Estimation of mean sojourn time in breast cancer screening using a Markov chain model of both entry to and exit from the preclinical detectable phase.
TL;DR: A two-parameter Markov chain model is proposed and developed to explicitly estimate the preclinical incidence rate and the rate of transition from preclinical to clinical state without using control data, and a new estimate of sensitivity is proposed, based on the estimated parameters of the Markov process.
Abstract: The sojourn time, time spent in the preclinical detectable phase (PCDP) for chronic diseases, for example, breast cancer, plays an important role in the design and assessment of screening programmes. Traditional methods to estimate it usually assume a uniform incidence rate of preclinical disease from a randomized control group or historical data. In this paper, a two-parameter Markov chain model is proposed and developed to explicitly estimate the preclinical incidence rate (λ1) and the rate of transition from preclinical to clinical state (λ2, equivalent to the inverse of mean sojourn time) without using control data. A new estimate of sensitivity is proposed, based on the estimated parameters of the Markov process. When this method is applied to the data from the Swedish two-county study of breast cancer screening in the age group 70–74, the estimate of MST is 2·3 with 95 per cent CI ranging from 2·1 to 2·5, which is close to the result based on the traditional method but the 95 per cent CI is narrower using the Markov model. The reason for the greater precision of the latter is the fuller use of all temporal data, since the continuous exact times to events are used in our method instead of grouping them as in the traditional method. Ongoing and future researches should extend this model to include, for example, the tumour size, nodal status and malignancy grade, along with methods of simultaneously estimating sensitivity and the transition rates in the Markov process.
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TL;DR: A small effect of breast cancer screening on breast cancer mortality in women aged younger than 50 years compared with older women and various possible reasons have been suggested are suggested.
Abstract: Background. Several studies have found a smaller effect of breast cancer screening on breast cancer mortality in women aged younger than 50 years compared with older women. Various possible reasons have been suggested for this, but none firmly is established. Methods. The Swedish Two-County Study is a randomized trial of breast cancer screening of women aged 40-74 years, comprising with 133,065 women with a 13-year follow-up of 2467 cancers. The Breast Cancer Detection Demonstration Project (BCDDP) is a nonrandomized screening program in the United States, with a 14-year follow-up of 3778 cancers in women aged 40-74 years. The Swedish results by age were updated. The lesser effect of screening at ages 40-49 years was investigated in terms of sojourn time (the duration of the preclinical but detectable phase) size, lymph node status, and histologic type of the tumors diagnosed in the Swedish Study and their subsequent effect on survival using survival data from both studies. Results. In the Swedish Trial, a 30% reduction in mortality associated with the invitation to screening of women aged 40-74 years was maintained after 13-years of follow-up. The reduction was 34% for women aged 50-74 years and 13% for women aged 40-49 years. Results indicated that the reduced effect on mortality for women aged 40-49 years was due to a differential effect of screening on the prognostic factors of tumor size, lymph node status, and histologic type. The mean sojourn times in the age groups 40-49 years, 50-59 years, 60-69 years, and 70-74 years were 1.7, 3.3, 3.8, and 2.6 years, respectively. Conclusions. These results suggest that much, although not all, of the smaller effect of screening on mortality in women aged 40-49 years is due to faster progression of a substantial proportion of tumors in this age group and the rapid increase in incidence during this decade of life. It is concluded that the interval between screenings should be shortened to achieve a greater benefit in this age group. It is estimated that a 19% reduction in mortality would result from an annual screening regime. Cancer 1995 ;75 :2507-17.
560 citations
TL;DR: The Swedish Two-County Trial as mentioned in this paper is a randomized controlled trial of invitation to breast cancer screening, with 133,000 women randomized between 1977 and 1979 to regular invitation to screening or to no invitation.
452 citations
TL;DR: The American Cancer Society published a summary of its guidelines for early cancer detection, data and trends in cancer screening rates, and select issues related to cancer screening as discussed by the authors, and provided the latest data on utilization of cancer screening from the National Health Interview Survey.
