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Teaching Statistics By Example Lisa M Sullivan, PhD Boston University.

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Presentation on theme: "Teaching Statistics By Example Lisa M Sullivan, PhD Boston University."— Presentation transcript:

1 Teaching Statistics By Example Lisa M Sullivan, PhD Boston University

2 Outline  Statistics in the News  The Introductory Statistics Requirement  Course Content  Recent Efforts at Reform  Modifications?  Examples for Specific Topics

3 News – Week of May 29, 2006  Preserve brain function with spicy foods. Kicking your food up a notch with spices could preserve brain function and keep your brain sharp and strong as you age. Turmeric, a spice that lends curries their yellow tint, can curb mental decline and even slow the effects of neurodegenerative diseases such as Alzheimer’s.

4 News – Week of May 29, 2006  Education linked to better fathers study U.S. data shows trend between education and time spent with kids Well-educated men tend to make better fathers, according a new U.S. government report on fatherhood.

5 News – Week of May 29, 2006  U.S. Releases Bird Flu Response Plan - Details outline containment policies; focus on worst-case scenarios may spread fear, experts say  Breast Cancer Survivors Lax About Mammograms - Only 33% get them annually for five years after diagnosis, study finds

6 News – Week of May 29, 2006  The FDA issued a long-awaited approval on a new human growth product. Approval of Omnitrope, made by Sandoz, was announced Tuesday in a statement on the FDA’s Web site. Omnitrope, also known as somatropin, is a hormone used to treat growth disorders in children and adults.

7 Statistics Requirement  Numerical literacy  Provide quantitative foundations for study in specific disciplines  Understand and interpret data  Perform independent research

8 Careers in Statistics  Business and Industry (Manufacturing, Marketing, Engineering)  Health and Medicine (Public Health, Clinical Trials, Epidemiology, Genetics, Health Communication)  Government (Census, Surveys)  Academia  Social Sciences  Health Insurance Demand for statisticians far exceeds supply today and this is expected to increase through 2008.

9 The Introductory Statistics Course  Difficult and frustrating for students  Difficult and frustrating for instructors  Hundreds of thousands of undergraduates across a variety of majors are required to take statistics - most not mathematically inclined

10 Typical Course Content  Descriptive Statistics Classification of Variables Means, Standard Deviations, Medians Graphical Displays  Principles of Probability  Probability Models Binomial, Poisson, Normal  Central Limit Theorem

11 Course Content  Estimation Point Estimates Margin of Error Precision  Hypothesis Testing Hypotheses Test Statistic Critical Region Level of Significance, P-values

12 Course Content  Associations Between Variables Regression Analysis Analysis of Variance Chi-Square Tests

13 Reforms in Undergraduate Education in Statistics  NSF funded various projects to improve teaching of undergraduate statistics courses  May 1999 the ASA’s Undergraduate Statistics Education Initiative (USEI) was launched

14 Focus of the Reform  Emphasize concepts over procedures  Teach students to: Formulate research questions Collect data Analyze data Interpret results

15 Focus of the Reform  Gain experience working with real data  Focus on active learning  Build communication skills  MAJOR FOCUS on statistical literacy/ statistical thinking

16 Statistical Thinking  Process of using wide ranging and interacting data to understand processes, problems, and solutions. The opposite of one factor at a time, where ones natural born tendency is to change one factor and “see” what happens. Statistical thinking is the tendency to want to understand how several control factors may be interacting at once to produce an outcome. Common cause variation becomes your friend and special cause variation your enemy. Attribute judgments of good and bad are replaced with estimates of significance with given confidence. ---Six Sigma

17 Statistical Thinking  Recognize and attempt to understand/explain variation  The process of asking a “good” question, collecting, analyzing and interpreting data and appropriately recognizing limitations  How do we teach statistical thinking?

18 Recommendations for Instructors  Provide working examples that include questions and processes to solve statistical problems  Allow students to practice using statistical thinking with open-ended questions and problems  Use technology to collect, manage and analyze data

19 Recommendations for Instructors  Use real data  Choose data and questions that are of interest to students  Reduce content to focus on key concepts in greater detail

20 What’s Missing  Study Design Issues and Implications  Real Data  Interpretation of Results  Practical Implications  Limitations of Inferences  Statistical Computing

21 Limitations/Interpretation  Association is not causation  Statistical significance is not practical importance  Lack of statistical significance does not imply no difference  Understand how to interpret news stories/articles with statistical information

22 Modifications?  Include design and analysis issues in curriculum  Sharpen skills in interpretation of results  Include projects with real data  Stress communication skills  Focus more on big picture

23 Big Picture  What is a statistical study?  How is sample constructed?  What are the key questions?  How is information collected – analyzed – interpreted?  What makes a good study? A poorly analyzed study can be re- analyzed. A poorly designed study cannot be re-designed.

24 Biostatistics  A specialized branch of applied mathematics/statistics that deals with the statistical evaluation of experimental research or clinical trial results.  Statistical applications in the medical or public health arena.

25 Biostatistics Mathematics/StatisticsMedicine/Public Health Biostatistics Computer Science

26 Examples  What proportion of college students drink alcohol, use illegal drugs?  Should driving age be increased?  Are cell phones safe for children?  How can we address these questions?

