Field Evaluation of Vaccine Efficacy*
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Descriptive Statistics (Part 2): Interpreting Study Results
Statistical Notes II Descriptive statistics (Part 2): Interpreting study results A Cook and A Sheikh he previous paper in this series looked at ‘baseline’. Investigations of treatment effects can be descriptive statistics, showing how to use and made in similar fashion by comparisons of disease T interpret fundamental measures such as the probability in treated and untreated patients. mean and standard deviation. Here we continue with descriptive statistics, looking at measures more The relative risk (RR), also sometimes known as specific to medical research. We start by defining the risk ratio, compares the risk of exposed and risk and odds, the two basic measures of disease unexposed subjects, while the odds ratio (OR) probability. Then we show how the effect of a disease compares odds. A relative risk or odds ratio greater risk factor, or a treatment, can be measured using the than one indicates an exposure to be harmful, while relative risk or the odds ratio. Finally we discuss the a value less than one indicates a protective effect. ‘number needed to treat’, a measure derived from the RR = 1.2 means exposed people are 20% more likely relative risk, which has gained popularity because of to be diseased, RR = 1.4 means 40% more likely. its clinical usefulness. Examples from the literature OR = 1.2 means that the odds of disease is 20% higher are used to illustrate important concepts. in exposed people. RISK AND ODDS Among workers at factory two (‘exposed’ workers) The probability of an individual becoming diseased the risk is 13 / 116 = 0.11, compared to an ‘unexposed’ is the risk. -
Limitations of the Odds Ratio in Gauging the Performance of a Diagnostic, Prognostic, Or Screening Marker
American Journal of Epidemiology Vol. 159, No. 9 Copyright © 2004 by the Johns Hopkins Bloomberg School of Public Health Printed in U.S.A. All rights reserved DOI: 10.1093/aje/kwh101 Limitations of the Odds Ratio in Gauging the Performance of a Diagnostic, Prognostic, or Screening Marker Margaret Sullivan Pepe1,2, Holly Janes2, Gary Longton1, Wendy Leisenring1,2,3, and Polly Newcomb1 1 Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA. 2 Department of Biostatistics, University of Washington, Seattle, WA. 3 Division of Clinical Research, Fred Hutchinson Cancer Research Center, Seattle, WA. Downloaded from Received for publication June 24, 2003; accepted for publication October 28, 2003. A marker strongly associated with outcome (or disease) is often assumed to be effective for classifying persons http://aje.oxfordjournals.org/ according to their current or future outcome. However, for this assumption to be true, the associated odds ratio must be of a magnitude rarely seen in epidemiologic studies. In this paper, an illustration of the relation between odds ratios and receiver operating characteristic curves shows, for example, that a marker with an odds ratio of as high as 3 is in fact a very poor classification tool. If a marker identifies 10% of controls as positive (false positives) and has an odds ratio of 3, then it will correctly identify only 25% of cases as positive (true positives). The authors illustrate that a single measure of association such as an odds ratio does not meaningfully describe a marker’s ability to classify subjects. Appropriate statistical methods for assessing and reporting the classification power of a marker are described. -
Guidance for Industry E2E Pharmacovigilance Planning
Guidance for Industry E2E Pharmacovigilance Planning U.S. Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER) Center for Biologics Evaluation and Research (CBER) April 2005 ICH Guidance for Industry E2E Pharmacovigilance Planning Additional copies are available from: Office of Training and Communication Division of Drug Information, HFD-240 Center for Drug Evaluation and Research Food and Drug Administration 5600 Fishers Lane Rockville, MD 20857 (Tel) 301-827-4573 http://www.fda.gov/cder/guidance/index.htm Office of Communication, Training and Manufacturers Assistance, HFM-40 Center for Biologics Evaluation and Research Food and Drug Administration 1401 Rockville Pike, Rockville, MD 20852-1448 http://www.fda.gov/cber/guidelines.htm. (Tel) Voice Information System at 800-835-4709 or 301-827-1800 U.S. Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER) Center for Biologics Evaluation and Research (CBER) April 2005 ICH Contains Nonbinding Recommendations TABLE OF CONTENTS I. INTRODUCTION (1, 1.1) ................................................................................................... 1 A. Background (1.2) ..................................................................................................................2 B. Scope of the Guidance (1.3)...................................................................................................2 II. SAFETY SPECIFICATION (2) ..................................................................................... -
Relative Risks, Odds Ratios and Cluster Randomised Trials . (1)
