Basic Biostatistics 1

Chapter 1: Chapter 1: MeasurementMeasurement

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In Chapter 1:

1.1 What is Biostatistics?

1.2 Organization of Data1.3 Types of Measurements

1.4 Data Quality

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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

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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

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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)

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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

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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

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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)

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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

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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

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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

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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

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Example: U.S. Census Form

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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

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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

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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

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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

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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