arXiv subjects

Pablo Martinez-Camblor

Publications and source records attributed to Pablo Martinez-Camblor.

4 recordsLinked to original sources

arXiv2025

Using functional information for binary classifications

The adequate use of information measured in a continuous manner along a period of time represents a methodological challenge. In the last decades, most of traditional statistical procedures have been extended for accommodating these functional data. The binary classification problem, which aims to correctly identify units as positive or negative based on marker values, is not aside of this scenario. The crucial point for making binary classifications based on a marker is to establish an order in the marker values, which is not immediate when these values are presented as functions. Here, we argue that if the marker is related to the characteristic under study, a trajectory from a positive participant should be more similar to trajectories from the positive population than to those drawn from the negative. With this criterion, a classification procedure based on the distance between the involved functions is proposed. Besides, we propose a fully non-parametric estimator for this so-called probability-based criterion, PBC. We explore its asymptotic properties, and its finite-sample behavior from an extensive Monte Carlo study. The observed results suggest that the proposed methodology works adequately, and frequently better than its competitors, for a wide variety of situations when the sample size in both the training and the testing cohorts is adequate. The practical use of the proposal is illustrated from real-world dataset. As online supplementary material, the manuscript includes a document with further simulations and additional comments. An R function which wraps up the implemented routines is also provided.

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arXiv2023

Comparing the effectiveness of k-different treatments through the area under the ROC curve

The area under the receiver-operating characteristic curve (AUC) has become a popular index not only for measuring the overall prediction capacity of a marker but also the association strength between continuous and binary variables. In the current study, it has been used for comparing the association size of four different interventions involving impulsive decision making, studied through an animal model, in which each animal provides several negative (pre-treatment) and positive (post-treatment) measures. The problem of the full comparison of the average AUCs arises therefore in a natural way. We construct an analysis of variance (ANOVA) type test for testing the equality of the impact of these treatments measured through the respective AUCs, and considering the random-effect represented by the animal. The use (and development) of a post-hoc Tukey's HSD type test is also considered. We explore the finite-sample behavior of our proposal via Monte Carlo simulations, and analyze the data generated from the original problem. An R package implementing the procedures is provided as supplementary material.

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arXiv2021

Estimating populational-average hazard ratios in the presence of unmeasured confounding

The Cox regression model and its associated hazard ratio (HR) are frequently used for summarizing the effect of treatments on time to event outcomes. However, the HR's interpretation strongly depends on the assumed underlying survival model. The challenge of interpreting the HR has been the focus of a number of recent works. Besides, several alternative measures have been proposed in order to deal with these concerns. The marginal Cox regression models include an identifiable hazard ratio without individual but populational causal interpretation. In this work, we study the properties of one particular marginal Cox regression model and consider its estimation in the presence of omitted confounder. We prove the large sample consistency of an estimation score which allows non-binary treatments. Our Monte Carlo simulations suggest that finite sample behavior of the procedure is adequate. The studied estimator is more robust than its competitors for weak instruments although it is slightly more biased for large effects of the treatment. The practical use of the presented techniques is illustrated through a real practical example using data from the vascular quality initiative registry. The used R code is provided as Supplementary Material.

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arXiv2017

Adjusting for bias introduced by instrumental variable estimation in the Cox Proportional Hazards Model

Instrumental variable (IV) methods are widely used for estimating average treatment effects in the presence of unmeasured confounders. However, the capability of existing IV procedures, and most notably the two-stage residual inclusion (2SRI) procedure recommended for use in nonlinear contexts, to account for unmeasured confounders in the Cox proportional hazard model is unclear. We show that instrumenting an endogenous treatment induces an unmeasured covariate, referred to as an individual frailty in survival analysis parlance, which if not accounted for leads to bias. We propose a new procedure that augments 2SRI with an individual frailty and prove that it is consistent under certain conditions. The finite sample-size behavior is studied across a broad set of conditions via Monte Carlo simulations. Finally, the proposed methodology is used to estimate the average effect of carotid endarterectomy versus carotid artery stenting on the mortality of patients suffering from carotid artery disease. Results suggest that the 2SRI-frailty estimator generally reduces the bias of both point and interval estimators compared to traditional 2SRI.

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