scholarly journals Graphing and reporting heterogeneous treatment effects through reference classes

2019 ◽  
Author(s):  
James A. Watson ◽  
Chris C. Holmes
Keyword(s):  
Predictive Model ◽  
Treatment Effect ◽  
Multiple Testing ◽  
Treatment Effects ◽  
Outcome Measurement ◽  
Background Information ◽  
Baseline Risk ◽  
Individual Treatment ◽  
Heterogeneous Treatment Effects ◽  
Reference Classes

AbstractBackgroundExploration and modelling of individual treatment effects and treatment heterogeneity is an important aspect of precision medicine in randomized controlled trials (RCTs). The usual approach is to develop a predictive model for individual outcomes and then look for an interaction effect between treatment allocation and important patient covariates. However, such models are prone to overfitting and multiple testing, and typically demand a transformation of the outcome measurement, for example, from the absolute risk in the original RCT to log-odds of risk in the predictive model.MethodsWe show how reference classes derived from background information can be used to alleviate this problem through a two-stage approach where we first estimate a key aspect of heterogeneity in the trial population and then explore for an interaction with the treatment effect along this axis of variation. This bypasses the search for interactions, protecting against multiple testing, and allows for exploration of heterogeneous treatment effects on the original outcome scale of the RCT. This would typically be applied to multivariate models of baseline risk to assess the stability of average treatment effects with respect to the distribution of risk in the population studied. We show how ‘local’ and ‘tilting’ schemes based on ranking patients by baseline risk can be used as a general approach for exploring heterogeneity of treatment effect.ResultsWe illustrate this approach using the single largest randomised treatment trial in severe falciparum malaria and show how the estimated treatment effect in terms of absolute mortality risk reduction increases considerably for higher risk strata.

scholarly journals Graphing and reporting heterogeneous treatment effects through reference classes

Trials ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
James A. Watson ◽  
Chris C. Holmes
Keyword(s):  
Predictive Model ◽  
Treatment Effect ◽  
Multiple Testing ◽  
Treatment Effects ◽  
Population Heterogeneity ◽  
Baseline Risk ◽  
Two Stage ◽  
Heterogeneous Treatment Effects ◽  
Reference Classes ◽  
Baseline Covariates

Abstract Background Exploration and modelling of heterogeneous treatment effects as a function of baseline covariates is an important aspect of precision medicine in randomised controlled trials (RCTs). Randomisation generally guarantees the internal validity of an RCT, but heterogeneity in treatment effect can reduce external validity. Estimation of heterogeneous treatment effects is usually done via a predictive model for individual outcomes, where one searches for interactions between treatment allocation and important patient baseline covariates. However, such models are prone to overfitting and multiple testing and typically demand a transformation of the outcome measurement, for example, from the absolute risk in the original RCT to log-odds of risk in the predictive model. Methods We show how reference classes derived from baseline covariates can be used to explore heterogeneous treatment effects via a two-stage approach. We first estimate a risk score which captures on a single dimension some of the heterogeneity in outcomes of the trial population. Heterogeneity in the treatment effect can then be explored via reweighting schemes along this axis of variation. This two-stage approach bypasses the search for interactions with multiple covariates, thus protecting against multiple testing. It also allows for exploration of heterogeneous treatment effects on the original outcome scale of the RCT. This approach would typically be applied to multivariable models of baseline risk to assess the stability of average treatment effects with respect to the distribution of risk in the population studied. Case study We illustrate this approach using the single largest randomised treatment trial in severe falciparum malaria and demonstrate how the estimated treatment effect in terms of absolute mortality risk reduction increases considerably in higher risk strata. Conclusions ‘Local’ and ‘tilting’ reweighting schemes based on ranking patients by baseline risk can be used as a general approach for exploring, graphing and reporting heterogeneity of treatment effect in RCTs. Trial registration ISRCTN clinical trials registry: ISRCTN50258054. Prospectively registered on 22 July 2005.

