CAPHIA WORKSHOP Perth Friday 19 September 2014 Ensuring competency in epidemiology and biostatistics among Master of Public Health graduates: teaching.

Slides:



Advertisements
Similar presentations
Dr Eva Batistatou. Outline of this presentation… What is epidemiology? The Fundamentals of Epidemiology course What is biostatistics? The Biostatistics.
Advertisements

Chapter 9: Simple Regression Continued
Lecture (11,12) Parameter Estimation of PDF and Fitting a Distribution Function.
School of Public Health and Community Medicine Ensuring competency attainment in Epidemiology and Biostatistics among MPH students Glenda Lawrence – UNSW.
July 1, 2008Lecture 17 - Regression Testing1 Testing Relationships between Variables Statistics Lecture 17.
EPIDEMIOLOGY AND BIOSTATISTICS DEPT Esimating Population Value with Hypothesis Testing.
Lecture 23: Tues., Dec. 2 Today: Thursday:
Basic Elements of Testing Hypothesis Dr. M. H. Rahbar Professor of Biostatistics Department of Epidemiology Director, Data Coordinating Center College.
BCOR 1020 Business Statistics Lecture 28 – May 1, 2008.
The Simple Regression Model
4-1 Statistical Inference The field of statistical inference consists of those methods used to make decisions or draw conclusions about a population.
Lecture 16 – Thurs, Oct. 30 Inference for Regression (Sections ): –Hypothesis Tests and Confidence Intervals for Intercept and Slope –Confidence.
Today Concepts underlying inferential statistics
Developing a Profession Specific Statistics Course for Nurses 1 Jane Oppenlander School of Management The Bioethics Program Union Graduate College eCOTS,
Survival analysis Brian Healy, PhD. Previous classes Regression Regression –Linear regression –Multiple regression –Logistic regression.
Chapter 9 Two-Sample Tests Part II: Introduction to Hypothesis Testing Renee R. Ha, Ph.D. James C. Ha, Ph.D Integrative Statistics for the Social & Behavioral.
Chapter 13: Inference in Regression
LEARNING PROGRAMME Hypothesis testing Intermediate Training in Quantitative Analysis Bangkok November 2007.
Overall agenda Part 1 and 2  Part 1: Basic statistical concepts and descriptive statistics summarizing and visualising data describing data -measures.
Biostatistics course Part 16 Lineal regression Dr. Sc. Nicolas Padilla Raygoza Department of Nursing and Obstetrics Division Health Sciences and Engineering.
T-Tests and Chi2 Does your sample data reflect the population from which it is drawn from?
Estimation of Various Population Parameters Point Estimation and Confidence Intervals Dr. M. H. Rahbar Professor of Biostatistics Department of Epidemiology.
Service Teaching, a Drop-in Help Centre and Statistics CDC Steele University of Manchester.
F OUNDATIONS OF S TATISTICAL I NFERENCE. D EFINITIONS Statistical inference is the process of reaching conclusions about characteristics of an entire.
Understanding Inferential Statistics—Estimation
1 - 1 Module 1: Introduction This module describes the purpose of the course and the general approach followed for addressing this purpose. Reviewed 15.
How to Teach Statistics in EBM Rafael Perera. Basic teaching advice Know your audience Know your audience! Create a knowledge gap Give a map of the main.
CHAPTER 14 MULTIPLE REGRESSION
● Final exam Wednesday, 6/10, 11:30-2:30. ● Bring your own blue books ● Closed book. Calculators and 2-page cheat sheet allowed. No cell phone/computer.
Marshall University School of Medicine Department of Biochemistry and Microbiology BMS 617 Lecture 8 – Comparing Proportions Marshall University Genomics.
Statistics for clinicians Biostatistics course by Kevin E. Kip, Ph.D., FAHA Professor and Executive Director, Research Center University of South Florida,
Various topics Petter Mostad Overview Epidemiology Study types / data types Econometrics Time series data More about sampling –Estimation.
An Interdisciplinary Approach in Statistics Courses for Biology Students Ramon Gomez Senior Instructor Dept. of Math & Statistics Florida International.
Inference for Regression Simple Linear Regression IPS Chapter 10.1 © 2009 W.H. Freeman and Company.
Contingency tables Brian Healy, PhD. Types of analysis-independent samples OutcomeExplanatoryAnalysis ContinuousDichotomous t-test, Wilcoxon test ContinuousCategorical.
Sampling Distribution Tripthi M. Mathew, MD, MPH.
CHAPTER 11 SECTION 2 Inference for Relationships.
Introduction to sample size and power calculations Afshin Ostovar Bushehr University of Medical Sciences.
STAT 3130 Statistical Methods I Lecture 1 Introduction.
STATISTICAL ANALYSIS FOR THE MATHEMATICALLY-CHALLENGED Associate Professor Phua Kai Lit School of Medicine & Health Sciences Monash University (Sunway.
Foundations of Sociological Inquiry Statistical Analysis.
Principles of Biostatistics ANOVA. DietWeight Gain (grams) Standard910 8 Junk Food Organic Table shows weight gains for mice on 3 diets.
Statistical planning and Sample size determination.
Inferential Statistics. The Logic of Inferential Statistics Makes inferences about a population from a sample Makes inferences about a population from.
Review Lecture 51 Tue, Dec 13, Chapter 1 Sections 1.1 – 1.4. Sections 1.1 – 1.4. Be familiar with the language and principles of hypothesis testing.
Lecture & tutorial material prepared by: Dr. Shaffi Shaikh Tutorial presented by: Dr. Rufaidah Dabbagh Dr. Nurah Al-Amro.
Introduction to the t Test Part 1: One-sample t test
Basics of Biostatistics for Health Research Session 1 – February 7 th, 2013 Dr. Scott Patten, Professor of Epidemiology Department of Community Health.
June 30, 2008Stat Lecture 16 - Regression1 Inference for relationships between variables Statistics Lecture 16.
Course Outline Presentation Reference Course Outline for MTS-202 (Statistical Inference) Fall-2009 Dated: 27 th August 2009 Course Supervisor(s): Mr. Ahmed.
Marshall University School of Medicine Department of Biochemistry and Microbiology BMS 617 Lecture 11: Models Marshall University Genomics Core Facility.
Sample Size Determination
Logistic Regression An Introduction. Uses Designed for survival analysis- binary response For predicting a chance, probability, proportion or percentage.
Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 10 Comparing Two Groups Section 10.3 Other Ways of Comparing Means and Comparing Proportions.
TEACHING STATISTICS ONLINE Dr Alison Bentley Research Coordinator School of Clinical Medicine Faculty of Health Sciences.
AP Statistics Friday, 01 April 2016 OBJECTIVE TSW review for the test covering two- sample inference. TEST: Two-Sample Inference is on Monday, 04 April.
Statistics and probability Dr. Khaled Ismael Almghari Phone No:
The 2 nd to last topic this year!!.  ANOVA Testing is similar to a “two sample t- test except” that it compares more than two samples to one another.
Marshall University School of Medicine Department of Biochemistry and Microbiology BMS 617 Lecture 10: Comparing Models.
Marshall University School of Medicine Department of Biochemistry and Microbiology BMS 617 Lecture 16 : Summary Marshall University Genomics Core Facility.
Data analysis and basic statistics KSU Fellowship in Clinical Pathology Clinical Biochemistry Unit
Criteria Rollout Meeting October 30, 2016
Criteria Rollout Meeting October 30, 2016
Computer aided teaching of statistics: advantages and disadvantages
Applied Biostatistics: Lecture 4
REGRESSION G&W p
Applied Biostatistics: Lecture 2
Data analysis and basic statistics
ADVANCED DATA ANALYSIS IN SPSS AND AMOS
First Semester Final Exam
Presentation transcript:

