Disease Incidence, Prevalence and Disability
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FDA Oncology Experience in Innovative Adaptive Trial Designs
Innovative Adaptive Trial Designs Rajeshwari Sridhara, Ph.D. Director, Division of Biometrics V Office of Biostatistics, CDER, FDA 9/3/2015 Sridhara - Ovarian cancer workshop 1 Fixed Sample Designs • Patient population, disease assessments, treatment, sample size, hypothesis to be tested, primary outcome measure - all fixed • No change in the design features during the study Adaptive Designs • A study that includes a prospectively planned opportunity for modification of one or more specified aspects of the study design and hypotheses based on analysis of data (interim data) from subjects in the study 9/3/2015 Sridhara - Ovarian cancer workshop 2 Bayesian Designs • In the Bayesian paradigm, the parameter measuring treatment effect is regarded as a random variable • Bayesian inference is based on the posterior distribution (Bayes’ Rule – updated based on observed data) – Outcome adaptive • By definition adaptive design 9/3/2015 Sridhara - Ovarian cancer workshop 3 Adaptive Designs (Frequentist or Bayesian) • Allows for planned design modifications • Modifications based on data accrued in the trial up to the interim time • Unblinded or blinded interim results • Control probability of false positive rate for multiple options • Control operational bias • Assumes independent increments of information 9/3/2015 Sridhara - Ovarian cancer workshop 4 Enrichment Designs – Prognostic or Predictive • Untargeted or All comers design: – post-hoc enrichment, prospective-retrospective designs – Marker evaluation after randomization (example: KRAS in cetuximab -
FORMULAS from EPIDEMIOLOGY KEPT SIMPLE (3E) Chapter 3: Epidemiologic Measures
FORMULAS FROM EPIDEMIOLOGY KEPT SIMPLE (3e) Chapter 3: Epidemiologic Measures Basic epidemiologic measures used to quantify: • frequency of occurrence • the effect of an exposure • the potential impact of an intervention. Epidemiologic Measures Measures of disease Measures of Measures of potential frequency association impact (“Measures of Effect”) Incidence Prevalence Absolute measures of Relative measures of Attributable Fraction Attributable Fraction effect effect in exposed cases in the Population Incidence proportion Incidence rate Risk difference Risk Ratio (Cumulative (incidence density, (Incidence proportion (Incidence Incidence, Incidence hazard rate, person- difference) Proportion Ratio) Risk) time rate) Incidence odds Rate Difference Rate Ratio (Incidence density (Incidence density difference) ratio) Prevalence Odds Ratio Difference Macintosh HD:Users:buddygerstman:Dropbox:eks:formula_sheet.doc Page 1 of 7 3.1 Measures of Disease Frequency No. of onsets Incidence Proportion = No. at risk at beginning of follow-up • Also called risk, average risk, and cumulative incidence. • Can be measured in cohorts (closed populations) only. • Requires follow-up of individuals. No. of onsets Incidence Rate = ∑person-time • Also called incidence density and average hazard. • When disease is rare (incidence proportion < 5%), incidence rate ≈ incidence proportion. • In cohorts (closed populations), it is best to sum individual person-time longitudinally. It can also be estimated as Σperson-time ≈ (average population size) × (duration of follow-up). Actuarial adjustments may be needed when the disease outcome is not rare. • In an open populations, Σperson-time ≈ (average population size) × (duration of follow-up). Examples of incidence rates in open populations include: births Crude birth rate (per m) = × m mid-year population size deaths Crude mortality rate (per m) = × m mid-year population size deaths < 1 year of age Infant mortality rate (per m) = × m live births No. -
Incidence and Secondary Transmission of SARS-Cov-2 Infections in Schools
