(EPOC) Data Collection Checklist
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Data Collection WP Template
Distr. GENERAL 02 September 2013 WP 14 ENGLISH ONLY UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE CONFERENCE OF EUROPEAN STATISTICIANS Seminar on Statistical Data Collection (Geneva, Switzerland, 25-27 September 2013) Topic (ii): Managing data collection functions in a changing environment THE BUSINESS STATISTICAL PORTAL: A NEW WAY OF ORGANIZING AND MANAGING DATA COLLECTION PROCESSES FOR BUSINESS SURVEYS IN ISTAT Working Paper Prepared by Natale Renato Fazio, Manuela Murgia, Andrea Nunnari, ISTAT – Italian National Statistical Institute I. Introduction 1. A broad shift of the social and technological context requires ISTAT to modify the way data are collected in order to improve the efficiency in producing timely estimates and, at the same time, to increase or at least maintain the level of data quality, while reducing respondent burden. All of this, within a climate of general budgets cuts. 2. In 2010, as a way to deal with these challenges, a medium-long term project was launched (Stat2015), envisaging the gradual standardisation and industrialisation of the entire cycle of ISTAT statistical processes, according to a model based on a metadata-driven, service-oriented architecture. With regard to data collection, as well for the other production phases, Stat2015 aims at outlining strong enterprise architecture standards. 3. A first step along these lines was to develop a comprehensive set of requirements according to which select, design or further develop the IT tools supporting data collection. The requirements encompassed four areas, covering the main functions of the entire (data collection) process: i) Survey unit management, ii) Data collection management, iii) Questionnaire, iv) Communication facilities. 4. A significant contribution to the definition and implementation of these requirements came with the development of the new ISTAT Business Statistical Portal, a single entry point for Web-based data collection from enterprises, which is at the same time an attempt to streamline the organization and management of business surveys as a whole. -
Standardization and Harmonization of Data and Data Collection Initiatives
Conference of the Parties to the WHO Framework Convention on Tobacco Control Fourth session FCTC/COP/4/15 Punta del Este, Uruguay, 15–20 November 2010 15 August 2010 Provisional agenda item 6.2 Standardization and harmonization of data and data collection initiatives Report of the Convention Secretariat INTRODUCTION 1. At its third session (Durban, South Africa, 17–22 November 2008) the Conference of the Parties requested the Convention Secretariat to compile a report on data collection measures, in decision FCTC/COP3(17). The decision outlined the fact that this should be undertaken under the guidance of the Bureau, and with the assistance of competent authorities within WHO, in particular WHO’s Tobacco Free Initiative, as well as relevant intergovernmental and nongovernmental organizations with specific expertise in this area. Paragraph 6 of the decision stated that the report should cover measures: to improve the comparability of data over time; to standardize1 collected data within and between Parties; to develop indicators and definitions to be used by Parties’ national and international data collection initiatives; and to further harmonize2 with other data collection initiatives. 2. The request to provide such a report is in line with Article 23.5 of the Convention, which requires the Conference of the Parties to “keep under regular review the implementation of the Convention” and to “promote and guide the development and periodic refinement of comparable methodologies for research and the collection of data, in addition to those provided for in Article 20, relevant to the implementation of the Convention”. 1 Standardization: the adoption of generally accepted uniform technical specifications, criteria, methods, processes or practices to measure an item. -
Data Collection and Analysis Plan FAMILY OPTIONS STUDY
Data Collection and Analysis Plan FAMILY OPTIONS STUDY UU.S..S. DDepaepartmentrtment ofof HouHousingsing andand UrUrbanban DevDevelopelopmmentent || OOfficeffice ofof PolicPolicyy DevDevelopelopmentment andand ResReseaearchrch Visit PD&R’s website www.huduser.org to find this report and others sponsored by HUD’s Office of Policy Development and Research (PD&R). Other services of HUD USER, PD&R’s research information service, include listservs, special interest reports, bimonthly publications (best practices, significant studies from other sources), access to public use databases, and a hotline (800-245-2691) for help accessing the information you need. Data Collection and Analysis Plan FAMILY OPTIONS STUDY Prepared for: U.S. Department of Housing and Urban Development Washington, D.C. Prepared by: Abt Associates Inc. Daniel Gubits Michelle Wood Debi McInnis Scott Brown Brooke Spellman Stephen Bell In partnership with : Vanderbilt University Marybeth Shinn March 2013 Disclaimer Disclaimer The contents of this report are the views of the authors and do not necessarily reflect the views or policies of the U.S. Department of Housing and Urban Development or the U.S. Government. Family Options Study Data Collection and Analysis Plan Table of Contents Family Options Study Revised Data Collection and Analysis Plan Table of Contents Chapter 1. Introduction and Evaluation Design .................................................................................. 1 1.1 Overview and Objectives ........................................................................................................ -
