1. Writing up Introduction
(What have you set out to do and why?)

2. Introduction
Funnel Down
• General to Specific
• Historical To Latest
• International To Local
Introduce Your Research Problem
With the help of Background
(Valid, Authentic References)
Finally tell what you plan to do to solve this
problem (Purpose, Rationale, Significance, )

3. Introduction
Classic introduction should have 3 paragraphs:
1. Background information: What is the problem
or issue?
2. Importance of the problem and list unresolved
issues.
3. Rationale for the current study. State your
research question or hypothesis.

4. 4
• What is the problem?
• Why have you chosen that subject?
• Why do you start?
• Why is it important?
Introduction (Concept)

5. Writing up Materials & Methods
(How will you do it?)

6. 6
Synonyms
• Methodology
• Patients and Methods
• Volunteers and Methods
• Subjects and Methods
Materials & Methods

7. 7
• How study is designed?
• How study will be carried out?
• How data will be collected?
• How data will be analyzed?
Materials & Methods

8. Material & Methods
Setting
Duration
Study Design
Sample Size
Sampling Technique
Inclusion Criteria
Exclusion Criteria
Data Collection Procedure
Data Analysis

9. Components of Synopsis
• Supervisor Certificate
• Title Page
• Introduction:
(Background, Problem, Rationale, Purported
Significance)
• Objective: (SMART)
• Operational Definition: (For all vague terms in
objectives or Title)
• Hypothesis (If Any) (Give Alternate
hypothesis only)

10. • Material & Methods
– Setting: (Short, precise)
– Duration : (At least 6 months)
– Study Design: (1 line only)
– Sample Size: (Total subjects + Name of Groups& basis of grouping)
– Sampling Technique: (Identify clearly)
– Inclusion Criteria: (you have to justify them in DCP)
– Exclusion Criteria: (you have to justify them in DCP)
– Data Collection Procedure (DCP): (Source, How included, How
Excluded, Risk/Benefit, Informed consent, Ethical committee
approval, steps of measuring variables)
– Data Analysis
• References
• Performa as Annexure
Components of Synopsis….

11. 11
Study Designs

12. Types of Epidemiological Studies
12
Non Experimental
Observational Studies
Experimental/
Interventional Studies
Population
Based
Individual
Based
Descriptive
(Health
Survey)
Analytic
(Ecological
Study)
Descriptive
Case reports
Case series
Analytic
Randomized
Control trial
or
(Clinical trial)
Non-randomized
Quasi-
Experimental
Field trial
Community Trial
Cross-sectional study
Or Prevalence study
Cohort study or
Follow-up study
Case-control study
Or Case-reference

13. 13
Descriptive Studies
Descriptive studies involve the systematic collection
and presentation of data to give a clear picture of a
particular situation and can be carried out on a small
or large scale.
• Case Report
• Case series
• Cross Sectional Survey

14. 14
Comparative or Analytical
Studies
• Attempts to establish association or determine risk
factors for certain problems. This is done by
comparing two or more groups, with or without the
outcome of interest/exposure of interest.
Types
Observational
Experimental

15. • A detailed report by a physician of an unusual
disease in a single person.
• In 1941 Australian Ophthalmologist Greg reported
a new syndrome Congenital Cataract linked to
Rubella in the mother during pregnancy
15
Case Report

16. 16
Case Report
• Classical example is that of a single case reported in
Germany in late 1959 of a congenital malformation
affecting the limbs and digits.
• More cases were reported in the following years. In
1961 a hypothesis was put forward that
thalidomide, a sleeping pill, was responsible for
congenital malformations.
• Subsequent analytic studies confirmed the link
between the drug and congenital malformation.