scholarly journals Flexible Estimation of Treatment Effect Parameters

2011 ◽  
Vol 101 (3) ◽  
pp. 544-551 ◽  
Author(s):  
Thomas MaCurdy ◽  
Xiaohong Chen ◽  
Han Hong
Keyword(s):  
Treatment Effect ◽  
Treatment Effects ◽  
Cell Structure ◽  
Structure Identification ◽  
Heterogeneous Treatment Effects ◽  
Common Cell

A variety of identification strategies have a common cell structure, in which the observed heterogeneity of the regression defines a partition of the sample into cells. Typically in the presence of exogenous covariates that define the cell structure, identification assumptions are imposed conditional on each value of the covariate, or cell by cell. Treatment effects across cells are typically heterogeneous. Researchers might be interested in unconditional parameters which are the averaged treatment effects across the cells. Alternatively, treatment effects can be estimated more efficiently if researchers are willing to impose additional parametric and semiparametric structures on the heterogeneous treatment effects across cells.

scholarly journals Individualized treatment effects with censored data via fully nonparametric Bayesian accelerated failure time models

Biostatistics ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 50-68 ◽  
Author(s):  
Nicholas C Henderson ◽  
Thomas A Louis ◽  
Gary L Rosner ◽  
Ravi Varadhan
Keyword(s):  
Treatment Effect ◽  
Treatment Effects ◽  
Failure Time ◽  
Enzyme Inhibitor ◽  
Model Specification ◽  
Accelerated Failure Time Model ◽  
Tuning Parameter ◽  
Individual Treatment ◽  
Accelerated Failure Time ◽  
Accelerated Failure

Summary Individuals often respond differently to identical treatments, and characterizing such variability in treatment response is an important aim in the practice of personalized medicine. In this article, we describe a nonparametric accelerated failure time model that can be used to analyze heterogeneous treatment effects (HTE) when patient outcomes are time-to-event. By utilizing Bayesian additive regression trees and a mean-constrained Dirichlet process mixture model, our approach offers a flexible model for the regression function while placing few restrictions on the baseline hazard. Our nonparametric method leads to natural estimates of individual treatment effect and has the flexibility to address many major goals of HTE assessment. Moreover, our method requires little user input in terms of model specification for treatment covariate interactions or for tuning parameter selection. Our procedure shows strong predictive performance while also exhibiting good frequentist properties in terms of parameter coverage and mitigation of spurious findings of HTE. We illustrate the merits of our proposed approach with a detailed analysis of two large clinical trials (N = 6769) for the prevention and treatment of congestive heart failure using an angiotensin-converting enzyme inhibitor. The analysis revealed considerable evidence for the presence of HTE in both trials as demonstrated by substantial estimated variation in treatment effect and by high proportions of patients exhibiting strong evidence of having treatment effects which differ from the overall treatment effect.

scholarly journals Higher treatment effect after total knee arthroplasty is associated with higher patient satisfaction

2020 ◽  
Author(s):  
Jörg Lützner ◽  
Franziska Beyer ◽  
Klaus-Peter Günther ◽  
Jörg Huber
Keyword(s):  
Total Knee Arthroplasty ◽  
Patient Satisfaction ◽  
Knee Arthroplasty ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Oxford Knee Score ◽  
Individual Treatment ◽  
Total Knee ◽  
The Mean ◽  
One Year

