Invited Speakers

Keynote Speakers

Professor Masoud Asgharian   Masoud Asgharian is a full professor in the Department of Mathematics and Statistics, McGill University. He earned his BSc and MSc degrees from Shahid Beheshti University, Tehran, and his PhD from McGill in 1998. He is the winner of the 1998 Pierre Robillard Award of the Statistical Society of Canada. His main areas of research are statistics, optimization, DEA, machine learning and neural networks. He has been an associate editor of the Journal of the American Statistical Association (JASA), The Canadian Journal of Statistics (CJS) and a Guest co-Editor of a Special Issue of the Central European Journal of OR. He is currently on the editorial board of the Croation Operational Research Review (CRORR).

 

Keynote Title: Prevalent Cohort Studies: Length-Biased Sampling with Right Censoring

 

Logistic or other constraints often preclude the possibility of conducting incident cohort studies. A feasible alternative in such cases is to conduct a cross-sectional prevalent cohort study for which we recruit prevalent cases, that is, subjects who have already experienced the initiating event, say the onset of a disease. When the interest lies in estimating the lifespan between the initiating event and a terminating event, say death for instance, such subjects may be followed prospectively until the terminating event or loss to follow-up, whichever happens first. It is well known that prevalent cases have, on average, longer lifespans. As such, they do not form a random sample from the target population; they comprise a biased sample. If the initiating events are generated from a stationary Poisson process, the so-called stationarity assumption, this bias is called length bias. My work revolves around developing statistical methodologies for analyzing such data. Our study is mainly motivated by challenges and questions raised in analyzing survival data collected on patients with dementia as part of a nationwide study in Canada, called the Canadian Study of Health and Aging (CSHA). I’ll use these and other real data for my work to discuss and motivate our methodologies and their applications.

Professor Yaser Samadi   S. Yaser Samadi is an Associate Professor in School of Mathematical and Statistical Sciences and an affiliated faculty in School of Computing at Southern Illinois University Carbondale, IL, USA. He obtained his PhD in Statistics from the University of Georgia in 2014. He was a Research Fellow at SAMSI & Duke University in 2020-2021.

Keynote Title: Multivariate Time Series Analysis through Reduced-Rank Envelope Vector Autoregressive Models

Vector autoregressive (VAR) models have historically been favored for their adaptability and simplicity in modeling multivariate time series data. However, the VAR framework often encounters overparameterization issues, particularly in high-dimensional time series datasets, limiting the incorporation of variables and lags. Several statistical approaches have been proposed to address dimension reduction in VAR models, yet, they prove inefficient in extracting relevant information from complex datasets, as they fail to distinguish between information aligned with scientific objectives and are also inefficient in addressing rank deficiency problems. In this context, envelope methods offer a promising solution by leveraging reduced subspaces to identify and eliminate irrelevant information, thereby enhancing efficiency in parameter estimation. This presentation introduces an innovative VAR model integrating envelope concepts within the reduced-rank framework, facilitating substantial dimension reduction without compromising parameter estimation accuracy. Through comprehensive simulation studies and real-world data analysis, we demonstrate the superior performance of our model compared to existing methodologies in the literature, underscoring its efficacy in capturing essential dynamics while mitigating the limitations of traditional VAR frameworks.

Professor Ding-Geng (Din) Chen Dr. Ding-Geng Chen (aka Din Chen) is a fellow of the American Statistical Association and is currently the executive director and professor in biostatistics at the College of Health Solutions, Arizona State University. He is also an extraordinary professor and the SARChI research chair in biostatistics at the University of Pretoria, an honorary professor at the University of KwaZulu-Natal, South Africa. Dr. Chen was the Wallacwe H. Kuralt distinguished professor in Biostatistics at the University of North Carolina at Chapel Hill,  a professor in biostatistics at the University of Rochester Medical School, and the Karl E. Peace Endowed Eminent Scholar Chair in Biostatistics at Georgia Southern University. He is a senior biostatistics consultant for biopharmaceuticals and government agencies with extensive expertise in biostatistics, clinical trials, and public health statistics. Dr. Chen has more than 200 referred professional publications and co-authored and co-edited 40 books on clinical trial methodology, meta-analysis, data science, causal inference, and public health research.

Keynote Title: How to Estimate COVID-19 Vaccine Efficacy

The COVID-19 pandemic has caused significant morbidity and mortality, as well as social and economic disruption worldwide. In order to reduce these effects, a global effort to develop effective vaccines against the COVID-19 virus has produced various options with the effectiveness assessed on the rate of infection between vaccinated and unvaccinated groups, which has been used for important policy decision-making on vaccination effectiveness ever since. However, the rate of infection is an over-simplified index in assessing the vaccination effectiveness overall, which should be strengthened to address the duration of protection with time-to-infection effect. The fundamental challenge in estimating the vaccination effect over time is that the time-to-infection for unvaccinated group is unknown due to nonexistent vaccination time. This presentation is to discuss the biostatistical methodological development to fill this knowledge gap to propose a Weibull regression model. This model treats the nonexistent vaccination time for the unvaccinated group as nuisance parameters and estimate the vaccination effectiveness along with these nuisance parameters. The performance of the proposed approach and its properties is empirically investigated through a simulation study, and its applicability is illustrated using a real-data example from the Arizona State University COVID-19 serological prevalence data.

