arXiv subjects

Craig Wang

Publications and source records attributed to Craig Wang.

7 recordsLinked to original sources

arXiv2025

Tipping Point Sensitivity Analysis for Missing Data in Time-to-Event Endpoints: Model-Based and Ad hoc Approaches

Treatment policy estimands are frequently favored by regulators, as they assess the effect of treatment assignment regardless of post-randomization events. Despite best efforts, missing data due to study discontinuation cannot be fully avoided and, for time-to-event endpoints, typically manifests as right censoring. Study discontinuation is often more likely following intercurrent events, particularly when it coincides with treatment discontinuation, raising concerns about violations of the independent censoring assumption. Although the independent censoring assumption is routinely adopted for the main analyses, it may be unrealistic in practice and could lead to biased estimation of the treatment effect under the treatment policy estimand. Tipping-point analyses provide a structured framework to assess the robustness of trial conclusions to departures from the independent censoring assumption. This paper describes and contrasts model-based and two ad hoc tipping point approaches, which involve "landmark" or "percentile sampling" based imputation. We illustrate their application using re-constructed examples based on real clinical trials, highlighting their underlying assumptions and implications for interpretation and clinical plausibility assessments of different tipping point approaches.

stat.ME↗

arXiv2024

Efficiency of nonparametric superiority tests based on restricted mean survival time versus the log-rank test under proportional hazards

Background: For RCTs with time-to-event endpoints, proportional hazard (PH) models are typically used to estimate treatment effects and logrank tests are commonly used for hypothesis testing. There is growing support for replacing this approach with a model-free estimand and assumption-lean analysis method. One alternative is to base the analysis on the difference in restricted mean survival time (RMST) at a specific time, a single-number summary measure that can be defined without any restrictive assumptions on the outcome model. In a simple setting without covariates, an assumption-lean analysis can be achieved using nonparametric methods such as Kaplan Meier estimation. The main advantage of moving to a model-free summary measure and assumption-lean analysis is that the validity and interpretation of conclusions do not depend on the PH assumption. The potential disadvantage is that the nonparametric analysis may lose efficiency under PH. There is disagreement in recent literature on this issue. Methods: Asymptotic results and simulations are used to compare the efficiency of a log-rank test against a nonparametric analysis of the difference in RMST in a superiority trial under PH. Previous studies have separately examined the effect of event rates and the censoring distribution on relative efficiency. This investigation clarifies conflicting results from earlier research by exploring the joint effect of event rate and censoring distribution together. Several illustrative examples are provided. Results: In scenarios with high event rates and/or substantial censoring across a large proportion of the study window, and when both methods make use of the same amount of data, relative efficiency is close to unity. However, in cases with low event rates but when censoring is concentrated at the end of the study window, the PH analysis has a considerable efficiency advantage.

stat.ME↗

arXiv2024

Image registration based automated lesion correspondence pipeline for longitudinal CT data

Patients diagnosed with metastatic breast cancer (mBC) typically undergo several radiographic assessments during their treatment. mBC often involves multiple metastatic lesions in different organs, it is imperative to accurately track and assess these lesions to gain a comprehensive understanding of the disease's response to treatment. Computerized analysis methods that rely on lesion-level tracking have often used manual matching of corresponding lesions, a time-consuming process that is prone to errors. This paper introduces an automated lesion correspondence algorithm designed to precisely track both targets' lesions and non-targets' lesions in longitudinal data. Here we demonstrate the applicability of our algorithm on the anonymized data from two Phase III trials. The dataset contains imaging data of patients for different follow-up timepoints and the radiologist annotations for the patients enrolled in the trials. Target and non-target lesions are annotated by either one or two groups of radiologists. To facilitate accurate tracking, we have developed a registration-assisted lesion correspondence algorithm. The algorithm employs a sequential two-step pipeline: (a) Firstly, an adaptive Hungarian algorithm is used to establish correspondence among lesions within a single volumetric image series which have been annotated by multiple radiologists at a specific timepoint. (b) Secondly, after establishing correspondence and assigning unique names to the lesions, three-dimensional rigid registration is applied to various image series at the same timepoint. Registration is followed by ongoing lesion correspondence based on the adaptive Hungarian algorithm and updating lesion names for accurate tracking. Validation of our automated lesion correspondence algorithm is performed through triaxial plots based on axial, sagittal, and coronal views, confirming its efficacy in matching lesions.

eess.IV↗

arXiv2023

Evaluating causal effects on time-to-event outcomes in an RCT in Oncology with treatment discontinuation