Abstract Purpose The aim of this study was to investigate what influence the treatment effect after total knee arthroplasty (TKA) had on patient satisfaction. Methods Prospective registry data of a University-based arthroplasty centre were used. 582 patients with unilateral bicondylar TKA were analyzed. Treatment effect (TE) was deduced from Oxford Knee Score (OKS) before and one year after surgery. Positive values correspond to improved symptoms (maximum 1.0 reflect no symptoms at all) and negative values correspond to deterioration of symptoms. Satisfaction on a visual-analogue scale from 0 to 10 and the willingness to undergo TKA surgery again was assessed one year after surgery. Results The mean OKS improved from 22.1 before to 36.7 one year after TKA. Treatment effects ranged from 1.0 to –0.62 with a mean TE of 0.56. Taking an individual treatment effect of 0.2 as a cut-off between responder and non-responder, a total of 85.8% would be classified as responder after TKA. The mean satisfaction score with the TKA was 8.1. There was a significant correlation between the individual treatment effect and satisfaction after TKA (p < 0.001). The majority of patients (84.5%) would undergo surgery again. Patients not willing to undergo surgery again or those uncertain about this had lower satisfaction scores, a lower treatment effect and were more often female compared to patients who would undergo surgery again. Conclusion Higher individual treatment effects resulted in higher patient satisfaction and willingness to undergo surgery again. However, some patients with a relatively low treatment effect were highly satisfied, which indicates the need for both information. Level of evidence II.

scholarly journals A New Framework for Online Testing of Heterogeneous Treatment Effect

2020 ◽  
Vol 34 (06) ◽  
pp. 10310-10317
Author(s):  
Miao Yu ◽  
Wenbin Lu ◽  
Rui Song
Keyword(s):  
Multiple Testing ◽  
Continuous Monitoring ◽  
Treatment Effects ◽  
Type I Error ◽  
Score Test ◽  
P Value ◽  
Type I ◽  
Online Testing ◽  
Heterogeneous Treatment Effects ◽  
New Framework

We propose a new framework for online testing of heterogeneous treatment effects. The proposed test, named sequential score test (SST), is able to control type I error under continuous monitoring and detect multi-dimensional heterogeneous treatment effects. We provide an online p-value calculation for SST, making it convenient for continuous monitoring, and extend our tests to online multiple testing settings by controlling the false discovery rate. We examine the empirical performance of the proposed tests and compare them with a state-of-art online test, named mSPRT using simulations and a real data. The results show that our proposed test controls type I error at any time, has higher detection power and allows quick inference on online A/B testing.

scholarly journals Heterogeneous Treatment Effects in the Presence of Self-Selection: A Propensity Score Perspective

2019 ◽  
Vol 50 (1) ◽  
pp. 350-385 ◽  
Author(s):  
Xiang Zhou ◽  
Yu Xie
Keyword(s):  
Propensity Score ◽  
Treatment Effect ◽  
Latent Variable ◽  
Treatment Effects ◽  
Social Research ◽  
Essential Feature ◽  
Economic Returns ◽  
Heterogeneous Treatment Effects ◽  
Self Selection ◽  
Wide Range

An essential feature common to all empirical social research is variability across units of analysis. Individuals differ not only in background characteristics but also in how they respond to a particular treatment, intervention, or stimulation. Moreover, individuals may self-select into treatment on the basis of anticipated treatment effects. To study heterogeneous treatment effects in the presence of self-selection, Heckman and Vytlacil developed a structural approach that builds on the marginal treatment effect (MTE). In this article, we extend the MTE-based approach through a redefinition of MTE. Specifically, we redefine MTE as the expected treatment effect conditional on the propensity score (rather than all observed covariates) as well as a latent variable representing unobserved resistance to treatment. As with the original MTE, the new MTE also can be used as a building block for evaluating standard causal estimands. However, the weights associated with the new MTE are simpler, more intuitive, and easier to compute. Moreover, the new MTE is a bivariate function and thus is easier to visualize than the original MTE. Finally, the redefined MTE immediately reveals treatment-effect heterogeneity among individuals who are at the margin of treatment. As a result, it can be used to evaluate a wide range of policy changes with little analytical twist and design policy interventions that optimize the marginal benefits of treatment. We illustrate the proposed method by estimating heterogeneous economic returns to college with National Longitudinal Study of Youth 1979 data.

scholarly journals Estimating the effect of adjuvant chemo-therapy for colon-cancer using registry data: a method comparison and validation

