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TUTORIAL in BIOSTATISTICS: the Self-Controlled Case Series Method
STATISTICS IN MEDICINE Statist. Med. 2005; 0:1–31 Prepared using simauth.cls [Version: 2002/09/18 v1.11] TUTORIAL IN BIOSTATISTICS: The self-controlled case series method Heather J. Whitaker1, C. Paddy Farrington1, Bart Spiessens2 and Patrick Musonda1 1 Department of Statistics, The Open University, Milton Keynes, MK7 6AA, UK. 2 GlaxoSmithKline Biologicals, Rue de l’Institut 89, B-1330 Rixensart, Belgium. SUMMARY The self-controlled case series method was developed to investigate associations between acute outcomes and transient exposures, using only data on cases, that is, on individuals who have experienced the outcome of interest. Inference is within individuals, and hence fixed covariates effects are implicitly controlled for within a proportional incidence framework. We describe the origins, assumptions, limitations, and uses of the method. The rationale for the model and the derivation of the likelihood are explained in detail using a worked example on vaccine safety. Code for fitting the model in the statistical package STATA is described. Two further vaccine safety data sets are used to illustrate a range of modelling issues and extensions of the basic model. Some brief pointers on the design of case series studies are provided. The data sets, STATA code, and further implementation details in SAS, GENSTAT and GLIM are available from an associated website. key words: case series; conditional likelihood; control; epidemiology; modelling; proportional incidence Copyright c 2005 John Wiley & Sons, Ltd. 1. Introduction The self-controlled case series method, or case series method for short, provides an alternative to more established cohort or case-control methods for investigating the association between a time-varying exposure and an outcome event. -
Supplementary File
Supplementary file Table S1. Characteristics of studies included in the systematic review. Authors, reference Type of article Number of patients Type of stroke Valderrama [1] case report 1 AIS Oxley [2] case series 5 AIS Morassi [3] case series 6 AIS (n = 4), HS (n = 2) Tunc [4] case series 4 AIS Avula [5] case series 4 AIS (n = 3), TIA (n = 1) Oliver [6] case report 1 AIS Beyrouti [7] case series 6 AIS Al Saiegh [8] case series 2 AIS (n = 1), SAH (n = 1) Gunasekaran [9] case report 1 AIS Goldberg [10] case report 1 AIS Viguier [11] case report 1 AIS Escalard [12] case series/ case control 10 AIS Wang [13] case series 5 AIS Fara [14] case series 3 AIS Moshayedi [15] case report 1 AIS Deliwala [16] case report 1 AIS Sharafi-Razavi [17] case report 1 HS retrospective cohort study (case Jain [18] 35 AIS (n = 26) HS (n = 9) control) retrospective cohort study (case Yaghi [19] 32 AIS control) AIS (n = 35), TIA (n = 5), HS Benussi [20] retrospective cohort study 43 (n = 3) Rudilosso [21] case report 1 AIS Lodigiani [22] retrospective cohort study 9 AIS Malentacchi [23] case report 1 AIS J. Clin. Med. 2020, 9, x; doi: FOR PEER REVIEW www.mdpi.com/journal/jcm J. Clin. Med. 2020, 9, x FOR PEER REVIEW 2 of 8 retrospective cohort study/case Scullen [24] 2 HS + AIS (n = 1), AIS (n = 1) series retrospective cohort study/ case AIS (n = 17), ST (n = 2), HS Sweid [25] 22 series (n = 3) AIS – acute ischemic stroke; HS – haemorrhagic stroke; SAH – subarachnoid haemorrhage; ST – sinus thrombosis; TIA-transient ischemic attack. -
Case Series: COVID-19 Infection Causing New-Onset Diabetes Mellitus?
