In honor of Dr. Charles L. Odoroff, the founding Director of the Division (now Department) of Biostatistics at the University of Rochester, the Department hosts an annual lecture by a distinguished statistician. This lecture series is supported by funds contributed by family and friends of Dr. Odoroff after his untimely death in 1987 at age 49.

2008 Charles L. Odoroff Memorial Lecture

Early Detection of Disease and Stochastic Models

The early detection of disease presents opportunities for using existing technologies to significantly improve patient benefit. The possibility of diagnosing a chronic disease early, while it is asymptomatic, may result in diagnosing the disease in an earlier stage leading to better prognosis. Many cancers, diabetes, tuberculosis, cardiovascular disease, HIV related diseases, etc. may have better prognosis when combined with an effective treatment. However gathering scientific evidence to demonstrate benefit has proved to be difficult. Clinical trials have been arduous to carry out, because of the need to have large numbers of subjects, long follow-up periods and problems of non-compliance. Implementing public health early detection programs have proved to be costly and not based on analytic considerations. Many of these difficulties are a result of not understanding the early disease detection process and the disease natural histories. One way to approach these problems is to model the early detection process. This talk will discuss stochastic models for the early detection of disease. Breast cancer will be used to illustrate some of the ideas. The talk will discuss breast cancer randomized trials, stage shift and benefit, scheduling of examinations, issue of screening younger and older women and the probability of over diagnosis of disease.

Tuesday, April 29, 2008
3:30 PM
Auditorium 3-6408 (K-307)

2007 Charles L. Odoroff Memorial Lecture

Targeted Maximum Likelihood Learning of Scientific Questions

The main point of this presentation is that choice of a statistical model and method must be based on careful consideration of the scientific question of interest in order to provide robust tests of the null hypothesis and to minimize bias in the parameter estimate. For this purpose we developed a new generally applicable targeted maximum likelihood estimation methodology.

As an example, I will distinguish between scientific questions concerned with prediction of an outcome based on a set of input variables versus scientific questions in which the goal is to estimate the variable importance or causal effect of one particular variable/treatment. I will show the limitations of fitting regression models for the purpose of learning about a causal effect or variable importance, and present the alternative targeted maximum likelihood approach. Both observational studies and randomized trials will be used to illustrate the advantages of the targeted approach. I will present results from data analyses in which the targeted approach is used to 1) analyze the importance of each of a set of HIV mutations for protease inhibitor resistance and 2) estimate the causal effect of interventions to improve adherence to antiretroviral drugs.

The differences between prediction and causal effect estimation are further highlighted by the additional assumptions needed for the estimation of the causal effect of an intervention in an observational study. Beyond the familiar ''no unmeasured confounding'' assumption, causal effect estimation also requires an experimental treatment assignment assumption, violation of which can cause severe bias and increased variance in a causal effect estimate. To address this problem, I will show that estimation of the causal effect of a "realistic" intervention (similar to the parameter one estimates in an intention-to-treat analysis) provides an important generalization which can always be fully identified from the data. Targeted estimators of this realistic parameter are also available.

Finally, I will discuss the advantages of applying targeted estimation in the context of a randomized trial. Like standard approaches, the targeted approach relies only on the randomization assumption. However, the targeted approach yields an improved estimate of the causal effect of a treatment in a randomized trial relative to the commonly used marginal estimate of the treatment effect.

Thursday, September 20, 2007
3:30 PM
Room 1-7619 (Adolph Auditorium)

2006 Charles L. Odoroff Memorial Lecture

Estimating Mean Response as a Function of Treatment Duration in an Observational Study, Where Duration May be Informatively Censored

In a recent clinical trial "ESPRIT" of patients with coronary heart disease who were scheduled to undergo percutaneous coronary intervention (PCI), patients randomized to receive Integrilin therapy had significantly better outcomes than patients randomized to placebo. The protocol recommended that Integrilin be given as a continuous infusion for 18--24 hours. There was debate among the clinicians on the optimal infusion duration in this 18--24-hour range, and we were asked to study this question statistically. Two issues complicated this analysis: (i) The choice of treatment duration was left to the discretion of the physician and (ii) treatment duration would have to be terminated (censored) if the patient experienced serious complications during the infusion period. To formalize the question, "What is the optimal infusion duration?" in terms of a statistical model, we developed a framework where the problem was cast using ideas developed for adaptive treatment strategies in causal inference. The problem is defined through parameters of the distribution of (unobserved) potential outcomes. We then show how, under some reasonable assumptions, these parameters could be estimated. The methods are illustrated using the data from the ESPRIT trial.

Thursday, May 11, 2006 at 3:30 P.M.