Prepublication Release Incidence and Secondary Transmission of SARS-CoV-2 Infections in Schools Kanecia O. Zimmerman, MD; Ibukunoluwa C. Akinboyo, MD; M. Alan Brookhart, PhD; Angelique E. Boutzoukas, MD; Kathleen McGann, MD; Michael J. Smith, MD, MSCE; Gabriela Maradiaga Panayotti, MD; Sarah C. Armstrong, MD; Helen Bristow, MPH; Donna Parker, MPH; Sabrina Zadrozny, PhD; David J. Weber, MD, MPH; Daniel K. Benjamin, Jr., MD, PhD; for The ABC Science Collaborative DOI: 10.1542/peds.2020-048090 Journal: Pediatrics Article Type: Regular Article Citation: Zimmerman KO, Akinboyo IC, Brookhart A, et al. Incidence and secondary transmission of SARS-CoV-2 infections in schools. Pediatrics. 2021; doi: 10.1542/peds.2020- 048090 This is a prepublication version of an article that has undergone peer review and been accepted for publication but is not the final version of record. This paper may be cited using the DOI and date of access. This paper may contain information that has errors in facts, figures, and statements, and will be corrected in the final published version. The journal is providing an early version of this article to expedite access to this information. The American Academy of Pediatrics, the editors, and authors are not responsible for inaccurate information and data described in this version. Downloaded from©2021 www.aappublications.org/news American Academy by of guest Pediatrics on September 27, 2021 Prepublication Release Incidence and Secondary Transmission of SARS-CoV-2 Infections in Schools Kanecia O. Zimmerman, MD1,2,3; Ibukunoluwa C. Akinboyo, MD1,2; M. Alan Brookhart, PhD4; Angelique E. Boutzoukas, MD1,2; Kathleen McGann, MD2; Michael J. -
Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests Document Issued On: March 13, 2007
Guidance for Industry and FDA Staff Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests Document issued on: March 13, 2007 The draft of this document was issued on March 12, 2003. For questions regarding this document, contact Kristen Meier at 240-276-3060, or send an e-mail to [email protected]. U.S. Department of Health and Human Services Food and Drug Administration Center for Devices and Radiological Health Diagnostic Devices Branch Division of Biostatistics Office of Surveillance and Biometrics Contains Nonbinding Recommendations Preface Public Comment Written comments and suggestions may be submitted at any time for Agency consideration to the Division of Dockets Management, Food and Drug Administration, 5630 Fishers Lane, Room 1061, (HFA-305), Rockville, MD, 20852. Alternatively, electronic comments may be submitted to http://www.fda.gov/dockets/ecomments. When submitting comments, please refer to Docket No. 2003D-0044. Comments may not be acted upon by the Agency until the document is next revised or updated. Additional Copies Additional copies are available from the Internet at: http://www.fda.gov/cdrh/osb/guidance/1620.pdf. You may also send an e-mail request to [email protected] to receive an electronic copy of the guidance or send a fax request to 240-276-3151 to receive a hard copy. Please use the document number 1620 to identify the guidance you are requesting. Contains Nonbinding Recommendations Table of Contents 1. Background....................................................................................................4 -
Estimated HIV Incidence and Prevalence in the United States
Volume 26, Number 1 Estimated HIV Incidence and Prevalence in the United States, 2015–2019 This issue of the HIV Surveillance Supplemental Report is published by the Division of HIV/AIDS Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention (CDC), U.S. Department of Health and Human Services, Atlanta, Georgia. Estimates are presented for the incidence and prevalence of HIV infection among adults and adolescents (aged 13 years and older) based on data reported to CDC through December 2020. The HIV Surveillance Supplemental Report is not copyrighted and may be used and reproduced without permission. Citation of the source is, however, appreciated. Suggested citation Centers for Disease Control and Prevention. Estimated HIV incidence and prevalence in the United States, 2015–2019. HIV Surveillance Supplemental Report 2021;26(No. 1). http://www.cdc.gov/ hiv/library/reports/hiv-surveillance.html. Published May 2021. Accessed [date]. On the Web: http://www.cdc.gov/hiv/library/reports/hiv-surveillance.html Confidential information, referrals, and educational material on HIV infection CDC-INFO 1-800-232-4636 (in English, en Español) 1-888-232-6348 (TTY) http://wwwn.cdc.gov/dcs/ContactUs/Form Acknowledgments Publication of this report was made possible by the contributions of the state and territorial health departments and the HIV surveillance programs that provided surveillance data to CDC. This report was prepared by the following staff and contractors of the Division of HIV/AIDS Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, CDC: Laurie Linley, Anna Satcher Johnson, Ruiguang Song, Sherry Hu, Baohua Wu, H. -
