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INTRODUCTION

When effects are large or rapidly changing, spline models may more appropriately describe the association. National Center for Biotechnology Information , U. Didn't get the message? Find out why Add to Clipboard. Add to Collections. Order articles. Fetching bibliography My Bibliography Add to Bibliography. Statistical Tools for Epidemiologic Research available in Hardcover. In this innovative new book, Steve Selvin provides readers with a clear understanding of intermediate biostatistical methods without advanced mathematics or statistical theory for example, no Bayesian statistics, no causal inference, no linear algebra and only a slight hint of calculus.


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This text answers the important question: After a typical first-year course in statistical methods, what next? Statistical Tools for Epidemiologic Research thoroughly explains not just how statistical data analysis works, but how the analysis is accomplished. From the basic foundation laid in the introduction, chapters gradually increase in sophistication with particular emphasis on regression techniques logistic, Poisson, conditional logistic and log-linear and then beyond to useful techniques that are not typically discussed in an applied context.

Intuitive explanations richly supported with numerous examples produce an accessible presentation for readers interested in the analysis of data relevant to epidemiologic or medical research. See All Customer Reviews. Studies to examine the relationship between an exposure and molecular pathologic signature of disease particularly cancer became increasingly common throughout the s. However, the use of molecular pathology in epidemiology posed unique challenges including lack of research guidelines and standardized statistical methodologies, and paucity of interdisciplinary experts and training programs.

To resolve these issues and advance population health science in the era of molecular precision medicine , " molecular pathology " and "epidemiology" was integrated to create a new interdisciplinary field of " molecular pathological epidemiology " MPE , [26] [27] defined as "epidemiology of molecular pathology and heterogeneity of disease". In MPE, investigators analyze the relationships between A environmental, dietary, lifestyle and genetic factors; B alterations in cellular or extracellular molecules; and C evolution and progression of disease.

A better understanding of heterogeneity of disease pathogenesis will further contribute to elucidate etiologies of disease. The MPE approach can be applied to not only neoplastic diseases but also non-neoplastic diseases. By it was recognized that many pathogens' evolution is rapid enough to be highly relevant to epidemiology, and that therefore much could be gained from an interdisciplinary approach to infectious disease integrating epidemiology and molecular evolution to "inform control strategies, or even patient treatment. Epidemiologists employ a range of study designs from the observational to experimental and generally categorized as descriptive, analytic aiming to further examine known associations or hypothesized relationships , and experimental a term often equated with clinical or community trials of treatments and other interventions.

In observational studies, nature is allowed to "take its course," as epidemiologists observe from the sidelines. Conversely, in experimental studies, the epidemiologist is the one in control of all of the factors entering a certain case study.

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The identification of causal relationships between these exposures and outcomes is an important aspect of epidemiology. Modern epidemiologists use informatics as a tool. Observational studies have two components, descriptive and analytical. Descriptive observations pertain to the "who, what, where and when of health-related state occurrence".

The term 'epidemiologic triad' is used to describe the intersection of Host , Agent , and Environment in analyzing an outbreak. Case-series may refer to the qualitative study of the experience of a single patient, or small group of patients with a similar diagnosis, or to a statistical factor with the potential to produce illness with periods when they are unexposed.

The former type of study is purely descriptive and cannot be used to make inferences about the general population of patients with that disease. These types of studies, in which an astute clinician identifies an unusual feature of a disease or a patient's history, may lead to a formulation of a new hypothesis. Using the data from the series, analytic studies could be done to investigate possible causal factors. These can include case-control studies or prospective studies. A case-control study would involve matching comparable controls without the disease to the cases in the series.

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A prospective study would involve following the case series over time to evaluate the disease's natural history. The latter type, more formally described as self-controlled case-series studies, divide individual patient follow-up time into exposed and unexposed periods and use fixed-effects Poisson regression processes to compare the incidence rate of a given outcome between exposed and unexposed periods.

This technique has been extensively used in the study of adverse reactions to vaccination and has been shown in some circumstances to provide statistical power comparable to that available in cohort studies. Case-control studies select subjects based on their disease status.

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It is a retrospective study. A group of individuals that are disease positive the "case" group is compared with a group of disease negative individuals the "control" group. The control group should ideally come from the same population that gave rise to the cases.

