ORCID Profile
0000-0003-1804-2463
Current Organisations
University of Zurich
,
Universitat Zurich
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Elsevier BV
Date: 02-2017
DOI: 10.1016/J.VETPAR.2016.12.007
Abstract: The prevalence of anthelmintic resistance has increased in recent years, as a result of the extensive use of anthelmintic drugs to reduce the infection of parasitic worms in livestock. In order to detect the resistance, the number of parasite eggs in animal faeces is counted. Typically a subs le of the diluted faeces is examined, and the mean egg counts from both untreated and treated animals are compared. However, the conventional method ignores the variabilities introduced by the counting process and by different infection levels across animals. In addition, there can be extra zero counts, which arise as a result of the unexposed animals in an infected population or animals. In this paper, we propose the zero-inflated Bayesian hierarchical models to estimate the reduction in faecal egg counts. The simulation study compares the Bayesian models with the conventional faecal egg count reduction test and other methods such as bootstrap and quasi-Poisson regression. The results show the Bayesian models are more robust and they perform well in terms of both the bias and the coverage. We further illustrate the advantages of our proposed model using a case study about the anthelmintic resistance in Swedish sheep flocks.
Publisher: Elsevier BV
Date: 12-2018
Publisher: IEEE
Date: 04-2015
Publisher: Wiley
Date: 23-08-2022
DOI: 10.1002/PST.2260
Abstract: In the context of clinical trials, there is interest in the treatment effect for subpopulations of patients defined by intercurrent events, namely disease‐related events occurring after treatment initiation that affect either the interpretation or the existence of endpoints. With the principal stratum strategy, the ICH E9(R1) guideline introduces a formal framework in drug development for defining treatment effects in such subpopulations. Statistical estimation of the treatment effect can be performed based on the principal ignorability assumption using multiple imputation approaches. Principal ignorability is a conditional independence assumption that cannot be directly verified therefore, it is crucial to evaluate the robustness of results to deviations from this assumption. As a sensitivity analysis, we propose a joint model that multiply imputes the principal stratum membership and the outcome variable while allowing different levels of violation of the principal ignorability assumption. We illustrate with a simulation study that the joint imputation model‐based approaches are superior to naive subpopulation analyses. Motivated by an oncology clinical trial, we implement the sensitivity analysis on a time‐to‐event outcome to assess the treatment effect in the subpopulation of patients who discontinued due to adverse events using a synthetic dataset. Finally, we explore the potential usage and provide interpretation of such analyses in clinical settings, as well as possible extension of such models in more general cases.
Publisher: Elsevier BV
Date: 03-2018
Publisher: Wiley
Date: 2019
DOI: 10.1002/STA4.216
Publisher: Wiley
Date: 20-11-2017
DOI: 10.1002/SIM.7549
Abstract: Information from historical trials is important for the design, interim monitoring, analysis, and interpretation of clinical trials. Meta-analytic models can be used to synthesize the evidence from historical data, which are often only available in aggregate form. We consider evidence synthesis methods for trials with recurrent event endpoints, which are common in many therapeutic areas. Such endpoints are typically analyzed by negative binomial regression. However, the in idual patient data necessary to fit such a model are usually unavailable for historical trials reported in the medical literature. We describe approaches for back-calculating model parameter estimates and their standard errors from available summary statistics with various techniques, including approximate Bayesian computation. We propose to use a quadratic approximation to the log-likelihood for each historical trial based on 2 independent terms for the log mean rate and the log of the dispersion parameter. A Bayesian hierarchical meta-analysis model then provides the posterior predictive distribution for these parameters. Simulations show this approach with back-calculated parameter estimates results in very similar inference as using parameter estimates from in idual patient data as an input. We illustrate how to design and analyze a new randomized placebo-controlled exacerbation trial in severe eosinophilic asthma using data from 11 historical trials.
No related grants have been discovered for Craig Wang.