1,721,073 research outputs found
Proportion of treatment effect explained: An overview of interpretations
No full textThe selection of the primary endpoint in a clinical trial plays a critical role in determining the trial's success. Ideally, the primary endpoint is the clinically most relevant outcome, also termed the true endpoint. However, practical considerations, like extended follow-up, may complicate this choice, prompting the proposal to replace the true endpoint with so-called surrogate endpoints. Evaluating the validity of these surrogate endpoints is crucial, and a popular evaluation framework is based on the proportion of treatment effect explained (PTE). While methodological advancements in this area have focused primarily on estimation methods, interpretation remains a challenge hindering the practical use of the PTE. We review various ways to interpret the PTE. These interpretations-two causal and one non-causal-reveal connections between the PTE principal surrogacy, causal mediation analysis, and the prediction of trial-level treatment effects. A common limitation across these interpretations is the reliance on unverifiable assumptions. As such, we argue that the PTE is only meaningful when researchers are willing to make very strong assumptions. These challenges are also illustrated in an analysis of three hypothetical vaccine trials.The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Agentschap Innoveren & Ondernemen (VLAIO) and Janssen through a Baekeland Mandate [grant number HBC.2022.0145]
A family of measures to evaluate scale reliability in a longitudinal setting
No full textThe concept of reliability denotes one of the most important psychometric properties of a measurement scale. Reliability refers to the capacity of the scale to discriminate between subjects in a given population. In classical test theory, it is often estimated by using the intraclass correlation coefficient based on two replicate measurements. However, the modelling framework that is used in this theory is often too narrow when applied in practical situations. Generalizability theory has extended reliability theory to a much broader framework but is confronted with some limitations when applied in a longitudinal setting. We explore how the definition of reliability can be generalized to a setting where subjects are measured repeatedly over time. On the basis of four defining properties for the concept of reliability, we propose a family of reliability measures which circumscribes the area in which reliability measures should be sought. It is shown how different members assess different aspects of the problem and that the reliability of the instrument can depend on the way that it is used. The methodology is motivated by and illustrated on data from a clinical study on schizophrenia. On the basis of this study, we estimate and compare the reliabilities of two different rating scales to evaluate the severity of the disorder. Copyright (c) 2009 Royal Statistical Society.
Identifying individual predictive factors for treatment efficacy
No full textGiven the heterogeneous responses to therapy and the high cost of treatments, there is an increasing interest in identifying pretreatment predictors of therapeutic effect. Clearly, the success of such an endeavor will depend on the amount of information that the patient-specific variables convey about the individual causal treatment effect on the response of interest. In the present work, using causal inference and information theory, a strategy is proposed to evaluate individual predictive factors for cancer immunotherapy efficacy. In a first step, the methodology proposes a causal inference model to describe the joint distribution of the pretreatment predictors and the individual causal treatment effect. Further, in a second step, the so-called predictive causal information (PCI), a metric that quantifies the amount of information the pretreatment predictors convey on the individual causal treatment effects, is introduced and its properties are studied. The methodology is applied to identify predictors of therapeutic success for a therapeutic vaccine in advanced lung cancer. A user-friendly R library EffectTreat is provided to carry out the necessary calculations.Alonso, A (corresponding author), Katholieke Univ Leuven, I BioStat, B-3000 Leuven, Belgium.