17. • It was a single case report that led to formulation of
hypothesis that OC use increases the risk of Venous
Thromo-embolism.
• I saw a patient who reported psychotic episodes
immediately after watching a TV Program “ Kaon
Baney Ga Karore Pati?”
• Limitation: Only 1 individual’s experience
17
Case Report

18. 18
Case Series
• When several unusual cases all with similar
conditions are described in a published report, this
is called a Case Series.
• In 1940 Alton Ochencer in US observed that
virtually all patients he was operating for Lung
Cancer gave history of smoking.
• Between Oct 1980 & May 1981, five cases….?
• A case series does not include a control group.
• Useful for hypothesis formation

19. • A detailed report by a physician of an unusual
disease in a single person.
Population: unknown
Select patient: (case report)
or patients (case series) with disease of interest
Assessment: Describe clinical findings
Analysis: Radiographs, lab reports, etc
Interpretation: Special features of this disease
Example: “Normal plasma cholesterol in an 88-
year-old man who eats 25 eggs a day” [Kern
J, NEJM 1991; 324:896–899]12
Case Reports and Case Series

20. Cross-sectional Study
• Data collected at a single point in time
• Describes associations
• Prevalence
• Burden of Disease
A “Snapshot”

21. Cross-Sectional Study: Definition
• Conducted at a single point in time or over a
short period of time. No Follow-up.
• Exposure status and disease status are
measured at one point in time or over a
period.
• Prevalence studies. Comparison of prevalence
among exposed and non-exposed.

22. 22
Cross-Sectional Surveys-Advantages
• Fairly quick and easy to perform.
• Inexpensive
• Useful for determining the prevalence of disease
for a defined population and can also measure
factors leading to it subsequent to group
formations
• Such data is of great value for Pub Health Adm in
assessing health status and needs of Population for
effective healthcare Planning

23. Cross-sectional: Disadvantages
• Difficult to separate cause from
effect, because measurement of exposure and
disease is conducted at the same time.
• A persons exposure status at the time of the
study may have little to do with their exposure
status at the time the disease began.

24. Cross-Sectional Studies
• Exposure and outcome status are determined
at the same time
• Examples include:
– Behavioral Risk Factor Surveillance System (BRFSS)
- http://www.cdc.gov/brfss/
– National Health and Nutrition Surveys (NHANES) -
http://www.cdc.gov/nchs/nhanes.htm
• Also include most opinion and political polls

25. Cohort studies
 longitudinal
 Prospective studies
 Forward looking study
 Incidence study
 starts with people free of disease
 assesses exposure at “baseline”
 assesses disease status at “follow-up”
25

26. Cohort Studies
Disease No Disease
Study
Population
Exposed Non-exposed
No DiseaseDisease
Exposure is
self selected
Follow through
time

27. Relative Risk (RR)
It is the “ratio of incidence of disease among
exposed to incidence of disease among non-
exposed”
Incidence among exposed
Relative Risk = ----------------------------------
Incidence among not exposed
27

28. 2 x 2 Tables
Used to summarize counts of disease and exposure in
order to do calculations of association
Outcome
Exposure Yes No Total
Yes a b a + b
No c d c + d
Total a + c b + d a + b + c + d

29. 2 x 2 Tables
a = number who are exposed and have the outcome
b = number who are exposed and do not have the outcome
c = number who are not exposed and have the outcome
d = number who are not exposed and do not have the outcome
*****************************************************
a + b = total number who are exposed
c + d = total number who are not exposed
a + c = total number who have the outcome
b + d = total number who do not have the outcome
a + b + c + d = total study population
a b
c d
Outcome
Yes No
Yes
Exposure
No

30. Relative Risk
• The relative risk is the risk of disease in the exposed
group divided by the risk of disease in the non-
exposed group
• RR is the measure used with cohort studies
a
a + b
RR =
c
c + d
a b
c d
Outcome
Yes No Total
Yes
Exposure
No
a + b
c + d
Risk among
the exposed
Risk among
the unexposed

31. Relative Risk Example
Escherichia coli?
Burger
Consumed Yes No
Total
Yes 23 10 33
No 7 60 67
Total 30 70 100
a / (a + b) 23 / 33
RR = = = 6.67
c / (c + d) 7 / 67

32. Selection of study subjects
• General population
– Whole population in an area
– A representative sample
• Special group of population
– Select group
• occupation group / professional group (Dolls study )
– Exposure groups
• Person having exposure to some physical, chemical or
biological agent, e.g. X-ray exposure to radiologists
32

33. Types of Cohort Studies
• Prospective:
– Exposure baseline in the present
– Follow-up period: present to future
• Retrospective:
– Exposure baseline in the past
– Follow-up period: past to present
• Historical prospective or ambispective:
– Exposure baseline in the past
– Follow-up period: past to present to future
33

