Apr 21, 2023

Chapter 1: Chapter 1: MeasurementMeasurement

In Chapter 1:

1.1 What is Biostatistics?

1.2 Organization of Data?1.3 Types of Measurements

1.4 Data Quality

Biostatistics • Statistics is not merely a compilation of

computational techniques• Statistics

– is a way of learning from data – is concerned with all elements of study

design, data collection and analysis of numerical data

– does require judgment

• Biostatistics is statistics applied to biological and health problems

Biostatisticians are: • Data detectives

– who uncover patterns and clues– This involves exploratory data analysis (EDA)

and descriptive statistics

• Data judges– who judge and confirm clues– This involves statistical inference

MeasurementP Measurement (defined): the assigning of

numbers and codes according to prior-set rules (Stevens, 1946).

P There are three broad types of measurements:P CategoricalP OrdinalP Quantitative

Measurement ScalesP Categorical - classify observations into

named categories, P e.g., HIV status classified as “positive” or

“negative”P Ordinal - categories that can be put in rank

orderP e.g., Stage of cancer classified as stage I,

stage II, stage III, stage IVP Quantitative – true numerical values that

can be put on a number lineP e.g., age (years)P e.g., Serum cholesterol (mg/dL)

Illustrative Example: Weight Change and Heart Disease

• This study sought to to determine the effect of weight change on coronary heart disease risk. It studied 115,818 women 30- to 55-years of age, free of CHD over 14 years. Measurements included

• Body mass index (BMI) at study entry

• BMI at age 18

• CHD case onset (yes or no)Source: Willett et al., 1995

Illustrative Example (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

Quantitative

Categorical

Examples of Variables

Ordinal

Variable, Value, Observation

P Observation the unit upon which measurements are made, can be an individual or aggregate

P Variable the generic thing we measure P e.g., AGE of a personP e.g., HIV status of a person

P Value a realized measurement P e.g.,“27”P e.g.,“positive”

Data Collection Form

Data Collection Form

Var1 (ID) 1Var2 (AGE) 27Var3 (SEX) FVar4 (HIV) YVar5 (KAPOSISARC) YVar6 (REPORTDATE)4/25/89Var7 (OPPORTUNIS) N

On this form, each questionnaire contains an observation

Each question corresponds to a variable

U.S. Census Form

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

Illustrative Example: Cigarette Consumption and Lung Cancer

Unit of observation in these data are individual regions, not individual people.

cig1930 = per capita cigarette use in 1930

mortality = lung cancer mortality per 100,000 in 1950

Data Quality

• An analysis is only as good as its data

• GIGO ≡ garbage in, garbage out

• Does a variable measure what it purports to? – 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

Choose Your Ethos

BS is manipulative and has a predetermined outcome.

Science “bends over backwards” to consider alternatives.

Blackburn, S. (2005). Oxford Univ. Press

Frankfurt, H. G. (2005). Princeton University Press

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