Design and Implementation of N-Of-1 Trials: a User's Guide
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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. -
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. -
(EPOC) Data Collection Checklist
Cochrane Effective Practice and Organisation of Care Review Group DATA COLLECTION CHECKLIST Page 2 Cochrane Effective Practice and Organisation of Care Review Group (EPOC) Data Collection Checklist CONTENTS Item Page Introduction 5-6 1 Inclusion criteria* 7-8 1.1 Study design* 7 1.1.1 Randomised controlled trial* 1.1.2 Controlled clinical trial* 1.1.3 Controlled before and after study* 1.1.4 Interrupted time series* 1.2 Methodological inclusion criteria* 8 2 Interventions* 9-12 2.1 Type of intervention 9 2.1.1 Professional interventions* 9 2.1.2 Financial interventions* 10 2.1.2.1 Provider interventions* 2.1.2.2 Patient interventions* 2.1.3 Organisational interventions* 11 2.1.3.1 Provider orientated interventions* 2.1.3.2 Patient orientated interventions* 2.1.3.3 Structural interventions* 2.1.4 Regulatory interventions* 12 2.2 Controls* 13 3 Type of targeted behaviour* 13 4 Participants* 14-15 4.1 Characteristics of participating providers* 14 4.1.1 Profession* Item Page Page 3 4.1.2 Level of training* 4.1.3 Clinical speciality* 4.1.4 Age 4.1.5 Time since graduation 4.2 Characteristics of participating patients* 15 4.2.1 Clinical problem* 4.2.2 Other patient characteristics 4.2.3 Number of patients included in the study* 5 Setting* 16 5.1 Reimbursement system 5.2 Location of care* 5.3 Academic Status* 5.4 Country* 5.5 Proportion of eligible providers from the sampling frame* 6 Methods* 17 6.1 Unit of allocation* 6.2 Unit of analysis* 6.3 Power calculation* 6.4 Quality criteria* 17-22 6.4.1 Quality criteria for randomised controlled trials -
Generalized Linear Models for Aggregated Data
Generalized Linear Models for Aggregated Data Avradeep Bhowmik Joydeep Ghosh Oluwasanmi Koyejo University of Texas at Austin University of Texas at Austin Stanford University [email protected] [email protected] [email protected] Abstract 1 Introduction Modern life is highly data driven. Datasets with Databases in domains such as healthcare are records at the individual level are generated every routinely released to the public in aggregated day in large volumes. This creates an opportunity form. Unfortunately, na¨ıve modeling with for researchers and policy-makers to analyze the data aggregated data may significantly diminish and examine individual level inferences. However, in the accuracy of inferences at the individ- many domains, individual records are difficult to ob- ual level. This paper addresses the scenario tain. This particularly true in the healthcare industry where features are provided at the individual where protecting the privacy of patients restricts pub- level, but the target variables are only avail- lic access to much of the sensitive data. Therefore, able as histogram aggregates or order statis- in many cases, multiple Statistical Disclosure Limita- tics. We consider a limiting case of gener- tion (SDL) techniques are applied [9]. Of these, data alized linear modeling when the target vari- aggregation is the most widely used technique [5]. ables are only known up to permutation, and explore how this relates to permutation test- It is common for agencies to report both individual ing; a standard technique for assessing statis- level information for non-sensitive attributes together tical dependency. Based on this relationship, with the aggregated information in the form of sample we propose a simple algorithm to estimate statistics. -
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 Using Individual Participant Data: One-Stage and Two-Stage Approaches, and Why They May Differ Danielle L
CORE Metadata, citation and similar papers at core.ac.uk Provided by Keele Research Repository Tutorial in Biostatistics Received: 11 March 2016, Accepted: 13 September 2016 Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/sim.7141 Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ Danielle L. Burke*† Joie Ensor and Richard D. Riley Meta-analysis using individual participant data (IPD) obtains and synthesises the raw, participant-level data from a set of relevant studies. The IPD approach is becoming an increasingly popular tool as an alternative to traditional aggregate data meta-analysis, especially as it avoids reliance on published results and provides an opportunity to investigate individual-level interactions, such as treatment-effect modifiers. There are two statistical approaches for conducting an IPD meta-analysis: one-stage and two-stage. The one-stage approach analyses the IPD from all studies simultaneously, for example, in a hierarchical