Dr. Ali Jalali, an assistant professor of population health sciences at Weill Cornell Medicine, was awarded the prestigious National Institutes of Health (NIH) Director’s New Innovator Award through its Helping to End Addiction Long-term (HEAL) Initiative. This highly competitive award supports exceptionally creative, early-stage investigators doing cutting-edge research with the potential to impact significant scientific challenges.
The $2.5 million grant over three years will support Dr. Jalali’s work which is addressing the difficulties of analyzing data from randomized controlled trials (RCTs) that evaluate opioid use disorder (OUD) treatment interventions.
“My research is focused on building new methods to improve the validity of often incomplete and inconsistent RCT data collected in clinically complex settings like jails, prisons and emergency departments,” Dr. Jalali explained. “The goal is to make more accurate assessment of which interventions are most effective and cost-effective in treating OUD in such high-risk environments.”
When Randomized Controlled Trials are Inconclusive
RCTs are studies that compare the effectiveness of different treatments or interventions by randomly assigning participants to groups, one of which is a control—the group that is untreated or receiving an established regimen. The groups are followed over time to uncover cause and effect relationships.
While RCTs are considered the gold standard for identifying the best treatments and guiding clinical and health policy decisions, they can be affected by issues that can lead to inconclusive results when using standard data analysis methods. These issues include failure to enroll enough participants in the study to evaluate clinical and economic endpoints; high rates of participant attrition or loss to follow-up correlated with economic endpoints (e.g., due to incarceration during the study); and inconsistencies in adhering to study protocols among participants randomized to different types of OUD medications.
As an alternative, the NIH and the Food and Drug Administration (FDA) have recognized the value of real-world evidence (RWE) study designs that use observational data from healthcare claims, electronic medical records and other sources to answer research questions that are difficult or impossible to implement in RCTs. However, RWE studies aren’t ideal due to their observational nature and potential for extraneous effects that cannot be accounted for.
Data Recycling May Provide Answers
The overarching goal of Dr. Jalali’s proposal is to pioneer an unconventional approach of integrating RWE information and data sources to enhance the precision and validity of RCTs. He revisited RCTs that evaluated OUD treatment interventions and prevention strategies, but originally generated inconclusive results.
“You can think of it as a form of data recycling,” Dr. Jalali said. “Inconclusive RCTs are considered a form of research waste. We collect this `waste,’ combine it with appropriate RWE data sources, and test whether we can generate reliable, causal conclusions to inform health policy.”
By leveraging the strengths of RCT research designs and depth of RWE data, this project aims to generate more reliable and actionable evidence for health policy and clinical decision-makers. Moreover, such an approach will take advantage of data that has already been collected, minimize the need to design and implement new and costly clinical trials and thereby reduce research waste. While his research primarily focuses on medication linkage and treatment strategies for OUD, Dr. Jalali’s work will also be applicable to other complex and chronic disorders that face similar data analytic challenges.
“This novel approach has the potential to address the pressing public health need to identify and implement cost-effective and sustainable strategies for combating public health emergencies such as the opioid epidemic,” Dr. Jalali concluded.
This article originally appeared in the WCM Newsroom.