34. 34
Retrospective Cohort Studies
• The investigator goes back into history to define a risk
group (e.g. *those children exposed to x-rays in utero vs.
those not), and follows the group members up to the
present to see what outcome (cancer) have occurred

35. 35
Cohort Study – Prospective
Unexposed (controls)
Exposed (cases)
With
outcome
Without
outcome
With
outcome
Without
outcome
Onset of study Direction of study
Cohort
selected
for study

36. 36
Retrospective Cohort Studies
Exposed (cases)
Unexposed (controls)
With
outcome
Without
outcome
With
outcome
Without
outcome
Direction of study
Records
selected
for study
Onset of study

37. Cohort Studies: Advantages
• Temporality: Exposure precedes outcome because the
cohort is disease free at baseline
• Efficient for studying rare exposures
• May be used to study multiple outcomes
• Allows for calculation of incidence of diseases in
exposed and unexposed individuals
• Minimizes recall bias
37

38. • Tend to be expensive (large sample size) and time
consuming (long follow-up period)
• Loss to follow-up
– When multiple outcomes or specific disease
incidence is the outcome of interest, it can be a
serious problem
• Inefficient to study rare diseases
Cohort Studies: Disadvantages
38

39. • Framingham, Massachusetts population was 28,000
• Study design called for a random sample of 6,500
• Enrollment questionnaire from targeted age range
30-59 years
• No clinical evidence of atherosclerotic
cardiovascular disease
• Cohort re-examined every two years
Framingham Study Design
39

40. The Framingham Study
• Exposures included:
– Smoking
– Alcohol use
– Obesity
– Elevated blood pressure
– Elevated cholesterol levels
– Low levels of physical activity, etc.
40

41. The Framingham Study
• Hypotheses:
– Persons with hypertension develop CHD at a greater rate
than those who are normotensive.
– Elevated blood cholesterol levels are associated with an
increased risk of CHD.
– Tobacco smoking and habitual use of alcohol are
associated with an increased incidence of CHD.
– Increased physical activity is associated with a decrease
in development of CHD.
– An increase in body weight predisposes a person to CHD.
41

42. Case-Control Studies
• Study population is grouped by outcome
• Cases are persons who have the outcome
• Controls are persons who do not have the
outcome
• Past exposure status is then determined

43. Case-Control Studies
Had Exposure No Exposure
Study
Population
Cases Controls
No ExposureHad Exposure

44. Case Control Study: Analysis
Exposure odds calculation for both case and control groups:
- exposure odds for cases =
- exposure odds for control group =
Odds Ratio (OR) =
c
a
d
b
cb
da
d
b
c
a

45. Odds Ratio
• In a case-control study, the risk of disease
cannot be directly calculated because the
population at risk is not known
• OR is the measure used with case-control
studies
a x d
OR =
b x c

46. Interpretation
Both the RR and OR are interpreted as follows:
= 1 - indicates no association
> 1 - indicates a positive association
< 1 - indicates a negative association

47. Odds Ratio Example
Autism
MMR
Vaccine? Yes No
Total
Yes 130 115 245
No 120 135 255
Total 250 250 500
a x d 130 x 135
OR = = = 1.27
b x c 115 x 120
The odds of being exposed to the MMR vaccine were 1.27 times higher in
those who had autism than in those who did not have autism.

48. Problems with Case Control Study Selection Bias
• In 1929, Raymond Pearl at John Hopkins, Baltimore
conducted a study to test the hypothesis tuberculosis
protected against cancer
• He selected 816 cases of cancer from 7500 consecutive
autopsies
• He also selected 816 controls from others on whom
autopsies had been carried out at John Hopkins
• Of the 816 CASES (with cancer), 6.6% had TB
• Of the 816 CONTROLS (without cancer), 16.3% had TB
• From the finding that the prevalence of TB was
considerably higher in the control group, Pearl concluded
that TB was protective against cancer
• Was Pearl’s conclusion justified?