CAPHIA WORKSHOP Perth Friday 19 September 2014 Ensuring competency in epidemiology and biostatistics among Master of Public Health graduates: teaching and learning approaches, dilemmas and needs The core introductory biostatistics unit in the UWA MPH degree – what’s in the unit and what are the challenges and issues Prof Matthew Knuiman APPLIED DATA FACULTY OF MEDICINE, DENTISTRY AND HEALTH SCIENCES SCHOOL OF POPULATION HEALTH

The University of Western Australia UWA PUBH4401 (Biostatistics I) This is a core (or mandatory) unit in the MPH by coursework (with or without a research project or dissertation) Other UWA postgraduate degrees (eg Master of Aboriginal Health, Master of Clinical Research, Master of Surgery, Doctor of Clinical Dentistry, Doctor of Clinical Podiatry, Doctor of Podiatric Medicine) and is also taken (with or without coercion) by many Masters by Research and PhD candidates. The unit is available in face-to-face mode (lectures, tutorials, comprehensive materials) and in on-line mode (recorded lectures, online discussion boards, comprehensive materials)

The University of Western Australia What are the learning objectives/aims?  Appreciate the role of statistics in health and medical practice and research.  Understand the statistical content of articles in general health and medical literature.  Know how to summarise and present data.  Understand statistical inference (sample to population) through confidence intervals and hypothesis testing.  Be able to apply statistical methods for comparison of means, proportions, incidence rates and survival curves, and know when to apply specific methods.  Understand and apply correlation coefficients and linear regression.  Be able to sensibly use the SPSS Statistics package. Assessment by home assignments, on-line MCQ tests, final written exam

The University of Western Australia What is the assumed prior knowledge and experience?  Basic (Year 10 high school) algebra to understand equations and formulae.  Familiarity with hand-held calculators. You must have your own calculator and know how to use it.  Familiarity with computing in a Windows environment.  No prior experience with SPSS is assumed.

The University of Western Australia What is the content? Summarising and presenting data Descriptive statistics, tables, charts Basics of statistical inferenceSampling distribution, 95% confidence interval estimate, hypothesis test and p-value Estimation and 95% confidence interval For a mean, proportion (prevalence, risk), rate, survival curve Comparing two groups (test and confidence interval) Comparing two means (t-test), proportions (chi- square test, odds ratio), rates (rate ratio), survival curves (logrank test) Correlation and linear regression Correlation coefficient, simple linear regression Sample size for estimationFormula to get required confidence interval width Sample size for testingPower calculations via free online program Review of published article2 articles reviewed in class (focus on statistical methods and results) Most calculations are done via SPSS (using pull down menus) but some also by hand-calculation. All data sets and examples are real.

The University of Western Australia Challenges and issues Some have math phobia – not confident with math notation and year 10 high school algebra eg ln x, e x and how to calculate (0.4 – 0.2)/[0.03/√100]. Some think not relevant to intended career (but statistical literacy is very important even if will not analyse their own data in future). Many enjoy the face-to-face discussions on review of published articles. Some rely on SPSS to ‘think’ for them instead of just doing their calculations (and trust all computer output is sensible!). The assessment requires ‘explanation and interpretation’ as well as correct formula and calculation/output, some have difficulty with constructing sensible sentences and don’t like losing marks because of this. Harder to answer questions and help students in online (discussion board) environment than in face-to-face situation. Cheating and collusion on home assignments is prevalent.