Endocrinology & Metabolism International Journal Case Series Open Access Case series: COVID-19 infection causing new-onset diabetes mellitus? Abstract Volume 9 Issue 1 - 2021 Objectives: This paper seeks to explore the hypothesis of the potential diabetogenic effect 1 2 of SARS-COV-2 (Severe Acute respiratory syndrome coronavirus). Rujuta Katkar, Narasa Raju Madam 1Consultant Endocrinologist at Yuma Regional Medical Center, Case series presentation: We present a case series of observation among 8 patients of age USA 2 group ranging from 34 to 74 years with a BMI range of 26.61 to 53.21 Kilogram/square Hospitalist at Yuma Regional Medical Center, USA meters that developed new-onset diabetes after COVID-19 infection. Correspondence: Rujuta Katkar, MD, Consultant Severe Acute Respiratory Syndrome Coronavirus (SARS-COV-2), commonly known as Endocrinologist at Yuma Regional Medical Center, 2851 S Coronavirus or COVID-19(Coronavirus infectious disease), gains entry into the cells by Avenue B, Bldg.20, Yuma, AZ-85364, USA, Tel 2034359976, binding to the Angiotensin-converting enzyme-2(ACE-2) receptors located in essential Email metabolic tissues including the pancreas, adipose tissue, small intestine, and kidneys. Received: March 25, 2021 | Published: April 02, 2021 The evidence reviewed from the scientific literature describes how ACE 2 receptors play a role in the pathogenesis of diabetes and the plausible interaction of SARS-COV-2 with ACE 2 receptors in metabolic organs and tissues. Conclusion: The 8 patients without a past medical history of diabetes admitted with COVID-19 infection developed new-onset diabetes mellitus due to plausible interaction of SARS-COV-2 with ACE 2 receptors. -
Observational Clinical Research
E REVIEW ARTICLE Clinical Research Methodology 2: Observational Clinical Research Daniel I. Sessler, MD, and Peter B. Imrey, PhD * † Case-control and cohort studies are invaluable research tools and provide the strongest fea- sible research designs for addressing some questions. Case-control studies usually involve retrospective data collection. Cohort studies can involve retrospective, ambidirectional, or prospective data collection. Observational studies are subject to errors attributable to selec- tion bias, confounding, measurement bias, and reverse causation—in addition to errors of chance. Confounding can be statistically controlled to the extent that potential factors are known and accurately measured, but, in practice, bias and unknown confounders usually remain additional potential sources of error, often of unknown magnitude and clinical impact. Causality—the most clinically useful relation between exposure and outcome—can rarely be defnitively determined from observational studies because intentional, controlled manipu- lations of exposures are not involved. In this article, we review several types of observa- tional clinical research: case series, comparative case-control and cohort studies, and hybrid designs in which case-control analyses are performed on selected members of cohorts. We also discuss the analytic issues that arise when groups to be compared in an observational study, such as patients receiving different therapies, are not comparable in other respects. (Anesth Analg 2015;121:1043–51) bservational clinical studies are attractive because Group, and the American Society of Anesthesiologists they are relatively inexpensive and, perhaps more Anesthesia Quality Institute. importantly, can be performed quickly if the required Recent retrospective perioperative studies include data O 1,2 data are already available. -
The Value of Case Reports in Systematic Reviews from Rare Diseases
International Journal of Environmental Research and Public Health Article The Value of Case Reports in Systematic Reviews from Rare Diseases. The Example of Enzyme Replacement Therapy (ERT) in Patients with Mucopolysaccharidosis Type II (MPS-II) Miguel Sampayo-Cordero 1,2,*, Bernat Miguel-Huguet 3, Andrea Malfettone 1,2, José Manuel Pérez-García 1,2,4, Antonio Llombart-Cussac 1,2,5, Javier Cortés 1,2,4,6, Almudena Pardo 7 and Jordi Pérez-López 8 1 Medica Scientia Innovation Research (MedSIR), Ridgewood, NJ 07450, USA; [email protected] (A.M.); [email protected] (J.M.P.-G.); [email protected] (A.L.