Abstract: Answer questions and earn CME/CNE Each year, the American Cancer Society publishes a summary of its guidelines for early cancer detection, data and trends in cancer screening rates, and select issues related to cancer screening. In this issue of the journal, the authors summarize current American Cancer Society cancer screening guidelines, describe an update of their guideline for using human papillomavirus vaccination for cancer prevention, describe updates in US Preventive Services Task Force recommendations for breast and colorectal cancer screening, discuss interim findings from the UK Collaborative Trial on Ovarian Cancer Screening, and provide the latest data on utilization of cancer screening from the National Health Interview Survey. CA Cancer J Clin 2017;67:100-121. © 2017 American Cancer Society.
393 citations
TL;DR: A general hidden Markov model for simultaneously estimating transition rates and probabilities of stage misclassification of chronic diseases, based on data from a trial of aortic aneurysm screening, in which the screening measurements are subject to error.
Abstract: Summary. Many chronic diseases have a natural interpretation in terms of staged progression. Multistate models based on Markov processes are a well-established method of estimating rates of transition between stages of disease. However, diagnoses of disease stages are sometimes subject to error. The paper presents a general hidden Markov model for simultaneously estimating transition rates and probabilities of stage misclassification. Covariates can be fitted to both the transition rates and the misclassification probabilities. For example, in the study of abdominal aortic aneurysms by ultrasonography, the disease is staged by severity, according to successive ranges of aortic diameter. The model is illustrated on data from a trial of aortic aneurysm screening, in which the screening measurements are subject to error. General purpose software for model implementation has been developed in the form of an R package and is made freely available.
294 citations
TL;DR: The American Cancer Society published a summary of its guidelines for early cancer detection, a report on data and trends in cancer screening rates, and select issues related to cancer screening as discussed by the authors.
Abstract: Answer questions and earn CME/CNE Each year the American Cancer Society publishes a summary of its guidelines for early cancer detection, a report on data and trends in cancer screening rates, and select issues related to cancer screening. In this issue of the journal, we summarize current American Cancer Society cancer screening guidelines. In addition, the latest data on the use of cancer screening from the National Health Interview Survey is described, as are several issues related to screening coverage under the Patient Protection and Affordable Care Act, including the expansion of the Medicaid program.
244 citations
References
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Book•
16 Jul 1993
TL;DR: Statistical Models Based on Counting Processes (SBP) as discussed by the authors is a monograph for mathematical statisticians and biostatisticians, although almost all methods are given in sufficient detail to be used in practice by other mathematically oriented researchers studying event histories.
Abstract: Modern survival analysis and more general event history analysis may be effectively handled in the mathematical framework of counting processes, stochastic integration, martingale central limit theory and product integration. This book presents this theory, which has been the subject of an intense research activity during the past one-and-a-half decades. The exposition of the theory is integrated with the careful presentation of many practical examples, based almost exlusively on the authors' experience, with detailed numerical and graphical illustrations. "Statistical Models Based on Counting Processes" may be viewed as a research monograph for mathematical statisticians and biostatisticians, although almost all methods are given in sufficient detail to be used in practice by other mathematically oriented researchers studying event histories (demographers, econometricians, epidemiologists, actuariala mathematicians, reliability engineers, biologists). Much of the material has so far only been available in the journal literature (if at all), and a wide variety of researchers will find this an invlauable survey of the subject.
3,012 citations
TL;DR: "Statistical Models Based on Counting Processes" may be viewed as a research monograph for mathematical statisticians and biostatisticians, although almost all methods are given in sufficient detail to be used in practice by other mathematically oriented researchers studying event histories.
