Predictive approaches to heterogeneous treatment effects: a scoping review

Rekkas, Alexandros; Paulus, Jessica K.; Raman, Gowri; Wong, John B.; Kent, David M.; Klaveren, David van

doi:10.1186/s12874-020-01145-1

Cited by 48 publications

(58 citation statements)

References 73 publications

(88 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The prediction of individualized or personalized treatment effect is an active field of research. Recent broad overviews on predictive approaches toward heterogeneity of treatment effect are available elsewhere and include a comprehensive overview of applied papers 5,58 . Related work approaches the problem from the missing data perspective 59,60 .…”

Section: Discussionmentioning

confidence: 99%

A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint

Hoogland

IntHout²,

Belias³

et al. 2021

Statistics in Medicine

View full text Add to dashboard Cite

Randomized trials typically estimate average relative treatment effects, but decisions on the benefit of a treatment are possibly better informed by more individualized predictions of the absolute treatment effect. In case of a binary outcome, these predictions of absolute individualized treatment effect require knowledge of the individual's risk without treatment and incorporation of a possibly differential treatment effect (ie, varying with patient characteristics). In this article, we lay out the causal structure of individualized treatment effect in terms of potential outcomes and describe the required assumptions that underlie a causal interpretation of its prediction. Subsequently, we describe regression models and model estimation techniques that can be used to move from average to more individualized treatment effect predictions. We focus mainly on logistic regression‐based methods that are both well‐known and naturally provide the required probabilistic estimates. We incorporate key components from both causal inference and prediction research to arrive at individualized treatment effect predictions. While the separate components are well known, their successful amalgamation is very much an ongoing field of research. We cut the problem down to its essentials in the setting of a randomized trial, discuss the importance of a clear definition of the estimand of interest, provide insight into the required assumptions, and give guidance with respect to modeling and estimation options. Simulated data illustrate the potential of different modeling options across scenarios that vary both average treatment effect and treatment effect heterogeneity. Two applied examples illustrate individualized treatment effect prediction in randomized trial data.

show abstract

Section: Discussionmentioning

confidence: 99%

A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint

Hoogland

IntHout²,

Belias³

et al. 2021

Statistics in Medicine

View full text Add to dashboard Cite

show abstract

“…It may also lead to differential model fit on trial arms, biasing treatment effect estimates across risk strata, and exaggerating HTE 1,7,31,33 . It can make completely ineffective treatments appear to be beneficial in high‐risk patients and harmful in low‐risk patients 2 …”

Section: Methodsmentioning

confidence: 99%

“…A risk modeling approach predicts the risk for patients based on their baseline characteristics. It then uses this risk to predict HTE at the absolute scale, typically within a randomized clinical trial (RCT) 1,2,7,10‐13 . In this sense, risk modeling deals better with dimensionality, low power and limited prior knowledge than an effect modification approach.…”

Section: Introductionmentioning

confidence: 99%

A two‐stage prediction model for heterogeneous effects of treatments

Chalkou

Steyerberg

Egger

et al. 2021

Statistics in Medicine

View full text Add to dashboard Cite

Treatment effects vary across different patients, and estimation of this variability is essential for clinical decision‐making. We aimed to develop a model estimating the benefit of alternative treatment options for individual patients, extending a risk modeling approach in a network meta‐analysis framework. We propose a two‐stage prediction model for heterogeneous treatment effects by combining prognosis research and network meta‐analysis methods where individual patient data are available. In the first stage, a prognostic model to predict the baseline risk of the outcome. In the second stage, we use the baseline risk score from the first stage as a single prognostic factor and effect modifier in a network meta‐regression model. We apply the approach to a network meta‐analysis of three randomized clinical trials comparing the relapses in Natalizumab, Glatiramer Acetate, and Dimethyl Fumarate, including 3590 patients diagnosed with relapsing‐remitting multiple sclerosis. We find that the baseline risk score modifies the relative and absolute treatment effects. Several patient characteristics, such as age and disability status, impact the baseline risk of relapse, which in turn moderates the benefit expected for each of the treatments. For high‐risk patients, the treatment that minimizes the risk of relapse in 2 years is Natalizumab, whereas Dimethyl Fumarate might be a better option for low‐risk patients. Our approach can be easily extended to all outcomes of interest and has the potential to inform a personalized treatment approach.