-C.); [email protected] (J.C.) 2 Medica Scientia Innovation Research (MedSIR), 08018 Barcelona, Spain 3 Department of Surgery, Hospital de Bellvitge, L’Hospitalet de Llobregat, 08907 Barcelona, Spain; [email protected] 4 Institute of Breast Cancer, Quiron Group, 08023 Barcelona, Spain 5 Hospital Arnau de Vilanova, Universidad Católica de Valencia “San Vicente Mártir”, 46015 Valencia, Spain 6 Vall d’Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain 7 Albiotech Consultores y Redacción Científica S.L., 28035 Madrid, Spain; [email protected] 8 Department of Internal Medicine, Hospital Vall d’Hebron, 08035 Barcelona, Spain; [email protected] * Correspondence: [email protected] Received: 3 August 2020; Accepted: 7 September 2020; Published: 10 September 2020 Abstract: Background: Case reports are usually excluded from systematic reviews. Patients with rare diseases are more dependent on novel individualized strategies than patients with common diseases. We reviewed and summarized the novelties reported by case reports in mucopolysaccharidosis type II (MPS-II) patients treated with enzyme replacement therapy (ERT). -
Download, And
medRxiv preprint doi: https://doi.org/10.1101/19002121; this version posted July 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Evaluation of Indicators of Reproducibility and Transparency in Published Cardiology Literature Short Title: Anderson et al.; Indicators of Reproducibility in Cardiology J. Michael Anderson, B.S.1, Bryan Wright, B.G.S.1, Daniel Tritz, B.S.1, Jarryd Horn, B.S.1, Ian Parker, D.O.2, Daniel Bergeron, D.O.2, Sharolyn Cook, D.O.2, Matt Vassar, PhD1 1. Oklahoma State University Center for Health Sciences, Tulsa, Oklahoma, USA 2. Oklahoma State University Medical Center - Department of Cardiology, Tulsa, Oklahoma, USA Corresponding Author: Mr. J. Michael Anderson, Oklahoma State University Center for Health Sciences, 1111 W 17th St Tulsa, OK 74107, United States; Fax: 918-561-8428; Telephone: 918-521-8774; Email: [email protected] Word Count: 7041 NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/19002121; this version posted July 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Abstract: Background The extent of reproducibility in cardiology research remains unclear. Therefore, our main objective was to determine the quality of research published in cardiology journals using eight indicators of reproducibility. -
Observational Studies and Bias in Epidemiology
The Young Epidemiology Scholars Program (YES) is supported by The Robert Wood Johnson Foundation and administered by the College Board. Observational Studies and Bias in Epidemiology Manuel Bayona Department of Epidemiology School of Public Health University of North Texas Fort Worth, Texas and Chris Olsen Mathematics Department George Washington High School Cedar Rapids, Iowa Observational Studies and Bias in Epidemiology Contents Lesson Plan . 3 The Logic of Inference in Science . 8 The Logic of Observational Studies and the Problem of Bias . 15 Characteristics of the Relative Risk When Random Sampling . and Not . 19 Types of Bias . 20 Selection Bias . 21 Information Bias . 23 Conclusion . 24 Take-Home, Open-Book Quiz (Student Version) . 25 Take-Home, Open-Book Quiz (Teacher’s Answer Key) . 27 In-Class Exercise (Student Version) . 30 In-Class Exercise (Teacher’s Answer Key) . 32 Bias in Epidemiologic Research (Examination) (Student Version) . 33 Bias in Epidemiologic Research (Examination with Answers) (Teacher’s Answer Key) . 35 Copyright © 2004 by College Entrance Examination Board. All rights reserved. College Board, SAT and the acorn logo are registered trademarks of the College Entrance Examination Board. Other products and services may be trademarks of their respective owners. Visit College Board on the Web: www.collegeboard.com. Copyright © 2004. All rights reserved. 2 Observational Studies and Bias in Epidemiology Lesson Plan TITLE: Observational Studies and Bias in Epidemiology SUBJECT AREA: Biology, mathematics, statistics, environmental and health sciences GOAL: To identify and appreciate the effects of bias in epidemiologic research OBJECTIVES: 1. Introduce students to the principles and methods for interpreting the results of epidemio- logic research and bias 2. -
Chapter 5 Experiments, Good And
