Search

Within the causal association paradigm, a method is proposed to assess the validity of a continuous outcome as a surrogate for a binary true endpoint. The methodology is based on a previously introduced information‐theoretic definition of surrogacy and has two main steps. In the first step, a new model is proposed to describe the joint distribution of the potential outcomes associated with the putative surrogate and the true endpoint of interest. The identifiability issues inherent to this type of models are handled via sensitivity analysis. In the second step, a metric of surrogacy new to this setting, the so‐called individual causal association is presented. The methodology is studied in detail using theoretical considerations, some simulations, and data from a randomized clinical trial evaluating an inactivated quadrivalent influenza vaccine. A user‐friendly R package Surrogate is provided to carry out the evaluation exercise. [ABSTRACT FROM AUTHOR]

Given 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. [ABSTRACT FROM AUTHOR]

In the meta‐analytic surrogate evaluation framework, the trial‐level coefficient of determination Rtrial2 quantifies the strength of the association between the expected causal treatment effects on the surrogate (S) and the true (T) endpoints. Burzykowski and Buyse supplemented this metric of surrogacy with the surrogate threshold effect (STE), which is defined as the minimum value of the causal treatment effect on S for which the predicted causal treatment effect on T exceeds zero. The STE supplements Rtrial2 with a more direct clinically interpretable metric of surrogacy. Alonso et al. proposed to evaluate surrogacy based on the strength of the association between the individual (rather than expected) causal treatment effects on S and T. In the current paper, the individual‐level surrogate threshold effect (ISTE) is introduced in the setting where S and T are normally distributed variables. ISTE is defined as the minimum value of the individual causal treatment effect on S for which the lower limit of the prediction interval around the individual causal treatment effect on T exceeds zero. The newly proposed methodology is applied in a case study, and it is illustrated that ISTE has an appealing clinical interpretation. The R package surrogate implements the methodology and a web appendix (supporting information) that details how the analyses can be conducted in practice is provided. [ABSTRACT FROM AUTHOR]

Abstract–In spite of medical and methodological advances, the identification of good surrogate endpoints has remained a challenging endeavor. This may, at least partially, be attributable to the fact that most researchers have only focused on univariate surrogates endpoints. In the present work, we argue in favor of using multivariate surrogates and introduce two new complementary metrics to assess their validity. The first one, the so-called individual causal association, quantifies the association between the individual causal treatment effects on the multivariate surrogate and true endpoints, while the second one quantifies the treatment-corrected association between the multivariate surrogate and the true endpoint outcomes. The newly proposed methodology is implemented in the R package Surrogate and a Web Appendix, detailing how the analysis can be conducted in practice, is provided. for this article are available online. [ABSTRACT FROM AUTHOR]

Surrogate 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. [ABSTRACT FROM AUTHOR]

In 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. [ABSTRACT FROM AUTHOR]

ABSTRACT In a causal inference framework, a new metric has been proposed to quantify surrogacy for a continuous putative surrogate and a binary true endpoint, based on information theory. The proposed metric, termed the individual causal association (ICA), was quantified using a joint causal inference model for the corresponding potential outcomes. Due to the non‐identifiability inherent in this type of models, a sensitivity analysis was introduced to study the behavior of the ICA as a function of the non‐identifiable parameters characterizing the aforementioned model. In this scenario, to reduce uncertainty, several plausible yet untestable assumptions like monotonicity, independence, conditional independence or homogeneous variance–covariance, are often incorporated into the analysis. We assess the robustness of the methodology regarding these simplifying assumptions via simulation. The practical implications of the findings are demonstrated in the analysis of a randomized clinical trial evaluating an inactivated quadrivalent influenza vaccine. [ABSTRACT FROM AUTHOR]