introduction to biostatistics (pubhlth 540) lecture 1: overview ed stanek
DESCRIPTION
Introduction to Biostatistics (PubHlth 540) Lecture 1: Overview Ed Stanek. Acknowledgement: Thanks to Professor Balasubramanian and Professor Pagano for lecture material. Topics. Course Logistics Why Biostatistics? Course outline: Data Presentation Inference Prediction. Topics. - PowerPoint PPT PresentationTRANSCRIPT
1
Introduction to Biostatistics
(PubHlth 540)
Lecture 1: Overview
Ed StanekAcknowledgement: Thanks to Professor Balasubramanianand Professor Pagano for lecture material
2
Topics
• Course Logistics• Why Biostatistics? • Course outline:
– Data Presentation– Inference– Prediction
3
Topics
• Course Logistics• Why Biostatistics? • Course outline:
– Data Presentation– Inference– Prediction
4
Course Logistics• Instructor: Ed Stanek
– Office Hours: Tu/Th, 4:00 – 5:15– Office: 401 Arnold– Email: [email protected]
Location:Morrill III Room 212
5
Course Logistics• Grading:
– Homework (n=10 assignments+reports): 10%
– Exam1: 30%– Exam2: 30%– Exam3: 30%– Final: 30%
Best 2 of 3
6
Topics
• Course Logistics• Why Biostatistics? • Course outline:
– Data Presentation– Inference– Prediction
7
• “There are three kind of lies: lies, damn lies, and statistics”
» Mark Twain (1835-1910)
• Bio --- bios Greek --- life • Statistics Latin --- statisticum collegium
(lecture about state affairs)
A branch of applied mathematics concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate population parameters -- www.hyperdictionary.com
Biostatistics
8
Course Outline
• Presentation
• Inference
• Prediction
9
Data presentation
• Data presentation techniques enable us to condense large amounts of information into a digestable form
• Examples: – Tables – Bar graphs– Histograms etc.
10
Data Presentation - example
11
Data Presentation - example
Data presentation - exampleData presentation - exampleAdults and children estimated to be livingAdults and children estimated to be living
with HIV as of end 2004 with HIV as of end 2004
Total: 39.4 (35.9 – 44.3) million
Western & Central Europe
610 000610 000[480 000 – 760 000][480 000 – 760 000]
North Africa & Middle East540 000540 000
[230 000 – 1.5 million][230 000 – 1.5 million]
Sub-Saharan Africa25.4 million25.4 million
[23.4 – 28.4 million][23.4 – 28.4 million]
Eastern Europe & Central Asia1.4 million 1.4 million
[920 000 – 2.1 million][920 000 – 2.1 million]
South & South-East Asia7.1 million7.1 million[4.4 – 10.6 million][4.4 – 10.6 million]
Oceania35 00035 000
[25 000 – 48 000][25 000 – 48 000]
North America1.0 million1.0 million
[540 000 – 1.6 million][540 000 – 1.6 million]
Caribbean440 000440 000
[270 000 – 780 000][270 000 – 780 000]
Latin America1.7 million1.7 million
[1.3 – 2.2 million][1.3 – 2.2 million]
East Asia1.1 million1.1 million
[560 000 – 1.8 million][560 000 – 1.8 million]
13
Election 2004 Results
http://www-personal.umich.edu/~mejn/election/
14
Course Outline
• Presentation
• Inference
• Prediction
15
But Do We Believe It?
InferenceSample from a population
From the sample infer (guess) characteristics of the population.
Inference
16
Inference
• Population: Entire group of interest
• Sample: a small subset of population to be studied
• Parameter: a summary measure or characteristic of a population (e.g. mean)
• Statistic: summary measure or characteristic of a sample
17
Inference
• Theory and methodology for generalizing from a sample to a population
Population sample
Sample drawn from population
Inference regarding the population made from sample
guess
18
Mortality before and after the 2003
invasion of Iraq: cluster sample survey
-- Lancet 2004; 364: 1857-64
“We estimate that there were 98000 extra deaths (95% CI 8000-194 000) during the post-war period in the 97% of Iraq represented by all the clusters except Falluja.”
Inference - example
19
Meat Consumption and Risk of
Colorectal Cancer –- JAMA 293 (2) Jan. 12, 2005
“In our analyses, the association between colon cancer risk and high intake of red (RR, 1.41; 95% CI, 1.12–1.78) and processed meat (RR, 1.33; 95% CI, 1.08–1.64) measured at a single time point is consistent with meta-analysis results, 50 adjusting for age and energy intake. However, the association was substantially attenuated with further adjustment for educational attainment, cigarette smoking, physical activity, and other lifestyle factors associated with red meat intake.”
Inference - example
20
Course Outline
• Presentation
• Inference
• Prediction
21
A diagnosis of diabetes can be suspected in the presence of the following signs and symptoms of hyperglycemia:
• Polydipsia (increased thirst)• Polyuria (increased urinary frequency with increased volume)• Fatigue• Polyphagia (increased appetite)• Weight loss• Abnormal healing• Blurred vision• Increased occurrence of infections, particularly those caused by yeast.
Prediction
22
The risk of diabetes is increased in asymptomatic individuals if any of the following risk factors are present:
• A strong family history of diabetes (parents or sibling) • Obesity (20% above ideal body weight)
• Certain races (American Indian, Hispanic, African, or Pacific Islander ancestry)
• Women with previous gestational diabetes or history of babies of 9 pounds (4Kg) or more at birth
• Previously identified impaired glucose tolerance (IGT)
• Hypertension or significant hypertriglyceridemia (> 250 mg/dL)
• 40 years of age with any of the preceding factors.
Prediction
23
Could you have diabetes and not know it? There are 18.2 million Americans with diabetes -- and
nearly one-third of them (or 5.2 million people) do not know it! Take this test to see if you are at risk for having diabetes. Diabetes is more common in African Americans, Latinos, Native Americans, Asian Americans and Pacific Islanders. If you are a member of one of these ethnic groups, you need to pay special attention to this test.
http://www.diabetes.org/diabetes-basics/prevention/diabetes-risk-test/
To find out if you are at risk, answer the following questions as they apply to you, then click the "CALCULATE" button to run the test and view your score.
Please select your age category. 0-4445-6465 or Older
Please select your height.
Please enter your weight in pounds.
I am a woman who has had a baby weighing more than nine pounds at birth.True or False
Prediction
24
I have a sister or brother with diabetes.TrueFalse
I have a parent with diabetes.TrueFalse
I am under 65 years of age and I get little or no exercise.TrueFalse
CALCULATE your score
Read our Frequently Asked Questions regarding this Risk Test.
The information contained in this American Diabetes Association (ADA) Web site is not a substitute for medical advice or treatment, and the ADA recommends consultation with your doctor or health care professional.
Prediction
25
Summary• Website:
– Syllabus– Lecture Notes– Homework Problems
• Course Outline:– Data Presentation – Inference– Prediction