27 Research Teams  Principal Investigator (Clinicians, Scientists)  Statistician/Biostatistician  Co-Investigators  Project Manager  Statistical Programmers  Research Assistants

28 Statistician’s Role on Team  Develop Study Design Research question Study sample Sample size Enrollment/Follow-up strategies On-going monitoring  Perform Interim and Final Analysis  Interpret and Report Results

29 Cell Phones for Children?  University of Washington scientist states 70-80% of the energy emitted from the antenna of a mobile phone is absorbed in the head: Children’s skulls are thinner and their growing brains may be much more susceptible to radiation exposure.  FDA states that scientific evidence does not show a danger to users of wireless communication devices including children.

30 Study of Cell Phone Safety  What is the outcome?  What is exposure/risk factor?  How can we assess relationship between cell phone use and health outcomes? Study Design Data Collection and Analysis Interpretation/Attribution

31 Issues for Biostatisticians  Children - Obesity, Immunizations, Asthma, Autism…..  Adolescents – Alcohol & Tobacco Use, Depression, STDs, Traffic Accidents….  Adults – Cancer, CVD, Substance Abuse, HIV/AIDS, Mental Health… What is #1 killer of men and women in US? What are the risk factors?

32 Research – Set Context  Framingham Heart Study  Pharmacologic Clinical Trials in Children with Autism  Effect of Alcohol Exposure in Pregnancy on SIDS

33 The Framingham Heart Study  5000+ men and women enrolled in 1948  Longitudinal cohort study  Exams every 2 years for cardiovascular risk factors - surveillance  Ancillary studies – hearing, exercise, nutrition, neurological studies  5000+ offspring & spouses enrolled in 1976  Third generation enrolled in 2002

34 Milestones from Framingham 1960 Cigarette smoking increases risk of heart disease 1961 Cholesterol & blood pressure increase risk of heart disease 1967 Physical activity reduces risk of heart disease, obesity increases risk of heart disease 1970 High blood pressure increases risk of stroke 1978 Psychosocial factors increases heart disease 1988 High levels of HDL cholesterol reduces risk of death More than 1500 scientific papers published

35 Framingham Study Risk Functions  Risk prediction models Predict likelihood that a person will have coronary heart disease in the next 10 years  Models designed to include risk factors that are readily available Age, blood pressure, cholesterol, smoking, diabetes, treatment for hypertension & high cholesterol, obesity Risk Calculator

36 Clinical Trial in Children with Autism  Autism-brain disorder usually diagnosed before age 3 that affects communication, social interaction, and creative play.  Affects over 500,000 children in the US  Trial to assess the efficacy of drug treatment in reducing repetitive behaviors  Children randomized to receive study drug or placebo

37 Clinical Trial in Children with Autism  144 children with autism aged 5-17 years followed every 2 weeks for 12 weeks for improvements in repetitive behaviors  Issues Randomization/Blinding Measurement of outcome (child, parent, teacher) Safety/Ethical issues

38 Effect of Alcohol Exposure in Pregnancy on SIDS  SIDS – Unexplained infant death before 1 year of life  Extremely high rates of SIDS among American Indians in Northern Plains of North and South Dakota and in Cape Town South Africa  High rates of alcohol consumption

39 Effect of Alcohol Exposure in Pregnancy on SIDS  SIDS – 0.57/1000 in US 3.4/1000 in Northern Plains 3.5/1000 in Cape Town  In US – 13% of women report drinking alcohol in pregnancy 58% in Northern Plains 41% in Cape Town

40 Effect of Alcohol Exposure in Pregnancy on SIDS  Study of 12,000 pregnant women in Northern Plains and Cape Town  Assess relationship between alcohol and SIDS  Issues Measuring alcohol exposure Ethical Issues – e.g., Autopsies

41 Examples for Specific Topics  Conditional Probability Performance of screening tests for prenatal diagnosis, prostate cancer, breast cancer, HIV

42 Prenatal Diagnosis  Your family is pregnant – should you have a screening test?  Standard of Care in the US is serum screen 68% sensitivity 5% false positive rate

43 Performance Characteristics of Screening Tests Disease +Disease – Test +ab Test -cd

44 Performance Characteristics  Sensitivity = True Positive Fraction = P(Test + | Disease)  False Positive Fraction = P(Test + | No Disease)

45 For the Patient  Positive Predictive Value = P(Disease | Test +)  Negative Predictive Value = P(No Disease | Test -)

46 Examples for Specific Topics  Normal Probability Model Percentiles Height, Weight, BMI for age

47 Statistical Inference  Estimation and Hypothesis Testing Clinical Trials  Search for clinical trials  Recent results (press releases and scientific articles)

48 Introductory Statistics  Big Picture and Make it Real! Real Data Relevant Examples Focus on Interpretation – Practical Importance

49 Academic Programs at Boston U  BA in Mathematics/Statistics  Minor in Applied Statistics  Summer Institute for Training in Biostatistics  MA in Biostatistics  PhD in Biostatistics  Minor in Public Health (Biostatistics, Epidemiology, Environmental Health, International Health, Health Law, Maternal and Child Health, Health Services, Social and Behavioral Science)

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