April 20, 2014
Distinguished Lecture Series in Statistical Science
December 6-7, 2012

Room 230, Fields Institute

Norman Breslow
University of Washington

Thursday, Dec. 6, 2012
at 3:30 p.m
Clinical Trials and Epidemiology: Reflections of the Statistician for the National Wilms Tumor Study
Friday, Dec. 7, 2012
at 10:00 a.m

Inference on Hazard Ratios and Survival Probabilities from Two-phase Stratified Samples

December 6, 2012 at 3:30 p.m

Clinical Trials and Epidemiology: Reflections of the Statistician for the National Wilms Tumor Study

The treatment of Wilms tumor is one of the great success stories in the annals of cancer chemotherapy. This lecture starts with a brief history of contributions to this success made by the National Wilms Tumor Study (NWTS): targeting high risk patients identified by histology and stage for the most intensive treatments and in so doing greatly reducing the use of radiation therapy. The NWTS commitment to evaluating the long term costs of cure continues in the Late Effects Study, which today relates the occurrence of congestive heart failure, secondary malignant neoplasms, end stage renal disease (ESRD) and adverse pregnancy outcomes to host factors and treatment histories.

The NWTS has also contributed to the epidemiology and biology of Wilms tumor. While attempting to confirm the applicability of the Knudson-Comings two-hit mutational model of cancer causation, epidemiologic evidence was developed for heterogeneity in Wilms tumor pathogenesis. Descriptive studies of age-at-onset, gender, birth weights and congenital anomaly syndromes in relation to the occurrence of distinct precursor lesions and histologic subtypes identified by NWTS pathologists led to the identification of (at least) two “ideal biological subtypes” of Wilms tumor. The first type likely involves a germline or somatic mutation of the WT1 tumor suppressor gene as the initiating event; the second involves loss of genomic imprinting of the insulin growth factor 2 gene IGF2. The virtual absence of the second type in Asians, and its lower frequency in Asian Americans, explains their lower incidence and earlier age-at-onset. Much of the ESRD occurring decades after Wilms tumor diagnosis occurs in patients with characteristics typical of the first type.

These NWTS contributions are possible due to systematic data collection using standardized definitions and codes spanning five clinical trials, decades long follow-up of survivors and close integration of the epidemiologic findings with data emanating from the laboratory.

References: http://www.nwtsg.org/bibliography/bibliography.html

December 7, 2012 at 10:00 a.m

Inference on Hazard Ratios and Survival Probabilities from Two-phase Stratified Samples

Norman E. Breslow (Department of Biostatistics, University of Washington, Seattle)
Thomas Lumley (Department of Statistics, University of Auckland, NZ)
Jon A. Wellner (Department of Biostatistics; Department of Statistics, University of Washington, Seattle)

Epidemiologists employ strati ed case-control studies nested within a de ned cohort so that collection of costly covariate information, such as bioassays of stored tissue samples, may be limited to the most informative participants. These designs involve two-phase stratified samples: a simple random sample (the main cohort) from an in nite super-population (model) at Phase I; and a nite population stratified (case-control) sample at Phase II. One approach to analysis involves inverse probability weighting (IPW) of general estimating equations.

In previous work we investigated IPW of in nite dimensional likelihood equations for both Euclidean and non-Euclidean parameters in semi-parametric models, of which the paradigm is the Cox model for survival data. The key idea was to separate the likelihood calculations, which are the same as those for simple random sampling, from weak convergence results for the IPW empirical process. For estimation of the Euclidean parameter (log hazard ratios), the problem was asymptotically equivalent to that of using the Phase II sample to estimate an unknown nite population total: the total of the unknown influence function contributions for subjects in the main cohort. Efficiency was improved, sometimes dramatically, through adjustment of the sampling weights by calibration to totals of auxiliary variables known for everyone or by estimation of the known weights using these same variables. After reviewing these results, this talk considers the extensions needed for joint estimation of hazard ratios and baseline hazard function in the Cox model, and hence for prediction of survival probabilities. The improvements in prediction possible with calibrated or estimated weights are illustrated via simulations conducted using Lumleys R survey package to analyze data from the National Wilms Tumor Study.


Breslow NE, Wellner JA. Scand J Stat 34:86-102, 2007; 35:186-192, 2008.
Breslow NE, Lumley T et al. Am J Epidemiol 35:1398-1405, 2009
Breslow NE, Lumley T et al. Stat Biosci 1:32-49, 2009
Breslow NE, Lumley T IMS Monograph Series { Wellner Festschrift, in press
Lumley T. Complex Surveys, New York: Wiley, 2010

The Distinguished Lecture Series in Statistical Science series was established in 2000 and takes place annually. It consists of two lectures by a prominent statistical scientist. The first lecture is intended for a broad mathematical sciences audience. The series occasionally takes place at a member university and is tied to any current thematic program related to statistical science; in the absence of such a program the speaker is chosen independently of current activity at the Institute. A nominating committee of representatives from the member universities solicits nominations from the Canadian statistical community and makes a recommendation to the Fields Scientific Advisory Panel, which is responsible for the selection of speakers.

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