Statistical considerations in intergenerational studies

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1 University Press Scholarship Online You are looking at of 38 items for: keywords : statistical methods Statistical considerations in intergenerational studies Dorothea Nitsch and Gita D Mishra in Family matters: Designing, analysing and understanding family based studies in life course epidemiology acprof:oso/ Intergenerational data necessarily reflect the time and place that the different generations of participants were living in. This chapter aims first to introduce simple concepts to provide an understanding of the founding assumptions and principles, before moving on to more complex analytic methods. As the objectives of analyses may vary substantially across intergenerational studies, there is no easy guideline for analyses, except perhaps that some a priori clarity on the main associations of interest is crucial. Since parents and their offspring are genetically related, intergenerational studies are to some extent genetically informative even if no genotyping was performed. Much of the analyses are concerned with identifying or unravelling the relationship between outcomes and genetic and environmental factors. Ways of handling missing data as well as approaches to deal with non-paternity are also discussed. Illustrative examples are drawn from the two cohort studies. Critical Appraisal of Epidemiological Studies and Clinical Trials Mark Elwood Published in print: 2007 Published Online: ISBN: eisbn: Item type: book acprof:oso/ This book presents a logical system of critical appraisal, and facilitates studies to be evaluated and carried out effectively. This system emphasizes the central importance of cause and effect relationships. Its great strength is that it is applicable to a wide range of issues, and both to intervention trials and observational studies. This system unifies the often different approaches used in epidemiology, health Page 1 of 6

2 services research, clinical trials, and evidence-based medicine, starting from a logical consideration of cause and effect. The book looks at the issues of study design, selection of subjects, bias, confounding, and the place of statistical methods. Systematic reviews, meta-analysis, and the applications of this logic to evidence-based medicine, knowledgebased health care, and health practice and policy are discussed. Current and often controversial examples are used, including screening for prostate cancer, publication bias in psychiatry, public health issues in developing countries, and conflicts between observational studies and randomized trials. Statistical issues are explained clearly without complex mathematics, and the most useful methods are summarized in the appendix. The final chapters give six applications of the critical appraisal of major studies: randomized trials of medical treatment and prevention, a prospective and a retrospective cohort study, a small matched case-control study, and a large case-control study. In these chapters, sections of the original papers are reproduced and the original studies placed in context by a summary of current developments. Family matters: Designing, analysing and understanding family based studies in life course epidemiology Deborah A. Lawlor and Gita D. Mishra (eds) Item type: book acprof:oso/ Family based studies, including intergenerational, sibling and twin studies, are increasingly used to explore life course epidemiology. However, understanding the underlying assumptions of these studies and hence the inferences that can be drawn from them is complex. Further, there are issues relating to study design and the statistical analysis of family-based studies that are not well understood. This book provides in one volume the knowledge and skills required to design, analyse, and correctly interpret family based studies. The book is divided into four sections that cover the theoretical underpinning of using family based studies in life course epidemiology; practical issues of data collection, storage, and collaborative use (including a chapters on conducting such studies in low and middle income countries and the use of information provided by other family members); statistical analysis and correct interpretation of results from family based studies and a section that illustrates the use of these studies in life course epidemiology, with examples from three areas cardiovascular disease, mental health and wellbeing, and reproductive health. Page 2 of 6