1 Biostatistics introduction

The case-control study looks back through time at potential exposures that both groups cases and controls may have encountered. If the OR is significantly greater than 1, then the conclusion is "those with the disease are more likely to have been exposed," whereas if it is close to 1 then the exposure and disease are not likely associated.

If the OR is far less than one, then this suggests that the exposure is a protective factor in the causation of the disease.


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Case-control studies are usually faster and more cost effective than cohort studies , but are sensitive to bias such as recall bias and selection bias. The main challenge is to identify the appropriate control group; the distribution of exposure among the control group should be representative of the distribution in the population that gave rise to the cases.

This can be achieved by drawing a random sample from the original population at risk. This has as a consequence that the control group can contain people with the disease under study when the disease has a high attack rate in a population. As the odds ratio approached 1, approaches 0; rendering case control studies all but useless for low odds ratios.

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For instance, for an odds ratio of 1. Cohort studies select subjects based on their exposure status.

The study subjects should be at risk of the outcome under investigation at the beginning of the cohort study; this usually means that they should be disease free when the cohort study starts. The cohort is followed through time to assess their later outcome status. An example of a cohort study would be the investigation of a cohort of smokers and non-smokers over time to estimate the incidence of lung cancer.

As with the OR, a RR greater than 1 shows association, where the conclusion can be read "those with the exposure were more likely to develop disease. Prospective studies have many benefits over case control studies. The RR is a more powerful effect measure than the OR, as the OR is just an estimation of the RR, since true incidence cannot be calculated in a case control study where subjects are selected based on disease status.

Temporality can be established in a prospective study, and confounders are more easily controlled for. However, they are more costly, and there is a greater chance of losing subjects to follow-up based on the long time period over which the cohort is followed. Although epidemiology is sometimes viewed as a collection of statistical tools used to elucidate the associations of exposures to health outcomes, a deeper understanding of this science is that of discovering causal relationships.

Introductory Course in Epidemiology & Medical Statistics | LSHTM

For epidemiologists, the key is in the term inference. Correlation, or at least association between two variables, is a necessary but not sufficient criteria for inference that one variable causes the other. Epidemiologists use gathered data and a broad range of biomedical and psychosocial theories in an iterative way to generate or expand theory, to test hypotheses, and to make educated, informed assertions about which relationships are causal, and about exactly how they are causal.

Epidemiologists emphasize that the " one cause — one effect " understanding is a simplistic mis-belief. If a necessary condition can be identified and controlled e. In , Austin Bradford Hill proposed a series of considerations to help assess evidence of causation, [41] which have come to be commonly known as the " Bradford Hill criteria ".

In contrast to the explicit intentions of their author, Hill's considerations are now sometimes taught as a checklist to be implemented for assessing causality. Epidemiological studies can only go to prove that an agent could have caused, but not that it did cause, an effect in any particular case:. This question, sometimes referred to as specific causation, is beyond the domain of the science of epidemiology.

Epidemiology has its limits at the point where an inference is made that the relationship between an agent and a disease is causal general causation and where the magnitude of excess risk attributed to the agent has been determined; that is, epidemiology addresses whether an agent can cause a disease, not whether an agent did cause a specific plaintiff's disease. In United States law, epidemiology alone cannot prove that a causal association does not exist in general.

Conversely, it can be and is in some circumstances taken by US courts, in an individual case, to justify an inference that a causal association does exist, based upon a balance of probability. The subdiscipline of forensic epidemiology is directed at the investigation of specific causation of disease or injury in individuals or groups of individuals in instances in which causation is disputed or is unclear, for presentation in legal settings. Epidemiological practice and the results of epidemiological analysis make a significant contribution to emerging population-based health management frameworks.

Modern population-based health management is complex, requiring a multiple set of skills medical, political, technological, mathematical etc. This task requires the forward looking ability of modern risk management approaches that transform health risk factors, incidence, prevalence and mortality statistics derived from epidemiological analysis into management metrics that not only guide how a health system responds to current population health issues, but also how a health system can be managed to better respond to future potential population health issues.

Each of these organizations use a population-based health management framework called Life at Risk that combines epidemiological quantitative analysis with demographics, health agency operational research and economics to perform:. Applied epidemiology is the practice of using epidemiological methods to protect or improve the health of a population.

Applied field epidemiology can include investigating communicable and non-communicable disease outbreaks, mortality and morbidity rates, and nutritional status, among other indicators of health, with the purpose of communicating the results to those who can implement appropriate policies or disease control measures.