Observational Study Design AI Prompt

Design an observational study and plan the appropriate analysis to control for confounding.

Exposure of interest: {{exposure}}
Outcome of interest: {{outcome}}
Available data: {{data_description}}
Study type: {{study_type}} (cross-sectional, case-control, cohort)

1. Study design selection:

   Cross-sectional:
   - Exposure and outcome measured at the same time
   - Pros: fast, cheap, good for prevalence estimation
   - Cons: cannot establish temporality; prone to reverse causation
   - Best for: estimating associations and generating hypotheses

   Case-control:
   - Sample based on outcome (cases vs controls), then measure past exposure
   - Pros: efficient for rare outcomes
   - Cons: recall bias; selection of controls is critical
   - Analysis: conditional or unconditional logistic regression; effect measure = odds ratio

   Prospective cohort:
   - Sample based on exposure status, follow forward to measure outcomes
   - Pros: can measure incidence, multiple outcomes, avoids recall bias
   - Cons: expensive, slow; loss to follow-up threatens validity
   - Analysis: survival analysis (Cox model), incidence rate ratio; effect measure = hazard ratio, RR

   Retrospective cohort:
   - Historical data used to construct a cohort; follow forward in time using existing records
   - Faster than prospective; subject to data quality of historical records

2. Confounding control methods:

   Design-stage:
   - Restriction: limit study to a homogeneous subgroup (removes confounding from that variable)
   - Matching: match cases and controls (or exposed and unexposed) on potential confounders
     - Advantage: guaranteed balance; disadvantage: cannot study matched variables as exposures

   Analysis-stage:
   - Multivariable regression: include confounders as covariates
   - Propensity score methods (see propensity score prompt)
   - Stratification: estimate effect within strata of the confounder, then pool with Mantel-Haenszel

3. Bias assessment:
   - Selection bias: is the study sample representative of the target population?
   - Information bias: are exposure and outcome measured accurately?
   - Confounding: have all major confounders been measured and controlled?
   - Use a directed acyclic graph (DAG) to identify the minimal sufficient adjustment set

4. Directed Acyclic Graph (DAG):
   - Draw the causal diagram: nodes = variables, arrows = direct causal effects
   - Identify confounders: common causes of exposure and outcome
   - Identify colliders: common effects of two variables (do NOT adjust for colliders — this opens a non-causal path)
   - Use the backdoor criterion to identify the adjustment set

Return: study design recommendation, confounding control plan, DAG specification, and bias assessment.