2005 Charles L. Odoroff Memorial Lecture

Prenatal Methylmercury Exposure and Childhood IQ

Controversy continues regarding the impact of chronic methylmercury exposures on childhood development. Adverse effects are difficult to quantify at low doses, and conflicting results have been obtained from several well-designed epidemiological studies, one in the Faroe Islands, one in the Seychelles and an older small study in New Zealand. We describe the use of hierarchical modeling techniques to combine data on several endpoints from these three studies. We find convincing evidence of an effect of methylmercury exposure on full scale IQ in children aged 6 to 9 years.

Wednesday, March 16, 2005 at 3:00 P.M.

2004 Charles L. Odoroff Memorial Lecture

A Generalization of the Levin-Robbins Procedure for Binomial Subset Selection and Recruitment Problems

We introduce a family of sequential selection and recruitment procedures for the subset identification problem in binomial populations. We demonstrate the general validity of a simple formula providing a lower bound for the probability of correct identification in a version of the family without sequential elimination or recruitment. A similar theorem is conjectured to hold for the more efficient version which employs sequential elimination or recruitment.

Thursday, April 15 2004 3:00 P.M.

2003 Charles L. Odoroff Memorial Lecture

Residuals are informative about the adequacy of regression models. Conventional residual analysis based on the plots of individual residuals is highly subjective, while most numerical goodness-of-fit tests provide little information about the nature of model misspecification. In this talk, we present objective and informative strategies for model selection and assessment based on the cumulative sums of residuals over certain coordinates (e.g., covariates or fitted values) or some related aggregates of residuals (e.g., moving averages and kernel smoothers). The distributions of these stochastic processes under the assumed model can be approximated by the distributions of certain zero-mean Gaussian processes whose realizations can be easily generated by computer simulation. Each observed residual pattern can then be compared, both graphically and numerically, with a number of realizations from the null distribution. Such comparisons enable one to assess objectively whether a specific aspect of the model (e.g., the functional form of a covariate, the link function or the proportional hazards assumption) has been correctly specified. They also provide helpful hints on how to obtain an appropriate model. We apply this approach to a wide variety of statistical models and data structures, and provide illustrations with several clinical and epidemiologic studies. The methods presented in this talk will be featured in the next release of SAS.

Wednesday, May 7 2003 10:30 A.M.

2002 Charles L. Odoroff Memorial Lecture

It is to be expected that responses to treatments, measurement techniques, and diseases may vary among individuals. Characterizing the underlying distributions of heterogeneous responses is often difficult in that it requires statistically removing the confounding influences of measurement error from the observed response distributions. This is an important problem, however, to the extent that there have been calls that drug approval be based on the nature of underlying response distributions, rather than merely average treatment effects. Some general approaches to the problem of estimating response distributions will be discussed. Three examples, from separate studies of postmenopausal hormone therapy, hypertension control, and carotid atherosclerosis, will be used as case studies. In each, multivariate hierarchical measurement error models were fitted with varying success to estimate characteristics of underlying response distributions. The goal of describing heterogeneous responses presents major challenges for study designs. Improved methodology and increased data sharing are needed.

Thursday, April 4 2002 3:00 P.M.

2001 Charles L. Odoroff Memorial Lecture

This talk will describe "SQUARE", a novel method for estimating the difference in means between two skewed distributions given one relatively smaller and one larger sample. This problem arises in assessing the medical costs of smoking.

We will give an overview of the statistical problem of determining smoking attributable expenditures which includes estimating the average cost of services for persons with smoking-caused diseases relative to otherwise similar persons without disease. We then introduce an estimator of this difference that relies on estimation of the ratio of the quantile functions for the two distributions. When the degrees of freedom to estimate this function is small, our estimator approximates the log-normal model mle; when they are large, it converges to the difference in sample means.

We illustrate SQUARE with an analysis of the difference in medical costs with persons who suffer lung cancer and chronic obstructive pulmonary disease as compared to otherwise similar people who do not.

Thursday, April 12 2001 3:15 P.M.

2000 Charles L. Odoroff Memorial Lecture

Wilms tumor is an embryonal tumor of the kidney that affects approximately one child per 10,000 before the age of 15 years. It was almost universally fatal a century ago, but cure rates today approach 90% due largely to the use of modern combination chemotherapy. The National Wilms Tumor Study Group (NWTSG), founded in 1969, has been in the forefront of the recent advances.

Wilms tumor has served as a model of complexity for understanding of the genetic origins of cancer. It was initially believed to follow the two-hit mutational model proposed by Knudson (1972) on the basis of his statistical analysis of data for retinoblastoma, another childhood tumor involving a paired organ. Simple descriptive analyses of the large NWTSG database, however, suggested that the genetics of Wilms tumor were more complex. This suggestion was subsequently confirmed by laboratory studies demonstrating a role for genomic imprinting in the etiology of Wilms tumor, the limited number of cases associated with mutations in the first (and to date only) Wilms tumor gene to be cloned (WT1), and the linkage of familial cases to at least two other distinct loci. This talk will present several examples of how simple descriptive statistical analyses can challenge prevailing genetic models and suggest new avenues for laboratory investigation.

