Evaluating time-to-event surrogates for time-to-event true endpoints: an information-theoretic approach based on causal inference.

Putative 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.

Testing for Sufficient Follow-Up in Censored Survival Data by Using Extremes.

In survival analysis, it often happens that some individuals, referred to as cured individuals, never experience the event of interest. When analyzing time-to-event data with a cure fraction, it is crucial to check the assumption of "sufficient follow-up," which means that the right extreme of the censoring time distribution is larger than that of the survival time distribution for the noncured individuals. However, the available methods to test this assumption are limited in the literature.

Assessing the Operational Characteristics of the Individual Causal Association as a Metric of Surrogacy in the Binary Continuous Setting.

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.

Predicting time-to-intubation after critical care admission using machine learning and cured fraction information.

Intubation for mechanical ventilation (MV) is one of the most common high-risk procedures performed in Intensive Care Units (ICUs). Early prediction of intubation may have a positive impact by providing timely alerts to clinicians and consequently avoiding high-risk late intubations. In this work, we propose a new machine learning method to predict the time to intubation during the first five days of ICU admission, based on the concept of cure survival models.

On variable selection in a semiparametric AFT mixture cure model.

In clinical studies, one often encounters time-to-event data that are subject to right censoring and for which a fraction of the patients under study never experience the event of interest. Such data can be modeled using cure models in survival analysis. In the presence of cure fraction, the mixture cure model is popular, since it allows to model probability to be cured (called the incidence) and the survival function of the uncured individuals (called the latency).

An information-theoretic approach for the assessment of a continuous outcome as a surrogate for a binary true endpoint based on causal inference: Application to vaccine evaluation.

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.

A nonparametric instrumental approach to confounding in competing risks models.

This paper discusses nonparametric identification and estimation of the causal effect of a treatment in the presence of confounding, competing risks and random right-censoring. Our identification strategy is based on an instrumental variable. We show that the competing risks model generates a nonparametric quantile instrumental regression problem.

Discussion on "Instrumented difference-in-differences" by Ting Ye, Ashkan Ertefaie, James Flory, Sean Hennessy, and Dylan S. Small.

We discuss Ye et al. 2022, which combines instrumental variables methods with difference in differences. First, we compare the paper to other works in the difference in differences literatures and argue that the main contribution lies in the multiply robust estimation approach.

Goodness-of-fit tests in proportional hazards models with random effects.

This paper deals with testing the functional form of the covariate effects in a Cox proportional hazards model with random effects. We assume that the responses are clustered and incomplete due to right censoring. The estimation of the model under the null (parametric covariate effect) and the alternative (nonparametric effect) is performed using the full marginal likelihood.

Semiparametric estimation of the nonmixture cure model with auxiliary survival information.

With rapidly increasing data sources, statistical methods that make use of external information are gradually becoming popular tools in medical research. In this article, we efficiently synthesize the auxiliary survival information and propose a semiparametric estimation method for the combined empirical likelihood in the framework of the nonmixture cure model, to enhance inference about the associations between exposures and disease outcomes. The auxiliary survival probabilities from external sources are first summarized as unbiased estimation equations, which help produce more efficient estimates of the effects of interest and improve the prediction accuracy for the risk of the event.

Infectious diseases epidemiology, quantitative methodology, and clinical research in the midst of the COVID-19 pandemic: Perspective from a European country.

Starting from historic reflections, the current SARS-CoV-2 induced COVID-19 pandemic is examined from various perspectives, in terms of what it implies for the implementation of non-pharmaceutical interventions, the modeling and monitoring of the epidemic, the development of early-warning systems, the study of mortality, prevalence estimation, diagnostic and serological testing, vaccine development, and ultimately clinical trials. Emphasis is placed on how the pandemic had led to unprecedented speed in methodological and clinical development, the pitfalls thereof, but also the opportunities that it engenders for national and international collaboration, and how it has simplified and sped up procedures. We also study the impact of the pandemic on clinical trials in other indications.

Nonparametric covariate hypothesis tests for the cure rate in mixture cure models.

In lifetime data, like cancer studies, there may be long term survivors, which lead to heavy censoring at the end of the follow-up period. Since a standard survival model is not appropriate to handle these data, a cure model is needed. In the literature, covariate hypothesis tests for cure models are limited to parametric and semiparametric methods.

Flexible parametric model for survival data subject to dependent censoring.

When modeling survival data, it is common to assume that the (log-transformed) survival time (T) is conditionally independent of the (log-transformed) censoring time (C) given a set of covariates. There are numerous situations in which this assumption is not realistic, and a number of correction procedures have been developed for different models. However, in most cases, either some prior knowledge about the association between T and C is required, or some auxiliary information or data is/are supposed to be available.

Hybrid combinations of parametric and empirical likelihoods.

This paper develops a hybrid likelihood (HL) method based on a compromise between parametric and nonparametric likelihoods. Consider the setting of a parametric model for the distribution of an observation with parameter . Suppose there is also an estimating function (·, ) identifying another parameter via E(, ) = 0, at the outset defined independently of the parametric model.

The single-index/Cox mixture cure model.

In survival analysis, it often happens that a certain fraction of the subjects under study never experience the event of interest, that is, they are considered "cured." In the presence of covariates, a common model for this type of data is the mixture cure model, which assumes that the population consists of two subpopulations, namely the cured and the non-cured ones, and it writes the survival function of the whole population given a set of covariates as a mixture of the survival function of the cured subjects (which equals one), and the survival function of the non-cured ones. In the literature, one usually assumes that the mixing probabilities follow a logistic model.

Flexible parametric approach to classical measurement error variance estimation without auxiliary data.

Measurement error in the continuous covariates of a model generally yields bias in the estimators. It is a frequent problem in practice, and many correction procedures have been developed for different classes of models. However, in most cases, some information about the measurement error distribution is required.

Inference in a survival cure model with mismeasured covariates using a simulation-extrapolation approach.

In many situations in survival analysis, it may happen that a fraction of individuals will never experience the event of interest: they are considered to be cured. The promotion time cure model takes this into account. We consider the case where one or more explanatory variables in the model are subject to measurement error, which should be taken into account to avoid biased estimators.

Robust estimation for homoscedastic regression in the secondary analysis of case-control data.

Primary analysis of case-control studies focuses on the relationship between disease and a set of covariates of interest (, ). A secondary application of the case-control study, which is often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated owing to the case-control sampling, where the regression of on is different from what it is in the population.

A simulation procedure based on copulas to generate clustered multi-state survival data.

Generating survival data with a clustered and multi-state structure is useful to study finite sample properties of multi-state models, competing risks models and frailty models. We propose a simulation procedure based on a copula model for each competing events block, allowing to introduce dependence between times of different transitions and between those of grouped subjects. The effect of simulated frailties and covariates can be added in a proportional hazards way.