1. Basic Biostatistics1

2. 2 In Chapter 1: 1.1 What is Biostatistics? 1.2 Organization of Data 1.3 Types of Measurements 1.4 Data Quality

3. Basic Biostatistics3 Biostatistics Statistics is not merely a compilation of computational techniques It is a way of learning from data Biostatistics is concerned with learning from biological, public health, and other health data

4. Basic Biostatistics4 Biostatisticians are: Data detectives who uncover patterns and clues through data description and exploration Data judges who confirm and ad adjudicate decision using inferential methods

5. Basic Biostatistics5 Measurement Measurement ≡ the assigning of numbers and codes according to prior-set rules (Stevens, 1946). Three main types of measurements: Categorical (nominal) Ordinal Quantitative (scale)

6. Basic Biostatistics6 Categorical Measurements Classify observations into named categories Examples HIV status (positive or negative) SEX (male or female) BLOOD PRESSURE classified as hypo-tensive, normo-tensive, borderline hypertensive, or hypertensive

7. Basic Biostatistics7 Ordinal Measurements Categories that can be put in rank order Examples: STAGE OF CANCER classified as stage I, stage II, stage III, stage IV OPINION classified as strongly agree (5), agree (4), neutral (3), disagree (2), strongly disagree (1); so-called Liekert scale

8. Basic Biostatistics8 Quantitative Measurements Numerical values with equal spacing between numerical values (like number line) Examples: AGE (years) SERUM CHOLESTEROL (mg/dL) T4 cell count (per dL)

9. Basic Biostatistics9 Example: Weight Change and Heart Disease Investigate effect of weight gain on coronary heart disease (CHD) risk 115,818 women 30- to 55-years of age, all free of CHD Follow over 14 years to determine CHD occurrence Measure the following variables: Source: Willett et al., 1995

10. Basic Biostatistics10 Measurement Scales Examples (cont.) Smoker (current, former, no) CHD onset (yes or no) Family history of CHD (yes or no) Non-smoker, light-smoker, moderate smoker, heavy smoker BMI (kgs/m 3 ) Age (years) Weight presently Weight at age 18 Quantitative vars Categorical vars Ordinal var

11. Basic Biostatistics11 Variable, Value, Observation Observation  unit upon which measurements are made, e.g., person, place, or thing Variable  the [generic] thing being measured, e.g., AGE, HIV status Value  a realized measurement, e.g., an age of “27”, a “positive” HIV test

12. Basic Biostatistics12 Data Collection Form Var1 (ID)1 Var2 (AGE) 27 Var3 (SEX)F Var4 (HIV)Y Var5(KAPOSISARC)Y Var6 (REPORTDATE)4/25/89 Var7 (OPPORTUNIS) N Each questionnaire contains an observation Each question corresponds to a variable

13. Basic Biostatistics13 Example: U.S. Census Form

14. Basic Biostatistics14 Data Table Each row corresponds to an observation Each column contains information on a variable Each cell in the table contains a value AGESEXHIVONSETINFECT 24MY12-OCT-07Y 14MN30-MAY-05Y 32FN11-NOV-06N

15. Basic Biostatistics15 Data Table Example 2: Cigarette Use and Lung Cancer Unit of observation is region, not individual Variables cig1930 = per capita cigarette use in 1930 mortality = lung cancer mortality per 100,000 in 1950

16. Basic Biostatistics16 Data Quality An analysis is only as good as its data GIGO ≡ garbage in, garbage out Validity = freedom from systematic error Objectivity = seeing things as they are without making it conform to a worldview Consider how the wording of a question can influence validity and objectivity

17. Basic Biostatistics17 Choose Your Ethos BS is manipulative and has a preferred outcome. Science bends over backwards to consider alternatives. Blackburn, S. (2005). Oxford Univ. Press Frankfurt, H. G. (2005). Princeton University Press

18. Basic Biostatistics18 Scientific Ethos “I cannot give any scientist of any age any better advice than this: The intensity of the conviction that a hypothesis is true has no bearing on whether it is true or not.” Peter Medawar