Understanding Relative Risk, Odds Ratio, and Related Terms: As Simple As It Can Get Chittaranjan Andrade, MD
Understanding Relative Risk, Odds Ratio, and Related Terms: As Simple as It Can Get Chittaranjan Andrade, MD Each month in his online Introduction column, Dr Andrade Many research papers present findings as odds ratios (ORs) and considers theoretical and relative risks (RRs) as measures of effect size for categorical outcomes. practical ideas in clinical Whereas these and related terms have been well explained in many psychopharmacology articles,1–5 this article presents a version, with examples, that is meant with a view to update the knowledge and skills to be both simple and practical. Readers may note that the explanations of medical practitioners and examples provided apply mostly to randomized controlled trials who treat patients with (RCTs), cohort studies, and case-control studies. Nevertheless, similar psychiatric conditions. principles operate when these concepts are applied in epidemiologic Department of Psychopharmacology, National Institute research. Whereas the terms may be applied slightly differently in of Mental Health and Neurosciences, Bangalore, India different explanatory texts, the general principles are the same. ([email protected]). ABSTRACT Clinical Situation Risk, and related measures of effect size (for Consider a hypothetical RCT in which 76 depressed patients were categorical outcomes) such as relative risks and randomly assigned to receive either venlafaxine (n = 40) or placebo odds ratios, are frequently presented in research (n = 36) for 8 weeks. During the trial, new-onset sexual dysfunction articles. Not all readers know how these statistics was identified in 8 patients treated with venlafaxine and in 3 patients are derived and interpreted, nor are all readers treated with placebo. These results are presented in Table 1. -
Ethics of Vaccine Research
COMMENTARY Ethics of vaccine research Christine Grady Vaccination has attracted controversy at every stage of its development and use. Ethical debates should consider its basic goal, which is to benefit the community at large rather than the individual. accines truly represent one of the mira- include value, validity, fair subject selection, the context in which it will be used and Vcles of modern science. Responsible for favorable risk/benefit ratio, independent acceptable to those who will use it. This reducing morbidity and mortality from sev- review, informed consent and respect for assessment considers details about the pub- eral formidable diseases, vaccines have made enrolled participants. Applying these princi- lic health need (such as the prevalence, bur- substantial contributions to global public ples to vaccine research allows consideration den and natural history of the disease, as health. Generally very safe and effective, vac- of some of the particular challenges inherent well as existing strategies to prevent or con- cines are also an efficient and cost-effective in testing vaccines (Box 1). trol it), the scientific data and possibilities way of preventing disease. Yet, despite their Ethically salient features of clinical vac- (preclinical and clinical data, expected brilliant successes, vaccines have always been cine research include the fact that it involves mechanism of action and immune corre- controversial. Concerns about the safety and healthy subjects, often (or ultimately) chil- lates) and the likely use of the vaccine (who untoward effects of vaccines, about disturb- dren and usually (at least when testing effi- will use and benefit from it, safety, cost, dis- ing the natural order, about compelling indi- cacy) in very large numbers. -
Prevalence and Determinants of Vaccine Hesitancy and Vaccines Recommendation Discrepancies Among General Practitioners in French-Speaking Parts of Belgium