Dr Nonhlanhla Yende-Zuma Dr. Nonhlanhla Yende-Zuma is a Specialist Statistician affiliated with the South African Medical Research Council (SAMRC). Before her tenure at SAMRC, she held the Head of Statistics and Data Management position at the Centre for the AIDS Programme of Research in South Africa (CAPRISA). She currently holds an honorary research associate position at CAPRISA.  Dr. Yende-Zuma earned her doctoral degree in Statistics from the University of KwaZulu-Natal. With a remarkable professional journey spanning over 17 years, Dr. Yende-Zuma has dedicated herself to conducting groundbreaking biomedical research in South Africa and various African countries. Her contribution to the field is evident in her authorship and co-authorship of more than 75 peer-reviewed journal articles. She is a statistical reviewer and advisor for prestigious publications such as Lancet HIV and Lancet Global Health journals. Beyond her institutional affiliations, Dr. Yende-Zuma plays a pivotal role in shaping global healthcare standards. In her personal capacity, she currently serves as a member of the World Health Organization (WHO) Technical Advisory Group on Development of Guidance on Best Practices for Clinical Trials. In addition to her professional commitments, Dr. Yende-Zuma dedicates her expertise and passion for statistical sciences to the academic arena. She holds an honorary senior lecturer position at the University of KwaZulu-Natal (UKZN), where she actively mentors and co-supervises postgraduate students in the field of Statistics.

Keynote Title: Causal inference methodology in the context of future  HIV prevention clinical trials

Randomised controlled trials (RCTs) remain the gold standard for evaluating the efficacy of new or emerging interventions. In the context of HIV prevention, we discuss that the use of a placebo as a comparator is becoming unethical and requires justification as highly effective long-acting injectable pre-exposure prophylaxis (PrEP) agents become available. On the other hand, active-controlled trials will require enormous sample sizes and probably prohibitive costs.

We provide alternative approaches that could be utilised in designing future HIV prevention trials, such as non-inferiority design, use of registrational cohorts, non-randomised comparator groups such as historical placebo controls and immune biomarkers as a mediator of prevention efficacy. However, these approaches could produce biased efficacy estimates due to potential confounding.

Most importantly, we discuss causal inference models such as propensity scores, instrumental variables, marginal structural models, etc, as they have the potential to provide robust estimates in this context.

Dr Tary-Lee Reddy Dr Tarylee Reddy is Director of the Biostatistics Research Unit at the South African Medical Research Council (SAMRC) and has an honorary appointment in the UKZN (University of KwaZulu Natal) School of Mathematics, Statistics and Computer Science.  She is the youngest Unit Director in the history of the SAMRC and the first female Director of the Biostatistics Research Unit. She holds a BSc degree in Actuarial Science (2008), a BSc Honours (summa cum laude, 2009) and Masters in Statistics (cum laude, 2011) from the University of KwaZulu Natal, South Africa and a PhD in Statistics (2017) from the University of Hasselt in Belgium. Dr Reddy has authored and co-authored more than 150 peer-reviewed publications and is an NRF C2 rated scientist.  She was awarded a silver SAMRC Scientific Merit Award in 2022 for her outstanding contribution to health research in the country. Dr Reddy is the current President of the South African region of the International Biometrics Society (IBS) and the Secretary of the IBS.

Keynote Title: Time to threshold estimation from longitudinal biomarker data: continuous, censored, discrete data and beyond

In longitudinal studies of biomarkers, an outcome of interest is the time at which a biomarker reaches a particular threshold. Due to the inherent variability of several studies have applied persistence criteria, designating the outcome as the time to the occurrence of two consecutive measurements less (or greater) than the threshold. In this presentation, we discuss a method to estimate the time to attainment of two consecutive measurements less than a meaningful threshold, which takes into account the patient-specific trajectory and measurement error. An expression for the expected time to threshold has been presented, which is a function of the fixed effects, random effects and residual variance. While the initial approach was motivated by continuous biomarkers, we present extensions of the methodology to accommodate censored observations as well as ordinal outcomes.  We present a range of specific applications to HIV, cardiology, SARS-CoV2 and schizophrenia demonstrating the relevance of the methodology. Through these applications   we demonstrate that the method proposed is computationally efficient, robust, and offers more flexibility than existing frameworks.

Guest Speakers

• Dr Bedilu Alamirie Ejigu: Department of Statistics, Addis Ababa University
• Dr Tsirizani Kaombe: Department of Mathematical Sciences, University of Malawi