1 Relative Risks, Odds Ratios and Cluster Randomised Trials Michael J Campbell, Suzanne Mason, Jon Nicholl Abstract It is well known in cluster randomised trials with a binary outcome and a logistic link that the population average model and cluster specific model estimate different population parameters for the odds ratio. However, it is less well appreciated that for a log link, the population parameters are the same and a log link leads to a relative risk. This suggests that for a prospective cluster randomised trial the relative risk is easier to interpret. Commonly the odds ratio and the relative risk have similar values and are interpreted similarly. However, when the incidence of events is high they can differ quite markedly, and it is unclear which is the better parameter . We explore these issues in a cluster randomised trial, the Paramedic Practitioner Older People’s Support Trial3 . Key words: Cluster randomized trials, odds ratio, relative risk, population average models, random effects models 1 Introduction Given two groups, with probabilities of binary outcomes of π0 and π1 , the Odds Ratio (OR) of outcome in group 1 relative to group 0 is related to Relative Risk (RR) by: . If there are covariates in the model , one has to estimate π0 at some covariate combination. One can see from this relationship that if the relative risk is small, or the probability of outcome π0 is small then the odds ratio and the relative risk will be close. One can also show, using the delta method, that approximately (1). ________________________________________________________________________________ Michael J Campbell, Medical Statistics Group, ScHARR, Regent Court, 30 Regent St, Sheffield S1 4DA [email protected] 2 Note that this result is derived for non-clustered data. -
Clinical Pharmacology 1: Phase 1 Studies and Early Drug Development
Clinical Pharmacology 1: Phase 1 Studies and Early Drug Development Gerlie Gieser, Ph.D. Office of Clinical Pharmacology, Div. IV Objectives • Outline the Phase 1 studies conducted to characterize the Clinical Pharmacology of a drug; describe important design elements of and the information gained from these studies. • List the Clinical Pharmacology characteristics of an Ideal Drug • Describe how the Clinical Pharmacology information from Phase 1 can help design Phase 2/3 trials • Discuss the timing of Clinical Pharmacology studies during drug development, and provide examples of how the information generated could impact the overall clinical development plan and product labeling. Phase 1 of Drug Development CLINICAL DEVELOPMENT RESEARCH PRE POST AND CLINICAL APPROVAL 1 DISCOVERY DEVELOPMENT 2 3 PHASE e e e s s s a a a h h h P P P Clinical Pharmacology Studies Initial IND (first in human) NDA/BLA SUBMISSION Phase 1 – studies designed mainly to investigate the safety/tolerability (if possible, identify MTD), pharmacokinetics and pharmacodynamics of an investigational drug in humans Clinical Pharmacology • Study of the Pharmacokinetics (PK) and Pharmacodynamics (PD) of the drug in humans – PK: what the body does to the drug (Absorption, Distribution, Metabolism, Excretion) – PD: what the drug does to the body • PK and PD profiles of the drug are influenced by physicochemical properties of the drug, product/formulation, administration route, patient’s intrinsic and extrinsic factors (e.g., organ dysfunction, diseases, concomitant medications, -
Basic Epidemiology for BPSU Studies
BPSU Study guidance – Basic epidemiology for BPSU studies Dr Simon Lenton on behalf of the Scientific Committee of the British Paediatric Surveillance Unit Updated 07 01 19 BPSU parent bodies: with support from: 1 Contents Introduction ................................................................................................................. 3 Concepts of disease development ..................................................................... 3 Public health surveillance ...................................................................................... 4 Epidemiology ............................................................................................................. 4 Descriptive epidemiology ...................................................................................... 5 Analytic epidemiology ............................................................................................ 5 Triangulation (cross verification) ........................................................................ 6 Capture-recapture .................................................................................................... 6 BPSU research design ............................................................................................. 6 BPSU resources .......................................................................................................... 9 Appendix .................................................................................................................... 10 References ................................................................................................................ -
Use of Antibiotics for Cholera Chemoprevention