Article Prevalence and Determinants of Vaccine Hesitancy and Vaccines Recommendation Discrepancies among General Practitioners in French-Speaking Parts of Belgium Cathy Gobert 1, Pascal Semaille 2, Thierry Van der Schueren 3 , Pierre Verger 4 and Nicolas Dauby 1,5,6,* 1 Department of Infectious Diseases, CHU Saint-Pierre, Université Libre de Bruxelles (ULB), 1000 Bruxelles, Belgium; [email protected] 2 Department of General Medicine, Université Libre de Bruxelles (ULB), 1070 Bruxelles, Belgium; [email protected] 3 Scientific Society of General Practice, 1060 Bruxelles, Belgium; [email protected] 4 Southeastern Health Regional Observatory (ORS PACA), 13005 Marseille, France; [email protected] 5 School of Public Health, Université Libre de Bruxelles (ULB), 1070 Bruxelles, Belgium 6 Institute for Medical Immunology, Université Libre de Bruxelles (ULB), 1070 Bruxelles, Belgium * Correspondence: [email protected] Abstract: General practitioners (GPs) play a critical role in patient acceptance of vaccination. Vaccine hesitancy (VH) is a growing phenomenon in the general population but also affects GPs. Few data exist on VH among GPs. The objectives of this analysis of a population of GPs in the Belgian Wallonia-Brussels Federation (WBF) were to: (1) determine the prevalence and the features of VH, (2) identify the correlates, and (3) estimate the discrepancy in vaccination’s behaviors between the GPs’ children and the recommendations made to their patients. An online survey was carried out Citation: Gobert, C.; Semaille, P.; Van among the population of general practitioners practicing in the WBF between 7 January and 18 der Schueren, T.; Verger, P.; Dauby, N. March 2020. A hierarchical cluster analysis was carried out based on various dimensions of vaccine Prevalence and Determinants of hesitancy: perception of the risks and the usefulness of vaccines as well as vaccine recommendations Vaccine Hesitancy and Vaccines for their patients. -
Incidence Density Sampling for Nested Case-Control Study Designs
Paper AS09 Incidence Density Sampling for Nested Case-Control Study Designs Quratul Ann, IQVIA, London, United Kingdom Rachel Tham, IQVIA, London, United Kingdom ABSTRACT The nested case-control design is commonly used in safety studies, as it is an efficient approach to an epidemiological investigation. When the design is implemented appropriately, it can yield unbiased estimates of relative risk; and in exchange for a small loss in precision, considerable improved efficiency and reduction in costs can be achieved. There are several ways of sampling controls to cases in a nested case-control design. The most sophisticated way is via incidence density sampling. Incidence density sampling matches cases to controls based on the dynamic risk set at the time of case occurrence, where the probability of control selection is proportional to the total person-time at risk. Incidence density sampling alleviates the rare disease assumption; however, it is rarely used due to its computational complexity. This paper presents a novel and simple Statistical Analysis System (SAS) program for incidence density sampling, which could minimise bias and introduce a more appropriate way of optimising the matching of controls to cases. INTRODUCTION Retrospective studies gather available previous exposure information for a clearly defined source population and the outcome event is determined for all members of the cohort4. Retrospective studies facilitate an efficient approach to draw inferences and determine the association between exposure and disease prevalence particularly for rare outcomes and with long-term disease conditions. Real-world data (RWD) are a valuable source of information for investigating health-related outcomes in human populations. -
Incidence, Risk, and Case Fatality of First Ever Stroke in the Elderly Population
J Neurol Neurosurg Psychiatry: first published as 10.1136/jnnp.74.3.317 on 1 March 2003. Downloaded from 317 PAPER Incidence, risk, and case fatality of first ever stroke in the elderly population. The Rotterdam Study M Hollander, P J Koudstaal, M L Bots, D E Grobbee, A Hofman,MMBBreteler ............................................................................................................................. J Neurol Neurosurg Psychiatry 2003;74:317–321 See end of article for authors’ affiliations ....................... Objective: To estimate the incidence, survival, and lifetime risk of stroke in the elderly population. Methods: The authors conducted a study in 7721 participants from the population based Rotterdam Correspondence to: Dr M M.B. Breteler, Study who were free from stroke at baseline (1990–1993) and were