-sample t-test comparing the full sample Cohen’s d = 0.82 and a generated distribution of subgroup Cohen’s d’s with mean d = 1.22, std d = 0.09). In addition, our modeling approach produces simple logical statements (if–then-else), termed a “rule list”, to ease interpretability for clinicians. A majority of the rule lists generated from cross-validation found two general psychopathology symptoms, disturbance of volition and uncooperativeness, to predict membership in the paliperidone-indicated subgroup. Conclusions These results help to technically validate our explainable AI approach to patient selection for a clinical trial by identifying a subgroup with an improved treatment effect. With these data, the explainable rule lists also suggest that paliperidone may provide an improved therapeutic benefit for the treatment of schizophrenia patients with either of the symptoms of high disturbance of volition or high uncooperativeness. Trial Registration: clincialtrials.gov identifier: NCT 00,083,668; prospectively registered May 28, 2004

scholarly journals Competing risk and heterogeneity of treatment effect in clinical trials

Trials ◽  
2008 ◽  
Vol 9 (1) ◽  
Author(s):  
David M Kent ◽  
Alawi Alsheikh-Ali ◽  
Rodney A Hayward
Keyword(s):  
Clinical Trials ◽  
Treatment Effect ◽  
Competing Risk ◽  
Heterogeneity Of Treatment Effect

scholarly journals Estimands: bringing clarity and focus to research questions in clinical trials

BMJ Open ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. e052953
Author(s):  
Timothy Peter Clark ◽  
Brennan C Kahan ◽  
Alan Phillips ◽  
Ian White ◽  
James R Carpenter
Keyword(s):  
Clinical Trial ◽  
Sensitivity Analysis ◽  
Clinical Trials ◽  
Clinical Research ◽  
Recent Work ◽  
Treatment Effect ◽  
Research Question ◽  
Probability Of Success ◽  
Research Questions

Precise specification of the research question and associated treatment effect of interest is essential in clinical research, yet recent work shows that they are often incompletely specified. The ICH E9 (R1) Addendum on Estimands and Sensitivity Analysis in Clinical Trials introduces a framework that supports researchers in precisely and transparently specifying the treatment effect they aim to estimate in their clinical trial. In this paper, we present practical examples to demonstrate to all researchers involved in clinical trials how estimands can help them to specify the research question, lead to a better understanding of the treatment effect to be estimated and hence increase the probability of success of the trial.

Sign in / Sign up

Export Citation Format

Share Document