Abstract: Modern survival analysis and more general event history analysis may be effectively handled in the mathematical framework of counting processes, stochastic integration, martingale central limit theory and product integration. This book presents this theory, which has been the subject of an intense research activity during the past one-and-a-half decades. The exposition of the theory is integrated with the careful presentation of many practical examples, based almost exlusively on the authors' experience, with detailed numerical and graphical illustrations. \"Statistical Models Based on Counting Processes\" may be viewed as a research monograph for mathematical statisticians and biostatisticians, although almost all methods are given in sufficient detail to be used in practice by other mathematically oriented researchers studying event histories (demographers, econometricians, epidemiologists, actuariala mathematicians, reliability engineers, biologists). Much of the material has so far only been available in the journal literature (if at all), and a wide variety of researchers will find this an invlauable survey of the subject.
2,852 citations
Journal Article•
TL;DR: Analysis of survival showed that relative to the control group, the cancers detected at prevalence screen, incidence screens, and in the interval between screens had a good prognosis, whereas cancers detected in those who had refused screening had a very poor prognosis.
715 citations
TL;DR: The purpose of this paper is to discuss statistical considerations associated with the evaluation of such early detection programmes, and to examine problems associated with screening programmes where an individual is examined periodically.
Abstract: SIUMMARY It is assumed that a chronic disease progresses from a pre-clinical state to a clinical state. If an individual, having pre-clinical disease, participates in an early detection programme, the disease may be detected in the pre-clinical state. The potential benefit of a screening programme is related to the lead time gained by early diagnosis. A stochastic model is developed for early detection programmes which leads to an estimate of the mean lead time as a function of observable variables. An investigation is also made of a non-progressive disease model in which individuals in a pre-clinical state may not necessarily advance to the clinical state. At the present time special diagnostic procedures are available for early detection of some chronic diseases. For example, chest X-rays have long been used to detect tuberculosis. Currently, there are many public health programmes to detect women having cancer of the uterine cervix by using Papanicolaou smears; other programmes designed to test for glaucoma and diabetes are in wide use. An especially interesting programme for early detection of breast cancer using soft tissue X-rays, mammography, is now being conducted by the Health Insurance Plan of Greater New York; see Shapiro, Strax & Venet (1967). The aim of all such programmes is to detect the disease earlier than it normally would be detected, the motivation being that earlier detection may result in a cure or better prognosis. Unfortunately, with only a few exceptions we know of no chronic disease in which unambiguous evidence has been coLlected showing that early detection has resulted in significantly improved prognosis. Even in cancer of the uterine cervix, the results are not without question, because the survival rate had been increasing before the widespread introduction of the Papanicolaou smear. It is the purpose of this paper to discuss statistical considerations associated with the evaluation of such early detection programmes. Attention is confined to screening programmes where an individual is examined only once. In a future paper, we shall examine problems associated with screening programmes where an individual is examined periodically. It will be assumed that a person having a particular chronic disease can be regarded as
394 citations
TL;DR: In this paper, a framework is proposed based on modelling each individual's dynamics in the Lexis diagram by a simple three-state stochastic process in the age direction and recruiting individuals from a Poisson process in time direction.
Abstract: In epidemiology incidence denotes the rate of occurrence of new cases (of disease), while prevalence is the frequency in the population (of diseased people). From a statistical point of view it is useful to understand incidence and prevalence in the parameter space, incidence as intensity (hazard) and prevalence as probability, and to relate observable quantities to these via a statistical model. In this paper such a framework is based on modelling each individual's dynamics in the Lexis diagram by a simple three‐state stochastic process in the age direction and recruiting individuals from a Poisson process in the time direction. The resulting distributions in the cross‐sectional population allow a rigorous discussion of the interplay between age‐specific incidence and prevalence as well as of the statistical analysis of epidemiological cross‐sectional data. For the latter, this paper focuses on methods from modern nonparametric continuous time survival analysis, including random censoring and truncation models and estimation under monotonicity constraints. The exposition is illustrated by examples, primarily from the author's epidemiological experience.
378 citations