Mauro Gasparini
A full Bayesian approach to the estimation of Vaccine Efficacy is presented, which is an improvement over the currently used exact method conditional on the total number of cases. As an example, we reconsider the statistical sections of the BioNTech/Pfizer protocol, which in 2020 has led to the first approved anti-Covid-19 vaccine.
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Lessons Learned from the Bayesian Design and Analysis for the BNT162b2 COVID-19 Vaccine Phase 3 Trial
针对BNT162b2新冠疫苗3期贝叶斯试验结果表述的混淆问题，阐明贝叶斯与常规统计报告的差异，提出四项规范建议及用BI区分贝叶斯可信区间与CI的方案，以提升临床决策支持效果。
Non-parametric Bayesian mixture model to study adverse events of COVID-19 vaccines
提出带Dirichlet过程混合先验并嵌入阴性对照程序的贝叶斯模型，以提升小计数不良事件估计并校正报告偏倚，模拟与VAERS数据验证了其有效性。
In vaccine studies for infectious diseases such as human immunodeficiency virus (HIV), the frequency and type of contacts between study participants and infectious sources are among the most informative risk factors, but are often not adequately adjusted for in standard analyses. Such adjustment can improve the assessment of vaccine efficacy as well as the assessment of risk factors. It can be attained by modeling transmission per contact with infectious sources. However, information about contacts that rely on self-reporting by study participants are subject to nontrivial measurement error in…
Studying Disease Reinfection Rates, Vaccine Efficacy and the Timing of Vaccine Rollout in the context of Infectious Diseases
本研究针对卡塔尔COVID-19疫情，构建两个贝叶斯模型分析疫苗效力与接种时机，发现早期接种可显著减少感染、再感染和死亡，主张优先早期接种以应对未来大流行。
A Bayesian mixed-effects model to evaluate the determinants of COVID-19 vaccine uptake in the US
针对美国COVID-19疫苗接种受多因素影响的问题，本研究采用贝叶斯混合效应模型评估相关社会人口学及空间因素，实现带不确定性量化的决定因素概率推断。
While vaccines are crucial to end the COVID-19 pandemic, public confidence in vaccine safety has always been vulnerable. Many statistical methods have been applied to VAERS (Vaccine Adverse Event Reporting System) database to study the safety of COVID-19 vaccines. However, all these methods ignored the adverse event (AE) ontology. AEs are naturally related; for example, events of retching, dysphagia, and reflux are all related to an abnormal digestive system. Explicitly bringing AE relationships into the model can aid in the detection of true AE signals amid the noise while reducing false posi…
Gaussian process (GP) based statistical surrogates are popular, inexpensive substitutes for emulating the outputs of expensive computer models that simulate real-world phenomena or complex systems. Here, we discuss the evolution of dynamic GP model - a computationally efficient statistical surrogate for a computer simulator with time series outputs. The main idea is to use a convolution of standard GP models, where the weights are guided by a singular value decomposition (SVD) of the response matrix over the time component. The dynamic GP model also adopts a localized modeling approach for bui…
In immunological studies, the characterization of small, functionally distinct cell subsets from blood and tissue is crucial to decipher system level biological changes. An increasing number of studies rely on assays that provide single-cell measurements of multiple genes and proteins from bulk cell samples. A common problem in the analysis of such data is to identify biomarkers (or combinations of thereof) that are differentially expressed between two biological conditions (e.g., before/after vaccination), where expression is defined as the proportion of cells expressing the biomarker or comb…
Robust Bayesian hierarchical models for basket trials enabling joint evaluation of toxicity and efficacy
针对篮式试验需同时评估毒性与疗效的需求，提出两种考虑亚组异质性的贝叶斯层次模型，仿真验证其在不同场景下稳健性更优，检验效能更高、错误率更低。