49. Problems with Case Control Studies
“Pearl’s Study”
• No!! At the time of the study, TB was one of the major
reasons for hospitalization at Johns Hopkins Hospital
• Pearl thought that the control group’s rate of TB would
represent the level of TB in the general population; but
because of the way he selected the controls, they came
from a pool that was heavily weighted with TB
• The way the controls are selected is a major
determinant of whether a conclusion is valid or not
9/16/2013

50. Problems with Case Control Studies
Coffee-drinking and Cancer of the Pancreas in Women*
• Cases were white cancer patients from 11 Boston and Rhode-Island
hospitals
• Controls were patients from GI Clinics
• McMohan found that coffee consumption was greater in cases than
controls
• Controls were patients who had reduced their coffee consumption
because of Physician’s advice
• The controls level of coffee consumption was not representative of
the general population
• When a difference in exposure is observed between cases and
controls we must ask “Is the level of exposure observed in the controls
really the expected level in the general population.
9/16/2013

51. Recall Bias
• Individuals who have experienced a
disease or other adverse health events
tend to think about possible causes &
thus are likely to recall histories of
exposure differently as compared to
controls.
9/16/2013

52. Advantages
1. only realistic study design for uncovering
etiology in rare diseases
2. important in understanding new diseases
3. commonly used in outbreak investigation
4. useful if induction period is long
5. relatively inexpensive

53. Disadvantages
1. Susceptible to bias if not carefully designed
(and matched)
2. Especially susceptible to exposure
misclassification
3. Especially susceptible to recall bias
4. Restricted to single outcome
5. Incidence rates not usually calculable
6. Cannot assess effects of matching variables

54. Experimental Study
• Only type of study design that can actually prove
causation
• Individuals are randomly allocated to at least two
groups. One group is subjected to an
intervention, while the other group(s) is not.
• The outcome of the intervention (effect of the
intervention on the dependent variable) is
obtained by comparing the two groups.
9/16/2013

55. 55
Interventional / Experimental Studies
• The researcher manipulates a situation and
measures the effects of the manipulation
amongst two groups, one in which the
intervention takes place (e.g. treatment with a
certain drug) and another group that remains
"untouched" (e.g., treatment with a placebo) .

56. Experimental Study Examples
• Randomized clinical trial to determine if giving
magnesium sulfate to pregnant women in
preterm labor decreases the risk of their
babies developing cerebral palsy
• Randomized community trial to determine if
fluoridation of the public water supply
decreases dental cavities

57. Sampling
A sample is a sub set of the population, with all
its inherent qualities. Inferences about the population
can be made from the measurements taken from a
sample, if the sample is truly representative of the
population. Since a sample is expected to represent the
whole population, the sampling procedure has to
follow three fundamentals:
1. Should be representative.
2. Large enough.
3. The selected elements should have been
properly approached, included and interviewed.

58.  Samples can be studied more quickly than
populations. Speed can be important if a physician
needs to determine something quickly, such as a
vaccine or treatment for a new disease.
 A study of a sample is less expensive than a study
of an entire population because a smaller number of
items or subjects are examined. This consideration is
especially important in the design of large studies
that require a long follow-up.
 A study of the entire population is impossible in
most situations.
Reasons for Using Samples

59. Steps in Sampling
1. Definition of the population
We first need to identify the population we wish to
draw the sample, from and do so somewhat formally
because any inferences we draw are really only
applicable to that population
2. Construction of a sampling frame (or thinking of an
alternate)
The list of all possible units that might be drawn in a
sample.

60. 3. Selection of a sampling procedure
This is a critical decision about how to
collect the sample. We will look at
some different sampling procedure in
the following slides.

61. TWO MAJOR TYPES OF SAMPLING PROCEDURES:
PROBABILITY Each element has the same chance of being
included in the sample like:
1. Simple random
2. Systematic
3. Cluster
4. Stratified
NON-PROBABILITY There is no assurance that each element will
have the same chance of being included in the sample:
1. Consecutive
2. Convenience
3. Purposive

62. Convenience
TYPES OF SAMPLING METHODS
Sampling
Non-Probability
Sampling
Consecutive Purposive
Probability Sampling
Simple
Random
Systematic
Stratified
Cluster

63. Simple Random Sampling
PREREQUISITES
1. Sampling frame
a unique number is assigned to each element
2. Elements are selected into the sample randomly
by 3 means:
 Table of Random Numbers
 Lottery Method
 Computer Generated Numbers

64. 64
Systematic Sampling
PREREQUISITES
1. Sampling frame (If available ) if not then too
systematic sampling can be undertaken. What is
required is an estimate of population size and
required sample size.

