CHAPTER 3 HEART AND LUNG TRANSPLANTATION. Editors: Mr Mohamed Ezani Md. Taib Dato Dr David Chew Soon Ping Dr Ashari Yunus
|
|
- Cornelius Harrison
- 5 years ago
- Views:
Transcription
1 CHAPTER 3 HEART AND LUNG TRANSPLANTATION Editors: Mr Mohamed Ezani Md. Taib Dato Dr David Chew Soon Ping Dr Ashari Yunus Expert Panel: Mr Mohamed Ezani Md. Taib (Chairperson) Dr Abdul Rais Sanusi Datuk Dr Aizai Azan Abdul Rahim Dr Ashari Yunus Dato Dr David Chew Soon Ping Contents 3.0 Introduction 3.1 Stock and Flow of Heart Transplantation 3.2 Recipients Characteristics Demographics and Clinical Status Primary Diagnosis 3.3 Transplant Practices Type of Transplant Immunosuppressive Therapy and Other Medications Duration of Waiting Time on the Waiting List 3.4 Transplant Outcomes Post Transplant Complications Patient Survival Causes of Death
2 HEART AND LUNG TRANSPLANTATION National Transplant Registry 2014 List of Tables Table 3.1.1a: Stock and Flow of Heart Transplantation, Table 3.2.1a: Distribution of Patients by Gender, Table 3.2.2a: Distribution of Patients by Ethnic Group, Table 3.2.3a: Distribution of Patients by Age, Table 3.2.4a: Distribution of Patients by Primary Diagnosis, Table 3.3.1a: Distribution of Patients by Heart Procedure, Table 3.3.2a: Distribution of Patients by Immunosuppressive Used, Table 3.3.3a: Immunosuppressive Used at Time of Last Follow-up up to Table 3.4.1: Post Transplant Events at Last Follow-up up to Table 3.4.2: Post Transplant Malignancies at Follow-up up to Table 3.4.3: Non-compliance at Follow-up up to Table 3.4.4: Patient Treated for Rejection at Follow-up up to Table 3.4.5a: Distribution of Patients by Time of Deaths, Table 3.4.6: Patient Survival, Table 3.4.7: Cause of Death at Discharge, Table 3.4.8: Cause of Death at Follow-up, Table 3.1.1b: Stock and Flow of Lung Transplantation, Table 3.2.1b: Distribution of Patients by Gender, Table 3.2.2b: Distribution of Patients by Ethnic Group, Table 3.2.3b: Distribution of Patients by Age, Table 3.2.4b: Distribution of Patients by Primary Diagnosis, Table 3.3.1b: Distribution of Patients by Heart Procedure, Table 3.3.3b: Immunosuppressive Used at Time of Last Follow-up up to Table 3.4.5b: Distribution of Patients by Time of Deaths, List of Figures Figure 3.1.1a: Stock and Flow of Heart Transplantation, Figure 3.4.6: Patient Survival, Figure 3.1.1b Stock and Flow of Lung Transplant and Heart Lung Transplant
3 National Transplant Registry 2014 HEART AND LUNG TRANSPLANTATION 3.0 INTRODUCTION The first heart transplant in Malaysia was in 1997, and the first lung transplant was in Since then the numbers of thoracic transplants have remained few and far between. For end stage heart failure patients, the use of left ventricular assist device (LVAD) as a bridge to heart transplant has been employed, to keep patients alive when their condition deteriorated while on the heart transplant waiting list. In 2014, there was only one thoracic organ transplant (heart transplant) performed. After 16 years since heart transplantation started in Malaysia, this option for the treatment of patients with end stage heart failure remains limited in availability. The Kaplan Meier survival curve is 54% at 1 year and 44% at 5 years. Most patients succumb early post heart transplant. The rest of the report that follows review the results of heart and lung transplantation in Malaysia till end of
4 HEART AND LUNG TRANSPLANTATION National Transplant Registry 2014 HEART TRANSPLANTATION 3.1 STOCK AND FLOW Table 3.1.1a: Stock and Flow of Heart Transplantation, New transplant patients Deaths Retransplanted Alive at 31 st December Note: The same patient was re-transplanted in the year 2007, thus only counted as one Figure 3.1.1a: Stock and Flow of Heart Transplant,
5 National Transplant Registry 2014 HEART AND LUNG TRANSPLANTATION 3.2 RECIPIENTS CHARACTERISTICS Table 3.2.1a: Distribution of Patients by Gender, TOTAL Gender n n n n n n n n n n n n n n n n n n n Male Female TOTAL Note: The same patient was re-transplanted in the year 2007, thus only counted as one Table 3.2.2a: Distribution of Patients by Ethnic Group, TOTAL Ethnic group n n n n n n n n n n n n n n n n n n n Malay Chinese Indian TOTAL Note: The same patient was re-transplanted in the year 2007, thus only counted as one Table 3.2.3a: Distribution of Patients by Age, TOTAL Age, years n n n n n n n n n n n n n n n n n n n TOTAL Mean SD Median Minimum Maximum Age=date of transplant-date of birth Note: The same patient was re-transplanted in the year 2007, thus only counted as one Age for 2007 patient was same for 1 st and 2 nd transplant Table 3.2.4a: Distribution of Patients by Primary Diagnosis, TOTAL Primary diagnosis n n n n n n n n n n n n n n n n n n n Ischaemic Cardiomyopathy Idiopathic Dilated Cardiomyopathy Restrictive Cardiomyopathy End Stage Valvular Heart Disease Hypertrophic Cardiomyopathy Others * 4 TOTAL *Non ischemic dilated cardiomyopathy Note: The same patient was re-transplanted in the year 2007, thus only counted as one 5