Parental refusal and delay of childhood vaccination has increased in recent years in the United States. This phenomenon challenges maintenance of herd immunity and increases the risk of outbreaks of vaccine-preventable diseases. We examine US county-level vaccine refusal for patients under five years of age collected during the period 2012--2015 from an administrative healthcare dataset. We model these data with a Bayesian zero-inflated negative binomial regression model to capture social and political processes that are associated with vaccine refusal, as well as factors that affect our measu…
We introduce an extension of nonparametric DS inference for arbitrary univariate CDFs to the case in which some failure times are (right)-censored, and then apply this to the problem of assessing evidence regarding assertions about relative risks across two populations. The approach enables exploration of the sensitivity of survival analyses to assumed independence of the missing data process and the failure proces. We present an application to the partially efficacious RV144 (HIV-1) vaccine trial, and show that the strength of conclusions of vaccine efficacy depend on assumptions about the ma…
Knowing the true effect size of clinical interventions in randomised clinical trials is key to informing the public health policies. Vaccine efficacy is defined in terms of the relative risk or the ratio of two disease risks. However, only approximate methods are available for estimating the variance of the relative risk. In this article, we show using a probabilistic model that uncertainty in the efficacy rate could be underestimated when the disease risk is low. Factoring in the baseline rate of the disease, we estimate broader confidence intervals for the efficacy rates of the vaccines rece…
Symmetric Vaccine Efficacy
研究传统疫苗效力衡量方法不对称问题，提出对称疫苗效力（SVE），通过对感染风险对称变换定义SVE，取值[-1, 1]，解决不对称性，还描述其与传统VE关系、推断方法并以HIV疫苗试验数据说明效用。
The COVID-19 pandemic due to the novel coronavirus SARS CoV-2 has inspired remarkable breakthroughs in development of vaccines against the virus and the launch of several phase 3 vaccine trials in Summer 2020 to evaluate vaccine efficacy (VE). Trials of vaccine candidates using mRNA delivery systems developed by Pfizer-BioNTech and Moderna have shown substantial VEs of 94-95%, leading the US Food and Drug Administration to issue Emergency Use Authorizations and subsequent widespread administration of the vaccines. As the trials continue, a key issue is the possibility that VE may wane over tim…
Debiasing hazard-based, time-varying vaccine effects using vaccine-irrelevant infections: An observational extension of a pivotal Phase 3 COVID-19 vaccine efficacy trial
针对Cox回归估计时变疫苗效力易受隐藏偏差的问题，提出利用疫苗无关感染校正偏差的方法，经COVE试验数据验证可更准确评估mRNA-1273加强针对奥密克戎的持久效力。
Assessing the Impact of Vaccination on Rotavirus Transmission Dynamics Using Bayesian Inference
本研究针对英国2013年引入轮状病毒疫苗后传播变化的问题，采用序贯蒙特卡洛方法校准随机仓室模型估计时变传播参数，揭示了疫苗推广前后传播能力的动态变化，为评估长期流行病学趋势提供了定量框架。
Estimates of national immunization coverage are crucial for guiding policy and decision-making in national immunization programs and setting the global immunization agenda. WHO and UNICEF estimates of national immunization coverage (WUENIC) are produced annually for various vaccine-dose combinations and all WHO Member States using information from multiple data sources and a deterministic computational logic approach. This approach, however, is incapable of characterizing the uncertainties inherent in coverage measurement and estimation. It also provides no statistically principled way of expl…
Semiparametric logistic regression for inference on relative vaccine efficacy in case-only studies with informative missingness
本文提出半参数逻辑回归方法，在仅病例研究中处理病毒株类型信息性缺失，利用目标最大似然估计和机器学习推断亚组特异性相对疫苗效力，并应用于ENSEMBLE COVID-19试验。
