Abstract
Purpose: Ideally, the objectives of a pharmacoepidemiologic comparative effectiveness or safety study should dictate its design and data analysis. This paper discusses how defining an estimand is instrumental to this process. Methods: We applied the ICH-E9 (Statistical Principles for Clinical Trials) R1 addendum on estimands – which originally focused on randomized trials – to three examples of observational pharmacoepidemiologic comparative effectiveness and safety studies. Five key elements specify the estimand: the population, contrasted treatments, endpoint, intercurrent events, and population-level summary measure. Results: Different estimands were defined for case studies representing three types of pharmacological treatments: (1) single-dose treatments using a case study about the effect of influenza vaccination versus no vaccination on mortality risk in an adult population of ≥60 years of age; (2) sustained-treatments using a case study about the effect of dipeptidyl peptidase 4 inhibitor versus glucagon-like peptide-1 agonist on hypoglycemia risk in treatment of uncontrolled diabetes; and (3) as needed treatments using a case study on the effect of nitroglycerin spray as-needed versus no nitroglycerin on syncope risk in treatment of stabile angina pectoris. Conclusions: The case studies illustrated that a seemingly clear research question can still be open to multiple interpretations. Defining an estimand ensures that the study targets a treatment effect that aligns with the treatment decision the study aims to inform. Estimand definitions further help to inform choices regarding study design and data-analysis and clarify how to interpret study findings.
| Original language | English |
|---|---|
| Pages (from-to) | 863-872 |
| Number of pages | 10 |
| Journal | Pharmacoepidemiology and Drug Safety |
| Volume | 32 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - Aug 2023 |
Keywords
- causal inference
- comparative effectiveness and safety research
- estimand
- ICH-E9(R1) addendum
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