Use of antibiotics for cholera chemoprevention Iza Ciglenecki, Médecins Sans Frontières GTFCC Case management meeting, 2018 Outline of presentation • Key questions: ₋ Rationale for household prophylaxis – household contacts at higher risk? ₋ Rationale for antibiotic use? ₋ What is the effectiveness? ₋ What is the impact on the epidemic? ₋ Risk of antimicrobial resistance ₋ Feasibility during outbreak control interventions • Example: Single-dose oral ciprofloxacin prophylaxis in response to a meningococcal meningitis epidemic in the African meningitis belt: a three-arm cluster-randomized trial • Prevention of cholera infection among contacts of case: a cluster-randomized trial of Azithromycine Rationale: risk for household contacts Forest plot of studies included in meta-analysis: association of presence of household contact with cholera with symptomatic cholera Richterman et al. Individual and Household Risk Factors for Symptomatic Cholera Infection: A Systematic Review and Meta-analysis. JID 2018 Rationale: clustering of cholera cases in time and space Relative risk for cholera among case cohorts compared with control cohorts at different spatio-temporal scales Living within 50 m of the index case: RR 36 (95% CI: 23–56) within 3 days of the index case presenting to the hospital Debes et al. Cholera cases cluster in time and space in Matlab, Bangladesh: implications for targeted preventive interventions. Int J Epid 2016 Rationale: clustering of cholera cases in time and space Relative risk of next cholera case occuring at different distances from primary case Relative risk of next cholera case being within specific distance to another case within days 0-4 Within 40 m: Ndjamena: RR 32.4 (95% 25-41) Kalemie: RR 121 (95% CI 90-165) Azman et al. -
MMWR Early and Were Reported to Two Respective Local Health Jurisdictions, Release on the MMWR Website (
Morbidity and Mortality Weekly Report High SARS-CoV-2 Attack Rate Following Exposure at a Choir Practice — Skagit County, Washington, March 2020 Lea Hamner, MPH1; Polly Dubbel, MPH1; Ian Capron1; Andy Ross, MPH1; Amber Jordan, MPH1; Jaxon Lee, MPH1; Joanne Lynn1; Amelia Ball1; Simranjit Narwal, MSc1; Sam Russell1; Dale Patrick1; Howard Leibrand, MD1 On May 12, 2020, this report was posted as an MMWR Early and were reported to two respective local health jurisdictions, Release on the MMWR website (https://www.cdc.gov/mmwr). without indication of a common source of exposure. On On March 17, 2020, a member of a Skagit County, March 17, the choir director sent a second e-mail stating that Washington, choir informed Skagit County Public Health 24 members reported that they had developed influenza-like (SCPH) that several members of the 122-member choir had symptoms since March 11, and at least one had received test become ill. Three persons, two from Skagit County and one results positive for SARS-CoV-2. The email emphasized the from another area, had test results positive for SARS-CoV-2, importance of social distancing and awareness of symptoms the virus that causes coronavirus disease 2019 (COVID-19). suggestive of COVID-19. These two emails led many members Another 25 persons had compatible symptoms. SCPH to self-isolate or quarantine before a delegated member of the obtained the choir’s member list and began an investigation on choir notified SCPH on March 17. March 18. Among 61 persons who attended a March 10 choir All 122 members were interviewed by telephone either practice at which one person was known to be symptomatic, during initial investigation of the cluster (March 18–20; 53 cases were identified, including 33 confirmed and 20 115 members) or a follow-up interview (April 7–10; 117); most probable cases (secondary attack rates of 53.3% among con- persons participated in both interviews. -
Measuring Ligand Efficacy at the Mu- Opioid Receptor Using A
RESEARCH ARTICLE Measuring ligand efficacy at the mu- opioid receptor using a conformational biosensor Kathryn E Livingston1,2, Jacob P Mahoney1,2, Aashish Manglik3, Roger K Sunahara4, John R Traynor1,2* 1Department of Pharmacology, University of Michigan Medical School, Ann Arbor, United States; 2Edward F Domino Research Center, University of Michigan, Ann Arbor, United States; 3Department of Pharmaceutical Chemistry, School of Pharmacy, University of California San Francisco, San Francisco, United States; 4Department of Pharmacology, University of California San Diego School of Medicine, La Jolla, United States Abstract The intrinsic efficacy of orthosteric ligands acting at G-protein-coupled receptors (GPCRs) reflects their ability to stabilize active receptor states (R*) and is a major determinant of their physiological effects. Here, we present a direct way to quantify the efficacy of ligands by measuring the binding of a R*-specific biosensor to purified receptor employing interferometry. As an example, we use the mu-opioid receptor (m-OR), a prototypic class A GPCR, and its active state sensor, nanobody-39 (Nb39). We demonstrate that ligands vary in their ability to recruit Nb39 to m- OR and describe methadone, loperamide, and PZM21 as ligands that support unique R* conformation(s) of m-OR. We further show that positive allosteric modulators of m-OR promote formation of R* in addition to enhancing promotion by orthosteric agonists. Finally, we demonstrate that the technique can be utilized with heterotrimeric G protein. The method is cell- free, signal transduction-independent and is generally applicable to GPCRs. DOI: https://doi.org/10.7554/eLife.32499.001 *For correspondence: [email protected] Competing interests: The authors declare that no Introduction competing interests exist. -