[email protected]
A reflection on the possibility of finding a good surrogate
No full textSurrogate endpoints need to be statistically evaluated before they can be used as substitutes of true endpoints in clinical studies. However, even though several evaluation methods have been introduced over the last decades, the identification of good surrogate endpoints remains practically and conceptually challenging. In the present work, the question regarding the existence of a good surrogate is addressed using information-theoretic concepts, within a causal-inference framework. The methodology can help practitioners to assess, given a clinically relevant true endpoint and a treatment of interest, the chances of finding a good surrogate endpoint in the first place. The methodology focuses on binary outcomes and is illustrated using data from the Initial Glaucoma Treatment Study. Furthermore, a newly developed and user friendly R package Surrogate is provided to carry out the necessary calculations.status: Publishe
Evaluating time-to-event surrogates for time-to-event true endpoints: an information-theoretic approach based on causal inference
No full textPutative surrogate endpoints must undergo a rigorous statistical evaluation before they can be used in clinical trials. Numerous frameworks have been introduced for this purpose. In this study, we extend the scope of the information-theoretic causal-inference approach to encompass scenarios where both outcomes are time-to-event endpoints, using the flexibility provided by D-vine copulas. We evaluate the quality of the putative surrogate using the individual causal association (ICA)-a measure based on the mutual information between the individual causal treatment effects. However, in spite of its appealing mathematical properties, the ICA may be ill defined for composite endpoints. Therefore, we also propose an alternative rank-based metric for assessing the ICA. Due to the fundamental problem of causal inference, the joint distribution of all potential outcomes is only partially identifiable and, consequently, the ICA cannot be estimated without strong unverifiable assumptions. This is addressed by a formal sensitivity analysis that is summarized by the so-called intervals of ignorance and uncertainty. The frequentist properties of these intervals are discussed in detail. Finally, the proposed methods are illustrated with an analysis of pooled data from two advanced colorectal cancer trials. The newly developed techniques have been implemented in the R package Surrogate.I. Van Keilegom received funding from the Research Foundation – Flanders and Fonds de la Recherche Scientifque – FNRS under the Excellence of Science (EOS) programme, project ASTeRISK [grant number 40007517]. F. Stijven received funding from Agentschap Innoveren & Ondernemen and Janssen through a Baekeland Mandate [grant number HBC.2022.0145]. The resources and services used in this work were provided by the Flemish Supercomputer Center, funded by the Research Foundation – Flanders and the Flemish Government
A reflection on the causal interpretation of individual-level surrogacy
No full textAt the beginning of the 21st century, a new paradigm was introduced for the evaluation of surrogate endpoints based on meta-analysis. In this paradigm, the putative surrogate is assessed at two different levels, the so-called, trial and individual level. Trial level surrogacy is defined as the association between the expected causal treatment effects across different trials populations, whereas the individual level is defined as the association between the surrogate and true endpoints, after adjusting by trial and treatment. It has been argued that the individual level surrogacy does not have a causal interpretation and, consequently, it is a poor metric of surrogacy. In the present work, an alternative definition of individual level surrogacy is introduced based on individual causal treatment effects. In addition, using the maximum entropy principle, a direct link between the individual level surrogacy, as defined in the meta-analytic approach, and the newly proposed definition is established. This new perspective sets the individual level surrogacy in a more coherent framework with respect to the trial level and bridges the two main schools of thought in this domain, namely, the causal inference and meta-analytic schools.status: Publishe
Fast and efficient joint modelling of multivariate longitudinal data and time-to-event data with a pairwise-fitting approach
No full textIn empirical studies, multiple outcomes are often measured repeatedly over time, and interest frequently lies in studying the association between these longitudinal outcomes and a time-to-event outcome. Therefore, shared-parameter joint models for longitudinal and time-to-event outcomes have been developed. However, while such joint models in theory also allow for multiple longitudinal outcomes, they are often restricted to a limited number of outcomes due to computational complexity when fitting the models. To address this problem, we propose a new joint model, which is based on correlated instead of shared random effects, and for which a pairwise-modelling strategy can be used. In this approach, the longitudinal outcomes are modelled with (generalized) linear mixed models and the survival outcome with a Weibull proportional hazards frailty model. Instead of fitting the full joint model, this approach involves fitting all possible bivariate models, and inference is based on pseudo-likelihood theory. The main advantage of our approach is that there is no restriction on the number of longitudinally measured outcomes that are jointly modelled with the time-to-event outcome.The authors received no financial support for the research, authorship and/or publication of this article
An information-theoretic approach for the evaluation of surrogate endpoints based on causal inference
No full textIn this work a new metric of surrogacy, the so-called individual causal association (ICA), is introduced using information-theoretic concepts and a causal inference model for a binary surrogate and true endpoint. The ICA has a simple and appealing interpretation in terms of uncertainty reduction and, in some scenarios, it seems to provide a more coherent assessment of the validity of a surrogate than existing measures. The identifiability issues are tackled using a two-step procedure. In the first step, the region of the parametric space of the distribution of the potential outcomes, compatible with the data at hand, is geometrically characterized. Further, in a second step, a Monte Carlo approach is proposed to study the behavior of the ICA on the previous region. The method is illustrated using data from the Collaborative Initial Glaucoma Treatment Study. A newly developed and user-friendly R package Surrogate is provided to carry out the evaluation exercise.sponsorship: Financial support from the IAP research network #P7/06 of the Belgian Government (Belgian Science Policy) is gratefully acknowledged. This research has also received funding from the European Seventh Framework programme [FP7 2007-2013] under grant agreement 602552. The authors gratefully acknowledge Dr David Musch, Coordinating Center Director and Brenda Gillespie, Study Statistician for providing the data from the CIGTS study. (IAP research network of the Belgian Government (Belgian Science Policy)|P7/06, European Seventh Framework programme [FP7]|602552)status: Publishe
- …