6 HEART AND LUNG TRANSPLANTATION National Transplant Registry TRANSPLANT PRACTICES Table 3.3.1a: Distribution of Patients by Heart Procedure, TOTAL Heart Procedure n n n n n n n n n n n n n n n n n n n Orthotopic Bicaval Orthotopic Traditional TOTAL Table 3.3.2a: Distribution of Patients by Immunosuppressive Used, Total Type of immunosuppressive n n n n n n n n n n n n n n n n n n n Steroids Prednisolone Methylprednisolone Calcineurin Inhibitors Neoral Tacrolimus (FK506) Antimetabolites Azathioprine (AZA) Mycophenolate Mofetil (MMF) Anti-lymphocyte Receptor Antibodies Anti-thymocyte globulin (ATG) Simulect TOTAL patients at notification Table 3.3.3a: Immunosuppressive Used at Time of Last Follow-up up to 2014 of follow up* Type of immunosuppressive n n n n n n n n n n n Steroids Prednisolone Methylprednisolone Everolimus Calcineurin Inhibitors Neoral FK Antimetabolites Azathioprine (AZA) Mycophenolate Mofetil (MMF) Everolimus TOTAL patients at follow-up *Data according to year of follow up of transplanted patients 6
7 National Transplant Registry 2014 HEART AND LUNG TRANSPLANTATION Table 3.3.4a: Duration of Waiting Time on Waiting List, TOTAL Duration (months)* n n n n n n n n n n n n n n n n n n n < TOTAL Mean SD Median Minimum Maximum *Duration=date of transplant-date added to wait list 3.4 TRANPLANT OUTCOMES Table 3.4.1: Post Transplant Events at Last Follow-up up to 2014 of transplant* Type of post transplant events Drug Treated Hypertension Bone Disease (Symptomatic) TOTAL n n n n n n n n n n n n n n n n n n n Chronic Liver Disease Cataracts Diabetes Renal Dysfunction Stroke Drug-Treated Hyperlipidaemia TOTAL patients at follow-up *Data according to year of transplant of patient 7
8 HEART AND LUNG TRANSPLANTATION National Transplant Registry 2014 Table 3.4.2: Post Transplant Malignancies at Follow-up up to 2014 of transplant* TOTAL Type of post transplant n n n n n n n n n n n n n n n n n n n malignancies Donor related Recurrence of pretransplant tumor De novo solid tumor De novo lymphoproliferative Skin Total patients at follow up *Data according to year of transplant of patient Table 3.4.3: Non-compliance at Follow-up up to 2014 of transplant* TOTAL Non-compliance during follow-up n n n n n n n n n n n n n n n n n n n Yes No TOTAL patients at follow-up *Data according to year of transplant of patient Table 3.4.4: Patient Treated for Rejection at Follow-up up to 2014 of transplant* TOTAL Patient treated for rejection n n n n n n n n n n n n n n n n n n n Yes No TOTAL patients at follow-up *Data according to year of transplant of patient Table 3.4.5a: Distribution of Patients by Time of Deaths, of discharge TOTAL Time of deaths* n n n n n n n n n n n n n n n n n n n <3 months (at discharge) <6 months months-1 year >1 year TOTAL patients who died *Time=Date of death date of transplant 8
9 National Transplant Registry 2014 HEART AND LUNG TRANSPLANTATION Table 3.4.6: Patient Survival, of Transplant Interval % Survival SE 1 year year year year Figure 3.4.6: Patient Survival,
10 HEART AND LUNG TRANSPLANTATION National Transplant Registry 2014 Table 3.4.7: Cause of Death at Discharge, TOTAL Cause of death n n n n n n n n n n n n n n n n n n n Hyperacute rejection Multi organ failure Respiratory failure secondary to septicaemia Respiratory failure, renal function and liver failure, ARDS, septicaemia Septicaemia, multiorgan failure Graft failure Severe Pneumonia TOTAL patients who died at discharge
11 National Transplant Registry 2014 HEART AND LUNG TRANSPLANTATION Table 3.4.8: Cause of Death at Follow-up, TOTAL Cause of death n n n n n n n n n n n n n n n n n n n Severe bleeding Lung cancer, small cell type bronchopneumonia Rejection due to non compliance Sudden death due most likely to graft CAD Unknown TOTAL patients who died at follow up
12 HEART AND LUNG TRANSPLANTATION National Transplant Registry 2014 LUNG TRANSPLANTATION Table 3.1.1b: Stock and Flow of Lung Transplantation, New Heart and Lung Transplant Patients New Lung Transplant Patients Deaths Alive at 31 st December Figure 3.1.1b Stock and Flow of Lung Transplant and Heart Lung Transplant 12
