Assessing The Treatment Effect Heterogeity with a Latent Variable

Yin, Yunjian; Liu, Lan; Geng, Zhi

doi:10.5705/ss.202016.0150

Cited by 5 publications

(6 citation statements)

References 7 publications

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“…PITE is one method for personalized medicine that uses latent variables to investigate heterogeneity in treatment response; others include methods to estimate the proportion of subjects who benefit from treatment. 33 The example includes (1) latent classes defined by heterogeneity in treatment response, 34 and (2) Bayesian analyses that assess differential treatment response as a function of continuous and categorical latent variables. 35 Once f t x ð Þ and f c x ð Þ are estimated from the trial data, individual-level PITE estimates for patients in the original trial and those who did not take part in it, can be obtained using equation (3).…”

Section: Permutation Test For Pitementioning

confidence: 99%

A permutation test for assessing the presence of individual differences in treatment effects

Chang

Jaki

Sadiq

et al. 2021

Stat Methods Med Res

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An important goal of personalized medicine is to identify heterogeneity in treatment effects and then use that heterogeneity to target the intervention to those most likely to benefit. Heterogeneity is assessed using the predicted individual treatment effects framework, and a permutation test is proposed to establish if significant heterogeneity is present given the covariates and predictive model or algorithm used for predicted individual treatment effects. We first show evidence for heterogeneity in the effects of treatment across an illustrative example data set. We then use simulations with two different predictive methods (linear regression model and Random Forests) to show that the permutation test has adequate type-I error control. Next, we use an example dataset as the basis for simulations to demonstrate the ability of the permutation test to find heterogeneity in treatment effects for a predicted individual treatment effects estimate as a function of both effect size and sample size. We find that the proposed test has good power for detecting heterogeneity in treatment effects when the heterogeneity was due primarily to a single predictor, or when it was spread across the predictors. Power was found to be greater for predictions from a linear model than from random forests. This non-parametric permutation test can be used to test for significant differences across individuals in predicted individual treatment effects obtained with a given set of covariates using any predictive method with no additional assumptions.

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Section: Permutation Test For Pitementioning

confidence: 99%

A permutation test for assessing the presence of individual differences in treatment effects

Chang

Jaki

Sadiq

et al. 2021

Stat Methods Med Res

View full text Add to dashboard Cite

show abstract

“…The heterogeneous treatment effect describes the effect variability due to varying characteristics and is widely utilized in the contexts of personalized medicine, policy design, and customized marketing recommendation (Kent et al, 2018;Yin, 2018;Imai and Strauss, 2011;Sato et al, 2019). In many of the application settings, the characteristics of treatment relevance are only a subset of baseline covariates (X = (X l , X −l )).…”

Section: Introductionmentioning

confidence: 99%

Propensity Score Regression for Causal Inference With Treatment Heterogeneity

Wu¹,

Han²,

Tong³

et al. 2024

STAT SINICA

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Understanding how treatment effects vary on several key characteristics is critical in the practice of personalized medicine. To estimate these conditional average treatment effects, non-parametric estimation is often desirable, but few methods are available due to the computational difficulty. Existing non-parametric methods such as the inverse probability weighting methods have limitations that hinder their use in many practical settings where the values of propensity scores are close to 0 or 1. We propose the propensity score regression (PSR) that allows the non-parametric estimation of such conditional average treatment effects in a wide context. PSR includes two non-parametric regressions in turn, where it first regresses on the propensity scores together with the characteristics of interest, to obtain an intermediate estimate; and then, regresses the intermediate estimate on the characteristics of interest only. By including propensity scores †contributed equally.

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“…In case of remaining dependence between individual's potential outcomes, estimation of the (conditional) percentage of individuals that would benefit from a exposure is complicated but bounds have been obtained (Tian & Pearl 2000, Li & Pearl 2019, Mueller et al 2022. Furthermore, assuming conditional independence of the ICE and the potential outcome under no exposure, parametric latent variable models have been proposed to estimate the TBR (Yin et al 2018) and the (conditional) ICE distribution (Shahn & Madigan 2017).…”

Section: Introductionmentioning

confidence: 99%

“…When the variance component of the random exposure effect is small one could use the CATE as an appropriate proxy for the ICE of individuals in the subpopulation. Otherwise, the TBR and THR in the subpopulation could be estimated from the LMM fit, as proposed byYin et al (2018). Again, these TBR and THR estimates will only be accurate when U | M and N Y | M , L are (approximately) Gaussian distributed.…”

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confidence: 99%

On the distribution of individual causal effects of binary exposures using latent variable models

Post¹,

Heuvel²

2022

Preprint

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In recent years the field of causal inference from observational data has emerged rapidly. This literature has focused on (conditional) average causal effect estimation. When (remaining) variability of individual causal effects (ICEs) is considerable, average effects may be less informative, and possibly misleading for an individual. The fundamental problem of causal inference precludes the estimation of the joint distribution of potential outcomes without making assumptions, while this distribution is necessary to describe the heterogeneity of causal effects. In this paper we describe these assumptions and present a family of flexible latent variable models that can be used to study individual effect modification and estimate the ICE distribution from cross-sectional data. We will also discuss how the distribution is affected by misspecification of the error distribution or ignoring possible confounding-effect heterogeneity. How latent variable models can be applied and validated in practice is illustrated in a case study on the effect of Hepatic Steatosis on a clinical precursor to heart failure. Assuming that there is (i) no unmeasured confounding and (ii) independence of the individual effect modifier and the potential outcome under no exposure, we conclude that the individual causal effect distribution deviates from Gaussian. We estimate that the 'treatment' benefit rate in the population is 23.7% (95% Bayesian credible interval: 2.6%, 53.7%) despite a harming average effect.

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Assessing The Treatment Effect Heterogeity with a Latent Variable

Cited by 5 publications

References 7 publications

A permutation test for assessing the presence of individual differences in treatment effects

A permutation test for assessing the presence of individual differences in treatment effects

Propensity Score Regression for Causal Inference With Treatment Heterogeneity

On the distribution of individual causal effects of binary exposures using latent variable models

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