Learning which antipsychotic treatments work best for older adults with schizophrenia
Robust Learning Approaches for Assessing Effects and Effect Heterogeneity of Real World Antipsychotic Treatment Regimes in Elderly Persons with Schizophrenia
Using real-world medical records and advanced computer methods to learn which antipsychotic drugs, combinations, and treatment patterns are safest and most helpful for older adults with schizophrenia.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Harvard Medical School NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project ID | NIH-11235193 on NIH RePORTER |
What this research studies
Researchers will analyze large linked healthcare datasets from racially and socially diverse older adults with schizophrenia who receive antipsychotic medications. They will apply modern machine-learning and causal inference methods to compare different drugs, sequences, combinations, and dosing patterns over time. The team will also connect patients' records to neighborhood-level measures like income and crime to see how social context affects adherence, safety, and benefits. The goal is to identify which treatments work best for which groups and under what circumstances.
Who could benefit from this research
Good fit: Older adults (typically age 65 and up) with a clinical diagnosis of schizophrenia who receive antipsychotic medications and whose care is captured in large U.S. health or public insurance databases would be the people represented by this work.
Not a fit: Younger people, individuals without schizophrenia, or those whose care is not included in the linked health databases (for example only privately insured or uninsured patients whose records aren't available) are unlikely to be represented or directly benefit from this project.
Why it matters
Potential benefit: Could help clinicians choose safer, more effective antipsychotic approaches tailored to older patients' health profiles and social circumstances.
How similar studies have performed: Prior studies using claims and electronic health records have provided useful safety and effectiveness signals, but this project applies newer machine-learning and causal methods to compare multiple treatment patterns and social-context effects, making it more advanced and partly novel.
Where this research is happening
Boston, United States
- Harvard Medical School — Boston, United States (Active)
Researchers
- Principal investigator: Normand, Sharon-Lise Teresa — Harvard Medical School
- Study coordinator: Normand, Sharon-Lise Teresa
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.