Ground Data Collection and Use
ANDREWS. BENSON WILLIAMC. DRAEGER LAWRENCER. PETTINGER University of California* Berkeley, Calif. 94720 Ground Data Collection and Use These are essential components of an agricultural resource survey. INTRODUCTION sions based on their efforts to perform re- HE IMPORTANCE OF collecting meaningful gional agricultural inventories in Maricopa T and timely ground data for resource in- County, Arizona, using space and high-alti- ventories which employ remote sensing tech- tude aerial photography, as part of an ongoing niques is often discussed, and appropriately NASA-sponsored research project (Draeger, so. However, those wishing to conduct inven- et a]., 1970). This paper draws upon examples tories frequently fail to devote as much time from this research. However, much of the dis- to the planning of field activities which occur cussion is relevant to other disciplines for in conjunction with an aircraft mission as which ground data is important. they do in planning the flight itself. As a re- sult. adequate remote sensing data mav be collected,-but no adequate sipporting infor- Preliminary evaluation of the geographical ABSTRACT:During the past two years, extensive studies have been conducted out in the Phoenix, Arizona area to ascertain the degree to which sequential high- altitude aircraft and spacecraft imagery can contribute to operational agri- cultural crop surveys. Data collected on the ground within the test site con- stituted an essential component of the three phases of the survey: (I)farniliariza- tion with the area and design of preliminary evalz~ationexperiments, (2) train- in,g of interpreters, and (3)providing the basis upon which image interpretation estimates can be adjusted and evaluated. -
Design and Implementation of N-Of-1 Trials: a User's Guide
Design and Implementation of N-of-1 Trials: A User’s Guide N of 1 The Agency for Healthcare Research and Quality’s (AHRQ) Effective Health Care Program conducts and supports research focused on the outcomes, effectiveness, comparative clinical effectiveness, and appropriateness of pharmaceuticals, devices, and health care services. More information on the Effective Health Care Program and electronic copies of this report can be found at www.effectivehealthcare.ahrq. gov. This report was produced under contract to AHRQ by the Brigham and Women’s Hospital DEcIDE (Developing Evidence to Inform Decisions about Effectiveness) Methods Center under Contract No. 290-2005-0016-I. The AHRQ Task Order Officer for this project was Parivash Nourjah, Ph.D. The findings and conclusions in this document are those of the authors, who are responsible for its contents; the findings and conclusions do not necessarily represent the views ofAHRQ or the U.S. Department of Health and Human Services. Therefore, no statement in this report should be construed as an official position of AHRQ or the U.S. Department of Health and Human Services. Persons using assistive technology may not be able to fully access information in this report. For assistance contact [email protected]. The following investigators acknowledge financial support: Dr. Naihua Duan was supported in part by NIH grants 1 R01 NR013938 and 7 P30 MH090322, Dr. Heather Kaplan is part of the investigator team that designed and developed the MyIBD platform, and this work was supported in part by Cincinnati Children’s Hospital Medical Center. Dr. Richard Kravitz was supported in part by National Institute of Nursing Research Grant R01 NR01393801. -
Impact of Blinding on Estimated Treatment Effects in Randomised Clinical Trials
RESEARCH Impact of blinding on estimated treatment effects in randomised BMJ: first published as 10.1136/bmj.l6802 on 21 January 2020. Downloaded from clinical trials: meta-epidemiological study Helene Moustgaard,1-4 Gemma L Clayton,5 Hayley E Jones,5 Isabelle Boutron,6 Lars Jørgensen,4 David R T Laursen,1-4 Mette F Olsen,4 Asger Paludan-Müller,4 Philippe Ravaud,6 5,7 5,7,8 5,7 1-3 Jelena Savović, , Jonathan A C Sterne, Julian P T Higgins, Asbjørn Hróbjartsson For numbered affiliations see ABSTRACT 1 indicated exaggerated effect estimates in trials end of the article. OBJECTIVES without blinding. Correspondence to: To study the impact of blinding on estimated RESULTS H Moustgaard treatment effects, and their variation between [email protected] The study included 142 meta-analyses (1153 trials). (or @HeleneMoustgaa1 on Twitter trials; differentiating between blinding of patients, The ROR for lack of blinding of patients was 0.91 ORCID 0000-0002-7057-5251) healthcare providers, and observers; detection bias (95% credible interval 0.61 to 1.34) in 18 meta- Additional material is published and performance bias; and types of outcome (the analyses with patient reported outcomes, and 0.98 online only. To view please visit MetaBLIND study). the journal online. (0.69 to 1.39) in 14 meta-analyses with outcomes C ite this as: BMJ 2020;368:l6802 DESIGN reported by blinded observers. The ROR for lack of http://dx.doi.org/10.1136/bmj.l6802 Meta-epidemiological study. blinding of healthcare providers was 1.01 (0.84 to Accepted: 19 November 2019 DATA SOURCE 1.19) in 29 meta-analyses with healthcare provider Cochrane Database of Systematic Reviews (2013-14). -
