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Marvin Zelen

Publications and source records attributed to Marvin Zelen.

14 recordsLinked to original sources
PubMed2006

Biostatisticians, biostatistical science and the future.

Biostatistical Science is in a 'golden period'. The definition of Biostatistical Science is the application of statistics, probability, mathematics and computing to advance our understanding of the subject matter in the biomedical sciences. Our field is experiencing unparallel developments due of the advances in communication and computing. We are becoming more global, we have resources which can expand educational opportunities for distant learning; the growth of quantitative methods in the biomedical sciences has made biostatistical science a key component in many research areas. What about the future? Are we receptive to change as many new scientific areas expand? Will the interaction between academia and industry likely to grow-especially in the training of future practitioners of biostatistical science. This paper discusses some of challenges facing our profession if we are to continue to be relevant in the biomedical sciences.

Biological Science Disciplines↗
PubMed2006

A stochastic model for predicting the mortality of breast cancer.

Consider a cohort of women, identified by year of birth, some of whom will eventually be diagnosed with breast cancer. A stochastic model is developed for predicting the U.S. breast cancer mortality that depends on advances in therapy and dissemination of mammographic screening. The predicted mortality can be compared with the same cohort having usual care with no screening program and absence of modern therapy, or a cohort in which only a proportion participate in a screening program and have modern therapy. The model envisions that a woman may be in four health states: i.e., 1) no disease or breast cancer that cannot be diagnosed (S0), 2) preclinical state (Sp), 3) clinical state (Sc), and 4) disease-specific death (Sd). The preclinical disease refers to breast cancer that is asymptomatic but that may be diagnosed with a special exam. The clinical state refers to symptomatic disease diagnosed under usual care. One of the basic assumptions of the model is that the disease is progressive; i.e., the transitions for the first three states are S0-->Sp-->Sc. The other basic assumption is that any reduction in mortality associated with earlier diagnosis is due to a stage shift in diagnosis; i.e., early diagnosis results in a larger proportion of earlier stage patients. The model is used to predict changes in female breast cancer mortality in the U.S. women for 1975-2000. The model is general and may predict mortality for other chronic diseases that satisfy the two basic assumptions.

Adult↗
PubMed2005

Effect of screening and adjuvant therapy on mortality from breast cancer.

BACKGROUND: We used modeling techniques to assess the relative and absolute contributions of screening mammography and adjuvant treatment to the reduction in breast-cancer mortality in the United States from 1975 to 2000. METHODS: A consortium of investigators developed seven independent statistical models of breast-cancer incidence and mortality. All seven groups used the same sources to obtain data on the use of screening mammography, adjuvant treatment, and benefits of treatment with respect to the rate of death from breast cancer. RESULTS: The proportion of the total reduction in the rate of death from breast cancer attributed to screening varied in the seven models from 28 to 65 percent (median, 46 percent), with adjuvant treatment contributing the rest. The variability across models in the absolute contribution of screening was larger than it was for treatment, reflecting the greater uncertainty associated with estimating the benefit of screening. CONCLUSIONS: Seven statistical models showed that both screening mammography and treatment have helped reduce the rate of death from breast cancer in the United States.

Adult↗
PubMed2005

Robust modeling in screening studies: estimation of sensitivity and preclinical sojourn time distribution.

In early-detection clinical trials, quantities such as the sensitivity of the screening modality and the preclinical duration of the disease are important to describe the natural history of the disease and its interaction with a screening program. Assume that the schedule of a screening program is periodic and that the sojourn time in the preclinical state has a piecewise density function. Modeling the preclinical sojourn time distribution as a piecewise density function results in robust estimation of the distribution function. Our aim is to estimate the piecewise density function and the examination sensitivity using both generalized least squares and maximum likelihood methods. We carried out extensive simulations to evaluate the performance of the methods of estimation. The different estimation methods provide complimentary tools to obtain the unknown parameters. The methods are applied to three breast cancer early-detection trials.

Adult↗
PubMed2004

Forward and backward recurrence times and length biased sampling: age specific models.

Consider a chronic disease process which is beginning to be observed at a point in chronological time. The backward recurrence and forward recurrence times are defined for prevalent cases as the time with disease and the time to leave the disease state, respectively, where the reference point is the point in time at which the disease process is being observed. In this setting the incidence of disease affects the recurrence time distributions. In addition, the survival of prevalent cases will tend to be greater than the population with disease due to length biased sampling. A similar problem arises in models for the early detection of disease. In this case the backward recurrence time is how long an individual has had disease before detection and the forward recurrence time is the time gained by early diagnosis, i.e., until the disease becomes clinical by exhibiting signs or symptoms. In these examples the incidence of disease may be age related resulting in a non-stationary process. The resulting recurrence time distributions are derived as well as some generalization of length-biased sampling.

Aged↗
PubMed2004

Overdiagnosis in early detection programs.

Overdiagnosis refers to the situation where a screening exam detects a disease that would have otherwise been undetected in a person's lifetime. The disease would have not have been diagnosed because the individual would have died of other causes prior to its clinical onset. Although the probability of overdiagnosis is an important quantity for understanding early detection programs it has not been rigorously studied. We analyze an idealized early detection program and derive the mathematical expression for the probability of overdiagnosis. The results are studied numerically for prostate cancer and applied to a variety of screening schedules. Our investigation indicates that the probability of overdiagnosis is remarkably high.