regression model with random effects. The two-stage approach derives aggregate data (such as effect estimates) in each study separately and then combines these in a traditional meta-analysis model. There have been numerous comparisons of the one-stage and two-stage approaches via theoretical consideration, simulation and empirical examples, yet there remains confusion regarding when each approach should be adopted, and indeed why they may differ. In this tutorial paper, we outline the key statistical methods for one-stage and two-stage IPD meta-analyses, and provide 10 key reasons why they may produce different summary results. We explain that most differences arise because of different modelling assumptions, rather than the choice of one-stage or two-stage itself. -
Learning to Personalize Medicine from Aggregate Data
medRxiv preprint doi: https://doi.org/10.1101/2020.07.07.20148205; this version posted July 8, 2020. 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. It is made available under a CC-BY-ND 4.0 International license . Learning to Personalize Medicine from Aggregate Data Rich Colbaugh Kristin Glass Volv Global Lausanne, Switzerland Abstract There is great interest in personalized medicine, in which treatment is tailored to the individual characteristics of pa- tients. Achieving the objectives of precision healthcare will require clinically-grounded, evidence-based approaches, which in turn demands rigorous, scalable predictive analytics. Standard strategies for deriving prediction models for medicine involve acquiring ‘training’ data for large numbers of patients, labeling each patient according to the out- come of interest, and then using the labeled examples to learn to predict the outcome for new patients. Unfortunate- ly, labeling individuals is time-consuming and expertise-intensive in medical applications and thus represents a ma- jor impediment to practical personalized medicine. We overcome this obstacle with a novel machine learning algo- rithm that enables individual-level prediction models to be induced from aggregate-level labeled data, which is read- ily-available in many health domains. The utility of the proposed learning methodology is demonstrated by: i.) lev- eraging US county-level mental health statistics to create a screening tool which detects individuals suffering from depression based upon their Twitter activity; ii.) designing a decision-support system that exploits aggregate clinical trials data on multiple sclerosis (MS) treatment to predict which therapy would work best for the presenting patient; iii.) employing group-level clinical trials data to induce a model able to find those MS patients likely to be helped by an experimental therapy. -
Good Statistical Practices for Contemporary Meta-Analysis: Examples Based on a Systematic Review on COVID-19 in Pregnancy
Review Good Statistical Practices for Contemporary Meta-Analysis: Examples Based on a Systematic Review on COVID-19 in Pregnancy Yuxi Zhao and Lifeng Lin * Department of Statistics, Florida State University, Tallahassee, FL 32306, USA; [email protected] * Correspondence: [email protected] Abstract: Systematic reviews and meta-analyses have been increasingly used to pool research find- ings from multiple studies in medical sciences. The reliability of the synthesized evidence depends highly on the methodological quality of a systematic review and meta-analysis. In recent years, several tools have been developed to guide the reporting and evidence appraisal of systematic reviews and meta-analyses, and much statistical effort has been paid to improve their methodological quality. Nevertheless, many contemporary meta-analyses continue to employ conventional statis- tical methods, which may be suboptimal compared with several alternative methods available in the evidence synthesis literature. Based on a recent systematic review on COVID-19 in pregnancy, this article provides an overview of select good practices for performing meta-analyses from sta- tistical perspectives. Specifically, we suggest meta-analysts (1) providing sufficient information of included studies, (2) providing information for reproducibility of meta-analyses, (3) using appro- priate terminologies, (4) double-checking presented results, (5) considering alternative estimators of between-study variance, (6) considering alternative confidence intervals, (7) reporting predic- Citation: Zhao, Y.; Lin, L. Good tion intervals, (8) assessing small-study effects whenever possible, and (9) considering one-stage Statistical Practices for Contemporary methods. We use worked examples to illustrate these good practices. Relevant statistical code Meta-Analysis: Examples Based on a is also provided. -
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