Identification of predicted individual treatment effects in randomized clinical trials

Lamont, Andrea; Lyons, Michael D.; Jaki, Thomas; Stuart, Elizabeth A.; Feaster, Daniel J.; Tharmaratnam, Kukatharmini; Oberski, Daniel L.; Ishwaran, Hemant; Wilson, Dawn K.; Horn, M. Lee Van

doi:10.1177/0962280215623981

Cited by 55 publications

(56 citation statements)

References 68 publications

(127 reference statements)

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“…An overview of several methods for obtaining the predicted individual treatment effect (PITE), including machine learning algorithms and non-parametric models such as tree-based methods is provided by Lamont et.al. [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Subgroup identification in clinical trials via the predicted individual treatment effect

et al. 2018

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Identifying subgroups of treatment responders through the different phases of clinical trials has the potential to increase success in drug development. Recent developments in subgroup analysis consider subgroups that are defined in terms of the predicted individual treatment effect, i.e. the difference between the predicted outcome under treatment and the predicted outcome under control for each individual, which in turn may depend on multiple biomarkers. In this work, we study the properties of different modelling strategies to estimate the predicted individual treatment effect. We explore linear models and compare different estimation methods, such as maximum likelihood and the Lasso with and without randomized response. For the latter, we implement confidence intervals based on the selective inference framework to account for the model selection stage. We illustrate the methods in a dataset of a treatment for Alzheimer disease (normal response) and in a dataset of a treatment for prostate cancer (survival outcome). We also evaluate via simulations the performance of using the predicted individual treatment effect to identify subgroups where a novel treatment leads to better outcomes compared to a control treatment.

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Section: Introductionmentioning

confidence: 99%

Subgroup identification in clinical trials via the predicted individual treatment effect

et al. 2018

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“…In sum, we have shown that, while conventional approaches to effect size estimation in policy interventions often resort to aggregated measures of impact, such as ATE or conditional ATE 35,40 , these evaluations take little note of individual characteristics that may alter individual responses to interventions. After all, an intervention that worked well on average may not be the best option for all 41 .…”

Section: Discussionmentioning

confidence: 99%

Uncovering Individualised Treatment Effect: Evidence from Educational Trials

Xiao¹,

Hauser²,

Kirkwood³

et al. 2020

Preprint

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The use of large-scale Randomised Controlled Trials (RCTs) is fast becoming "the gold standard" of testing the causal effects of policy, social, and educational interventions. RCTs are typically evaluated — and ultimately judged — by the economic, educational, and statistical significance of the Average Treatment Effect (ATE) in the study sample. However, many interventions have heterogeneous treatment effects across different individuals, not captured by the ATE. One way to identify heterogeneous treatment effects is to conduct subgroup analyses, such as focusing on low-income Free School Meal pupils as required for projects funded by the Education Endowment Foundation (EEF) in England. These subgroup analyses, as we demonstrate in 48 EEF-funded RCTs involving over 200,000 students, are usually not standardised across studies and offer flexible degrees of freedom to researchers, potentially leading to mixed results. Here, we develop and deploy a machine-learning and regression-based framework for systematic estimation of Individualised Treatment Effect (ITE), which can show where a seemingly ineffective and uninformative intervention worked, for whom, and by how much. Our findings have implications for decision-makers in education, public health, and medical trials.

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“…Treatment recommendations can be based on ATE when response-modifying patient characteristics are absent. 33 …”

Section: State-of-the-art For Developing Prediction-based Recommendatmentioning

confidence: 99%

“…This may not apply if the clinical response is modulated by other (observable) variables Z (called effect modifiers) and if the individual patient deviates significantly from the average through his set of modifiers. 33 A simple example is the different response to prasugrel across several subgroups, which can be attributed to effect modifiers (eg, age, weight, and medical history). More complex cases are often not well described, also because they neither have been identified nor comprehensively evaluated nor adequately reported.…”

Section: State-of-the-art For Developing Prediction-based Recommendatmentioning

confidence: 99%

<p>Using the Causal Inference Framework to Support Individualized Drug Treatment Decisions Based on Observational Healthcare Data</p>

Meid¹,

Ruff²,

Wirbka³

et al. 2020

CLEP

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When healthcare professionals have the choice between several drug treatments for their patients, they often experience considerable decision uncertainty because many decisions simply have no single “best” choice. The challenges are manifold and include that guideline recommendations focus on randomized controlled trials whose populations do not necessarily correspond to specific patients in everyday treatment. Further reasons may be insufficient evidence on outcomes, lack of direct comparison of distinct options, and the need to individually balance benefits and risks. All these situations will occur in routine care, its outcomes will be mirrored in routine data, and could thus be used to guide decisions. We propose a concept to facilitate decision-making by exploiting this wealth of information. Our working example for illustration assumes that the response to a particular (drug) treatment can substantially differ between individual patients depending on their characteristics (heterogeneous treatment effects, HTE), and that decisions will be more precise if they are based on real-world evidence of HTE considering this information. However, such methods must account for confounding by indication and effect measure modification, eg, by adequately using machine learning methods or parametric regressions to estimate individual responses to pharmacological treatments. The better a model assesses the underlying HTE, the more accurate are predicted probabilities of treatment response. After probabilities for treatment-related benefit and harm have been calculated, decision rules can be applied and patient preferences can be considered to provide individual recommendations. Emulated trials in observational data are a straightforward technique to predict the effects of such decision rules when applied in routine care. Prediction-based decision rules from routine data have the potential to efficiently supplement clinical guidelines and support healthcare professionals in creating personalized treatment plans using decision support tools.

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Identification of predicted individual treatment effects in randomized clinical trials

Cited by 55 publications

References 68 publications

Subgroup identification in clinical trials via the predicted individual treatment effect

Subgroup identification in clinical trials via the predicted individual treatment effect

Uncovering Individualised Treatment Effect: Evidence from Educational Trials

<p>Using the Causal Inference Framework to Support Individualized Drug Treatment Decisions Based on Observational Healthcare Data</p>

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