Aged↗
PubMed2004

Early detection of disease and scheduling of screening examinations.

Special examinations exist for many chronic diseases, which can diagnose the disease while it is asymptomatic, with no signs or symptoms. The earlier detection of disease may lead to more cures or longer survival. This possibility has led to public health programs which recommend populations to have periodic screening examinations for detecting specific chronic diseases, for example, cancer, diabetes, cardiovascular disease and so on. Such examination schedules when embedded in a public health program are invariably costly and are ordinarily not chosen on the basis of possible trade-offs in costs and benefits for different screening schedules. The possible candidate number of examination schedules is so large that it is not feasible to carry out clinical trials to compare different schedules. Instead, this problem can be investigated by developing a theoretical model which can predict the eventual disease specific mortality for different examination schedules. We have developed such a model. It is a stochastic model which assumes that i) the natural history of the disease is progressive and ii) any benefit from earlier diagnosis is due to a change in the distribution of disease stages at diagnosis (stage shift). The model is general and can be applied to any chronic disease which satisfies our two basic assumptions. We discuss the basic ideas of schedule sensitivity and lifetime schedule sensitivity and its relation to the reduction in disease specific mortality. Our theory is illustrated by applications to breast cancer screening. The investigation of schedules compares not only examination schedules with equal intervals between examinations but also staggered schedules using the threshold method. (Examinations are carried out when an individual's risk status reaches a preassigned threshold value.).

Adult↗
PubMed2004

Planning of randomized early detection trials.

Consider a randomized clinical trial to evaluate the benefit of screening an asymptomatic population. Suppose that the subjects are randomized into a usual care and a study group. The study group receives one or more periodic early detection examinations aimed at diagnosing disease early, when there are no signs or symptoms. Early detection clinical trials differ from therapeutic trials in that power is affected by: i) the number of exams, ii) the time between exams and iii) the ages at which exams will be given. These design options do not exist in therapeutic trials. Furthermore; long-term follow-up may result in a reduction of power. In general, power increases with number of examinations, and the optimal follow-up time is dependent on the spacing between examinations. Clinical trials in which the usual care group receives benefit are also discussed. Two designs are discussed, for example the 'up-front design' in which all subjects receive an initial exam and then are randomized to the usual care and study groups and the 'close-out design' in which the usual care group receives an exam which is timed to be given at the same time as the last exam in the study group. Both families of designs significantly reduce the power. Power calculations are made for two clinical trials, which actually used these two designs.

Adult↗
PubMed2003

The theory of case-control studies for early detection programs.

Although case-control studies are widely used for evaluating the benefit of early detection programs, the theoretical basis underlying this application has not been well developed. In this paper the properties of chronic disease case-control studies for evaluating early detection programs are investigated. An idealized case-control study is analyzed and the theoretical expression for the odds ratio associated with the benefit of screening is derived. The odds ratio is related to the natural history of disease and the screening program. Our results indicate that case-control studies result in odds ratios that are surprisingly close to unity and consequently have low power.

Age Factors↗
PubMed2002

Experimental design issues for the early detection of disease: novel designs.

This paper investigates two experimental designs which have been used to evaluate the benefit of the early detection of breast cancer. They have some advantages over a classical design (the screening program versus usual medical care) in that subjects in a control group may benefit by participating in the study. We refer to the two experimental designs as the up-front (UFD) and close-out (COD) designs. The UFD consists of offering an initial exam to all participants. Then they can be randomized to a usual care group or a screening group receiving one or more special examinations. If the outcome of the initial examination is included in the analysis, then the study can answer the question of the benefit of an additional screening program after an initial examination. If the analysis excludes all the cases diagnosed at the initial examination, then the analysis evaluates the benefit of a screening program after elimination of the prevalent cases. These prevalent cases are most likely to be affected by length bias sampling and consequently will tend to have less aggressive disease and live longer. As a result, the UFD can answer two scientific questions. The COD consists of randomizing subjects to a usual care group and a screened group. However, the usual care group receives an examination which coincides at the time of the last exam in the study group. In this paper the power of these two designs have been evaluated. In both cases the power is severely reduced compared to the usual control group receiving no special exams. The power is a function of the sensitivity of the exam, the number and spacings of the exams given to the screened group as well as the sample size, disease incidence of the population and the survival distribution. The theoretical results on power are applied to the Canadian National Breast Cancer Study (ages 40-49) which used an UFD and the Stockholm Mammography Breast Cancer Screening Trial which utilized a COD.

Journal Article↗
PubMed2002

Modeling and optimization in early detection programs with a single exam.

The choice of timing of screening examinations is an important element in determining the efficacy of strategies for the early detection of occult disease. In this article, we describe a flexible decision-making framework for the design of early detection programs, and we investigate the choice of timing when each individual in the screening program is examined only once. We focus on the theoretical relation between the optimal examination time and the distributions of sojourn times in health-related states. Specifically, we derive closed-form solutions of the optimal age using two specifications of utility functions, discuss the effects of natural history and utility specifications on the optimal solution, and present an application to early detection of colorectal cancer by once-only sigmoidoscopy or colonoscopy.

Age Factors↗