The COVID-19 pandemic has caused severe public health consequences in the United States. In this study, we use a hierarchical Bayesian model to estimate the age-specific COVID-19 attributable deaths over time in the United States. The model is specified by a novel non-parametric spatial approach, a low-rank Gaussian Process (GP) projected by regularised B-splines. We show that this projection defines a new GP with attractive smoothness and computational efficiency properties, derive its kernel function, and discuss the penalty terms induced by the projected GP. Simulation analyses and benchmar…
Misclassification of Vaccination Status in Electronic Health Records: A Bayesian Approach in Cluster Randomized Trials
针对电子健康记录中疫苗接种状态错误分类问题，提出一种贝叶斯逻辑回归扩展方法，通过专家先验扰动诊所数据，用于集群随机试验的敏感性分析，并在退伍军人疫苗接种研究中验证。
In many application domains, such as computational biology, the goal of graphical model structure learning is to uncover discrete relationships between entities. For example, in our problem of interest concerning HIV vaccine design, we want to infer which HIV peptides interact with which immune system molecules (HLA molecules). For problems of this nature, we are interested in determining the number of nonspurious arcs in a learned graphical model. We describe both a Bayesian and frequentist approach to this problem. In the Bayesian approach, we use the posterior distribution over model struct…
Accurate estimates of malaria vaccine efficacy require a reliable definition of a malaria case. However, the symptoms of clinical malaria are unspecific, overlapping with other childhood illnesses. Additionally, children in endemic areas tolerate varying levels of parasitemia without symptoms. Together, this makes finding a gold-standard case definition challenging. We present a method to identify and estimate malaria vaccine efficacy that does not require an observable gold-standard case definition. Instead, we leverage genetic traits that are protective against malaria but not against other …
COVID-19 vaccines are highly efficacious at preventing symptomatic infection, severe disease, and death. Most of the evidence that COVID-19 vaccines also reduce transmission of SARS-CoV-2 is based on retrospective, observational studies. Specifically, an increasing number of studies are evaluating vaccine efficacy against the secondary attack rate of SARS-CoV-2 using data available in existing healthcare databases or contact tracing databases. Since these types of databases were designed for clinical diagnosis or management of COVID-19, they are limited in their ability to provide accurate inf…
Multi-Signal Safety Surveillance with Bayesian Latent Factor Modeling and Bias Correction
该研究针对观察性医疗数据多信号安全监测的复杂问题，提出结合经验偏差校正与低秩潜在因子建模的多信号贝叶斯序贯监测框架，经美国保险理赔数据库的疫苗安全监测研究验证有效。
Vaccine Efficacy Estimands Implied by Common Estimators Used in Individual Randomized Field Trials
本文探讨了个体随机现场试验中常用估计器隐含的疫苗效力估计量，分析了其在不同时间点的解释困难及敏感性分析方法。
Vaccine efficacy for binary post-infection outcomes under misclassification without monotonicity
提出一种非参数点识别方法，在无需单调性假设且允许测量误差的情况下，利用地理分散试验和预处理协变量识别疫苗对感染及感染后结局的主效应，并支持多处理场景。
Suppose one has data from one or more completed vaccine efficacy trials and wishes to estimate the efficacy in a new setting. Often logistical or ethical considerations make running another efficacy trial impossible. Fortunately, if there is a biomarker that is the primary modifier of efficacy, then the biomarker-conditional efficacy may be identical in the completed trials and the new setting, or at least informative enough to meaningfully bound this quantity. Given a sample of this biomarker from the new population, we might hope we can bridge the results of the completed trials to estimate …
Adoption and implication of the Biased-Annotator Competence Estimation (BACE) model into COVID-19 vaccine Twitter data: Human annotation for latent message features
本文针对传统人工编码假设编码者同等准确的弱点，提出将BACE模型应用于COVID-19疫苗Twitter数据，通过贝叶斯建模考虑编码者偏倚并将真实标签视为潜在参数，以改进潜在消息特征的人工编码。