13 National Transplant Registry 2014 HEART AND LUNG TRANSPLANTATION 3.2 RECIPIENTS CHARACTERISTICS Table 3.2.1b: Distribution of Patients by Gender, TOTAL Gender n n n n n n n n n n n Male Female TOTAL Table 3.2.2b: Distribution of Patients by Ethnic Group, TOTAL Ethnic group n n n n n n n n n n n Malay Chinese Indian Bumiputra Sarawak TOTAL Table 3.2.3b: Distribution of Patients by Age, TOTAL Age, n n n n n n n n n n n years TOTAL Age=date of transplant-date of birth Table 3.2.4b: Distribution of Patients by Primary Diagnosis, TOTAL Primary diagnosis n n n n n n n n n n n Idiopathic pulmonary fibrosis Idiopathic pulmonary arterial hypertension Chronic obstructive pulmonary disease Bronchiectasis Others * TOTAL * ventricular septal defect (VSD) and Eisenmenger s syndrome 13
14 HEART AND LUNG TRANSPLANTATION National Transplant Registry TRANSPLANT PRACTICES Table 3.3.1b: Distribution of Patients by Thoracic Transplant Procedure, TOTAL Heart Procedure n n n n n n n n n n n Single lung transplant Double lung transplant Heart-Lung tranplant TOTAL Table 3.3.3b: Immunosuppressive Used at Time of Last Follow-up up to 2014 of follow up* Type of immunosuppressive n n n n n n n n n n Steroids: Prednisolone Methylprednisolone Calcineurin Inhibitors: Neoral FK506 (Tacrolimus) Antimetabolites: Mycophenolate Mofetil (MMF) TOTAL patients at followup *Data according to year of follow up of transplanted patients 3.4 TRANSPLANT OUTCOMES Table 3.4.5b: Distribution of Patients by Time of Deaths, of discharge TOTAL Time of deaths* n n n n n n n n n n n <3 months (at discharge) <6 months months-1 year >1 year TOTAL patients who died *Time=Date of death date of transplant 14
CHAPTER 3 HEART AND LUNG TRANSPLANTATION. Editors: Mr Mohamed Ezani Md. Taib Dato Dr David Chew Soon Ping Dr Ashari Yunus
CHAPTER 3 Editors: Mr Mohamed Ezani Md. Taib Dato Dr David Chew Soon Ping Dr Ashari Yunus Expert Panel: Mr Mohamed Ezani Md. Taib (Chairperson) Dr Abdul Rais Sanusi Datuk Dr Aizai Azan Abdul Rahim Dr Ashari
More informationCHAPTER 8. Editors: Dr Omar Sulaiman Dr Hooi Lai Seong
CHAPTER 8 DECEASED (CADAVERIC) ORGAN Editors: Dr Omar Sulaiman Dr Hooi Lai Seong Expert Panel: Dr Omar Sulaiman (Chairperson) Dr Hooi Lai Seong Dr Rosnawati Yahya Dato' Dr Sharifah Suraya Syed Mohd Tahir
More informationCHAPTER 8 DECEASED (CADAVERIC) ORGAN AND TISSUE DONATION. Editor: Datin Dr Fadilah Zowyah Lela Yasmin Mansor Dr Hooi Lai Seong
CHAPTER 8 DECEASED (CADAVERIC) ORGAN AND TISSUE DONATION Editor: Datin Dr Fadilah Zowyah Lela Yasmin Mansor Dr Hooi Lai Seong Expert Panel Datin Dr Fadilah Zowyah Lela Yasmin Mansor (Chairperson) Dr Hooi
More informationIndications and outcomes after UD HSCT
Indications and outcomes after UD HSCT Jakob R Passweg 1 Impact on Outcome: Patient Age, Disease Severity I II III Title: Optimization of Therapy for Severe AplasticAnemia Based on Clinical, Biological
More informationSupplementary Online Content
Supplementary Online Content Sellers MM, Keele LJ, Sharoky CE, Wirtalla C, Bailey EA, Kelz RR. Association of surgical practice patterns and clinical outcomes with surgeon training in university- or nonuniversity-based
More informationLecture 7 Time-dependent Covariates in Cox Regression
Lecture 7 Time-dependent Covariates in Cox Regression So far, we ve been considering the following Cox PH model: λ(t Z) = λ 0 (t) exp(β Z) = λ 0 (t) exp( β j Z j ) where β j is the parameter for the the
More informationSrdan Verstovsek, MD, PhD Professor, Department of Leukemia, The University of Texas MD Anderson Cancer Center
Efficacy and Safety of Pegylated Interferon Alpha- 2a in Patients with Essential Thrombocythemia (ET) and Polycythemia vera (PV): Results after a Median 7-year Follow-up of a Phase 2 Study Srdan Verstovsek,
More informationInitial Certification
Initial Certification Medical Physics Part 1 Content Guide Part 1 Content Guides and Sample Questions PLEASE NOTE: List of Constants and Physical Values for Use on the Part 1 Physics Exam The ABR provides
More informationDEATHS. In 2011, Florida resident deaths increased to 172,856. This is a 0.2 percent increase from 2010.
Deaths DEATHS In 2011, Florida resident deaths increased to 172,856. This is a 0.2 percent increase from 2010. The overall resident death rate decreased slightly from 9.2 in 2010 to 9.1 per 1,000 population
More informationJanuary NCCTG Protocol No: N0927 Opened: September 3, 2009
January 2011 0915-1 RTOG Protocol No: 0915 Protocol Status: NCCTG Protocol No: N0927 Opened: September 3, 2009 Title: A Randomized Phase II Study Comparing 2 Stereotactic Body Radiation Therapy (SBRT)
More informationAllogeneic Bone Marrow Transplant Crossword Puzzle on Discharge Education
Allogeneic Bone Marrow Transplant Crossword Puzzle on Discharge Education Across 1. If you have, it is important to inform their school that you must be notified of communicable diseases like measles,
More information. In 2009, Florida resident deaths decreased to 169,854. This is a 0.4 percent decrease from 2008.