followed up for stroke until Department of 1 January 1999. Age and sex specific incidence, case fatality rates, and lifetime risks of stroke were Epidemiology and calculated. Biostatistics, Erasmus Results: Mean follow up was 6.0 years and 432 strokes occurred. The incidence rate of stroke per Medical Centre Rotterdam, 1000 person years increased with age and ranged from 1.7 (95% CI 0.4 to 6.6) in men aged 55 to PO Box 1738, 3000 DR Rotterdam, Netherlands; 59 years to 69.8 (95% CI 22.5 to 216.6) in men aged 95 years or over. Corresponding figures for [email protected] women were 1.2 (95% CI 0.3 to 4.7) and 33.1 (95% CI 17.8 to 61.6). Men and women had similar absolute lifetime risks of stroke (21% for those aged 55 years). The survival after stroke did not differ Received 17 April 2002 Accepted in revised form according to sex. -
EPI Case Study 1 Incidence, Prevalence, and Disease
EPIDEMIOLOGY CASE STUDY 1: Incidence, Prevalence, and Disease Surveillance; Historical Trends in the Epidemiology of M. tuberculosis STUDENT VERSION 1.0 EPI Case Study 1: Incidence, Prevalence, and Disease Surveillance; Historical Trends in the Epidemiology of M. tuberculosis Estimated Time to Complete Exercise: 30 minutes LEARNING OBJECTIVES At the completion of this Case Study, participants should be able to: ¾ Explain why denominators are necessary when comparing changes in morbidity and mortality over time ¾ Distinguish between incidence rates and prevalence ratios ¾ Calculate and interpret cause-specific morbidity and mortality rates ¾ Describe how changes in mortality or morbidity could be due to an artifact rather than a real change ASPH EPIDEMIOLOGY COMPETENCIES ADDRESSED C. 3. Describe a public health problem in terms of magnitude, person, place, and time C. 6. Apply the basic terminology and definitions of epidemiology C. 7. Calculate basic epidemiology measures C. 9. Draw appropriate inference from epidemiologic data C. 10. Evaluate the strengths and limitations of epidemiologic reports ASPH INTERDISCIPLINARY/CROSS-CUTTING COMPETENCIES ADDRESSED F.1. [Communication and Informatics] Describe how the public health information infrastructure is used to collect, process, maintain, and disseminate data J.1. [Professionalism] Discuss sentinel events in the history and development of the public health profession and their relevance for practice in the field L.2. [Systems Thinking] Identify unintended consequences produced by changes made to a public health system This material was developed by the staff at the Global Tuberculosis Institute (GTBI), one of four Regional Training and Medical Consultation Centers funded by the Centers for Disease Control and Prevention. It is published for learning purposes only. -
Likelihood Ratios, Predictive Values, and Post-Test Probabilities The
Likelihood ratios, predictive values, and post-test probabilities The purpose of this quick tutorial is to help you better understand how to use likelihood ratios (LRs), predictive values (PVs), and post-test probabilities in medical writing. The key point is that LRs are calculated differently depending on whether a test has only two possible results (dichotomous), or whether it has more than two possible outcomes (multichotomous). Examples of the latter include a clinical decision rule (CDR) that identifies low, moderate and high-risk groups, or reporting serum ferritin as < 20, 20 – 60, 61 – 100, and > 100 mg/dl. Dichotomous tests A likelihood ratio (LR) for a dichotomous test is defined as the likelihood of a test result in patients with the disease divided by the likelihood of the test result in patients without the disease. For example, the likelihood of abnormal fecal immunochemical test (FIT) in patients with colorectal cancer, divided by the likelihood of an abnormal FIT in those without cancer. A LR close to 1 means that the test result does not change the likelihood of disease or the outcome of interest appreciably. The more the likelihood ratio for a positive test (LR+) is greater than 1, the more likely the disease or outcome. The more a likelihood ratio for a negative test is less than 1, the less likely the disease or outcome. Thus, LRs correspond nicely to the clinical concepts of ruling in and ruling out disease. One suggested way to interpret LR is as follows (Ebell, 2005 Information Mastery AAFP Home Study): > 10 Large