Chapter 5 Experiments, Good and Bad Point of both observational studies and designed experiments is to identify variable or set of variables, called explanatory variables, which are thought to predict outcome or response variable. Confounding between explanatory variables occurs when two or more explanatory variables are not separated and so it is not clear how much each explanatory variable contributes in prediction of response variable. Lurking variable is explanatory variable not considered in study but confounded with one or more explanatory variables in study. Confounding with lurking variables effectively reduced in randomized comparative experiments where subjects are assigned to treatments at random. Confounding with a (only one at a time) lurking variable reduced in observational studies by controlling for it by comparing matched groups. Consequently, experiments much more effec- tive than observed studies at detecting which explanatory variables cause differences in response. In both cases, statistically significant observed differences in average responses implies differences are \real", did not occur by chance alone. Exercise 5.1 (Experiments, Good and Bad) 1. Randomized comparative experiment: effect of temperature on mice rate of oxy- gen consumption. For example, mice rate of oxygen consumption 10.3 mL/sec when subjected to 10o F. temperature (Fo) 0 10 20 30 ROC (mL/sec) 9.7 10.3 11.2 14.0 (a) Explanatory variable considered in study is (choose one) i. temperature ii. rate of oxygen consumption iii. mice iv. mouse weight 25 26 Chapter 5. Experiments, Good and Bad (ATTENDANCE 3) (b) Response is (choose one) i. temperature ii. rate of oxygen consumption iii. -
Analytic Epidemiology Analytic Studies: Observational Study
Analytic Studies: Observational Study Designs Cohort Studies Analytic Epidemiology Prospective Cohort Studies Retrospective (historical) Cohort Studies Part 2 Case-Control Studies Nested case-control Case-cohort studies Dr. H. Stockwell Case-crossover studies Determinants of disease: Analytic Epidemiology Epidemiology: Risk factors Identifying the causes of disease A behavior, environmental exposure, or inherent human characteristic that is associated with an important health Testing hypotheses using epidemiologic related condition* studies Risk factors are associated with an increased probability of disease but may Goal is to prevent disease (deterrents) not always cause the diseases *Last, J. Dictionary of Epidemiology Analytic Studies: Cohort Studies Panel Studies Healthy subjects are defined by their Combination of cross-sectional and exposure status and followed over time to cohort determine the incidence of disease, symptoms or death Same individuals surveyed at several poiiiints in time Subjects grouped by exposure level – exposed and unexposed (f(reference group, Can measure changes in individuals comparison group) Analytic Studies: Case-Control Studies Nested case-control studies A group of individuals with a disease Case-control study conducted within a (cases) are compared with a group of cohort study individuals without the disease (controls) Advantages of both study designs Also called a retrospective study because it starts with people with disease and looks backward for ppprevious exposures which might be -
1 Sensitivity Analysis in Observational Research: Introducing the E-‐Value
Sensitivity Analysis in Observational Research: Introducing the E-value Tyler J. VanderWeele, Ph.D., Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health Peng Ding, Ph.D., Department of Statistics, University of California, Berkeley This is the prepublication, author-produced version of a manuscript accepted for publication in Annals of Internal Medicine. This version does not include post-acceptance editing and formatting. The American College of Physicians, the publisher of Annals of Internal Medicine, is not responsible for the content or presentation of the author-produced accepted version of the manuscript or any version that a third party derives from it. Readers who wish to access the definitive published version of this manuscript and any ancillary material related to this manuscript (e.g., correspondence, corrections, editorials, linked articles) should go to Annals.org or to the print issue in which the article appears. Those who cite this manuscript should cite the published version, as it is the official version of record: VanderWeele, T.J. and Ding, P. (2017). Sensitivity analysis in observational research: introducing the E-value. Annals of Internal Medicine, 167(4):268-274. 