Deaths DEATHS. In 2009, Florida resident deaths decreased to 169,854. This is a 0.4 percent decrease from 2008.. The overall resident death rate per 1,000 population decreased 1.1 percent from 9.1 per
More informationMultistate models in survival and event history analysis
Multistate models in survival and event history analysis Dorota M. Dabrowska UCLA November 8, 2011 Research supported by the grant R01 AI067943 from NIAID. The content is solely the responsibility of the
More informationDeep Temporal Generative Models of. Rahul Krishnan, Uri Shalit, David Sontag
Deep Temporal Generative Models of Rahul Krishnan, Uri Shalit, David Sontag Patient timeline Jan 1 Feb 12 May 15 Blood pressure = 130 WBC count = 6*10 9 /L Temperature = 98 F A1c = 6.6% Precancerous cells
More informationPhilip J Cimo DDS PA 650 West Bough Lane Ste #160 Houston TX
Philip J Cimo DDS PA 650 West Bough Lane Ste #160 Houston TX 770024 O: (713)464-1887 F: (713)461-0605 PATIENT INFORMATION Date: / / Patient Name: First MI Last Address: Date of Birth: Social Security #:
More informationMcGill University. Faculty of Science MATH 204 PRINCIPLES OF STATISTICS II. Final Examination
McGill University Faculty of Science MATH 204 PRINCIPLES OF STATISTICS II Final Examination Date: 23rd April 2007 Time: 2pm-5pm Examiner: Dr. David A. Stephens Associate Examiner: Dr. Russell Steele Please
More informationIndividualized Treatment Effects with Censored Data via Nonparametric Accelerated Failure Time Models
Individualized Treatment Effects with Censored Data via Nonparametric Accelerated Failure Time Models Nicholas C. Henderson Thomas A. Louis Gary Rosner Ravi Varadhan Johns Hopkins University July 31, 2018
More informationCHART D-1: RESIDENT DEATHS AND RATES PER 100,000 POPULATION, BY RACE AND GENDER, FLORIDA AND UNITED STATES, CENSUS YEARS AND
Deaths CHART D-1: RESIDENT DEATHS AND RATES PER 100,000 POPULATION, BY RACE AND GENDER, FLORIDA AND UNITED STATES, CENSUS YEARS 1970-2000 AND 2005-2015 WHITE BLACK YEAR TOTAL WHITE BLACK OTHER MALE FEMALE
More information2011/04 LEUKAEMIA IN WALES Welsh Cancer Intelligence and Surveillance Unit
2011/04 LEUKAEMIA IN WALES 1994-2008 Welsh Cancer Intelligence and Surveillance Unit Table of Contents 1 Definitions and Statistical Methods... 2 2 Results 7 2.1 Leukaemia....... 7 2.2 Acute Lymphoblastic
More informationBIOL 51A - Biostatistics 1 1. Lecture 1: Intro to Biostatistics. Smoking: hazardous? FEV (l) Smoke
BIOL 51A - Biostatistics 1 1 Lecture 1: Intro to Biostatistics Smoking: hazardous? FEV (l) 1 2 3 4 5 No Yes Smoke BIOL 51A - Biostatistics 1 2 Box Plot a.k.a box-and-whisker diagram or candlestick chart
More informationCritical Illness Cover
Critical Illness Cover Competitor Comparison This is not a consumer advertisement and should not be relied upon by private investors or any other persons for making financial decisions. Condition CI Extra
More informationEstimating Causal Effects of Organ Transplantation Treatment Regimes
Estimating Causal Effects of Organ Transplantation Treatment Regimes David M. Vock, Jeffrey A. Verdoliva Boatman Division of Biostatistics University of Minnesota July 31, 2018 1 / 27 Hot off the Press
More informationADVANCED STATISTICAL ANALYSIS OF EPIDEMIOLOGICAL STUDIES. Cox s regression analysis Time dependent explanatory variables
ADVANCED STATISTICAL ANALYSIS OF EPIDEMIOLOGICAL STUDIES Cox s regression analysis Time dependent explanatory variables Henrik Ravn Bandim Health Project, Statens Serum Institut 4 November 2011 1 / 53
More informationJOINT STRATEGIC NEEDS ASSESSMENT (JSNA) Key findings from the Leicestershire JSNA and Charnwood summary
JOINT STRATEGIC NEEDS ASSESSMENT (JSNA) Key findings from the Leicestershire JSNA and Charnwood summary 1 What is a JSNA? Joint Strategic Needs Assessment (JSNA) identifies the big picture in terms of
More informationREGRESSION ANALYSIS FOR TIME-TO-EVENT DATA THE PROPORTIONAL HAZARDS (COX) MODEL ST520
REGRESSION ANALYSIS FOR TIME-TO-EVENT DATA THE PROPORTIONAL HAZARDS (COX) MODEL ST520 Department of Statistics North Carolina State University Presented by: Butch Tsiatis, Department of Statistics, NCSU
More informationSupplementary Material
1 ORIGINAL RESEARCH ARTICLE Pharmacoeconomics 2008; 26 (1): Supplementary Material 1170-7690/08/001-0001/$48.00/0 2008 Adis Data Information BV. All rights reserved. Economic Burden of Bilateral Neovascular
More informationSection IX. Introduction to Logistic Regression for binary outcomes. Poisson regression
Section IX Introduction to Logistic Regression for binary outcomes Poisson regression 0 Sec 9 - Logistic regression In linear regression, we studied models where Y is a continuous variable. What about
More informationLIST OF TABLES END STAGE RENAL DISEASE NETWORK 8, INC.
