Introduction to epidemiological study designs

2020 ◽  
pp. 83-98
Author(s):  
Tamsin Ford ◽  
Jayati Das-Munshi ◽  
Martin Prince
Keyword(s):  
Mixed Methods ◽  
Data Collection ◽  
Cohort Studies ◽  
Epidemiological Study ◽  
Study Design ◽  
Case Control ◽  
Psychiatric Epidemiology ◽  
Case Control Studies ◽  
Cross Sectional ◽  
Study Designs

This chapter provides a brief overview for each of the main types of study design commonly used in psychiatric epidemiology. The chapter begins with a discussion of the importance of study design. This is followed by a section on classifying study design, including descriptive studies, ecological studies, cross-sectional surveys, cohort studies, case–control studies, intervention studies, and qualitative and mixed methods studies. The chapter concludes with a description of the basic steps which should be observed in the conduct of studies employing quantitative methodologies (including cross-sectional, cohort, and case–control studies), as well as discussing interviews/assessments, and data collection and processing.

Study designs which can demonstrate and test causation

2017 ◽  
Author(s):  
Mark Elwood
Keyword(s):  
Cohort Studies ◽  
Case Control ◽  
Control Group ◽  
Case Control Studies ◽  
Retrospective Case ◽  
Community Based ◽  
Cross Sectional ◽  
Healthcare Interventions ◽  
Intervention Trials ◽  
Study Designs

This chapter presents study designs which can test and show causation. Cohort and intervention studies compare people exposed to an agent or intervention with those unexposed or less exposed. Case-control studies compare people affected by a disease or outcome with a control group of unaffected people or representing a total population. Surveys select a sample of people, not chosen by exposure or outcome. Cohort studies may be prospective or retrospective; case-control studies are retrospective; surveys are cross-sectional in time, but retrospective or prospective aspects can be added. In part two, strengths, weaknesses and applications of these designs are shown. Intervention trials, ideally randomised, are the prime method of assessing healthcare interventions; special types include crossover trials and community-based trials. Non-randomised trials are noted. The strengths and weaknesses of cohort studies, case-control studies, and surveys are shown.

Epidemiology

2017 ◽  
Author(s):  
Sadie Costello ◽  
Jennifer M. Cavallari ◽  
David H. Wegman ◽  
Marie S. O’Neill ◽  
Ellen A. Eisen
Keyword(s):  
Case Control ◽  
Case Control Studies ◽  
Cross Sectional ◽  
Information Selection ◽  
Basic Principles ◽  
Potential Source ◽  
The Common ◽  
Key Aspects ◽  
Study Designs

This chapter describes the basic principles of epidemiology, emphasizing the aspects most relevant to studies of health effects from occupational and environmental exposures. Numerous examples are provided of how epidemiology can be used to identify and quantify the relations between recent or long-term exposure and health outcomes, such as prevalence or incidence of disease, injury, or mortality. The chapter describes the common study designs, including cohort studies, case-control studies, and cross-sectional studies, with examples of their application. Key aspects of exposure assessment and characterizing and quantifying exposure, are described. The three types of bias in epidemiology, information, selection, and confounding, are defined as well as the healthy worker effect, a potential source of bias unique in occupational studies. Study designs and analytic methods that can reduce or eliminate specific types of bias are also described. Finally, the chapter provides guidance on how to interpret the results of studies, with an eye toward causal inference.

