Item-level heterogeneous treatment effects of selective serotonin reuptake inhibitors (SSRIs) on depression: implications for inference, generalizability, and identification
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Joshua B. Gilbert
,
Fredrik Hieronymus
,
Elias Eriksson
Abstract
Objectives
In analysis of randomized controlled trials (RCTs) with patient-reported outcome measures (PROMs), Item Response Theory (IRT) models that allow for heterogeneity in the treatment effect at the item level merit consideration. These models for “item-level heterogeneous treatment effects” (IL-HTE) can provide more accurate statistical inference, allow researchers to better generalize their results, and resolve critical identification problems in the estimation of interaction effects. In this study, we extend the IL-HTE model to polytomous data and apply the model to determine how the effect of selective serotonin reuptake inhibitors (SSRIs) on depression varies across the items on a depression rating scale.
Methods
We first conduct a Monte Carlo simulation study to assess the performance of the polytomous IL-HTE model under a range of conditions. We then apply the IL-HTE model to item-level data from 24 RCTs measuring the effect of SSRIs on depression using the 17-item Hamilton Depression Rating Scale (HDRS-17) and estimate heterogeneity by subscale (HDRS-6).
Results
Our simulation results show that ignoring IL-HTE can yield standard errors that are as much as 50 % too small and create significant bias in treatment by covariate interaction effects when item-specific treatment effects are correlated with item location, and that the application of the IL-HTE model resolves these issues. Our empirical application shows that while the average effect of SSRIs on depression is beneficial (i.e., negative) and statistically significant, there is substantial IL-HTE, with estimates of the standard deviation of item-level effects nearly as large as the average effect. We show that this substantial IL-HTE is driven primarily by systematically larger effects on the HDRS-6 subscale items.
Conclusions
The IL-HTE model has the potential to provide new insights for the inference, generalizability, and identification of treatment effects in clinical trials using PROMs.
Funding source: Jacobs Foundation
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: Conceptualization: Author 1, Author 4, Methodology: Author 1, Author 4, Software: Author 1, Formal Analysis: Author 1, Author 2, Writing – original draft preparation: Author 1, Writing – review and editing: Author 2, Author 3, Author 4.
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Competing interests: Authors 1 and 4 report no competing interests. Author 2 has received speaker’s fees from Janssen Pharmaceuticals in the last five years and is a board member of the Swedish Serotonin Society. Author 3 has received speaker’s fees from Janssen Pharmaceuticals in the last five years.
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Research funding: This work was funded in part by the Jacobs Foundation.
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© 2024 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Perspective
- Integrating Hill’s classical considerations with modern causal inference methods in observational studies: a ‘How-Questions’ framework
- Research Articles
- A compound representation of the multiple treatment propensity score with applications to marginal structural modeling
- Non-plug-in estimators could outperform plug-in estimators: a cautionary note and a diagnosis
- Item-level heterogeneous treatment effects of selective serotonin reuptake inhibitors (SSRIs) on depression: implications for inference, generalizability, and identification
- Estimation of the number needed to treat, the number needed to be exposed, and the exposure impact number with instrumental variables
- Bounds for selection bias using outcome probabilities
Articles in the same Issue
- Perspective
- Integrating Hill’s classical considerations with modern causal inference methods in observational studies: a ‘How-Questions’ framework
- Research Articles
- A compound representation of the multiple treatment propensity score with applications to marginal structural modeling
- Non-plug-in estimators could outperform plug-in estimators: a cautionary note and a diagnosis
- Item-level heterogeneous treatment effects of selective serotonin reuptake inhibitors (SSRIs) on depression: implications for inference, generalizability, and identification
- Estimation of the number needed to treat, the number needed to be exposed, and the exposure impact number with instrumental variables
- Bounds for selection bias using outcome probabilities