LIST OF TABLES Table 1. ESRD Incidence... 75 Table 2. ESRD Dialysis Prevalence... 796 Table 3. Dialysis Modality - In Home... 80 Table 4. Dialysis Modality - In Center...95 Table 5. Renal Transplant by
More informationAnalysis of Longitudinal Data. Patrick J. Heagerty PhD Department of Biostatistics University of Washington
Analysis of Longitudinal Data Patrick J Heagerty PhD Department of Biostatistics University of Washington Auckland 8 Session One Outline Examples of longitudinal data Scientific motivation Opportunities
More informationMASSACHUSETTS INSTITUTE OF TECHNOLOGY
Harvard-MIT Division of Health Sciences and Technology HST.54J: Quantitative Physiology: Organ Transport Systems Instructors: Roger Mark and Jose Venegas MASSACHUSETTS INSTITUTE OF TECHNOLOGY Departments
More informationIntroduction to Inferential Statistics. Jaranit Kaewkungwal, Ph.D. Faculty of Tropical Medicine Mahidol University
Introduction to Inferential Statistics Jaranit Kaewkungwal, Ph.D. Faculty of Tropical Medicine Mahidol University 1 Data & Variables 2 Types of Data QUALITATIVE Data expressed by type Data that has been
More informationMulti-state Models: An Overview
Multi-state Models: An Overview Andrew Titman Lancaster University 14 April 2016 Overview Introduction to multi-state modelling Examples of applications Continuously observed processes Intermittently observed
More informationPh.D. course: Regression models. Introduction. 19 April 2012
Ph.D. course: Regression models Introduction PKA & LTS Sect. 1.1, 1.2, 1.4 19 April 2012 www.biostat.ku.dk/~pka/regrmodels12 Per Kragh Andersen 1 Regression models The distribution of one outcome variable
More informationApproach to identifying hot spots for NCDs in South Africa
Approach to identifying hot spots for NCDs in South Africa HST Conference 6 May 2016 Noluthando Ndlovu, 1 Candy Day, 1 Benn Sartorius, 2 Karen Hofman, 3 Jens Aagaard-Hansen 3,4 1 Health Systems Trust,
More informationStudy No: Title : Rationale: Phase: Study Period: Study Design: Centres: Indication: Treatment: Objectives: Statistical Methods:
The study listed may include approved and non-approved uses, formulations or treatment regimens. The results reported in any single study may not reflect the overall results obtained on studies of a product.
More informationDefinitions and examples Simple estimation and testing Regression models Goodness of fit for the Cox model. Recap of Part 1. Per Kragh Andersen
Recap of Part 1 Per Kragh Andersen Section of Biostatistics, University of Copenhagen DSBS Course Survival Analysis in Clinical Trials January 2018 1 / 65 Overview Definitions and examples Simple estimation
More informationPh.D. course: Regression models. Regression models. Explanatory variables. Example 1.1: Body mass index and vitamin D status
Ph.D. course: Regression models Introduction PKA & LTS Sect. 1.1, 1.2, 1.4 25 April 2013 www.biostat.ku.dk/~pka/regrmodels13 Per Kragh Andersen Regression models The distribution of one outcome variable
More informationAlternative Approaches to Thoracoscopic Lobectomy: Uniportal, Supxiphoid,
Alternative Approaches to Thoracoscopic Lobectomy: Uniportal, Supxiphoid, Thomas A. D Amico MD Gary Hock Endowed Professor Chief Thoracic Surgery Chief Medical Officer, Duke Cancer Institute Disclosures
More informationBayesian Nonparametric Accelerated Failure Time Models for Analyzing Heterogeneous Treatment Effects
Bayesian Nonparametric Accelerated Failure Time Models for Analyzing Heterogeneous Treatment Effects Nicholas C. Henderson Thomas A. Louis Gary Rosner Ravi Varadhan Johns Hopkins University September 28,
More informationYear 8: Living World- Functioning Organisms
Year 8: Living World- Functioning Organisms Revise assumed knowledge: ST3-10LW describes how structural features and other adaptations of living things help them to survive in their environment Check Date
More informationYou know I m not goin diss you on the internet Cause my mama taught me better than that I m a survivor (What?) I m not goin give up (What?
You know I m not goin diss you on the internet Cause my mama taught me better than that I m a survivor (What?) I m not goin give up (What?) I m not goin stop (What?) I m goin work harder (What?) Sir David
More informationCASE REPORT FORM (April 2012)
CASE REPORT FORM (April 2012) Surveillance of Paediatric Dengue National Paediatric Hospital, Phnom Penh Kingdom of Cambodia Study contact: I am confident that the information supplied in this case record
More informationCOOK ISLANDS TE MARAE ORA
COOK ISLANDS MINISTRY OF HEALTH TE MARAE ORA ANNUAL STATISTICAL TABLES HEALTH STATISTICAL TABLES 2008-2010 MEDICAL RECORDS UNIT Rarotonga Hospital It should be noted that information contained in this
More informationPart III Measures of Classification Accuracy for the Prediction of Survival Times
Part III Measures of Classification Accuracy for the Prediction of Survival Times Patrick J Heagerty PhD Department of Biostatistics University of Washington 102 ISCB 2010 Session Three Outline Examples
More informationMulti-state models: prediction
Department of Medical Statistics and Bioinformatics Leiden University Medical Center Course on advanced survival analysis, Copenhagen Outline Prediction Theory Aalen-Johansen Computational aspects Applications
More informationInternational Journal of Scientific & Engineering Research, Volume 5, Issue 4, April ISSN
International Journal of Scientific & Engineering Research, Volume 5, Issue 4, April2014 1474 Pulmonary Function Test in Normal Healthy School Children Kundan Mittal, Tanu Satija, Jyoti Yadav, K B Gupta,
More informationSTAC51: Categorical data Analysis
STAC51: Categorical data Analysis Mahinda Samarakoon January 26, 2016 Mahinda Samarakoon STAC51: Categorical data Analysis 1 / 32 Table of contents Contingency Tables 1 Contingency Tables Mahinda Samarakoon
More informationLynsi Rahorst, MT(ASCP) IRL Staff Technologist II. Everything But the Kitchen Sink!
Lynsi Rahorst, MT(ASCP) IRL Staff Technologist II Everything But the Kitchen Sink! 49 year old African American male with sickle cell anemia Requests for units Hospital A 6/03/2009-2 E-, Jk(b-) units 6/08/2009-2
More informationA homo-dimer of annexin V protects against ischemia reperfusion injury in lung transplantation
A homo-dimer of annexin V protects against ischemia reperfusion injury in lung transplantation K Hashimoto, H Kim, H Oishi, M Chen, I Iskender, J Sakamoto, A Ohsumi, Z Guan, DM Hwang, TK Waddell, M Cypel,
More informationDISCRETE PROBABILITY DISTRIBUTIONS
DISCRETE PROBABILITY DISTRIBUTIONS REVIEW OF KEY CONCEPTS SECTION 41 Random Variable A random variable X is a numerically valued quantity that takes on specific values with different probabilities The
More informationChronic Granulomatous Disease Medical Management
Chronic Granulomatous Disease Medical Management N I C H O L A S H A R T O G, M D D i r e c t o r o f P e d i a t r i c / A d u l t P r i m a r y I m m u n o d e f i c i e n c y C l i n i c A s s i s t
More informationChapter 4 Fall Notations: t 1 < t 2 < < t D, D unique death times. d j = # deaths at t j = n. Y j = # at risk /alive at t j = n
Bios 323: Applied Survival Analysis Qingxia (Cindy) Chen Chapter 4 Fall 2012 4.2 Estimators of the survival and cumulative hazard functions for RC data Suppose X is a continuous random failure time with
More informationLog-linearity for Cox s regression model. Thesis for the Degree Master of Science
Log-linearity for Cox s regression model Thesis for the Degree Master of Science Zaki Amini Master s Thesis, Spring 2015 i Abstract Cox s regression model is one of the most applied methods in medical
More informationSTAT 526 Spring Final Exam. Thursday May 5, 2011
STAT 526 Spring 2011 Final Exam Thursday May 5, 2011 Time: 2 hours Name (please print): Show all your work and calculations. Partial credit will be given for work that is partially correct. Points will
More informationYear 8: Living World- Functioning Organisms.
Year 8: Living World- Functioning Organisms. Revise assumed knowledge: ST3-10LW describes how structural features and other adaptations of living things help them to survive in their environment Check
More informationAuxiliary-variable-enriched Biomarker Stratified Design
Auxiliary-variable-enriched Biomarker Stratified Design Ting Wang University of North Carolina at Chapel Hill tingwang@live.unc.edu 8th May, 2017 A joint work with Xiaofei Wang, Haibo Zhou, Jianwen Cai
More informationHarvard-MIT Division of Health Sciences and Technology HST.952: Computing for Biomedical Scientists. Data and Knowledge Representation Lecture 2
Harvard-MIT Division of Health Sciences and Technology HST.952: Computing for Biomedical Scientists Data and Knowledge Representation Lecture 2 Last Time We Talked About Why is knowledge/data representation
More informationUse of frequentist and Bayesian approaches for extrapolating from adult efficacy data to design and interpret confirmatory trials in children
Use of frequentist and Bayesian approaches for extrapolating from adult efficacy data to design and interpret confirmatory trials in children Lisa Hampson, Franz Koenig and Martin Posch Department of Mathematics
More informationPractical APLIS-based Structured/Synoptic Reporting
Practical APLIS-based Structured/Synoptic Reporting Friday, August 18, 2006 David L.Booker, MD Anil Parwani, MD., PhD Synoptic Reporting Workshop: Goals In this workshop, we will describe our experience
More informationSurvival Analysis. 732G34 Statistisk analys av komplexa data. Krzysztof Bartoszek
Survival Analysis 732G34 Statistisk analys av komplexa data Krzysztof Bartoszek (krzysztof.bartoszek@liu.se) 10, 11 I 2018 Department of Computer and Information Science Linköping University Survival analysis
More informationSEYCHELLES NATIONAL CANCER REGISTRY REPORT FOR
SEYCHELLES NATIONAL CANCER REGISTRY REPORT FOR 2009-2011 MINISTRY OF HEALTH, SEYCHELLES HOSPITAL, JANUARY 2014 Compiled by: Dr Maxwell, Donald Parkin (AFRN Consultant & Epidemiologist) Ms Anne Finesse
More informationMAS3301 / MAS8311 Biostatistics Part II: Survival
MAS3301 / MAS8311 Biostatistics Part II: Survival M. Farrow School of Mathematics and Statistics Newcastle University Semester 2, 2009-10 1 13 The Cox proportional hazards model 13.1 Introduction In the
More informationSample Size and Power I: Binary Outcomes. James Ware, PhD Harvard School of Public Health Boston, MA
Sample Size and Power I: Binary Outcomes James Ware, PhD Harvard School of Public Health Boston, MA Sample Size and Power Principles: Sample size calculations are an essential part of study design Consider
More informationNew Immigrant Survey Section D - Health
Section D: Health D1 {CP, IM, SP - Next I have some questions about your health. Would} {OS Would} you say your health is excellent, very good, good, fair, or poor? 1. EXCELLENT [D2; OS: E1a] 2. VERY GOOD
More informationJOINT MODELING OF MULTIVARIATE LONGITUDINAL DATA AND COMPETING RISKS DATA JEEVANANTHAM RAJESWARAN
JOINT MODELING OF MULTIVARIATE LONGITUDINAL DATA AND COMPETING RISKS DATA by JEEVANANTHAM RAJESWARAN Submitted in partial fulfillment of the requirements For the degree of Doctor of Philosophy Dissertation
More informationData Analysis and Statistical Methods Statistics 651
Data Analysis and Statistical Methods Statistics 651 http://www.stat.tamu.edu/~suhasini/teaching.html Lecture 6 (MWF) Conditional probabilities and associations Suhasini Subba Rao Review of previous lecture
More informationGIS and Health Geography. What is epidemiology?
GIS and Health Geography { What is epidemiology? TOC GIS and health geography Major applications for GIS Epidemiology What is health (and how location matters) What is a disease (and how to identify one)
More informationTime-dependent covariates
Time-dependent covariates Rasmus Waagepetersen November 5, 2018 1 / 10 Time-dependent covariates Our excursion into the realm of counting process and martingales showed that it poses no problems to introduce
More informationOnline supplement. Absolute Value of Lung Function (FEV 1 or FVC) Explains the Sex Difference in. Breathlessness in the General Population
Online supplement Absolute Value of Lung Function (FEV 1 or FVC) Explains the Sex Difference in Breathlessness in the General Population Table S1. Comparison between patients who were excluded or included
More informationNCCTG Status Report for Study N May 2010
Phase I/II Trial of Imatinib Mesylate; (Gleevec; STI-571) in Treatment of Recurrent Oligodendroglioma and Mixed Oligoastrocytoma Purpose of - Objectives Study: 1) Study 1: To identify the MTD of imatinib
More informationLung Exchange. November 23, Abstract
Lung Exchange Haluk Ergin Tayfun Sönmez M. Utku Ünver November 23, 2015 Abstract Owing to the worldwide shortage of deceased donor organs for transplantation, tissue/organ donations from living donors
More informationInterplay Between S-adenosylmethionine, Folate, Cobalamin, and Arsenic Methylation in Bangladesh
Interplay Between S-adenosylmethionine, Folate, Cobalamin, and Arsenic Methylation in Bangladesh Caitlin Howe Columbia University, Environmental Health Sciences Dr. Mary Gamble s Lab Arsenic Exposure in
More informationCompare Predicted Counts between Groups of Zero Truncated Poisson Regression Model based on Recycled Predictions Method
Compare Predicted Counts between Groups of Zero Truncated Poisson Regression Model based on Recycled Predictions Method Yan Wang 1, Michael Ong 2, Honghu Liu 1,2,3 1 Department of Biostatistics, UCLA School
More informationModular Program Report
Modular Program Report Disclaimer The following report(s) provides findings from an FDA initiated query using Sentinel. While Sentinel queries may be undertaken to assess potential medical product safety
More informationSupplementary webappendix
Supplementary webappendix This webappendix formed part of the original submission and has been peer reviewed. We post it as supplied by the authors. Supplement to: Elter T, Gercheva-Kyuchukova L, Pylylpenko
More information4. Comparison of Two (K) Samples
4. Comparison of Two (K) Samples K=2 Problem: compare the survival distributions between two groups. E: comparing treatments on patients with a particular disease. Z: Treatment indicator, i.e. Z = 1 for
More informationUsing rjags for survival data with right censoring
Using rjags for survival data with right censoring Malcolm Farrow Newcastle University October 4, 2016 1 Method using data augmentation Here is a simple example of a model specification for a survival
More informationProbability and Probability Distributions. Dr. Mohammed Alahmed
Probability and Probability Distributions 1 Probability and Probability Distributions Usually we want to do more with data than just describing them! We might want to test certain specific inferences about
More informationTreatment Intake Form
Sally Valentine, PhD, LCSW 1 W. Camino Real, Suite 202, Boca Raton, FL 33432 drsallyvalentine@me.com 561.391.3305 Treatment Intake Form Please complete all information on this form and bring it to your
More informationPhD course: Statistical evaluation of diagnostic and predictive models
PhD course: Statistical evaluation of diagnostic and predictive models Tianxi Cai (Harvard University, Boston) Paul Blanche (University of Copenhagen) Thomas Alexander Gerds (University of Copenhagen)
More informationChapter 7 Fall Chapter 7 Hypothesis testing Hypotheses of interest: (A) 1-sample
Bios 323: Applied Survival Analysis Qingxia (Cindy) Chen Chapter 7 Fall 2012 Chapter 7 Hypothesis testing Hypotheses of interest: (A) 1-sample H 0 : S(t) = S 0 (t), where S 0 ( ) is known survival function,
More informationPhone number: When and how did your pain begin? (a date is required for Medicare and some insurance policies) Date of onset:
C H I R O P R A C T I C O R T H O P E D I C S A N D R E H A B I L I T A T I O N ILJXAi Pain Relief Clinics Please complete all sections. Full Name: Nickname: Gender: M F Age: Race: Date of Birth: I I Family
More informationTextbook: Survivial Analysis Techniques for Censored and Truncated Data 2nd edition, by Klein and Moeschberger
Lecturer: James Degnan Office: SMLC 342 Office hours: MW 12:00 1:00 or by appointment E-mail: jamdeg@unm.edu Please include STAT474 or STAT574 in the subject line of the email to make sure I don t overlook
More informationStatistics in medicine