show abstract

“…31,[33][34][35][36][37][38] While randomized controlled trials (RCTs) are the gold standard for evidence generation, with treatment effect heterogeneity, RCTs cannot generate personalized evidence for many patients. 4,[39][40][41][42] Comparative effectiveness research (CER) using large observational databases has been suggested as an alternative to develop personalized evidence. 1,2,[43][44][45][46] Observational data provide the perspective of "real-world" practicebased evidence and a diversity of patients well beyond who are usually evaluated in RCTs.…”

Section: Introductionmentioning

confidence: 99%

“…14,50,[72][73][74][75][76] Third, because alternative treatments may have distinct beneficial and detrimental effects across outcomes, CER must provide personalized evidence across the outcomes affected by treatment choice. 14,41,42 These issues are addressed by extending the novel Instrumental Variable Causal Forest Algorithm (IV-CFA) described by Athey and colleagues 77 to assemble evidence across reference classes of patients on the effects of early surgery of Medicare patients with new proximal humerus fractures (PHFs). Data are from a prior instrumental variable (IV) study for Medicare fee-for-service patients with a new PHF in 2011 that used local area surgery rates as an instrumental variable and showed positive associations between early surgery rates and detriments (1-year mortality and morbidity rates) which varied when patients were stratified by single baseline factors.…”

Section: Introductionmentioning

confidence: 99%

Personalizing Evidence for Shoulder Fracture Patients Using an Extended Instrumental Variable Causal Forest Algorithm

Brooks

Chapman

Floyd

et al. 2021

Preprint

View full text Add to dashboard Cite

Objective: To assess the ability of an extended Instrumental Variable Causal Forest Algorithm (IV-CFA) to provide personalized evidence of early surgery effects on benefits and detriments for elderly shoulder fracture patients. Data Sources/Study Setting: Population of 72,751 fee-for-service Medicare beneficiaries with proximal humerus fractures (PHFs) in 2011 who survived a 60-day treatment window after an index PHF and were continuously Medicare fee-for-service eligible over the period 12 months prior to index to the minimum of 12 months after index or death. Study Design: IV-CFA estimated early surgery effects on both beneficial and detrimental outcomes for each patient in the study population. Classification and regression trees (CART) were applied to these estimates to create patient reference classes. Two-stage least squares (2SLS) estimators were applied to patients in each reference class to scrutinize the estimates relative to the known 2SLS properties. Principal Findings: This approach uncovered distinct reference classes of elderly PHF patients with respect to early surgery effects on benefit and detriment. Older, frailer patients with more comorbidities, and lower utilizers of healthcare were less likely to gain benefit and more likely to have detriment from early surgery. Reference classes were characterized by the appropriateness of early surgery rates with respect to benefit and detriment. Conclusions: Extended IV-CFA provides an illuminating method to uncover reference classes of patients based on treatment effects using observational data with a strong instrumental variable. This study isolated reference classes of new PHF patients in which changes in early surgery rates would improve patient outcomes. The inability to measure fracture complexity in Medicare claims means providers will need to discuss the appropriateness of these estimates to patients within a reference class in context of this missing information.

show abstract

Predictive approaches to heterogeneous treatment effects: a scoping review

Cited by 48 publications

References 73 publications

A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint

A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint

A two‐stage prediction model for heterogeneous effects of treatments

Personalizing Evidence for Shoulder Fracture Patients Using an Extended Instrumental Variable Causal Forest Algorithm

Contact Info

Product

Resources

About