2020 ◽  
Author(s):  
Lingjie Shen ◽  
Erik Visser ◽  
Hans de Wilt ◽  
Henk Verheul ◽  
Felice van Erning ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Observational Data ◽  
Treatment Effect ◽  
Gold Standard ◽  
Large Scale ◽  
Treatment Effects ◽  
Direct Method ◽  
Registry Data ◽  
Individual Treatment ◽  
Simple Models

Abstract Background: Although randomized controlled trials (RCT) are the gold standard to estimate treatment effects, they are often criticized in terms of generalizability. Observational data might alleviate this problem by being readily available in large quantities. However, observational data are potentially confounded. In this methodological study we use a large-scale RCT as the gold standard to examine the performance of various statistical methods to control for potential confounding in observational data. Methods: In this paper we compare three types of methods that allow researchers to correct for such potential confounding: direct methods, inverse probability weighting (IPW) methods and doubly robust (DR) methods. We uniquely compare estimates obtained from the population-wide Netherlands Cancer Registry (NCR) on colon cancer (n=52621) with estimates obtained from a large-scale RCT. As the RCT differs from the observational data both in its sampling mechanism and in its treatment assignment mechanism, we first resample the NCR data to reflect the distribution in RCT data. Next, we correct for potential confounding using three alternative types of methods and consequentially evaluate their estimates to those obtained in the RCT. Results: We find that while all estimators qualitatively approximate to findings in the RCT, methods that can flexibly model the response (i.e., direct estimation and DR estimation) performed consistently superior to the inverse propensity score method. Subgroup analysis indicates that relatively simple models allow us to properly estimate the treatment effect. However, these simple models do not properly identify heterogeneous treatment effects in stage2 colon cancer. Careful sensitivity analysis using more flexible models demonstrates both the uncertainty and the potential heterogeneous treatment effect in stage2 cancer and provides robust estimation of treatment effect in stage3 cancer. Conclusions: Our results suggest that both the direct method and the DR method, when executed with care, can be used to reliably estimate treatment effects based on registry data. This methodological validation opens the door to more extensive use of registry data for the estimation of (individual) treatment effects. Additionally, our identification of potentially meaningful subgroups of stage2 colon cancer patients who, based on our analysis seem to benefit from chemotherapy, should be further explored.

scholarly journals A Unified Survey of Treatment Effect Heterogeneity Modelling and Uplift Modelling

2022 ◽  
Vol 54 (8) ◽  
pp. 1-36
Author(s):  
Weijia Zhang ◽  
Jiuyong Li ◽  
Lin Liu
Keyword(s):  
Treatment Effect ◽  
Treatment Effects ◽  
Causal Effect ◽  
Personalised Medicine ◽  
Online Marketing ◽  
Heterogeneous Treatment Effects ◽  
Source Codes ◽  
Outcome Framework ◽  
Treatment Effect Heterogeneity ◽  
Effect Heterogeneity

A central question in many fields of scientific research is to determine how an outcome is affected by an action, i.e., to estimate the causal effect or treatment effect of an action. In recent years, in areas such as personalised healthcare, sociology, and online marketing, a need has emerged to estimate heterogeneous treatment effects with respect to individuals of different characteristics. To meet this need, two major approaches have been taken: treatment effect heterogeneity modelling and uplifting modelling. Researchers and practitioners in different communities have developed algorithms based on these approaches to estimate the heterogeneous treatment effects. In this article, we present a unified view of these two seemingly disconnected yet closely related approaches under the potential outcome framework. We provide a structured survey of existing methods following either of the two approaches, emphasising their inherent connections and using unified notation to facilitate comparisons. We also review the main applications of the surveyed methods in personalised marketing, personalised medicine, and sociology. Finally, we summarise and discuss the available software packages and source codes in terms of their coverage of different methods and applicability to different datasets, and we provide general guidelines for method selection.

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