Development of Validated Instruments
Development of Validated Instruments Michelle Tarver, MD, PhD Medical Officer Division of Ophthalmic and Ear, Nose, and Throat Devices Center for Devices and Radiological Health Food and Drug Administration No Financial Conflicts to Disclose 2 Overview • Define Patient Reported Outcomes (PROs) • Factors to Consider when Developing PROs • FDA Guidance for PROs • Use of PROs in FDA Clinical Trials 3 Patient Reported Outcomes (PROs) • Any report of the status of a patient’s health condition that comes directly from the patient, without interpretation of the patient’s response by a clinician or anyone else • Can be measured in absolute terms (e.g., severity of a symptom) or as a change from a previous measure • In trials, measures the effect of a medical intervention on one or more concepts – Concept is the thing being measured (e.g., symptom, effects on function, severity of health condition) 4 Concepts a PRO May Capture • Symptoms • Symptom impact and functioning • Disability/handicap • Adverse events • Treatment tolerability • Treatment satisfaction • Health-related quality of life 5 Criteria to Consider in PRO Development • Appropriateness – Does the content address the relevant questions for the device? • Acceptability – Is the questionnaire acceptable to patients? • Feasibility – Is it easy to administer and process/analyze? • Interpretability – Are the scores interpretable? Abstracted from (1) Patient Reported Outcomes Measurement Group: University of Oxford (2) NIH PROMIS Instrument Development and Validation Standards 6 Criteria -
Development of a Person-Centered Conceptual Model of Perceived Fatigability
Quality of Life Research (2019) 28:1337–1347 https://doi.org/10.1007/s11136-018-2093-z Development of a person-centered conceptual model of perceived fatigability Anna L. Kratz1 · Susan L. Murphy2 · Tiffany J. Braley3 · Neil Basu4 · Shubhangi Kulkarni1 · Jenna Russell1 · Noelle E. Carlozzi1 Accepted: 17 December 2018 / Published online: 2 January 2019 © Springer Nature Switzerland AG 2019 Abstract Purpose Perceived fatigability, reflective of changes in fatigue intensity in the context of activity, has emerged as a poten- tially important clinical outcome and quality of life indicator. Unfortunately, the nature of perceived fatigability is not well characterized. The aim of this study is to define the characteristics of fatigability through the development of a conceptual model informed by input from key stakeholders who experience fatigability, including the general population, individuals with multiple sclerosis (MS), and individuals with fibromyalgia (FM). Methods Thirteen focus groups were conducted with 101 participants; five groups with n = 44 individuals representing the general population, four groups with n = 26 individuals with MS, and four groups with n = 31 individuals with FM. Focus group data were qualitatively analyzed to identify major themes in the participants’ characterizations of perceived fatigability. Results Seven major themes were identified: general fatigability, physical fatigability, mental fatigability, emotional fati- gability, moderators of fatigability, proactive and reactive behaviors, and temporal aspects of fatigability. Relative to those in the general sample, FM or MS groups more often described experiencing fatigue as a result of cognitive activity, use of proactive behaviors to manage fatigability, and sensory stimulation as exacerbating fatigability. Conclusions Fatigability is the complex and dynamic process of the development of physical, mental, and/or emotional fatigue. -
Collapsing the Distinction Between Experimentation and Treatment in the Regulation of New Drugs
File: LAAKMANN EIC PUBLISH.doc Created on: 3/30/2011 4:56:00 PM Last Printed: 4/7/2011 11:58:00 AM COLLAPSING THE DISTINCTION BETWEEN EXPERIMENTATION AND TREATMENT IN THE REGULATION OF NEW DRUGS Anna B. Laakmann* The explosion of scientific knowledge in recent decades has simulta- neously increased pressure on the Food and Drug Administration (FDA) both to expedite market entry of promising medical breakthroughs and to safeguard the public from harms caused by new products. Ironically, as the FDA and drug manufacturers spend huge sums in the premarketing phase gathering and interpreting data from planned trials, an enormous amount of valuable information about postmarketing clinical experiences with FDA-regulated drugs is not being effectively captured. The Article asserts that the FDA should formally recognize the blurred line between experimentation and treatment by adopting a more fluid approach to its review of new medical technologies. It argues that the FDA should shift its focus toward harnessing and distilling accumulated experiential knowledge about the effects of new drugs so as to enable patients and doctors to make rational treatment decisions based on inevitably imperfect information. The Article sets forth an alternative regulatory approach that seamlessly incor- porates prospective outcomes data into the FDA drug review process. This approach would be facilitated by the creation of a centralized database that serves as a clearinghouse of information about treatment outcomes. The proposed scheme would help to address current failures in the imple- mentation of the agency’s “fast-track” programs. More broadly, it could serve as the instrumentality to effectuate a shift in the perceived role of the FDA from market gatekeeper to consolidator and purveyor of information about drug safety and efficacy. -
