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A practical introduction to epidemiology, biostatistics, and research methodology for the whole health care community This comprehensive text, which has been extensively revised with new material and additional topics, utilizes a practical slant to introduce health professionals and students to epidemiology, biostatistics, and research methodology. It draws examples from a wide range of topics, covering all of the main contemporary health research methods, including survival analysis, Cox regression, and systematic reviews and meta-analysis—the explanation of which go beyond introductory concepts. This second edition of Quantitative Methods for Health Research: A Practical Interactive Guide to Epidemiology and Statistics also helps develop critical skills that will prepare students to move on to more advanced and specialized methods. A clear distinction is made between knowledge and concepts that all students should ensure they understand, and those that can be pursued further by those who wish to do so. Self-assessment exercises throughout the text help students explore and reflect on their understanding. A program of practical exercises in SPSS (using a prepared data set) helps to consolidate the theory and develop skills and confidence in data handling, analysis, and interpretation. Highlights of the book include: * Combining epidemiology and bio-statistics to demonstrate the relevance and strength of statistical methods * Emphasis on the interpretation of statistics using examples from a variety of public health and health care situations to stress relevance and application * Use of concepts related to examples of published research to show the application of methods and balance between ideals and the realities of research in practice * Integration of practical data analysis exercises to develop skills and confidence * Supplementation by a student companion website which provides guidance on data handling in SPSS and study data sets as referred to in the text Quantitative Methods for Health Research, Second Edition is a practical learning resource for students, practitioners and researchers in public health, health care and related disciplines, providing both a course book and a useful introductory reference. 

This comprehensive text focuses on reasoning, critical thinking and pragmatic decision making in medicine. Based on the author's extensive experience and filled with definitions, formulae, flowcharts and checklists, this fully revised second edition continues to provide invaluable guidance to the crucial role that clinical epidemiology plays in the expanding field of evidence-based medicine. Key Features: * Considers evidence-based medicine as a universal initiative common to all health sciences and professions, and all specialties within those disciplines * Demonstrates how effective practice is reliant on proper foundations, such as clinical and fundamental epidemiology, and biostatistics * Introduces the reader to basic epidemiological methods, meta-analysis and decision analysis * Shows that structured, modern, argumentative reasoning is required to build the best possible evidence and use it in practice and research * Outlines how to make the most appropriate decisions in clinical care, disease prevention and health promotion Presenting a range of topics seldom seen in a single resource, the innovative blend of informal logic and structured evidence-based reasoning makes this book invaluable for anyone seeking broad, in-depth and readable coverage of this complex and sometimes controversial field.

In the analysis of data from longitudinal cohort studies, there is a growing interest in the analysis of developmental trajectories in subpopulations of the cohort under study. There are different advanced statistical methods available to analyze these trajectories, but in the epidemiologic literature, most of those are never used. The purpose of the present study is to compare five statistical methods to detect developmental trajectories in a longitudinal epidemiological data set. All five statistical methods (K-means clustering, a “two-step” approach with mixed modeling and K-means clustering, latent class analysis [LCA], latent class growth analysis [LCGA], and latent class growth mixture modeling [LCGMM]) were performed on a real-life data set and two manipulated data sets. The first manipulated data set contained four different linear developments over time, whereas the second contained two linear and two quadratic developments. For the real-life data set, all five classification methods revealed comparable trajectories. Regarding the manipulated data sets, LCGA performed best in detecting linear trajectories, whereas none of the methods performed well in detecting a combination of linear and quadratic trajectories. Furthermore, the optimal solution for LCA and LCGA contained more classes compared with LCGMM. Although LCGA and LCGMM seem to be preferable above the more simple methods, all classification methods should be applied with great caution.

Purpose of ReviewInstrumental variable (IV) methods continue to be applied to questions ranging from genetic to social epidemiology. In the epidemiologic literature, discussion of whether the assumptions underlying IV analyses hold is often limited to only certain assumptions and even then, arguments are mostly made using subject matter knowledge. To complement subject matter knowledge, there exist a variety of falsification strategies and other tools for weighing the plausibility of the assumptions underlying IV analyses.Recent FindingsThere are many tools that can refute the IV assumptions or help estimate the magnitude or direction of possible bias if the conditions do not hold perfectly. Many of these tools, including both recently developed strategies and strategies described decades ago, are underused or only used in specific applications of IV methods in epidemiology.SummaryAlthough estimating causal effects with IV analyses relies on unverifiable assumptions, the assumptions can sometimes be refuted. We suggest that the epidemiologists using IV analyses employ all the falsification strategies that apply to their research question in order to avoid settings that demonstrably violate a core condition for valid inference.

