2022 colloquia will be temporarily replaced by closed faculty candidate seminars. Department faculty and students will receive emails with details.
Prevalent Cohort Studies: Length-Biased Sampling with Right Censoring
Masoud Asgharian, Ph.D.
Logistic or other constraint often preclude the possibility of conducting incident cohort studies. A feasible alternative in such cases is to conduct a cross-section 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 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. I present the basics of nonparametric inference using length-biased right censored failure time data. I’ll then discuss some recent progress and current challenges. 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 data throughout the talk to discuss and motivate our methodology and its applications.
Thursday, May 5, 2022
Assessing Personalization in Digital Health
Susan Murphy, Ph.D.
Visit Charles L. Odoroff Memorial Lecture for details
Thursday, May 19, 2022
Estimands and Estimation of COVID-19 Vaccine Effectiveness Under the Test-Negative Design: Connections to Causal Inference
Mireille Schnitzer, Ph.D.
University of Montreal
The test-negative design (TND) is routinely used for the monitoring of seasonal flu vaccine effectiveness. More recently, it has become integral to the estimation of COVID-19 vaccine effectiveness, in particular for more severe disease outcomes. Distinct from the case-control study, the design typically involves recruitment of participants with a common symptom presentation who are being tested for the infectious disease in question. Participants who test positive for the target infection are the “cases” and those who test negative are the “controls”. Logistic regression is the only statistical method that has been proposed to estimate vaccine effectiveness under the TND while adjusting for confounders. While under strong modeling assumptions it produces estimates of a causal risk ratio, it may be biased in the presence of effect modification by a confounder. I will present and justify an inverse probability of treatment weighting (IPTW) estimator for the marginal risk ratio, which is valid under effect modification. I’ll discuss connections between the estimands targeted by these two methods and causal parameters under different interference assumptions. I will then describe the results of a simulation study to illustrate and confirm the derivations and to evaluate the performance of the estimators.
Thursday, September 8, 2022
Maps: A Statistical View
Lance Waller, Ph.D.
Visit Andrei Yakovlev Colloquium for details
Thursday, September 22, 2022
Causal Influence, Causal Effect, and the Identification of Mediation Parameters
Iván Diaz, Ph.D.
NYU Grossman School of Medicine
Recent approaches to causal inference have focused on the identification and estimation of causal effects, defined as (properties of) the distribution of counterfactual outcomes under hypothetical actions that alter the nodes of a graphical model. In this article we explore an alternative approach using the concept of causal influence, defined through operations that alter the information propagated through the edges of a directed acyclic graph. Causal influence may be more useful than causal effects in settings in which interventions on the causal agents are infeasible or of no substantive interest, for example when considering gender, race, or genetics as a causal agent. Furthermore, the "information transfer" interventions proposed allow us to solve a long-standing problem in causal mediation analysis, namely the non-parametric identification of path-specific effects in the presence of treatment-induced mediator-outcome confounding. We propose efficient non-parametric estimators for a covariance version of the proposed causal influence measures, using data-adaptive regression coupled with semi-parametric efficiency theory to address model misspecification bias while retaining root-n-consistency and asymptotic normality. We illustrate the use of our methods in two examples using publicly available data.
Thursday, October 13, 2022
3:30 p.m. – 5:00 p.m.
Helen Wood Hall Room 1W-510