Studies of disease causation

2018 ◽  
pp. 71-89
Author(s):  
Alan J. Silman ◽  
Gary J. Macfarlane ◽  
Tatiana Macfarlane
Keyword(s):  
Cohort Studies ◽  
Epidemiological Study ◽  
Disease Status ◽  
Case Control ◽  
Careful Selection ◽  
Sampling Frame ◽  
Case Control Studies ◽  
Disease Causation ◽  
Selection Of

The most important aspect that will influence the validity of any epidemiological study is the careful selection of the subjects for investigation. Separate issues relate to the sampling of subjects for disease status in case–control studies, and sampling by exposure status in cohort studies. In simplest terms, the issues are who should be the cases and, given that, who should be the controls. Thus, in each instance the needs are to identify the sampling frame and then what should be the process for selecting the specific sample or subsamples needed for study. In addition to consideration of who to study, other factors such as how to identify and verify plus the size of the planned study are all topics to be addressed.

scholarly journals Association of multiple sclerosis with vitiligo: a systematic review and meta-analysis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Meng-Han Shen ◽  
Chau Yee Ng ◽  
Kuo-Hsuan Chang ◽  
Ching-Chi Chi
Keyword(s):  
Multiple Sclerosis ◽  
Cohort Studies ◽  
Genetic Factors ◽  
Meta Analysis ◽  
Case Control ◽  
Systemic Review ◽  
Case Control Studies ◽  
Cross Sectional ◽  
Pooled Data ◽  
Newcastle Ottawa Scale

Abstract Polyautoimmunity implicates that some autoimmune diseases share common etiopathogenesis. Some studies have reported an association between multiple sclerosis (MS) and vitiligo; meanwhile, other studies have failed to confirm this association. We performed a systemic review and meta-analysis to examine the association of MS with vitiligo. We searched the MEDLINE and Embase databases on March 8, 2020 for relevant case–control, cross-sectional, and cohort studies. The Newcastle–Ottawa Scale was used to evaluate the risk of bias of the included studies. Where applicable, we performed a meta-analysis to calculate the pooled odds ratio (OR) for case–control/cross-sectional studies and risk ratio for cohort studies with 95% confidence interval (CI). Our search identified 285 citations after removing duplicates. Six case–control studies with 12,930 study subjects met our inclusion criteria. Our meta-analysis found no significant association of MS with prevalent vitiligo (pooled OR 1.33; 95% CI 0.80‒2.22). Analysis of the pooled data failed to display any increase of prevalent vitiligo in MS patients compared with controls. Ethnic and genetic factors may play an important role for sporadically observed associations between MS and vitiligo. Future studies of this association should therefore consider stratification by ethnic or genetic factors.

scholarly journals Neurology and neuropsychiatry of COVID-19: a systematic review and meta-analysis of the early literature reveals frequent CNS manifestations and key emerging narratives

2021 ◽  
pp. jnnp-2021-326405
Author(s):  
Jonathan P Rogers ◽  
Cameron J Watson ◽  
James Badenoch ◽  
Benjamin Cross ◽  
Matthew Butler ◽  
...  
Keyword(s):  
Systematic Review ◽  
Cohort Studies ◽  
Meta Analysis ◽  
Neuropsychiatric Symptoms ◽  
Case Series ◽  
Case Control ◽  
Case Control Studies ◽  
Cross Sectional ◽  
Early Literature ◽  
Cross Sectional Studies

There is accumulating evidence of the neurological and neuropsychiatric features of infection with SARS-CoV-2. In this systematic review and meta-analysis, we aimed to describe the characteristics of the early literature and estimate point prevalences for neurological and neuropsychiatric manifestations. We searched MEDLINE, Embase, PsycINFO and CINAHL up to 18 July 2020 for randomised controlled trials, cohort studies, case-control studies, cross-sectional studies and case series. Studies reporting prevalences of neurological or neuropsychiatric symptoms were synthesised into meta-analyses to estimate pooled prevalence. 13 292 records were screened by at least two authors to identify 215 included studies, of which there were 37 cohort studies, 15 case-control studies, 80 cross-sectional studies and 83 case series from 30 countries. 147 studies were included in the meta-analysis. The symptoms with the highest prevalence were anosmia (43.1% (95% CI 35.2% to 51.3%), n=15 975, 63 studies), weakness (40.0% (95% CI 27.9% to 53.5%), n=221, 3 studies), fatigue (37.8% (95% CI 31.6% to 44.4%), n=21 101, 67 studies), dysgeusia (37.2% (95% CI 29.8% to 45.3%), n=13 686, 52 studies), myalgia (25.1% (95% CI 19.8% to 31.3%), n=66 268, 76 studies), depression (23.0% (95% CI 11.8% to 40.2%), n=43 128, 10 studies), headache (20.7% (95% CI 16.1% to 26.1%), n=64 613, 84 studies), anxiety (15.9% (5.6% to 37.7%), n=42 566, 9 studies) and altered mental status (8.2% (95% CI 4.4% to 14.8%), n=49 326, 19 studies). Heterogeneity for most clinical manifestations was high. Neurological and neuropsychiatric symptoms of COVID-19 in the pandemic’s early phase are varied and common. The neurological and psychiatric academic communities should develop systems to facilitate high-quality methodologies, including more rapid examination of the longitudinal course of neuropsychiatric complications of newly emerging diseases and their relationship to neuroimaging and inflammatory biomarkers.

