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Journal ArticleDOI

Changepoint inference in the presence of missing covariates for principal surrogate evaluation in vaccine trials

Tao Yang, +2 more
- 15 Nov 2021 - 
- Vol. 108, Iss: 4, pp 829-843
TLDR
A regression methodology that allows joint estimation of all model parameters as well as a two-step method that separates the estimation of the threshold parameter from the rest of the parameters is developed and the asymptotic properties of the proposed estimators are established.
Abstract
We consider the use of threshold-based regression models for evaluating immune response biomarkers as principal surrogate markers of a vaccine's protective effect. Threshold-based regression models, which allow the relationship between a clinical outcome and a covariate to change dramatically across a threshold value in the covariate, have been studied by various authors under fully observed data. Limited research, however, has examined these models in the presence of missing covariates, such as the counterfactual potential immune responses of a participant in the placebo arm of a standard vaccine trial had s/he been assigned to the vaccine arm instead. Based on a hinge model for a threshold effect of the principal surrogate on vaccine efficacy, we develop a regression methodology that consists of two components: (1) The estimated likelihood method is employed to handle missing potential outcomes, and (2) a penalty is imposed on the estimated likelihood to ensure satisfactory finite sample performance. We develop a method that allows joint estimation of all model parameters as well as a two-step method that separates the estimation of the threshold parameter from the rest of the parameters. Stable iterative algorithms are developed to implement the two methods and the asymptotic properties of the proposed estimators are established. In simulation studies, the proposed estimators are shown to have satisfactory finite sample performance. The proposed methods are applied to analyze a real dataset collected from dengue vaccine efficacy trials to predict how vaccine efficacy varies with an individual's potential immune response if receiving vaccine.

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Posted Content

Online network change point detection with missing values.

TL;DR: In this article, a polynomial-time change point detection algorithm was proposed for dynamic networks with heterogeneous missing pattern across the networks and the time course, where the missingness probabilities, the networks' entrywise sparsity, the rank of the networks, and the jump size in terms of the Frobenius norm, are all allowed to vary as functions of the pre-change sample size.
References
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Journal ArticleDOI

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David Firth
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Journal ArticleDOI

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Journal ArticleDOI

Correlates of Protection Induced by Vaccination

TL;DR: This paper attempts to summarize current knowledge about immune responses to vaccines that correlate with protection, finding some vaccines have no true correlates, but only useful surrogates, for an unknown protective response.
Journal ArticleDOI

On the existence of maximum likelihood estimates in logistic regression models

TL;DR: For multinomial logistic regression models, this article proved existence theorems by considering the possible patterns of data points, which fall into three mutually exclusive and exhaustive categories: complete separation, quasicomplete separation and overlap.
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