65. SYSTEMATIC SAMPLING
• Decide on sample size: n
•Determine population size = N
• Divide population of N individuals into groups of
k individuals: k = N/n
• Randomly select one individual from the 1st group.
• Select every k-th individual thereafter.
N = 64
n = 8
k = 8
First Group

66. STRATIFIED SAMPLING
One of the main purposes of stratified sampling
is to compare different strata, which may not be
possible with simple random sampling alone.
Pre requisite: Sampling frame
 The population is first divided into groups of elements called
strata.
 Each element in the population belongs to one and only one
stratum.
 Best results are obtained when the elements within each
stratum are as much alike as possible (i.e. homogeneous
group).
 A simple random sample is taken from each stratum.

67. CLUSTER SAMPLING
When a list of the entire area is not available
and it is not physically possible to visit the entire
area (e.g. the city, or country) one can divide the
area into several equal size clusters or units.
E.g.: Mohallas, Apartment Buildings, Villages, Schools
One can select (randomly) only a few cluster,
number all the units within it and draw either:
1. A random sample or
2. A systematic sample

68. NONPROBABILITY SAMPLING
Non-probability sampling design are often more
practical than probability designs for some clinical
research. Because statistical significance test are
based on the assumption that a probability sample has
been used, the objective in non-probability sampling
is to produce a facsimile, for the search question at
hand of the probability sample.

69. CONSECUTIVE
CONVENIENCE
PURPOSIVE
Three major types of nonprobability sampling are

70. CONSECUTIVE SAMPLING
– It involves taking every patient who meets the
selection criteria over a specified time interval
or number of patients.
– It is the best of the nonprobability techniques
and one that is very often practical.

71. CONVENIENCE SAMPLING
1. It is the process of taking those members of the
accessible population who are easily available.
2. Sample is selected in a haphazard fashion.
3. It is widely used because of its obvious
advantages in cost and logistics, however this
type of sampling technique in fraught with
biases.

72. Purposive Sampling
• Judgemental Sampling
• done on the basis of some pre determined idea
(clinical knowledge)
• Specific targets interviewed, as they posses the
desired information.
• Experimenter exercises deliberate subjective choice
in drawing what he regards as the representative
sample
• Personal prejudices / lack of knowledge
• e.g. All Hypertensive patients of a certain age
9/16/2013

73. Quota Sampling
• Strata Identified
• Researcher determines proportion of
elements needed from sub groups
• No yard stick to measure representativeness
• e.g. Male and Female population –
researcher decides the percentage
9/16/2013

74. Snowball Sampling
• Technique where existing study subjects recruit
future subjects from among their acquaintances
thus the sample group appears to grow like a
snowball.
• Used for hidden populations difficult for
researchers to access
• Examples would be ?
• commercial sex workers, one would be able to get
information on more subjects by getting their
contacts from those initially interviewed.
9/16/2013

75. INTRODUCTION TO
BIOSTATISTICS

76. Statistics refers to numerical facts.
Field of statistics – how data are
presented
calculated
analysed
interpreted
When the data we use are biological, medical or
health related the subject is called Biostatistics

77. • Set or group of discrete observations of
attributes or events that carry little meaning
when considered alone.
• Data is the raw material for any research
• Data (plural) – Singular ?
– Singular for Data is ‘Datum’
• Comprises of observations made on
VARIABLES
15 September 2013

78. • A characteristic that takes different
values in different persons, places or
things or different values in the same
person at different times:
–Heights of adult males
–Weight of preschool children
–RBCs / ml of blood
–Age of patients in a medical OPD
• Any quantity that varies.
15 September 2013

79. Independent Variable
The variables that are used to describe
or measure the factors that are
assumed to cause or at least to
influence the problem are called the
INDEPENDENT (exposure) variables.
e.g. Smoking
15 September 2013

80. Dependent Variable
15 September 2013
–The variable that is used to describe
or measure the problem under study
(outcome) is called the DEPENDENT
variable.
–e.g Lung cancer

81. Categorical
(Qualitative)
15 September 2013
Numerical
(Quantitative)
Nominal Ordinal Discrete Continuous

82. • The characteristic which can’t be
expressed numerically like sex, ethnicity
, healing etc.
• Types
– Nominal
– Ordinal
15 September 2013

83. