Abstracts tagged "Statistical methods"

Abstract Number: 0613 • ACR Convergence 2020
Modeling the Effects of Covid-19 Protective Behaviors and Healthcare Delivery on the Health of Patients with Rheumatic Disease
Kevin Kennedy¹, Emily Sirotich², Salman Surangiwala³, Maggie Larche², Mitchell Levine¹ and Jonathan Hausmann⁴, ¹McMaster University, Hamilton, Canada, ²McMaster University, Hamilton, ON, Canada, ³Queen’s School of Medicine, Kingston, Canada, ⁴Boston Children's Hospital / Beth Israel Deaconess Medical Center, Cambridge, MA
Background/Purpose: COVID-19 has caused global disruptions in the management of chronic illnesses. The extent to which patients with rheumatic disease have been affected by COVID-19…
Abstract Number: 1009 • ACR Convergence 2020
Missing Data and Multiple Imputation in Rheumatoid Arthritis Registries Using Sequential Random Forest Method
Ahmed Al-Saber¹, Adeeba Al-Herz², Jiazhu Pan¹, Khulood Saleh³, Adel Al-Awadhi⁴, Waleed Al-Kandari³, Eman Hasan⁵, Aqeel Ghanem⁶, Mohammed Hussain⁵, Yaser Ali⁷, Ebrahim Nahar⁷, Ahmad Alenizi⁸, Sawsan Hayat⁷, Fatemah Abutiban⁹, Ali Aldei⁵, Amjad Alkadi¹⁰, Heba Alhajeri⁷, Husain Behbehani³, Naser Alhadhood³, Khaled Mokaddem⁵, Ahmed Khadrawy³, Ammad Fazal³, Agaz Zaman⁷, Ghada Mazloum⁷, Youssef Bartella⁵, Sally Hamed⁵ and Ramia Alsouk⁸, ¹Department of Mathematics and Statistics, University of Strathclyde, Glasgow, United Kingdom, ²Al-Amiri Hospital, Kuwait City, Kuwait, ³Farwania Hosiptal, Farwania, Kuwait, ⁴Faculty of Medicine, Kuwait University, Jabria, Kuwait, ⁵Al-Amiri Hospital, Kuwait, Kuwait, ⁶Mubarak Al-Kabeer Hospital, Kuwait City, Kuwait, ⁷Mubarak Al-Kabeer Hospital, Hawally, Kuwait, ⁸Jahra Hospital, Jahra, Kuwait, ⁹Rheumatology Unit, Department of Medicine, Jaber Alahmed Alsabah hospital, State of Kuwait, Jahra, Kuwait, ¹⁰Sabah Hospital, Kuwait, Kuwait
Background/Purpose: Missing data in clinical epidemiological researches violate the intention to treat principle, reduce statistical power and can induce bias if they are related to…
Abstract Number: 1419 • ACR Convergence 2020
Clinical Disease Might Be Divided into Two Phenotypes in ANCA Associated Vasculitis; Results of a Cluster Analysis
Elif Ediboglu¹, Onay Gercik², Emre Bilgin³, Dilek Solmaz⁴, Irfan Ocal⁵, Arzu Saglam⁶, Zeki Soypacacı², Haluk Cinaklı⁵, Sedat Kiraz³, Ömer Karadağ⁷ and Servet Akar², ¹Izmir Katip Celebi University, Izmir, Izmir, Turkey, ²Izmir Katip Celebi University, Izmir, Turkey, ³Hacettepe University, Ankara, Turkey, ⁴Izmir Katip Celebi University, Department of Internal Medicine, Division of Rheumatology, Izmir, TURKEY, Izmir, Turkey, ⁵Izmir Katip Celebi University, Izmir, ⁶Hacettepe University, Ankara, ⁷Hacettepe University, Faculty of Medicine, Department of Internal Medicine, Division of Rheumatology, Ankara,, Ankara, Turkey
Background/Purpose: One of the controversial matters in ANCA associated vasculitis is the definition of disease based on clinical characteristics since there is a remarkable overlap…
Abstract Number: 1470 • ACR Convergence 2020
Machine Learning-Based Prediction of Knee Replacement in Persons with and Without Radiographic Osteoarthritis Using Clinical and Imaging Features of Osteoarthritis: The Multicenter Osteoarthritis Study
S. Reza Jafarzadeh¹, David Felson¹, Michael Nevitt², James Torner³, Cora Lewis⁴, Frank Roemer⁵, Ali Guermazi¹ and Tuhina Neogi¹, ¹Boston University School of Medicine, Boston, MA, ²University of California, San Francisco, San Francisco, CA, ³University of Iowa, Iowa City, IA, ⁴University of Alabama Birmingham, Birmingham, AL, ⁵Boston University School of Medicine, Boston, MA, and Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany, Erlangen, Germany
Background/Purpose: Prior studies of predictors of knee replacement (KR) have often included only a limited set of risk factors, been conducted primarily in knees with…
Abstract Number: 1487 • ACR Convergence 2020
Biclustering Reveals Potential Knee Osteoarthritis Phenotypes in Exploratory Analyses: Data from the Osteoarthritis Initiative
Amanda Nelson¹, Thomas Keefe², Todd Schwartz³, Richard Loeser¹, Yvonne Golightly⁴, Liubov Arbeeva¹ and J Marron², ¹University of North Carolina at Chapel Hill Thurston Arthritis Research Center, Chapel Hill, NC, ²University of North Carolina at Chapel Hill, Chapel Hill, NC, ³University of North Carolina at Chapel Hill Dept of Biostatistics, Chapel Hill, NC, ⁴University of North Carolina at Chapel Hill Dept of Epidemiology, Chapel Hill, NC
Background/Purpose: To utilize novel methodologies to explore subgroups within the OAI clinical data.Methods: From the OAI baseline dataset (n=4796 individuals with or at risk of…
Abstract Number: 1734 • ACR Convergence 2020
The Challenge of Assessing Wellbeing from the Patients` Perspective in Early Rheumatoid Arthritis
Diederik De Cock¹, Tianna Poffe², Geert Verbeke², Sofia Pazmino¹, Veerle Stouten¹, Delphine Bertrand¹, Rene Westhovens³ and Patrick Verschueren⁴, ¹KU Leuven, Leuven, Belgium, ²KU Leuven, Leuven, ³University Hospitals Leuven, Belgium, Leuven, Belgium, ⁴University Hospital Leuven, Leuven, Belgium
Background/Purpose: Although more effective therapeutics and treatment strategies for Rheumatoid Arthritis (RA) have improved many patient-reported outcomes (PROs), still a sizeable number of patients in…
Abstract Number: 2412 • 2019 ACR/ARP Annual Meeting
Use of Minimal Important Difference (MID) in Randomized Clinical Trials of Pain in Osteoarthritis
Lavalley Michael¹, Matthew Parkes ², Daniel White ³, Stephan Reichebach ⁴, Timothy McAlindon ⁵ and David Felson ⁶, ¹Boston University, Boston, ²University of Manchester, Manchester, England, United Kingdom, ³University of Delaware, Newark, DE, ⁴University of Bern, Bern, Switzerland, ⁵Tufts Medical Center, Boston, MA, ⁶Boston University School of Medicine, Department of Rheumatology, Boston
Background/Purpose: Minimal important differences (MID), based on within-subject evaluation of attaining an improvement in a continuous outcome such as a pain scale, are important for…
Abstract Number: 2535 • 2019 ACR/ARP Annual Meeting
Identifying Subgroups of SLE Patients with Differential Responses to a BLyS Inhibitor: Application of a Machine Learning Algorithm to Clinical Trial Data
Mimi Kim¹, Kith Pradhan ¹, Peter Izmirly ², Kenneth Kalunian ³, Leslie Hanrahan ⁴ and Joan Merrill ⁵, ¹Albert Einstein College of Medicine, Bronx, NY, ²New York University School of Medicine, New York, ³University of California at San Diego, San Diego, ⁴Lupus Foundation of America, Washington DC, ⁵Oklahoma Medical Research Foundation, Oklahoma City
Background/Purpose: Given the heterogeneity of systemic lupus erythematosus (SLE), the effect of any intervention is expected to vary. The ability to identify those most and…
Abstract Number: 1157 • 2018 ACR/ARHP Annual Meeting
Acknowledged Biostatistical Help and the Quality of Statistical Analyses in Randomized Controlled Trials in Rheumatology
Elif Dincses¹, Gul Guzelant², Gulen Hatemi² and Hasan Yazici³, ¹Istanbul University, Cerrahpasa Medical Faculty, Department of Internal Medicine, Division of Rheumatology, İstanbul, Turkey, ²Istanbul University, Cerrahpasa Medical Faculty, Department of Internal Medicine, Division of Rheumatology, Istanbul, Turkey, ³Istanbul Academic Hospital, Istanbul, Turkey
Background/Purpose: The quality of statistical analysis reporting is wanting even in our most prestigious journals. It stands to reason that active participation of biostatisticians/epidemiologists (b/e)…
Abstract Number: 2051 • 2017 ACR/ARHP Annual Meeting
The Lasso Selection Model in Rheumatology Epidemiologic Studies
Sofia Pedro¹, Bella Mehta², Gulsen Ozen^3,4 and Kaleb Michaud^1,5, ¹National Data Bank for Rheumatic Diseases, Wichita, KS, ²Rheumatology, Hospital of Special Surgery, New York, NY, ³Rheumatology, Marmara University, School of Medicine, Rheumatology, Istanbul, Turkey, ⁴Department of Rheumatology, University of Nebraska Medical Center, Omaha, NE, ⁵University of Nebraska Medical Center, Omaha, NE
Background/Purpose: Selecting the best model in an epidemiologic analysis is challenging as it addresses problems like confounding and allows the estimation of unbiased results. Stepwise…

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