Related Experiment Video
Updated: Sep 28, 2025
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
Published on: September 11, 2021
The impact of moderator by confounder interactions in the assessment of treatment effect modification: a simulation
Antonia Mary Marsden1, William G Dixon2, Graham Dunn3
1Centre for Biostatistics, School of Health Sciences, The University of Manchester, Manchester Academic Health Science Centre, Jean McFarlane Building, Oxford Road, Manchester, M13 9PL, UK. antonia.marsden@manchester.ac.uk.
Background:
When performed in an observational setting, treatment effect modification analyses should account for all confounding, where possible. Often, such studies only consider confounding between the exposure and outcome. However, there is scope for misspecification of the confounding adjustment when estimating moderation as the effects of the confounders may themselves be influenced by the moderator. The aim of this study was to investigate bias in estimates of treatment effect modification resulting from failure to account for an interaction between a binary moderator and a confounder on either treatment receipt or the outcome, and to assess the performance of different approaches to account for such interactions.
Methods:
The theory behind the reason for bias and factors that impact the magnitude of bias is explained. Monte Carlo simulations were used to assess the performance of different propensity scores adjustment methods and regression adjustment where the adjustment 1) did not account for any moderator-confounder interactions, 2) included moderator-confounder interactions, and 3) was estimated separately in each moderator subgroup. A real-world observational dataset was used to demonstrate this issue.
Results:
Regression adjustment and propensity score covariate adjustment were sensitive to the presence of moderator-confounder interactions on outcome, whilst propensity score weighting and matching were more sensitive to the presence of moderator-confounder interactions on treatment receipt. Including the relevant moderator-confounder interactions in the propensity score (for methods using this) or the outcome model (for regression adjustment) rectified this for all methods except propensity score covariate adjustment. For the latter, subgroup-specific propensity scores were required. Analysis of the real-world dataset showed that accounting for a moderator-confounder interaction can change the estimate of effect modification.
Conclusions:
When estimating treatment effect modification whilst adjusting for confounders, moderator-confounder interactions on outcome or treatment receipt should be accounted for.
Insights
To accurately estimate treatment effect modification in observational studies, it is crucial to account for interactions between moderators and confounders. Failure to do so can lead to biased results, impacting treatment effect modification analyses.
Area of Science:
- Observational studies
- Epidemiology
- Biostatistics
Background:
- Treatment effect modification analyses in observational settings require comprehensive confounding adjustment.
- Confounding adjustment can be misspecified if confounder effects are influenced by the moderator.
- Bias in treatment effect modification estimates can arise from unaddressed moderator-confounder interactions.
Purpose of the Study:
- Investigate bias in treatment effect modification estimates due to unaddressed moderator-confounder interactions.
- Assess the performance of different adjustment methods for these interactions.
- Evaluate the impact on treatment receipt and outcome.
Main Methods:
- Utilized Monte Carlo simulations to evaluate bias and method performance.
- Compared regression adjustment and propensity score methods (covariate adjustment, weighting, matching).
- Assessed adjustments that did not account for interactions, included interactions, or used subgroup-specific estimates.
Main Results:
- Regression and propensity score covariate adjustment were sensitive to outcome interactions; propensity score weighting/matching were sensitive to treatment receipt interactions.
- Including moderator-confounder interactions in models or using subgroup-specific propensity scores generally corrected bias.
- Real-world data analysis confirmed that accounting for interactions alters effect modification estimates.
Conclusions:
- When estimating treatment effect modification in observational studies, it is essential to account for moderator-confounder interactions.
- These interactions can occur on either treatment receipt or the outcome.
- Properly accounting for these interactions is necessary for unbiased estimation.
Related Concept Videos
01:25Strategies for Assessing and Addressing Confounding
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
01:27Confounding in Epidemiological Studies
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
01:11Blinding
01:13Randomized Experiments
Simple randomization
Simple...
01:26Modeling in Therapy
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...