Thursday, March 23, 2000 3:00 P.M.

1999 Charles L. Odoroff Memorial Lecture

Phase 3 clinical trials, which evaluate the effect that new interventions have on the clinical outcomes of particular relevance to the patient (such as death, loss of vision, or other major symptomatic event), often require many participants to be followed for a long time. There has recently been great interest in using surrogate end points, such as tumor shrinkage or changes in cholesterol level, blood pressure, CD4 cell count, or other laboratory measures, to reduce the cost and duration of clinical trials. In theory, for a surrogate end point to be an effective substitute for the clinical outcome, effects of the intervention on the surrogate must reliably predict the overall effect on the clinical outcome. In practice, this requirement frequently fails. Among several explanations for this failure is the possibility that the disease process could affect the clinical outcome through several causal pathways that are not medated through the surrogate, with the intervention's effect on these pathways differing from its effect on the surrogate. Even more likely, the intervention might also affect the clinical outcome by unintended, unanticipated, and unrecognized mechanisms of action that operate independently of the disease process. Examples from several disease areas illustrate how surrogate end points have been misleading about the actual effects that treatments have on the health of patients. Surrogate end points can be useful in phase 2 screening trials for identifying whether a new intervention is biologically active and for guiding decisions about whether the intervention is promising enough to justify a large definitive trial with clinically meaningful outcomes. In definitive phase 3 trials, except for rare circumstances in which the validity of the surrogate end point has already been rigorously established, the primary end point should be the true clinical outcome.

Thursday, April 8, 1999 3:15 P.M.

1998 Charles L. Odoroff Memorial Lecture

The Development of methods for "exact" small-sample analyses has been a major advance of the past decade in contingency table analysis. Exact methods guarantee that the size of a test is no greater than some prespecified level and that the coverage probability for a confidence interval is at least the nominal level. A variety of exact methods now exist, both of a conditional and unconditional nature. The great variability in results that can occur with different methods reflects complications due to discreteness. As discreteness increases, exact tests and confidence intervals tend to become overly conservative. We illustrate these issues by studying interval estimation of two basic parameters -- the proportion and the odds ratio. In each case, even for small samples one can argue that "large-sample" solutions are superior to exact ones for many purposes. There will always be an important niche for exact methods, but issues discussed here suggest that statisticians should perhaps reconsider how to evaluate inference procedures.

Thursday, April 16, 1998, 3:45 p.m.

1997 Charles L. Odoroff Memorial Lecture

Thursday, April 24, 1997, 3:30 p.m.

1996 Charles L. Odoroff Memorial Lecture

This talk describes two of the most important developments in medical investigation: the adoption of randomization in clinical trials, and the use of statistical ideas, including the case-control design, to establish and investigate the association between smoking and lung cancer. I shall discuss these two developments and two statisticians who contributed enormously to their realization, Austin Bradford Hill and Jerome Cornfield.

Thursday, April 18, 1996, 3:30 p.m.

1995 Charles L. Odoroff Memorial Lecture

Tuesday, April 25, 1995, 2:30 p.m.

1994 Charles L. Odoroff Memorial Lecture

Tuesday, April 26, 1994, 3:30 p.m.

1993 Charles L. Odoroff Memorial Lecture

Wednesday, March 3, 1993, 1:30 p.m.

1992 Charles L. Odoroff Memorial Lecture

Thursday, March 19, 1992, 3:30 p.m.

1991 Charles Odoroff Memorial Lecture

A review of various definitions of causality, with special reference to epidemiological applications. Implications for empirical statistical analysis and for clinical trials.

Thursday, May 23, 1991, 3:30 p.m.

1990 Charles L. Odoroff Memorial Lecture

For 20 different studies, we tabulate numerical averages of opinions on quantitative meanings of 52 qualitative probabilistic expressions. Populations with differing occupations, mainly students, physicians, other medical workers, and science writers contributed. In spite of the variety of populations, format of question, instructions, and context, the variation of the averages for most of the expressions was modest.

The paper also reviews studies that show stability of meanings over 20 years, mild effects of translation into other languages, context, small order effects, and effects of scale for reporting on extreme values.

Wednesday, March 28, 1990, 3:00 p.m.

1989 Charles L. Odoroff Memorial Lecture

In the 1960's it was reported that patients who suffered heart attacks were seldom regular takers of aspirin. From that time forward many studies have been devoted to the question of whether there may be a possible therapeutic benefit of aspirin in preventing myocardial infarction. Although the benefit of aspirin prophylactically (i.e., in preventing a new infarction) remains unclear, it has at least been demonstrated that aspirin is beneficial therapeutically (i.e., in acute post-infarct survival).

This example well illustrates the strengths and weaknesses of different styles of clinical evaluation, a topic central to the life and work of Charles Odoroff.

Wednesday, March 8, 1989, 3:00 p.m.