Statistics in medicine Lecture 4: and multivariable regression Fatma Shebl, MD, MS, MPH, PhD Assistant Professor Chronic Disease Epidemiology Department Yale School of Public Health Fatma.shebl@yale.edu
More informationLow-Income African American Women's Perceptions of Primary Care Physician Weight Loss Counseling: A Positive Deviance Study
Thomas Jefferson University Jefferson Digital Commons Master of Public Health Thesis and Capstone Presentations Jefferson College of Population Health 6-25-2015 Low-Income African American Women's Perceptions
More informationEnrollment of Students with Disabilities
Enrollment of Students with Disabilities State legislation, which requires the Board of Higher Education to monitor the participation of specific groups of individuals in public colleges and universities,
More informationChapter Six: Two Independent Samples Methods 1/51
Chapter Six: Two Independent Samples Methods 1/51 6.3 Methods Related To Differences Between Proportions 2/51 Test For A Difference Between Proportions:Introduction Suppose a sampling distribution were
More informationSurvival Analysis with Time- Dependent Covariates: A Practical Example. October 28, 2016 SAS Health Users Group Maria Eberg
Survival Analysis with Time- Dependent Covariates: A Practical Example October 28, 2016 SAS Health Users Group Maria Eberg Outline Why use time-dependent covariates? Things to consider in definition of
More informationBias in Markov Models of Disease
Bias in Markov Models of Disease Daniel M. Faissol, Paul M. Griffin, and Julie L. Swann Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, GA 30332-0205 Phone:
More informationApply Grey Relational Grade And Rough Set Theory for The Factor Weighting Analysis in Liver Function
Apply Grey Relational Grade And Rough Set Theory for The Factor Weighting Analysis in Liver Function Kun-Li Wen, Mei-Li You, 3 Bih-Yun Lee *,Corresponding Author Department of Electrical Engineering, Chienkuo
More informationDistributed analysis in multi-center studies
Distributed analysis in multi-center studies Sharing of individual-level data across health plans or healthcare delivery systems continues to be challenging due to concerns about loss of patient privacy,
More informationJefferies Gene Editing/Therapy Summit Matthew Kapusta, Interim Chief Executive Officer OCTOBER 11, 2016
Jefferies Gene Editing/Therapy Summit Matthew Kapusta, Interim Chief Executive Officer OCTOBER 11, 2016 This presentation contains forward-looking statements. All statements other than statements of historical
More informationPractice problems from chapters 2 and 3
Practice problems from chapters and 3 Question-1. For each of the following variables, indicate whether it is quantitative or qualitative and specify which of the four levels of measurement (nominal, ordinal,
More informationMRI of the airways and lungs Including hyperpolarized techniques
MRI of the airways and lungs Including hyperpolarized techniques Pulmonary Medicine Jason C. Woods Radiology Neonatology Disclosure statements: Financial relationships: Dr. Woods is a consultant to and
More informationWelfare and Equity Consequences of Transplant Organ Allocation Policies
Welfare and Equity Consequences of Transplant Organ Allocation Policies Tayfun Sönmez Department of Economics, Boston College Distinguished Research Fellow, Koç University M. Utku Ünver Department of Economics,
More informationCARDIAC METASTASIS MASQUERADE AS STEMI D R S R E E K A N T H K O D U R
CARDIAC METASTASIS MASQUERADE AS STEMI D R S R E E K A N T H K O D U R MR OR, 68 YRS Smoker No prior cardiac hx Lives near Muswellbrook area Called ambulance 3 am Atypical chest pain Life net ecg transmitted
More informationMixture Models for Capture- Recapture Data
Mixture Models for Capture- Recapture Data Dankmar Böhning Invited Lecture at Mixture Models between Theory and Applications Rome, September 13, 2002 How many cases n in a population? Registry identifies
More informationConstrained Maximum Likelihood Estimation for Model Calibration Using Summary-level Information from External Big Data Sources
Constrained Maximum Likelihood Estimation for Model Calibration Using Summary-level Information from External Big Data Sources Yi-Hau Chen Institute of Statistical Science, Academia Sinica Joint with Nilanjan
More informationThe Design of a Survival Study
The Design of a Survival Study The design of survival studies are usually based on the logrank test, and sometimes assumes the exponential distribution. As in standard designs, the power depends on The
More informationRobust estimates of state occupancy and transition probabilities for Non-Markov multi-state models
Robust estimates of state occupancy and transition probabilities for Non-Markov multi-state models 26 March 2014 Overview Continuously observed data Three-state illness-death General robust estimator Interval
More information