Patient Reported Outcomes (PROS) in Performance Measurement
Patient-Reported Outcomes Table of Contents Introduction ............................................................................................................................................ 2 Defining Patient-Reported Outcomes ............................................................................................ 2 How Are PROs Used? ............................................................................................................................ 3 Measuring research study endpoints ........................................................................................................ 3 Monitoring adverse events in clinical research ..................................................................................... 3 Monitoring symptoms, patient satisfaction, and health care performance ................................ 3 Example from the NIH Collaboratory ................................................................................................................... 4 Measuring PROs: Instruments, Item Banks, and Devices ....................................................... 5 PRO Instruments ............................................................................................................................................... 5 Item Banks........................................................................................................................................................... 8 Devices ................................................................................................................................................................. -
Meta-Analysis: Methods for Quantitative Data Synthesis
Department of Health Sciences M.Sc. in Evidence Based Practice, M.Sc. in Health Services Research Meta-analysis: methods for quantitative data synthesis What is a meta-analysis? Meta-analysis is a statistical technique, or set of statistical techniques, for summarising the results of several studies into a single estimate. Many systematic reviews include a meta-analysis, but not all. Meta-analysis takes data from several different studies and produces a single estimate of the effect, usually of a treatment or risk factor. We improve the precision of an estimate by making use of all available data. The Greek root ‘meta’ means ‘with’, ‘along’, ‘after’, or ‘later’, so here we have an analysis after the original analysis has been done. Boring pedants think that ‘metanalysis’ would have been a better word, and more euphonious, but we boring pedants can’t have everything. For us to do a meta-analysis, we must have more than one study which has estimated the effect of an intervention or of a risk factor. The participants, interventions or risk factors, and settings in which the studies were carried out need to be sufficiently similar for us to say that there is something in common for us to investigate. We would not do a meta-analysis of two studies, one of which was in adults and the other in children, for example. We must make a judgement that the studies do not differ in ways which are likely to affect the outcome substantially. We need outcome variables in the different studies which we can somehow get in to a common format, so that they can be combined. -
The Assisi Think Tank Meeting Breast Large Database for Standardized Data Collection in Breast Cancer—ATTM.BLADE
Journal of Personalized Medicine Article The Assisi Think Tank Meeting Breast Large Database for Standardized Data Collection in Breast Cancer—ATTM.BLADE Fabio Marazzi 1, Valeria Masiello 1,* , Carlotta Masciocchi 2, Mara Merluzzi 3, Simonetta Saldi 4, Paolo Belli 5, Luca Boldrini 1,6 , Nikola Dino Capocchiano 6, Alba Di Leone 7 , Stefano Magno 7 , Elisa Meldolesi 1, Francesca Moschella 7, Antonino Mulé 8, Daniela Smaniotto 1,6, Daniela Andreina Terribile 8, Luca Tagliaferri 1 , Gianluca Franceschini 6,7 , Maria Antonietta Gambacorta 1,6, Riccardo Masetti 6,7, Vincenzo Valentini 1,6 , Philip M. P. Poortmans 9,10 and Cynthia Aristei 11 1 Fondazione Policlinico Universitario “A. Gemelli” IRCCS, UOC di Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, 00168 Roma, Italy; [email protected] (F.M.); [email protected] (L.B.); [email protected] (E.M.); [email protected] (D.S.); [email protected] (L.T.); [email protected] (M.A.G.); [email protected] (V.V.) 2 Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Roma, Italy; [email protected] 3 Radiation Oncology Section, Department of Medicine and Surgery, University of Perugia, 06123 Perugia, Italy; [email protected] 4 Radiation Oncology Section, Perugia General Hospital, 06123 Perugia, Italy; [email protected] 5 Fondazione Policlinico Universitario