Evidence That Coronavirus Superspreading Is Fat-Tailed BRIEF REPORT Felix Wonga,B,C and James J
Evidence that coronavirus superspreading is fat-tailed BRIEF REPORT Felix Wonga,b,c and James J. Collinsa,b,c,d,1 aInstitute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139; bDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139; cInfectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142; and dWyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115 Edited by Simon A. Levin, Princeton University, Princeton, NJ, and approved September 28, 2020 (received for review September 1, 2020) Superspreaders, infected individuals who result in an outsized CoV and SARS-CoV-2 exhibited an exponential tail. We number of secondary cases, are believed to underlie a significant searched the scientific literature for global accounts of SSEs, in fraction of total SARS-CoV-2 transmission. Here, we combine em- which single cases resulted in numbers of secondary cases pirical observations of SARS-CoV and SARS-CoV-2 transmission and greater than R0, estimated to be ∼3 to 6 for both coronaviruses extreme value statistics to show that the distribution of secondary (1, 7). To broadly sample the right tail, we focused on SSEs cases is consistent with being fat-tailed, implying that large super- resulting in >6 secondary cases, and as data on SSEs are sparse, spreading events are extremal, yet probable, occurrences. We inte- perhaps due in part to a lack of data sharing, we pooled data for grate these results with interaction-based network models of disease SARS-CoV and SARS-CoV-2. Moreover, to avoid higher- transmission and show that superspreading, when it is fat-tailed, order transmission obfuscating the cases generated directly by leads to pronounced transmission by increasing dispersion. -
Epidemiology of and Risk Factors for COVID-19 Infection Among Health Care Workers: a Multi-Centre Comparative Study
International Journal of Environmental Research and Public Health Article Epidemiology of and Risk Factors for COVID-19 Infection among Health Care Workers: A Multi-Centre Comparative Study Jia-Te Wei 1, Zhi-Dong Liu 1, Zheng-Wei Fan 2, Lin Zhao 1,* and Wu-Chun Cao 1,2,* 1 Institute of EcoHealth, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China; [email protected] (J.-T.W.); [email protected] (Z.-D.L.) 2 State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China; [email protected] * Correspondence: [email protected] (L.Z.); [email protected] (W.-C.C.) Received: 31 August 2020; Accepted: 28 September 2020; Published: 29 September 2020 Abstract: Healthcare workers (HCWs) worldwide are putting themselves at high risks of coronavirus disease 2019 (COVID-19) by treating a large number of patients while lacking protective equipment. We aim to provide a scientific basis for preventing and controlling the COVID-19 infection among HCWs. We used data on COVID-19 cases in the city of Wuhan to compare epidemiological characteristics between HCWs and non-HCWs and explored the risk factors for infection and deterioration among HCWs based on hospital settings. The attack rate (AR) of HCWs in the hospital can reach up to 11.9% in Wuhan. The time interval from symptom onset to diagnosis in HCWs and non-HCWs dropped rapidly over time. From mid-January, the median time interval of HCW cases was significantly shorter than in non-HCW cases. -
Antimalarial Drug Efficacy and Drug Resistance: 2000–2010 WHO Library Cataloguing-In-Publication Data
GLOBAL REPORT ON ANTIMALARIAL DRUG EFFICACY AND DRUG RESISTANCE: 2000–2010 WHO Library Cataloguing-in-Publication Data Global report on antimalarial drug efficacy and drug resistance: 2000-2010. 1.Malaria - prevention and control. 2.Malaria - drug therapy. 3.Antimalarials - therapeutic use. 4.Epidemiologic surveillance - methods. 5.Drug resistance. 6.Drug monitoring. 7.Treatment outcome. 8.Thailand. 8.Cambodia. I.World Health Organization. ISBN 978 92 4 150047 0 (NLM classification: QV 256) © World Health Organization 2010 All rights reserved. Publications of the World Health Organization can be obtained from WHO Press, World Health Organization, 20 Avenue Appia, 1211 Geneva 27, Switzerland (tel.: +41 22 791 3264; fax: +41 22 791 4857; e-mail: [email protected]). Requests for permission to reproduce or translate WHO publications – whether for sale or for noncommercial distribution – should be addressed to WHO Press, at the above address (fax: +41 22 791 4806; e-mail: [email protected]). The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the World Health Organization concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted lines on maps represent approximate border lines for which there may not yet be full agreement. The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by the World Health Organization in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters.