Data Analysis: Simple Statistical Tests
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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. -
Levels of Evidence Table
Levels of Evidence All clinically related articles will require a Level-of-Evidence rating for classifying study quality. The Journal has five levels of evidence for each of four different study types; therapeutic, prognostic, diagnostic and cost effectiveness studies. Authors must classify the type of study and provide a level - of- evidence rating for all clinically oriented manuscripts. The level-of evidence rating will be reviewed by our editorial staff and their decision will be final. The following tables and types of studies will assist the author in providing the appropriate level-of- evidence. Type Treatment Study Prognosis Study Study of Diagnostic Test Cost Effectiveness of Study Study LEVEL Randomized High-quality Testing previously Reasonable I controlled trials prospective developed costs and with adequate cohort study diagnostic criteria alternatives used statistical power with > 80% in a consecutive in study with to detect follow-up, and series of patients values obtained differences all patients and a universally from many (narrow enrolled at same applied “gold” standard studies, study confidence time point in used multi-way intervals) and disease sensitivity follow up >80% analysis LEVEL Randomized trials Prospective cohort Development of Reasonable costs and II (follow up <80%, study (<80% follow- diagnostic criteria in a alternatives used in Improper up, patients enrolled consecutive series of study with values Randomization at different time patients and a obtained from limited Techniques) points in disease) universally -
Meta-Analysis of Proportions
NCSS Statistical Software NCSS.com Chapter 456 Meta-Analysis of Proportions Introduction This module performs a meta-analysis of a set of two-group, binary-event studies. These studies have a treatment group (arm) and a control group. The results of each study may be summarized as counts in a 2-by-2 table. The program provides a complete set of numeric reports and plots to allow the investigation and presentation of the studies. The plots include the forest plot, radial plot, and L’Abbe plot. Both fixed-, and random-, effects models are available for analysis. Meta-Analysis refers to methods for the systematic review of a set of individual studies with the aim to combine their results. Meta-analysis has become popular for a number of reasons: 1. The adoption of evidence-based medicine which requires that all reliable information is considered. 2. The desire to avoid narrative reviews which are often misleading. 3. The desire to interpret the large number of studies that may have been conducted about a specific treatment. 4. The desire to increase the statistical power of the results be combining many small-size studies. The goals of meta-analysis may be summarized as follows. A meta-analysis seeks to systematically review all pertinent evidence, provide quantitative summaries, integrate results across studies, and provide an overall interpretation of these studies. We have found many books and articles on meta-analysis. In this chapter, we briefly summarize the information in Sutton et al (2000) and Thompson (1998). Refer to those sources for more details about how to conduct a meta- analysis. -
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) ..................................................................................... -
Contingency Tables Are Eaten by Large Birds of Prey
Case Study Case Study Example 9.3 beginning on page 213 of the text describes an experiment in which fish are placed in a large tank for a period of time and some Contingency Tables are eaten by large birds of prey. The fish are categorized by their level of parasitic infection, either uninfected, lightly infected, or highly infected. It is to the parasites' advantage to be in a fish that is eaten, as this provides Bret Hanlon and Bret Larget an opportunity to infect the bird in the parasites' next stage of life. The observed proportions of fish eaten are quite different among the categories. Department of Statistics University of Wisconsin|Madison Uninfected Lightly Infected Highly Infected Total October 4{6, 2011 Eaten 1 10 37 48 Not eaten 49 35 9 93 Total 50 45 46 141 The proportions of eaten fish are, respectively, 1=50 = 0:02, 10=45 = 0:222, and 37=46 = 0:804. Contingency Tables 1 / 56 Contingency Tables Case Study Infected Fish and Predation 2 / 56 Stacked Bar Graph Graphing Tabled Counts Eaten Not eaten 50 40 A stacked bar graph shows: I the sample sizes in each sample; and I the number of observations of each type within each sample. 30 This plot makes it easy to compare sample sizes among samples and 20 counts within samples, but the comparison of estimates of conditional Frequency probabilities among samples is less clear. 10 0 Uninfected Lightly Infected Highly Infected Contingency Tables Case Study Graphics 3 / 56 Contingency Tables Case Study Graphics 4 / 56 Mosaic Plot Mosaic Plot Eaten Not eaten 1.0 0.8 A mosaic plot replaces absolute frequencies (counts) with relative frequencies within each sample. -
Patient Safety Culture and Associated Factors Among Health Care Providers in Bale Zone Hospitals, Southeast Ethiopia: an Institutional Based Cross-Sectional Study
Drug, Healthcare and Patient Safety Dovepress open access to scientific and medical research Open Access Full Text Article ORIGINAL RESEARCH Patient Safety Culture and Associated Factors Among Health Care Providers in Bale Zone Hospitals, Southeast Ethiopia: An Institutional Based Cross-Sectional Study This article was published in the following Dove Press journal: Drug, Healthcare and Patient Safety Musa Kumbi1 Introduction: Patient safety is a serious global public health issue and a critical component of Abduljewad Hussen 1 health care quality. Unsafe patient care is associated with significant morbidity and mortality Abate Lette1 throughout the world. In Ethiopia health system delivery, there is little practical evidence of Shemsu Nuriye2 patient safety culture and associated factors. Therefore, this study aims to assess patient safety Geroma Morka3 culture and associated factors among health care providers in Bale Zone hospitals. Methods: A facility-based cross-sectional study was undertaken using the “Hospital Survey 1 Department of Public Health, Goba on Patient Safety Culture (HSOPSC)” questionnaire. A total of 518 health care providers Referral Hospital, Madda Walabu University, Goba, Ethiopia; 2Department were interviewed. Analysis of variance (ANOVA) was employed to examine statistical of Public Health, College of Health differences between hospitals and patient safety culture dimensions. We also computed Science and Medicine, Wolayta Sodo internal consistency coefficients and exploratory factor analysis. Bivariate and multivariate University, Sodo, Ethiopia; 3Department of Nursing, Goba Referral Hospital, linear regression analyses were performed using SPSS version 20. The level of significance Madda Walabu University, Goba, Ethiopia was established using 95% confidence intervals and a p-value of <0.05. Results: The overall level of patient safety culture was 44% (95% CI: 43.3–44.6) with a response rate of 93.2%. -
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. -
Quasi-Experimental Studies in the Fields of Infection Control and Antibiotic Resistance, Ten Years Later: a Systematic Review
HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Infect Control Manuscript Author Hosp Epidemiol Manuscript Author . Author manuscript; available in PMC 2019 November 12. Published in final edited form as: Infect Control Hosp Epidemiol. 2018 February ; 39(2): 170–176. doi:10.1017/ice.2017.296. Quasi-experimental Studies in the Fields of Infection Control and Antibiotic Resistance, Ten Years Later: A Systematic Review Rotana Alsaggaf, MS, Lyndsay M. O’Hara, PhD, MPH, Kristen A. Stafford, PhD, MPH, Surbhi Leekha, MBBS, MPH, Anthony D. Harris, MD, MPH, CDC Prevention Epicenters Program Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland. Abstract OBJECTIVE.—A systematic review of quasi-experimental studies in the field of infectious diseases was published in 2005. The aim of this study was to assess improvements in the design and reporting of quasi-experiments 10 years after the initial review. We also aimed to report the statistical methods used to analyze quasi-experimental data. DESIGN.—Systematic review of articles published from January 1, 2013, to December 31, 2014, in 4 major infectious disease journals. METHODS.—Quasi-experimental studies focused on infection control and antibiotic resistance were identified and classified based on 4 criteria: (1) type of quasi-experimental design used, (2) justification of the use of the design, (3) use of correct nomenclature to describe the design, and (4) statistical methods used. RESULTS.—Of 2,600 articles, 173 (7%) featured a quasi-experimental design, compared to 73 of 2,320 articles (3%) in the previous review (P<.01). Moreover, 21 articles (12%) utilized a study design with a control group; 6 (3.5%) justified the use of a quasi-experimental design; and 68 (39%) identified their design using the correct nomenclature. -