New forms of evidence are needed to complement evidence generated from randomised controlled trials (RCTs). Real-World Evidence (RWE) is a potential new form of evidence, but remains undefined.This paper sets to fill that gap by defining RWE as the output from a rigorous research process which: (1) includes a clear a priori statement of a hypothesis to be tested or research question to be answered; (2) defines the data sources that will be used and critically appraises their strengths and weaknesses; and (3) applies appropriate methods, including advanced analytics. These elements should be set out in advance of the study commencing, ideally in a published protocol.The strengths of RWE studies are that they are more inclusive than RCTs and can enable an evidence base to be developed around real-world effectiveness and to start to address the complications of managing other real-world problems such as multimorbidity. Computerised medical record systems and big data provide a rich source of data for RWE studies.However, guidance is needed to help assess the rigour of RWE studies so that the strength of recommendations based on their output can be determined. Additionally, RWE advanced analytics methods need better categorisation and validation.We predict that the core role of RCTs will shift towards assessing safety and achieving regulatory compliance. RWE studies, notwithstanding their limitations, may become established as the best vehicle to assess efficacy.

The first edition of Applied Health Economics did an expert job of showing how the availability of large scale data sets and the rapid advancement of advanced econometric techniques can help health economists and health professionals make sense of information better than ever before.This second edition has been revised and updated throughout and includes a new chapter on the description and modelling of individual health care costs, thus broadening the book's readership to those working on risk adjustment and health technology appraisal. The text also fully reflects the very

Background Long-term multicentre studies are subject to numerous factors that may affect the integrity of their conclusions. Quality control and standardization of data collection are crucial to minimise the biases induced by these factors. Nevertheless, tools implemented to manage biases are rarely described in publications about population-based cohorts. This report aims to describe the processes implemented to control biases in the Constances cohort taking lung function results as an example. Methods Constances is a general-purpose population-based cohort of 200,000 participants. Volunteers attend physical examinations at baseline and then every 5 years at selected study sites. Medical device specifications and measurement methods have to comply with Standard Operating Procedures developed by experts. Protocol deviations are assessed by on-site inspections and database controls. In February 2016, more than 94,000 participants yielding around 30 million readings from physical exams, had been covered by our quality program. Results Participating centres accepted to revise their practices in accordance with the study research specifications. Distributors of medical devices were asked to comply with international guidelines and Constances requirements. Close monitoring enhanced the quality of measurements and recordings of the physical exams. Regarding lung function testing, spirometry acceptability rates per operator doubled in some sites within a few months and global repeatability reached 96.7 % for 29,772 acceptable maneuvers. Conclusions Despite Constances volunteers being followed in multiple sites with heterogeneous materials, the investment of significant resources to set up and maintain a continuous quality management process has proved effective in preventing drifts and improving accuracy of collected data.

Purpose of ReviewWe provided an overview of sampling methods for hard-to-reach populations and guidance on implementing one of the most popular approaches: respondent-driven sampling (RDS).Recent FindingsLimitations related to generating a sampling frame for marginalized populations can make them “hard-to-reach” when conducting population health research. Data analyzed from non-probability-based or convenience samples may produce estimates that are biased or not generalizable to the target population. In RDS and time-location sampling (TLS), factors that influence inclusion can be estimated and accounted for in an effort to generate representative samples. RDS is particularly equipped to reach the most hidden members of hard-to-reach populations.SummaryTLS, RDS, or a combination can provide a rigorous method to identify and recruit samples from hard-to-reach populations and more generalizable estimates of population characteristics. Researchers interested in sampling hard-to-reach populations should expand their toolkits to include these methods.

Purpose of ReviewNegative controls are a powerful tool to detect and adjust for bias in epidemiological research. This paper introduces negative controls to a broader audience and provides guidance on principled design and causal analysis based on a formal negative control framework.Recent FindingsWe review and summarize causal and statistical assumptions, practical strategies, and validation criteria that can be combined with subject-matter knowledge to perform negative control analyses. We also review existing statistical methodologies for the detection, reduction, and correction of confounding bias, and briefly discuss recent advances towards nonparametric identification of causal effects in a double-negative control design.SummaryThere is great potential for valid and accurate causal inference leveraging contemporary healthcare data in which negative controls are routinely available. Design and analysis of observational data leveraging negative controls is an area of growing interest in health and social sciences. Despite these developments, further effort is needed to disseminate these novel methods to ensure they are adopted by practicing epidemiologists.

Purpose of ReviewSurvival analyses are common and essential in medical research. Most readers are familiar with Kaplan–Meier curves and Cox models; however, very few are familiar with multistate models. Although multistate models were introduced in 1965, they only recently receive more attention in the medical research community. The current review introduces common terminologies and quantities that can be estimated from multistate models. Examples from published literature are used to illustrate the utility of multistate models.Recent FindingsA figure of states and transitions is a useful depiction of a multistate model. Clinically meaningful quantities that can be estimated from a multistate model include the probability in a state at a given time, the average time in a state, and the expected number of visits to a state; all of which describe the absolute risks of an event. Relative risk can also be estimated using multistate hazard models.SummaryMultistate models provide a more general and flexible framework that extends beyond the Kaplan-Meier estimator and Cox models. Multistate models allow simultaneous analyses of multiple disease pathways to provide insights into the natural history of complex diseases. We strongly encourage the use of multistate models when analyzing time-to-event data.