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Basic Biostatistics 2
In Chapter 1:
1.1 What is Biostatistics?
1.2 Organization of Data1.3 Types of Measurements
1.4 Data Quality
Basic Biostatistics 3
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
Basic Biostatistics 4
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
Basic Biostatistics 5
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)
Basic Biostatistics 6
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
Basic Biostatistics 7
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
Basic Biostatistics 8
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)
Basic Biostatistics 9
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
Basic Biostatistics 10
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/m3)• Age (years)• Weight presently• Weight at age 18
Quantitativevars
Categoricalvars
Ordinal var
Basic Biostatistics 11
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
Basic Biostatistics 12
Data Collection Form
Data Collection Form
Var1 (ID) 1Var2 (AGE) 27Var3 (SEX) FVar4 (HIV) Y
Var5 (KAPOSISARC) YVar6 (REPORTDATE)4/25/89Var7 (OPPORTUNIS) N
Each questionnaire contains an observation
Each question corresponds to a
variable
Basic Biostatistics 14
Data Table
• Each row corresponds to an observation• Each column contains information on a variable• Each cell in the table contains a value
AGE SEX HIV ONSET INFECT
24 M Y 12-OCT-07 Y
14 M N 30-MAY-05 Y
32 F N 11-NOV-06 N
Basic Biostatistics 15
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
Basic Biostatistics 16
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
Basic Biostatistics 17
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