scholarly journals Research made Easy: Answering Important Questions with Valid Designs

2012 ◽  
Vol 46 (1) ◽  
pp. 8-11
Author(s):  
Harman Chaudhry ◽  
Mohit Bhandari
Keyword(s):  
Clinical Study ◽  
Cohort Studies ◽  
Case Reports ◽  
Research Question ◽  
Case Control ◽  
Controlled Trials ◽  
Case Control Studies ◽  
Randomized Controlled ◽  
Observational Cohort ◽  
Study Designs

ABSTRACT Clinical research fundamentally involves finding answers to questions. Next to asking important questions, determining what type of study design to use is arguably the most pivotal step for a researcher. In this article, we provide an overview of various clinical study designs, including case reports and series, case-control studies, observational cohort studies, randomized controlled trials and systematic reviews. We aim to elucidate the utility, advantages and drawbacks of these study designs in order to assist researchers in selecting the most valid design for their research question. How to cite this article Chaudhry H, Bhandari M. Research made Easy: Answering Important Questions with Valid Designs. J Postgrad Med Edu Res 2012;46(1):8-11

Observational Studies

2018 ◽  
pp. 324-361
Author(s):  
Minou Djannatian ◽  
Clarissa Valim ◽  
Andre Brunoni ◽  
Felipe Fregni
Keyword(s):  
Cohort Studies ◽  
Observational Studies ◽  
Real Life ◽  
Case Control ◽  
Sample Size Determination ◽  
Size Determination ◽  
Cross Sectional ◽  
Life Conditions ◽  
Study Designs

This chapter on observational studies provides an understanding of the main concepts in epidemiology, introduces common study designs, such as cross-sectional, case-control, and cohort studies, and outlines their importance for clinical research. The hallmark of epidemiological research is that it observes unexposed and exposed individuals under “real-life conditions” without intervening itself. The chapter emphasizes the important role of bias and confounding in interpreting results from such studies and explains how bias and confounding can be controlled. It furthermore discusses specific aspects of sample size determination that are relevant to observational studies. The chapter concludes with a brief review of the special nature of surgical research.

Introduction

2017 ◽  
Author(s):  
Mark Elwood
Keyword(s):  
Cohort Studies ◽  
Case Control ◽  
Randomised Trials ◽  
Case Control Studies ◽  
Causal Explanations ◽  
Bradford Hill ◽  
Definition Of ◽  
Meta Analyses ◽  
Observation Bias ◽  
Study Designs

This chapter shows the plan of the book. Later chapters will cover the definition of causation, study designs that can demonstrate causation, how results are presented, the interpretation of studies stressing the non-causal explanations of observation bias, confounding, and chance variation. Then come positive aspects of causation, the Bradford-Hill principles, systematic reviews and meta-analyses, and an overall scheme for assessing studies and diagnosing causation. Further chapters present appraisals of six published studies: randomised trials, cohort studies, and case-control studies. An appendix presents statistical methods with examples.

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