Statistical Analysis 8: Two-Way Analysis of Variance (ANOVA)
Statistical Analysis 8: Two-way analysis of variance (ANOVA) Research question type: Explaining a continuous variable with 2 categorical variables What kind of variables? Continuous (scale/interval/ratio) and 2 independent categorical variables (factors) Common Applications: Comparing means of a single variable at different levels of two conditions (factors) in scientific experiments. Example: The effective life (in hours) of batteries is compared by material type (1, 2 or 3) and operating temperature: Low (-10˚C), Medium (20˚C) or High (45˚C). Twelve batteries are randomly selected from each material type and are then randomly allocated to each temperature level. The resulting life of all 36 batteries is shown below: Table 1: Life (in hours) of batteries by material type and temperature Temperature (˚C) Low (-10˚C) Medium (20˚C) High (45˚C) 1 130, 155, 74, 180 34, 40, 80, 75 20, 70, 82, 58 2 150, 188, 159, 126 136, 122, 106, 115 25, 70, 58, 45 type Material 3 138, 110, 168, 160 174, 120, 150, 139 96, 104, 82, 60 Source: Montgomery (2001) Research question: Is there difference in mean life of the batteries for differing material type and operating temperature levels? In analysis of variance we compare the variability between the groups (how far apart are the means?) to the variability within the groups (how much natural variation is there in our measurements?). This is why it is called analysis of variance, abbreviated to ANOVA. This example has two factors (material type and temperature), each with 3 levels. Hypotheses: The 'null hypothesis' might be: H0: There is no difference in mean battery life for different combinations of material type and temperature level And an 'alternative hypothesis' might be: H1: There is a difference in mean battery life for different combinations of material type and temperature level If the alternative hypothesis is accepted, further analysis is performed to explore where the individual differences are. -
Antiretroviral Adverse Drug Reactions Pharmacovigilance in Harare City
bioRxiv preprint doi: https://doi.org/10.1101/358069; this version posted June 28, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 Original Manuscript 2 Title: Antiretroviral Adverse Drug Reactions Pharmacovigilance in Harare City, 3 Zimbabwe, 2017 4 Hamufare Mugauri1, Owen Mugurungi2, Tsitsi Juru1, Notion Gombe1, Gerald 5 Shambira1 ,Mufuta Tshimanga1 6 7 1. Department of Community Medicine, University of Zimbabwe 8 2. Ministry of Health and Child Care, Zimbabwe 9 10 Corresponding author: 11 Tsitsi Juru [email protected] 12 Office 3-66 Kaguvi Building, Cnr 4th/Central Avenue 13 University of Zimbabwe, Harare, Zimbabwe 14 Phone: +263 4 792157, Mobile: +263 772 647 465 1 bioRxiv preprint doi: https://doi.org/10.1101/358069; this version posted June 28, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 15 Abstract 16 Introduction: Key to pharmacovigilance is spontaneously reporting all Adverse Drug 17 Reactions (ADR) during post-market surveillance. This facilitates identification and 18 evaluation of previously unreported ADR’s, acknowledging the trade-off between 19 benefits and potential harm of medications. Only 41% ADR’s documented in Harare 20 city clinical records for January to December 2016 were reported to Medicines 21 Control Authority of Zimbabwe (MCAZ). -
Communicable Disease Control in Emergencies: a Field Manual Edited by M
Communicable disease control in emergencies A field manual Communicable disease control in emergencies A field manual Edited by M.A. Connolly WHO Library Cataloguing-in-Publication Data Communicable disease control in emergencies: a field manual edited by M. A. Connolly. 1.Communicable disease control–methods 2.Emergencies 3.Disease outbreaks–prevention and control 4.Manuals I.Connolly, Máire A. ISBN 92 4 154616 6 (NLM Classification: WA 110) WHO/CDS/2005.27 © World Health Organization, 2005 All rights reserved. The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the pert 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 map 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. All reasonable precautions have been taken by WHO to verify he information contained in this publication. However, the published material is being distributed without warranty of any kind, either express or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall the World Health Organization be liable for damages arising in its use.