A Bayesian adaptive design for dual-agent phase I-II oncology trials integrating efficacy data across stages
该研究针对双药联合肿瘤I-II期剂量寻找问题，提出可适应阶段间人群变化的两阶段贝叶斯自适应设计，通过带可交换/不可交换假设的分层随机效应模型跨阶段共享疗效信息，模拟显示其改善了疗效评估的操作特性。
Dose Selection Balancing Efficacy and Toxicity Using Bayesian Model Averaging
针对药物研发中平衡疗效与毒性的剂量选择难题，提出采用BMA-Mod贝叶斯模型平均框架的模型无关策略，经多场景模拟验证可稳定识别一致的可接受剂量范围。
Subgroup analysis is a frequently used tool for evaluating heterogeneity of treatment effect and heterogeneity in treatment harm across observed baseline patient characteristics. While treatment efficacy and adverse event measures are often reported separately for each subgroup, analyzing their within-subgroup joint distribution is critical for better informed patient decision-making. In this paper, we describe Bayesian models for performing a subgroup analysis to compare the joint occurrence of a primary endpoint and an adverse event between two treatment arms. Our approaches emphasize estima…
Efficient estimation of cumulative incidence curves via data fusion with surrogates: application to integrated analysis of vaccine trial and immunobridging data
本文提出通过历史疫苗效力试验与免疫桥梁研究的数据融合，高效估计累积发病率曲线，针对多病原体如登革热病毒和流感，通过模拟研究评估所提方法的有限样本性能，并应用于新冠变异体免疫学景观试验的数据分析。
Statistical methods are developed for analysis of clinical and virus genetics data from phase 3 randomized, placebo-controlled trials of vaccines against novel coronavirus COVID-19. Vaccine efficacy (VE) of a vaccine to prevent COVID-19 caused by one of finitely many genetic strains of SARS-CoV-2 may vary by strain. The problem of assessing differential VE by viral genetics can be formulated under a competing risks model where the endpoint is virologically confirmed COVID-19 and the cause-of-failure is the infecting SARS-CoV-2 genotype. Strain-specific VE is defined as one minus the cause-spec…
Is Repeated Bayesian Interim Analysis Consequence-Free?
本文探讨了重复贝叶斯中间分析对效能和多重性的影响，发现即使先验匹配时，某些统计特性仍会改变，强调了先验指定和操作特性评估的重要性。
Bayesian predictive probability based on a bivariate index vector for single-arm phase II study with binary efficacy and safety endpoints
本文针对单臂II期试验中疗效与安全性双终点监测问题，提出一种基于新二元指标向量的贝叶斯预测概率方法，用于期中继续/停止决策，并通过模拟验证其操作特性良好。
BOP2-TE: Bayesian Optimal Phase 2 Design for Jointly Monitoring Efficacy and Toxicity with Application to Dose Optimization
本文提出BOP2-TE贝叶斯最优II期设计，通过Dirichlet-多项模型联合监测毒性与疗效，在多种场景下严格控制I类错误并优化效能，支持多剂量随机试验和无缝剂量优化设计。
Distinguishing immunological and behavioral effects of vaccination
本文引入术语区分疫苗效力估计量，明确免疫与行为效应，证明在测量信念变量后常规试验可识别政策相关估计量，并通过流感疫苗试验示例说明其实际意义。
The advent and subsequent widespread availability of preventive vaccines has altered the course of public health over the past century. Despite this success, effective vaccines to prevent many high-burden diseases, including HIV, have been slow to develop. Vaccine development can be aided by the identification of immune response markers that serve as effective surrogates for clinically significant infection or disease endpoints. However, measuring immune response marker activity is often costly, which has motivated the usage of two-phase sampling for immune response evaluation in clinical tria…
U-DESPE: a Bayesian Utility-based methodology for dosing regimen optimization in early-phase oncology trials based on Dose-Exposure, Safety, Pharmacodynamics, Efficacy
U-DESPE是一种基于剂量-暴露的贝叶斯方法，用于早期肿瘤试验中的剂量方案优化，通过整合药代动力学数据和效用函数，提高剂量选择的科学性和安全性。
It has long been noticed that the efficacy observed in small early phase studies is generally better than that observed in later larger studies. Historically, the inflation of the efficacy results from early proof-of-concept studies is either ignored, or adjusted empirically using a frequentist or Bayesian approach. In this article, we systematically explained the underlying reason for the inflation of efficacy results in small early phase studies from the perspectives of measurement error models and selection bias. A systematic method was built to adjust the early phase study results from bot…
