| Definition |
Population-averaged treatment effects, computed by averaging predictions across a sample (e.g., risk differences, odds ratios). |
Effects estimated conditional on specific covariate values, accounting for patient-specific characteristics. |
| Generalizability |
Limited; assumes trial sample is a random sample from the target population, which is rarely true in RCTs. |
More generalizable; adjusts for covariates, reducing bias from non-representative samples. |
| Clinical Relevance |
Less relevant for individual patients; obscures heterogeneity (e.g., risk magnification in high-risk groups). |
More relevant; provides patient-specific estimates, closer to crossover study insights. |
| Robustness |
Perceived as robust to model misspecification but sensitive to sampling assumptions, leading to potential bias. |
Slightly sensitive to model misspecification but typically more robust due to covariate adjustment. |
| Statistical Power |
Lower power; unadjusted models ignore outcome heterogeneity. |
Higher power; covariate adjustment accounts for heterogeneity, improving precision. |
| Interpretation |
Simpler for population-level summaries but harder in nonlinear models (e.g., logistic regression). |
More complex but directly interpretable for specific patient profiles. |
| Harrell’s Recommendation |
Avoid one-number summaries (e.g., marginal odds ratios); report distribution of unit-level differences. |
Preferred approach; use tools like R rms package for robust modeling and visualization. |
| Example Application |
Estimating average risk difference in an RCT without covariate adjustment. |
Using ANCOVA or logistic regression with covariates to estimate treatment effects. |