3 Trial-by-trial data analysis using computational models: (Tutorial Review) Nathaniel D. Daw in Decision Making, Affect, and Learning: Attention and Performance XXIII Published in print: 2011 Published Online: May 2011 ISBN: eisbn: acprof:oso/ Researchers have recently begun to integrate computational models into the analysis of neural and behavioural data, particularly in experiments on reward learning and decision making. This chapter aims to review and rationalize these methods. It exposes these tools as instances of broadly applicable statistical techniques, considers the questions they are suited to answer, provides a practical tutorial and tips for their effective use, and, finally, suggests some directions for extension or improvement. The techniques are illustrated with fits of simple models to simulated datasets. Throughout, the chapter flags interpretational and technical pitfalls of which authors, reviewers, and readers should be aware. Random effects models for sibling and twin-based studies in life course epidemiology Samuli Ripatti in Family matters: Designing, analysing and understanding family based studies in life course epidemiology acprof:oso/ This chapter introduces statistical methods for twin and sibling studies using random effects models. The classic twin study begins from assessing the variance of a trait (called a phenotype by geneticists) in a large group, and attempts to estimate how much of this is due to genetic effects (heritability), how much appears to be due to shared environmental effects, and how much is due to unique environmental effects events occurring to one twin but not another. This chapter shows how three families of random effect models, random effects models for normally distributed traits, generalized linear mixed models for binary and count data, and frailty models for survival data may be used for modelling variances in longitudinal, or life course setting. The methods are illustrated using data from The Swedish Adoption/Twin Page 3 of 6

4 Study of Aging. The appendix provides the R and Winbugs codes that are used in the analyses. Statistical methods for geographical correlation studies S. Richardson in Geographical and Environmental Epidemiology: Methods for Small Area Studies Published in print: 1996 Published Online: ISBN: eisbn: acprof:oso/ This chapter discusses statistical method for geographical correlation studies. It focuses on models that acknowledge, estimate, and adjust for spatial correlation. It also discusses problems of ecological bias, which can lead to inaccurate estimates of risk coefficients. It also looks at issues of study design. Event Time Analysis Ezra Susser, Sharon Schwartz, Alfredo Morabia, and Evelyn J. Bromet in Psychiatric Epidemiology: Searching for the Causes of Mental Disorders Published in print: 2006 Published Online: ISBN: eisbn: acprof:oso/ This chapter describes statistical methods for taking account of unequal attrition, that is, different follow-up times across exposed and unexposed groups in a cohort study. It considers methods for analyzing time to an event as an outcome in its own right. The chapter defines the outcome as the time until disease onset. From this perspective, a nondiseased subject in the study is not a control as in a case-control study, but someone who has not yet developed the disease. To analyze disease incidence data from this perspective, methods that come under the interchangeable headings of survival analysis, time-to-event analysis, or failure-time analysis are used. Page 4 of 6

5 Discussant chapter statistical considerations in family-based life course studies Amanda Sacker in Family matters: Designing, analysing and understanding family based studies in life course epidemiology acprof:oso/ This chapter summarizes some of the issues that have been described in the previous two chapters on statistical considerations in family studies. It highlights some of the assumptions underlying the analytic methods and discusses how their use can impact the results. The statistical analyses outlined in this section share common features aimed at quantifying the association between genetic and environmental factors with phenotypic outcomes. For some research, the focus is on heritability while for other work, the focus is on environmental issues while controlling for genetic influences. Modelling approaches for each are discussed, emphasizing potential problems and providing guidelines for careful interpretation. Examples from published empirical epidemiological work is used to illustrate the breadth of analytical strategies adopted for family studies research. Associations between Exposure and Disease Theodore R. Holford in Multivariate Methods in Epidemiology Published in print: 2002 Published Online: ISBN: eisbn: acprof:oso/ This chapter begins with a discussion of the strategies for studying disease in a population. It considers the rationale for studying more than one factor at a time. It then discusses the quantitative framework that can guide the approach to formulating studies of disease etiology. An overview of the different parts of the book is presented. Page 5 of 6

6 A Framework for Investigating Event Occurrence Judith D. Singer and John B. Willett in Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence Published in print: 2003 Published Online: ISBN: eisbn: acprof:oso/ This chapter introduces the essential features of event occurrence data, explaining how and why they create the need for new analytic methods. Section 9.1 provides a context by describing three studies of event occurrence, each conducted in a different discipline. Section 9.2 identifies the three major methodological features of each study: a welldefined event whose occurrence is being explored; a clearly identified beginning of time ; and a substantively meaningful metric for clocking time. Section 9.3 concludes by highlighting the hallmark feature of event occurrence data that makes the new statistical methods we soon discuss necessary the problem known as censoring. Page 6 of 6