Introduction to epidemiology and biostatistics in mid level medical schools.

1. EPIDEMIOLGY AND BIOSTATISTICS
CMT 05101

2. INTRODUCTION TO BIOSTATISTICS
Learning Objectives
By the end of this session students should be able to:
• Define terms used in Biostatistics
• Explain the significance for studying biostatistics in
medical field
• Mention the application of statistics
• Describe descriptive methods for qualitative data

3. INTRODUCTION
Biostatistics
• Development and application of statistical
techniques to scientific research relating to life
(Human, plant and Animal).
• But here the focus is on human life and health
– Pharmacology
– Medicine
– Epidemiology
– Anatomy and physiology, etc.

4. Statistics
• Branch of mathematics concerned
with collection, classification, analysis, and
interpretation of numerical facts or data,
for drawing inferences on the basis of
their quantifiable likelihood (probability).
• Statistics can interpret aggregates of data too
large to be intelligible by ordinary observation
because such data tend to behave in regular,
predictable manner
INTRODUCTION

5. Statistics
• A field which examines the collection,
organization, summarization and analysis of
data, and draws inferences regarding that
data for a population through observation of a
sample.
INTRODUCTION

6. Data
• A representation of facts, concepts or
instructions in a formalised manner suitable for
communication, interpretation, or processing by
humans or by automatic means. (Hicks [1993:
668] quoted by Checkland and Holwell [1998])
• The raw material of statistics, consisting of
numbers of measurement or counting of a
population sample.
INTRODUCTION

7. Population
• The totality of individuals or units of interest.
For example, there could be a population of
blood samples collected in a year. If the
interest is restricted to only suspected cases
of liver diseases, the population comprises
blood samples of such cases only. If the
interest is further restricted to the cases
attending OPD in a group of hospitals, the
population is also accordingly restricted.
INTRODUCTION

8. Sample
• A set of data collected and/or selected from
a statistical population. It is therefore a part
of a population obtained by a defined
procedure.
INTRODUCTION

9. Parameter
• A summary measure for any characteristic in
the target population, for example,
percentage of cirrhosis patients with high
Aspartate Aminotransferase, or rate of
increase of systolic blood pressure in healthy
subjects per year of age. The parameter
pertains to the entire population of interest
and not to the sample.
• A descriptive measure calculated from the
data of a population.
INTRODUCTION

10. Variable
• A characteristic that varies from person to
person, or from situation to situation. For
example, Platelet count in different persons is
variable but number of eyes or number of
fingers is not a variable.
• There are two main types of variable
– Qualitative variable
– Quantitative variable
INTRODUCTION

11. Qualitative variable
• Data that is not given numerically, e.g place of
birth, gender/sex, favorite of food, level of
education etc
Quantitative variable
• Given numerically. Subdivided into two types
– Discrete variable —Take specific numeric value or
number of possible values, for example, Parity for
a woman, number of patients are discrete
variables.
INTRODUCTION

12. – Continuous variable — A variable that can
theoretically have infinite number of possible
values within a short range. Age is continuous
since within 8 and 12, it can be 8.17, 10.874, 9.756
years, etc. Age can be measured in terms of days,
hours and minutes, although practically there is
no need to do this. Blood pressure is a continuous
variable but measured in integers for
convenience. Parity is not a continuous variable
because there is no possibility of it being 2.75 or
1.6.
INTRODUCTION

13. Levels of Variable Measurement
• Four levels of measurement have been
identified. These levels differ in how closely
they approach the structure of the number
system we use.
• Understanding the level of measurement of
variables used in research is important
because the level of measurement determines
the types of statistical analyses that can be
conducted.

14. • The conclusions that can be drawn from research
depend on the statistical analysis used.
• Nominal level measurement uses symbols to
classify observations into mutually exclusive and
exhaustive categories.
– Mutually exclusive means the categories must be
distinct so that no observation falls into more than
one category.
– Exhaustive means sufficient categories must exist so
that all observations fall into some category.
Levels of Variable Measurement

15. Levels of Measurement: Nominal
• This is the most basic level of measurement.
• At this level we can determine only whether
two observations are alike or different.
• Example: In a survey of teachers, sex was
determined by a question. Observations were
sorted into two mutually exclusive and
exhaustive categories, male and female.
Observations could be labeled with the letters
M and F, or the numerals 0 and 1.

16. • In the same survey the variable of marital status
could be measured by two categories, married
and unmarried.
• But, these categories must each be defined so
that all possible observations will fit into one
category but no more than one: legally married,
common-law marriage, religious marriage, civil
marriage, living together, never married,
divorced, informally separated, legally
separated, widowed, etc
Levels of Measurement: Nominal

17. • In nominal measurement, all observations in
one category are alike on some property and
differ from the members in the other category
on that property (e.g., sex, martial status).
• On ordering of categories exists. We cannot
say one category is better or worse, or more
or less than another.
Levels of Measurement: Nominal

18. • Ordinal level of measurement uses symbols to
classify observations into categories that are
not only mutually exclusive and exhaustive. In
addition, the categories have some explicit
relationship among them.
• Observations may be classified into categories
such as taller and shorter, greater and lesser,
faster and slower, harder and easier, and so
forth.
• The categories must be exhaustive and
mutually exclusive.
Levels of Measurement: Ordinal

19. • Most questionnaires use Likert type items. For
example, we may ask teachers about their job
satisfaction.
• Asking whether a teachers is very satisfied,
satisfied, neutral, dissatisfied, or very
dissatisfied is using an ordinal scale of
measurement.
Levels of Measurement: Ordinal

20. Level of Measurement: Interval
• The interval level of measurement classifies
observations into mutually exclusive and
exhaustive categories that have some explicit
relationship among them, and the relationship
between the categories is known and exact.
This is the first quantitative application of
numbers.

21. • In the interval level of measurement, a
common and constant unit of measurement is
established between the categories. For
example, measures of temperature are
interval scales.
• A temperature of 75° is one degree cooler
than a temperature of 76°; likewise, a
temperature of 32° is one degree warmer
than a temperature of 31°.
Level of Measurement: Interval

22. • Numbers may be assigned to observations
because the relationship between any two
categories is assumed to be the same as the
relationship between numbers in the number
system. For example, 76-1=75 and 31+1=32.
• Intervals between categories are equal but
they originate from some arbitrary point of
origin. No meaningful zero point exists.
Level of Measurement: Interval

23. Levels of Measurement: Ratio
• The ratio level is the same as the interval level
with the addition of a meaningful and non-
arbitrary zero point.
• Examples: Weight, area, speed, velocity. In
education, budgets and number of students
are measured on ratio scales.

24. Descriptive Methods for Qualitative
DataFrequency distribution
• A statistical distribution of subjects that displays
the number of subjects with different levels of
measurement, e.g., how many have diastolic
blood pressure <70 mmHg, how many between
70-74, 75-79, etc.
• It gives a picture of the shape of the distribution
of the data.

25. Unimodal, Bimodal and Multimodal Distribution
• Distributions of data can have few or many
peaks. Distributions with one clear peak are
called unimodal, and distributions with two
clear peaks are called bimodal. That with
more than two peaks is called multimoda data
• Frequency distribution can be displayed as a
table, bar chart, histogram or pie chat

26. What is the Role of Biostatistics in
Modern Medicine?
• Helps to determine disease burden in the
population
• Finding new drug treatment for diseases
• Planning and allocation of resources
• Used in research projects
• Used in Quality Improvement programs
• Used to measure performance outcome