next up previous index
Next: ims.24 Up: Institute of Mathematical Statistics Previous: ims.22



Session Slot: 4:00- 5:50 Sunday

Estimated Audience Size: 150

AudioVisual Request: None

Session Title: Observational Studies

Theme Session: No

Applied Session: No

Session Organizer: Wasserman, Larry Carnegie Mellon

Address: Dept. of Stat, Carnegie Mellon

Phone: 412-268-8727

Fax: 412-268-7828


Session Timing: 110 minutes total (Sorry about format):

Opening Remarks by Chair - 0 minutes First Speaker - 30 minutes Second Speaker - 30 minutes Third Speaker - 30 minutes Discussant - 0 minutes Floor Discusion - 15 minutes

Session Chair: Wasserman, Larry Carnegie Mellon

Address: Dept. of Stat, Carnegie Mellon

Phone: 412-268-8727

Fax: 412-268-7828


1. Sensitivity Analysis in Observational Studies: Two Simple Examples

Rosenbaum, Paul,   Univ. of Pennsylvania

Address: Paul R. Rosenbaum
The Wharton School
University Of Pennsylvania
Philadelphia, PA 19104-6302

Phone: 215-898-3120

Fax: 215-898-1280


Abstract: In an observational study, treatments are not randomly assigned to experimental subjects, so treated and control subjects may differ prior to treatment in ways that matter for the outcomes under study. Even when treated and control subjects are matched on recorded covariates, they may nonetheless differ in ways that have not been recorded. A sensitivity analysis asks how hidden biases of various magnitudes might alter the conclusions of a study. Two simple examples of sensitivity analysis will be presented, one from epidemiology and one from economics. In the economics example, the sensitivity of an instrumental variable estimator will also be examined.

2. Experiments, Randomized Trials, Observational Studies, and Causal Inference

Scheines, Richard,   Carnegie Mellon University

Address: Dept. of Philosophy, CMU, Pittsburgh, PA 15213

Phone: 412-268-8571

Fax: 412-268-1440


Abstract: Causal Inference from controlled experiments and large randomized clinical trials is relatively uncontroversial, whereas inference from uncontrolled observational studies is almost taboo. In this paper I use graphical causal models to show how the structure of inference is similar in all of these contexts. I show the assumptions needed to infer that a relationship is causal, how controlled experiments and randomized trials give us reason to endorse these assumptions, and how patterns of independence can do the same job in observational studies.

3. A New Approach to Observational Studies of Disease-specific Mortality

Joffe, Marshall,   University of Pennsylvania

Address: Department of Biostatistics and Epidemiology
602 Blockley Hall
423 Guardian Drive
University of Pennsylvania School of Medicine
Philadelphia, PA 19104-6021

Phone: 215- 573-7395

Fax: 215- 573-4865


Abstract: In observational research, investigation of possible causal links between a treatment or exposure and outcome often rely on subjects' self-report regarding exposure or treatment received; obtaining this information can be expensive. When the outcome event is rare, it is more efficient to obtain such self-report from all subjects who experience the event and a randomly chosen subset of the rest of the study cohort. Such case-control designs are problematic for mortality outcomes, because dead subjects cannot report information on previous exposures. For cause-specific mortality, a feasible hybrid design (Weiss and Lazovich, 1996) can often preserve much of the cost efficiency of case-control design. In this design, exposure is assessed at the time of disease diagnosis for all subjects diagnosed with the disease of interest, and for a subset of the remaining subjects. Unfortunately, analyzing data gathered this way as if it were standard case-control data can give biased results. When, as is usually the case with cancer outcomes, only a small proportion of potential subjects will be diagnosed with the condition of interest, an alternate method of analysis of such data is possible. This approach, which is based on formal definitions of the effect of a time-varying treatment and on the G-computation algorithm (Robins, 1986), may be applied even when the sampling fraction of non-diseased subjects is not known. We illustrate and compare conventional and new methods with data from a hypothetical study of breast cancer mortality.

List of speakers who are nonmembers: None

next up previous index
Next: ims.24 Up: Institute of Mathematical Statistics Previous: ims.22
David Scott