1 Abstract. Sensitivity analysis can be useful in assessing how robust associations are to potential unmeasured or uncontrolled confounding. In this paper we introduce a new measure that we call the “E-value,” a measure related to the evidence for causality in observational studies, when they are potentially subject to confounding. The E-value is defined as the minimum strength of association on the risk ratio scale that an unmeasured confounder would need to have with both the treatment and the outcome to fully eXplain away a specific treatment-outcome association, conditional on the measured covariates. -
Chapter 20 Design and Analysis of Experiments and Observational
Chapter 20 Design and Analysis of Experiments and Observational Studies Point of both observational studies and designed experiments is to identify variable or set of variables, called explanatory variables, which are thought to predict outcome or response variable. Observational studies are an investigation of the association between treatment and response, where subject who decide whether or not they take a treatment. In experimental designs, experimenter decides who is given a treat- ment and who is to be a control, the experimenter alters levels of treatments when investigating how these alterations cause change in the response. 20.1 Observational Studies Exercise 20.1 (Observational Studies) 1. Observational Study versus Designed Experiment. (a) Effect of air temperature on rate of oxygen consumption (ROC) of four mice is investigated. ROC of one mouse at 0o F is 9.7 mL/sec for example. temperature (Fo) 0 10 20 30 ROC (mL/sec) 9.7 10.3 11.2 14.0 Since experimenter (not a mouse!) decides which mice are subjected to which temperature, this is (choose one) observational study / designed experiment. (b) Indiana police records from 1999–2001 on six drivers are analyzed to de- termine if there is an association between drinking and traffic accidents. One heavy drinker had 6 accidents for example. 169 170Chapter 20. Design and Analysis of Experiments and Observational Studies (lecture notes 10) drinking → heavy light 3 1 6 2 2 1 This is an observational study because (choose one) i. police decided who was going to drink and drive and who was not. ii. drivers decided who was going to drink and drive and who was not. -
Overview of Epidemiological Study Designs
University of Massachusetts Medical School eScholarship@UMMS PEER Liberia Project UMass Medical School Collaborations in Liberia 2018-11 Overview of Epidemiological Study Designs Richard Ssekitoleko Yale University Let us know how access to this document benefits ou.y Follow this and additional works at: https://escholarship.umassmed.edu/liberia_peer Part of the Clinical Epidemiology Commons, Epidemiology Commons, Family Medicine Commons, Infectious Disease Commons, Medical Education Commons, and the Quantitative, Qualitative, Comparative, and Historical Methodologies Commons Repository Citation Ssekitoleko R. (2018). Overview of Epidemiological Study Designs. PEER Liberia Project. https://doi.org/ 10.13028/fp3z-mv12. Retrieved from https://escholarship.umassmed.edu/liberia_peer/5 This material is brought to you by eScholarship@UMMS. It has been accepted for inclusion in PEER Liberia Project by an authorized administrator of eScholarship@UMMS. For more information, please contact [email protected]. Overview of Epidemiological study Designs Richard Ssekitoleko Department of Global Health Yale University 1.1 Objectives • Understand the different epidemiological study types • Get to know what is involved in each type of study • Understand the strengths and Limitations of each study type Key terms • Population • Consists of all elements and is the group from which a sample is drawn • A sample is a subset of a population • Parameters • Summary data from a population • Statistics • Summary data from a sample • Validity • Extent to which a conclusion or statistic is well-founded and likely corresponds accurately to the parameter. Hierarchy of Evidence Study type Observational Interventional Descriptive Experiment Ecological Randomized Controlled Trial Cross-sectional Case-control Cohort Overview of Epidemiologic Study Designs Validity *anecdotes Cost Understanding What Physicians Mean: In my experience … once.