Immune correlates of protection (CoPs) are immunologic biomarkers accepted as a surrogate for an infectious disease clinical endpoint and thus can be used for traditional or provisional vaccine approval. To study CoPs in randomized, placebo-controlled trials, correlates of risk (CoRs) are first assessed in vaccine recipients. This analysis does not assess causation, as a CoR may fail to be a CoP. We propose a causal CoP analysis that estimates the controlled vaccine efficacy curve across biomarker levels $s$, $CVE(s)$, equal to one minus the ratio of the controlled-risk curve $r_C(s)$ at $s$ a…
In recent years new cancer treatments improved survival in multiple histologies. Some of these therapeutics, and in particular treatment combinations, are often associated with severe treatment-related adverse events (AEs). It is therefore important to identify alternative de-intensified therapies, for example dose-reduced therapies, with reduced AEs and similar efficacy. We introduce a sequential design for multi-arm de-intensification studies. The design evaluates multiple de-intensified therapies at different dose levels, one at the time, based on modeling of toxicity and efficacy endpoints…
We propose a new integrated phase I/II trial design to identify the most efficacious dose combination that also satisfies certain safety requirements for drug-combination trials. We first take a Bayesian copula-type model for dose finding in phase I. After identifying a set of admissible doses, we immediately move the entire set forward to phase II. We propose a novel adaptive randomization scheme to favor assigning patients to more efficacious dose-combination arms. Our adaptive randomization scheme takes into account both the point estimate and variability of efficacy. By using a moving refe…
The human body is able to generate a diverse set of high affinity antibodies, the soluble form of B cell receptors (BCRs), that bind to and neutralize invading pathogens. The natural development of BCRs must be understood in order to design vaccines for highly mutable pathogens such as influenza and HIV. BCR diversity is induced by naturally occurring combinatorial "V(D)J" rearrangement, mutation, and selection processes. Most current methods for BCR sequence analysis focus on separately modeling the above processes. Statistical phylogenetic methods are often used to model the mutational dynam…
Quantification of vaccine waning as a challenge effect
针对疫苗效力衰退难以进行因果量化的问题，提出利用挑战效应并在非参数假设下确定其界限，基于BNT162b2疫苗试验数据证实接种2个月后保护力衰退。
PK/PD-integrated Bayesian platform design for phase II dose regimen optimization
针对剂量无法充分刻画治疗方案的问题，提出整合PK/PD建模的贝叶斯II期适应性平台设计PROP，以改善方案选择与决策，模拟显示其优于基于剂量的方法。
The use of drug combinations in clinical trials is increasingly common during the last years since a more favorable therapeutic response may be obtained by combining drugs. In phase I clinical trials, most of the existing methodology recommends a one unique dose combination as "optimal", which may result in a subsequent failed phase II clinical trial since other dose combinations may present higher treatment efficacy for the same level of toxicity. We are particularly interested in the setting where it is necessary to wait a few cycles of therapy to observe an efficacy outcome and the phase I …
In this article we have conducted a reanalysis of the phase III aducanumab (ADU) summary statistics announced by Biogen, in particular the result of the Clinical Dementia Rating-Sum of Boxes (CDR-SB). The results showed that the evidence on the efficacy of the drug is very low and a more clearer view of the results of clinical trials are presented in the Bayesian framework that can be useful for future development and research in the field.
