Research Interests
Event History Analysis
In many branches of science including demography, epidemiology, medicine and engineering, a considerable amount of information is collected on the nature and timing of events of interest. In the context of medical research this data may represent the time and nature of a variety of clinically important health related events occurring over the course of a patient’s life time as well as any additional explanatory variables. In the context of immunologic research, for example, this data may be the dates of infection with the HIV, diagnosis with AIDS, various opportunistic infections, and death. In cancer research it may represent the dates of diagnosis with bladder cancer, of subsequent recurrences, of metasteses, or death. Finally, in cardiovascular trials, event history data may consist of the dates and types of various types of cardiac events (i.e. angina attacks, arrythmias, myocardial infarction), strokes (left/right hemisphere, etc.), and thromboses. Event history analysis is concerned with the application of statistical methods to this type of data, typically with a view to one of the following objectives: i) to accurately reflect aspects of the natural history of the disease, ii) to identify risk factors for disease progression, iii) to provide measures of the effect of medical or surgical interventions, or iv) to provide a basis for prediction about the future course of the disease at the patient or population level. I collaborate with Jerry Lawless for much of this work.
In many branches of science including demography, epidemiology, medicine and engineering, a considerable amount of information is collected on the nature and timing of events of interest. In the context of medical research this data may represent the time and nature of a variety of clinically important health related events occurring over the course of a patient’s life time as well as any additional explanatory variables. In the context of immunologic research, for example, this data may be the dates of infection with the HIV, diagnosis with AIDS, various opportunistic infections, and death. In cancer research it may represent the dates of diagnosis with bladder cancer, of subsequent recurrences, of metasteses, or death. Finally, in cardiovascular trials, event history data may consist of the dates and types of various types of cardiac events (i.e. angina attacks, arrythmias, myocardial infarction), strokes (left/right hemisphere, etc.), and thromboses. Event history analysis is concerned with the application of statistical methods to this type of data, typically with a view to one of the following objectives: i) to accurately reflect aspects of the natural history of the disease, ii) to identify risk factors for disease progression, iii) to provide measures of the effect of medical or surgical interventions, or iv) to provide a basis for prediction about the future course of the disease at the patient or population level. I collaborate with Jerry Lawless for much of this work.
Clustered/Longitudinal Data
Longitudinal data arise when individuals are assessed repeatedly over time and responses and explanatory variables of interest are recorded at each assessment. The most suitable method for analysing data from a particular study depends on the primary scientific question, but all valid methods must address the serial correlation in the responses over time. The most common methods are based on random effect models, marginal (population-averaged) models, and transitional models. My primary interest is in the development of extension of these methods for the analysis of longitudinal data which has cross-sectional clustering, incomplete responses, measurement error, and other challenging features. I collaborate with Grace Y. Yi on much of this work.
Longitudinal data arise when individuals are assessed repeatedly over time and responses and explanatory variables of interest are recorded at each assessment. The most suitable method for analysing data from a particular study depends on the primary scientific question, but all valid methods must address the serial correlation in the responses over time. The most common methods are based on random effect models, marginal (population-averaged) models, and transitional models. My primary interest is in the development of extension of these methods for the analysis of longitudinal data which has cross-sectional clustering, incomplete responses, measurement error, and other challenging features. I collaborate with Grace Y. Yi on much of this work.
Clinical Trial Methodology
The need for efficient use of available resources in medical research has led to the increased appeal of clinical trial designs based on multiple outcomes. One of my interests in the recent past has been in the development of methods that facilitate the design and analysis of randomized trials in which treatment comparisons are to be made on the basis on multivariate responses. Issues that require consideration in the area include the estimate of approximate multivariate distribution and multiple comparisons.
The need for efficient use of available resources in medical research has led to the increased appeal of clinical trial designs based on multiple outcomes. One of my interests in the recent past has been in the development of methods that facilitate the design and analysis of randomized trials in which treatment comparisons are to be made on the basis on multivariate responses. Issues that require consideration in the area include the estimate of approximate multivariate distribution and multiple comparisons.
Sequential Methods
The primary purpose of sequential and group sequential designs is to allow repeated tests of significance on data as it accumulates over the course of a trial, while maintaining a specified experimental type I error rate. The interim analyses are generally required to meet ethical and economic constraints. The ethical requirements relate to the need to identify superior or inferior treatments as soon as possible. Once sufficient data is collected to establish a statistically significant effect size, it is considered unethical to continue the randomization process. Further, it is economically inefficient to spend valuable resources on a trial that has sufficient information to identify a statistically significant difference between therapies. Some of my work in this area has been to develop sequential methods for multivariate outcomes, as well as for cross-over trials.
The primary purpose of sequential and group sequential designs is to allow repeated tests of significance on data as it accumulates over the course of a trial, while maintaining a specified experimental type I error rate. The interim analyses are generally required to meet ethical and economic constraints. The ethical requirements relate to the need to identify superior or inferior treatments as soon as possible. Once sufficient data is collected to establish a statistically significant effect size, it is considered unethical to continue the randomization process. Further, it is economically inefficient to spend valuable resources on a trial that has sufficient information to identify a statistically significant difference between therapies. Some of my work in this area has been to develop sequential methods for multivariate outcomes, as well as for cross-over trials.
Collaborative Medical Research
I collaborate with oncologists, epidemiologists, and rheumatologists.
I collaborate with oncologists, epidemiologists, and rheumatologists.