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Statistical epidemiology
About: Statistical epidemiology is a research topic. Over the lifetime, 160 publications have been published within this topic receiving 14230 citations.
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01 Jan 1980
TL;DR: Case-control studies have come into increasing favour, and they are now one of the commonest forms of epidemiol-ogical studies.
Abstract: Substantial advances have been made over the past 10 years or so in the study of statistical techniques for the analysis of observational (as opposed to experimental) data. Partly as a result of this, case-control studies have come into increasing favour, and they are now one of the commonest forms of epidemiol-ogical Other CABI sites
3,422 citations
Book•
01 Jan 1987
TL;DR: The scope ranges from an account of the elementary and descriptive approaches to cohort analysis to the fitting of regression models for incidence rates with general risk functions, and particular attention is given to the use of a case-control approach embedded in a cohort study.
Abstract: This book complements the first volume in the series, on case-control studies, and the two together provide a comprehensive account of the analysis of the major types of study in cancer epidemiology. In addition, this volume has a chapter on study design, covering both the case-control and cohort approach. The scope ranges from an account of the elementary and descriptive approaches to cohort analysis to the fitting of regression models for incidence rates with general risk functions. Particular attention is given to the use of a case-control approach embedded in a cohort study. As in the first volume, all the methods described are illustrated by examples from real studies, and the data from these studies are provided in appendices to enable the reader to go through the computations themselves. The book is intended for the medical epidemiologist with an interest in the quantitative aspects of the subject, and the statistician who is looking for a reasonably complete development of the statistical concepts and methods in current use in this area of epidemiology, as well as medical epidemiologists; statisticians; oncologists; students in biostatistics and related fields.
1,722 citations
Book•
01 Aug 1999
TL;DR: Beyond the Basics as discussed by the authors is an intermediate level epidemiology text specifically designed to expand student knowledge without complex statistical formulations, which guides students who have a good understanding of basic epidemiologic principles through more rigorous discussions of concepts and methods.
Abstract: Finally! A first-of-its-kind intermediate level epidemiology text specifically designed to expand student knowledge without complex statistical formulations! "Epidemiology": Beyond the Basics guides your students who have a good understanding of basic epidemiologic principles through more rigorous discussions of concepts and methods. Generous use of eye-catching graphics and real-life examples makes the material engaging and easy to understand.
1,533 citations
Book•
05 Aug 1993
TL;DR: This self-contained account of the statistical basis of epidemiology has been written specifically for those with a basic training in biology, therefore no previous knowledge is assumed and the mathematics is deliberately kept at a manageable level.
Abstract: Statistical models in epidemiology , Statistical models in epidemiology , کتابخانه مرکزی دانشگاه علوم پزشکی تهران
1,527 citations
TL;DR: This issue focuses on statistical methods in medical research and proposes two probabilistic models to estimate male-to-female HIV-1 transmission rate in one sexual contact.
Abstract: Since John Snow first conducted a modern epidemiological study in 1854 during a cholera epidemic in London, statistics has been associated with medical research. After Austin Bradford Hill published a series of articles on the use of statistical methodology in medical research in 1937, statistical considerations and computational tools have been paramount in conductingmedical research [1]. For the past century, statistics has played an important role in the advancement of medical research and medical research has stimulated rapid development of statistical methods. For example, the development of modern survival analysis-an important branch of statistics has aimed to solve problems encountered in clinical trials and large-scale epidemiological studies. In this era of evidence-based medicine, the development of novel statistical methods will continue to be crucial in medical research. With the expansion of computer capacity and advancement of computational techniques, it is inevitable that modern statistical methods will likely incorporate, to a greater degree, complex computational procedures. This issue focuses on statistical methods in medical research. Several novel methods aiming on solving different medical research questions are introduced. Some unique approaches of statistical analysis are also present. Hanagal and Sharma contribute two papers. The first one deals with a bivariate survival model. They examine a parameter estimation issue when the samples are taken from a bivariate log-logistic distribution with shared gamma frailty. They propose to use a Bayesian approach along with theMarkov ChainMonte Carlo computational technique for implementation. The computer simulation is conducted for performance evaluation. Two well-known datasets, one about acute leukemia and the other about kidney infection are applied as examples. The second paper contributed by Hanagal and Sharma examines the shared inverse Gaussian frailty model with the bivariate exponential baseline hazard. They first derive the likelihood of the joint survival function. In their Bayesian approach, the parameters of the baseline hazard are assumed to follow a gamma distribution while the coefficients of the regression relationship are assumed to follow an independent normal distribution. The dependence of two components of the survival function is tested. Three information criteria are used for model comparisons. The proposed method is applied to analyze diabetic retinopathy data. The paper by Chang, Lyer, Bullitt and Wang provides a method to find determinants of the brain arterial system. They represent the brain arterial system as a binary tree and apply the mixed logistic regression model to find significant covariates. The authors also demonstrate model selection methods for both fixed and random effects. A case study is presented using the method. This paper provides a rigorous approach for analyzing the binary branching structure data. It is potentially applicable to other tree structure data. Chakraborty proposes two probabilistic models to estimate male-to-female HIV-1 transmission rate in one sexual contact. One model is applicable when the transmitter cell counts are known and the other model is applicable when the receptor cell counts are known. By first uniformizing each transmitter (or receptor) cell count and assuming as a beta distribution, this paper algebraically derives the transition probability by imposing some boundary conditions based on scientific phenomena related to HIV infection. The paper by Yeh, Jiang, Garrard, Lei and Gajewski proposes to use a zero-truncated Poisson model to analyze human cancer tissues transplanted to mice when the positive counts of affected ducts is subject to right censoring. A Bayesian approach choosing a Gamma distribution as the prior is adopted. After implementing through complex computational procedures, this paper obtains the estimates of the coefficients and demonstrates model fitting through
1,127 citations