Combating the SARS-CoV2 pandemic will require the fast development of effective preventive vaccines. Regulatory agencies may open accelerated approval pathways for vaccines if an immunological marker can be established as a mediator of a vaccine's protection. A rich source of information for identifying such correlates are large-scale efficacy trials of COVID-19 vaccines, where immune responses are measured subject to a case-cohort sampling design. We propose two approaches to estimation of mediation parameters in the context of case-cohort sampling designs. We establish the theoretical large-…
Bayesian Optimization for Identification of Optimal Biological Dose Combinations in Personalized Dose-Finding Trials
针对个性化剂量探索试验样本量小、难识别最佳生物剂量组合的问题，提出融合疗效与毒性信息的贝叶斯优化方法，经模拟验证其表现良好。
Fast Bayesian inference for spatial mean-parameterized Conway-Maxwell-Poisson models
提出空间均值参数化 COMP 模型及快速贝叶斯计算框架，利用空间滤波、可逆跳 MCMC、辅助变量和预计算近似处理复杂空间计数数据。
In vaccine development, the temporal profiles of relative abundance of subtypes of immune cells (T-cells) is key to understanding vaccine efficacy. Complex and expensive experimental studies generate very sparse time series data on this immune response. Fitting multi-parameter dynamic models of the immune response dynamics-- central to evaluating mechanisms underlying vaccine efficacy-- is challenged by data sparsity. The research reported here addresses this challenge. For HIV/SIV vaccine studies in macaques, we: (a) introduce novel dynamic models of progression of cellular populations over t…
Bayesian Hierarchical Methods for Surveillance of Cervical Dystonia Treatments
本研究利用贝叶斯分层方法分析109名患者的多中心随机对照试验数据，比较B型肉毒杆菌毒素不同剂量对颈部肌张力障碍的疗效，发现5,000单位剂量效果更优且男性反应更好，为个体化治疗提供了概率框架。
PKBOIN-12: A Bayesian optimal interval Phase I/II design incorporating pharmacokinetics outcomes to find the optimal biological dose
提出PKBOIN-12及扩展TITE-PKBOIN-12设计，将药代动力学信息整合入BOIN12，以更准确识别最优生物剂量并优化患者分配。
BOP2-ENR: a Bayesian optimal phase II design for adaptive enrichment with family-wise error rate control and a bound on joint-claim power
提出BOP2-ENR贝叶斯最优II期设计，用于自适应富集，通过分别监测生物标志物阴性和阳性队列控制家系错误率并限制联合声明功效，扩展了BOP2框架。
Adaptive approaches, allowing for more flexible trial design, have been proposed for individually randomized trials to save time or reduce sample size. However, adaptive designs for cluster-randomized trials in which groups of participants rather than individuals are randomized to treatment arms are less common. Motivated by a cluster-randomized trial designed to assess the effectiveness of a machine-learning based clinical decision support system for physicians treating patients with depression, two Bayesian adaptive designs for cluster-randomized trials are proposed to allow for early stoppi…
In this paper, we describe a novel shape classification method which is embedded in the Bayesian paradigm. We discuss the modelling and the resulting shape classification algorithm for two and three dimensional data shapes. We conclude by evaluating the efficiency and efficacy of the proposed algorithm on the Kimia shape database for the two dimensional case.
We address discrete-marks survival analysis, also known as categorical sieve analysis, for a setting of a randomized placebo-controlled treatment intervention to prevent infection by a pathogen to which multiple exposures are possible, with a finite number of types of "failure". In particular, we address the case of interventions that are partially efficacious due to a combination of failure-type-dependent efficacy and subject-dependent efficacy, for an intervention that is "non-leaky" (where "leaky" interventions are those for which each exposure event has a chance of resulting in a "failure"…
Chain event graphs have been established as a practical Bayesian graphical tool. While bespoke diagnostics have been developed for Bayesian Networks, they have not yet been defined for the statistical class of Chain Event Graph models. Mirroring the methodology of prequential diagnostics for Bayesian Networks, in this paper we design a number of new Bayesian diagnostics for this new class. These can be used to check whether a selected model--presumably the best within the class--captures most of the salient features of the observed process. These are designed to check the continued validity of…
Decision making in the face of a disaster requires the consideration of several complex factors. In such cases, Bayesian multi-criteria decision analysis provides a framework for decision making. In this paper, we present how to construct a multi-attribute decision support system for choosing between countermeasure strategies, such as lockdowns, designed to mitigate the effects of COVID-19. Such an analysis can evaluate both the short term and long term efficacy of various candidate countermeasures. The expected utility scores of a countermeasure strategy capture the expected impact of the pol…
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