Research Design Consultation
A Devil's Advocate AI that stress-tests your study design, exposes hidden biases, and recommends the most rigorous methodology — from RCTs to Target Trial Emulation.
- Fill in your research question, constraints, and available resources.
- Click AI Run — receive tailored research design recommendations directly in the chat.
- Discuss trade-offs or explore alternative designs through follow-up questions.
Selecting the right study design is one of the most technically demanding aspects of clinical research planning. The wrong design can introduce irreducible biases that make a study's conclusions unreliable regardless of how well the data collection is executed. Conversely, choosing a design more complex than necessary wastes resources and may impose unnecessary patient burden. Most investigators, including experienced clinicians, have gaps in their formal training on modern causal inference methods — gaps that this tool is designed to bridge.
This tool functions as a "Devil's Advocate" research design consultant. Rather than simply validating the investigator's initial instincts, it systematically stress-tests the proposed design by identifying threats to internal validity, sources of bias, confounding structures, and inferential limitations. This adversarial approach — modeled on the rigorous peer review process — surfaces problems while they can still be corrected, before the study begins.
The tool covers the full spectrum of clinical study designs: observational designs (cross-sectional, case-control, retrospective and prospective cohort), interventional designs (pilot trials, pragmatic RCTs, cluster RCTs, platform trials), and modern causal inference approaches including Target Trial Emulation (TTE) for situations where RCT data is unavailable but observational data must approximate a hypothetical trial. TTE is increasingly recognized as the methodological gold standard for extracting causal estimates from electronic health records.
For each design recommendation, the tool provides bias risk assessment aligned with standard frameworks (Newcastle-Ottawa for observational studies, Cochrane RoB 2 for trials), explains which confounders must be measured and controlled, and identifies which statistical methods are appropriate — with explicit warnings against outdated approaches like stepwise variable selection that inflate Type I error.
The available resources input (patient data access, funding, timeline, staffing) is critical: it prevents the AI from recommending a five-year prospective RCT to an investigator with six months and a single research assistant. The tool consistently recommends the most rigorous design that is achievable within the stated constraints.
After receiving the design recommendation, users are encouraged to consult with an institutional biostatistician for formal sample size calculation and analysis plan finalization before submitting a protocol or ethics application.