September 7, 2020:
Some papers are listed below; others may be added to the list (ordered by year of publication) during the term. Subject to availability of time and scheduling constraints, I am happy to supervise any of these papers.
If one of the listed papers interests you, contact me to arrange a meeting to discuss what you would like to do in connection with that paper. In general terms, I will require a written report as well as an oral presentation of your work on the paper. However, exactly what that work might be is open to negotiation. You should have specific ideas about what you want to do for any paper that interests you.
I am also willing to consider supervising other papers. If you have a paper you would like to study for STAT 548 and you think it might interest me, feel free to inquire if I am willing to supervise your study of it. As with the listed papers, you should have specific ideas about what you would like to do in connection with that paper.
This discussion paper develops an approach to assessing the sensitivity of inferences to various types of selection bias; for example, in the way that subjects may have been allocated to treatments. The approach is to extend simple statistical models by including an additional parameter that models the degree of non-randomness in the mechanism generating the data, and to study inference conditional on a range of different values of that parameter. Several problems are investigated, including non-ignorable nonresponse. The paper requires no advanced knowledge so is suitable for any new PhD student. Note: Almost half of the paper consists of comments by multiple discussants. You will not be required to review these although they may provide useful perspectives on the content of the paper.
Cluster randomized trials (CRT) are often used to evaluate therapies or interventions in situations where individual randomization is not possible or not desirable. This paper discusses a particular type of CRT, particularly applicable for large-scale public health interventions, in which different clusters "cross over" (switch from the control to intervention treatment) at different time points. No advanced background is necessary for comprehension of this paper, so it is suitable for any new PhD student with an interest in designs for clinical and public health experiments.
This paper investigates the efficiency of response-adaptive two-stage designs, where after the first stage the data are used to determine a locally optimum design for the second stage, relative to a fixed design without adaptation. The comparisons are based on an explicit expression for the (asymptotic) Fisher information of such designs. This paper is suitable for a student with an interest in designs for clinical experiments, particularly a student wanting to gain some familiarity with the current rage for adaptive designs.
This paper proposes a new testing strategy for clinical trials contexts where a binary biomarker is available that should be able to identify a subset of patients who can be expected to derive particular benefit from the treatment under investigation. Existing "biomarker-guided" designs are described and their pros and cons are discussed. Properties of the MaST design are evaluated and compared. This paper is suitable for a student with an interest in designs for clinical experiments, particularly a student wanting to gain some familiarity with the designs that have been proposed for this context of targeted therapies.
This paper develops methods for statistical inference for missing data mechanisms through a parametric regression model based on both fully and partially observed variables, estimated from a semiparametric likelihood using an EM algorithm. The approach is introduced for a simple monotone missing data pattern and then extended to deal with arbitrary nonmonotone missing data patterns. This paper is suitable for a student with an interest in issues related to missing data. Previous exposure to the basic notions of missing data mechanisms would be useful, though not essential.