Research: An event or an environment?

A SuperLearner-enforced approach for the estimation of treatment effect in pediatric trials

Background

Randomized Clinical Trials (RCT) represent the gold standard among scientific evidence. RCTs are tailored to control selection bias and the confounding effect of baseline characteristics on the effect of treatment. However, trial conduction and enrolment procedures could be challenging, especially for rare diseases and paediatric research. In these research frameworks, the treatment effect estimation could be compromised. A potential countermeasure is to develop predictive models on the probability of the baseline disease based on previously collected observational data. Machine learning (ML) algorithms have recently become attractive in clinical research because of their flexibility and improved performance compared to standard statistical methods in developing predictive models.

Objective

This manuscript proposes an ML-enforced treatment effect estimation procedure based on an ensemble SuperLearner (SL) approach, trained on historical observational data, to control the confounding effect.

Methods

The REnal SCarring Urinary infEction trial served as a motivating example. Historical observational study data have been simulated through 10,000 Monte Carlo (MC) runs. Hypothetical RCTs have been also simulated, for each MC run, assuming different treatment effects of antibiotics combined with steroids. For each MC simulation, the SL tool has been applied to the simulated observational data. Furthermore, the average treatment effect (ATE), has been estimated on the trial data and adjusted for the SL predicted probability of renal scar.

Results

The simulation results revealed an increased power in ATE estimation for the SL-enforced estimation compared to the unadjusted estimates for all the algorithms composing the ensemble SL.

Handling Poor Accrual in Pediatric Trials: A Simulation Study Using a Bayesian Approach

In the conduction of trials, a common situation is related to potential difficulties in recruiting the planned sample size as provided by the study design. A Bayesian analysis of such trials might provide a framework to combine prior evidence with current evidence, and it is an accepted approach by regulatory agencies. However, especially for small trials, the Bayesian inference may be severely conditioned by the prior choices. The Renal Scarring Urinary Infection (RESCUE) trial, a pediatric trial that was a candidate for early termination due to underrecruitment, served as a motivating example to investigate the effects of the prior choices on small trial inference. The trial outcomes were simulated by assuming 50 scenarios combining different sample sizes and true absolute risk reduction (ARR). The simulated data were analyzed via the Bayesian approach using 0%, 50%, and 100% discounting factors on the beta power prior. An informative inference (0% discounting) on small samples could generate data-insensitive results. Instead, the 50% discounting factor ensured that the probability of confirming the trial outcome was higher than 80%, but only for an ARR higher than 0.17. A suitable option to maintain data relevant to the trial inference is to define a discounting factor based on the prior parameters. Nevertheless, a sensitivity analysis of the prior choices is highly recommended.

A community-academic partnership to explore informational needs of African American women as a primer for cancer clinical trial recruitment

Background: African Americans (AAs) are less likely to participate in cancer clinical trials (CCTs) despite experiencing disproportionately higher rates of cancer mortality. As a way to address these ongoing disparities, this study sought to qualitatively explore informational needs regarding CCTs among AA women and identify message considerations for educational information targeting AA women and their community. Methods: Three focus groups were conducted in which AA women viewed a DVD created as a decisional tool for CCT participation and provided feedback regarding content. Results: Results indicated general fear regarding CCTs, which is partially attributable to the impact of historic research abuses, lack of information regarding CCTs, and lack of cultural relevance of the education and outreach materials for AA communities. Recruitment of AAs to CCTs may be enhanced by educational and outreach approaches that increase awareness of CCTs as well as involvement of the AA community in developing such interventions. Conclusion: Interventions should include the perspectives of AA women, as key stakeholders and decision-makers for their family and provide research information in a multimedia format that will facilitate family discussion and decision-making regarding CCTs.