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Tailoring treatments using treatment effect modification
A F Schmidt1,2,3,4, O H Klungel1,2, M Nielen3
1Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands.
Background And Objective:
Applying results from clinical studies to individual patients can be a difficult process. Using the concept of treatment effect modification (also referred to as interaction), defined as a difference in treatment response between patient groups, we discuss whether and how treatment effects can be tailored to better meet patients' needs.
Results:
First we argue that contrary to how most studies are designed, treatment effect modification should be expected. Second, given this expected heterogeneity, a small number of clinically relevant subgroups should be a priori selected, depending on the expected magnitude of effect modification, and prevalence of the patient type. Third, by defining generalizability as the absence of treatment effect modification we show that generalizability can be evaluated within the usual statistical framework of equivalence testing. Fourth, when equivalence cannot be confirmed, we address the need for further analyses and studies tailoring treatment towards groups of patients with similar response to treatment. Fifth, we argue that to properly frame, the entire body of evidence on effect modification should be quantified in a prior probability.
Insights
Treatment effect modification, a difference in response between patient groups, should be expected in clinical studies. Tailoring treatments to specific patient subgroups can improve outcomes when generalizability is not confirmed.
Area of Science:
- Biostatistics
- Clinical Epidemiology
- Pharmacology
Background:
- Applying clinical study results to individual patients is challenging.
- Treatment effect modification (interaction) is defined as differing treatment responses across patient groups.
Purpose of the Study:
- To discuss the expectation and application of treatment effect modification.
- To explore methods for tailoring treatment effects to patient needs.
Main Methods:
- Argument for expecting treatment effect modification in study designs.
- A priori selection of clinically relevant subgroups based on effect modification and prevalence.
- Utilizing equivalence testing to evaluate generalizability (absence of effect modification).
Main Results:
- Treatment effect modification is expected, contrary to common study designs.
- Generalizability can be statistically evaluated by defining it as the absence of effect modification.
- When generalizability is not confirmed, further analyses are needed for tailored treatments.
Conclusions:
- Clinical study results should account for expected treatment effect modification.
- Tailoring treatments to patient subgroups enhances personalized medicine.
- Quantifying evidence on effect modification is crucial for robust clinical decision-making.
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