In clinical trials, patients may discontinue treatments prematurely, breaking the initial randomization and, thus, challenging inference. Stakeholders in drug development are generally interested in going beyond the Intention-To-Treat (ITT) analysis, which provides valid causal estimates of the effect of treatment assignment but does not inform on the effect of the actual treatment receipt. Our study is motivated by an RCT in oncology, where patients assigned the investigational treatment may discontinue it due to adverse events. We propose adopting a principal stratum strategy and decomposing the overall ITT effect into principal causal effects for groups of patients defined by their potential discontinuation behavior. We first show how to implement a principal stratum strategy to assess causal effects on a survival outcome in the presence of continuous time treatment discontinuation, its advantages, and the conclusions one can draw. Our strategy deals with the time-to-event intermediate variable that may not be defined for patients who would not discontinue; moreover, discontinuation time and the primary endpoint are subject to censoring. We employ a flexible model-based Bayesian approach to tackle these complexities, providing easily interpretable results. We apply this Bayesian principal stratification framework to analyze synthetic data of the motivating oncology trial. We simulate data under different assumptions that reflect real scenarios where patients' behavior depends on critical baseline covariates. Supported by a simulation study, we shed light on the role of covariates in this framework: beyond making structural and parametric assumptions more credible, they lead to more precise inference and can be used to characterize patients' discontinuation behavior, which could help inform clinical practice and future protocols.

stat.AP↗

arXiv2022

EggCounts: a Bayesian hierarchical toolkit to model faecal egg count reductions

This is a vignette for the R package eggCounts version 2.0. The package implements a suite of Bayesian hierarchical models dealing with faecal egg count reductions. The models are designed for a variety of practical situations, including individual treatment efficacy, zero inflation, small sample size (less than 10) and potential outliers. The functions are intuitive to use and their output are easy to interpret, such that users are protected from being exposed to complex Bayesian hierarchical modelling tasks. In addition, the package includes plotting functions to display data and results in a visually appealing manner. The models are implemented in Stan modelling language, which provides efficient sampling technique to obtain posterior samples. This vignette briefly introduces different models, and provides a short walk-through analysis with example data.

stat.CO↗

arXiv2019

Combining Heterogeneous Spatial Datasets with Process-based Spatial Fusion Models: A Unifying Framework

In modern spatial statistics, the structure of data that is collected has become more heterogeneous. Depending on the type of spatial data, different modeling strategies for spatial data are used. For example, a kriging approach for geostatistical data; a Gaussian Markov random field model for lattice data; or a log Gaussian Cox process for point-pattern data. Despite these different modeling choices, the nature of underlying scientific data-generating (latent) processes is often the same, which can be represented by some continuous spatial surfaces. In this paper, we introduce a unifying framework for process-based multivariate spatial fusion models. The framework can jointly analyze all three aforementioned types of spatial data (or any combinations thereof). Moreover, the framework accommodates different conditional distributions for geostatistical and lattice data. We show that some established approaches, such as linear models of coregionalization, can be viewed as special cases of our proposed framework. We offer flexible and scalable implementations in R using Stan and INLA. Simulation studies confirm that the predictive performance of latent processes improves as we move from univariate spatial models to multivariate spatial fusion models. The introduced framework is illustrated using a cross-sectional study linked with a national cohort dataset in Switzerland, we examine differences in underlying spatial risk patterns between respiratory disease and lung cancer.

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arXiv2016

Local-likelihood transformation kernel density estimation for positive random variables

The kernel estimator is known not to be adequate for estimating the density of a positive random variable X. The main reason is the well-known boundary bias problems that it suffers from, but also its poor behaviour in the long right tail that such a density typically exhibits. A natural approach to this problem is to first estimate the density of the logarithm of X, and obtaining an estimate of the density of X using standard results on functions of random variables (`back-transformation'). Although intuitive, the basic application of this idea yields very poor results, as was documented earlier in the literature. In this paper, the main reason for this underachievement is identified, and an easy fix is suggested. It is demonstrated that combining the transformation with local likelihood density estimation methods produces very good estimators of R+-supported densities, not only close to the boundary, but also in the right tail. The asymptotic properties of the proposed `local likelihood transformation kernel density estimators' are derived for a generic transformation, not only for the logarithm, which allows one to consider other transformations as well. One of them, called the `probex' transformation, is given more focus. Finally, the excellent behaviour of those estimators in practice is evidenced through a comprehensive simulation study and the analysis of several real data sets. A nice consequence of articulating the method around local-likelihood estimation is that the resulting density estimates are typically smooth and visually pleasant, without oversmoothing important features of the underlying density.

stat.ME↗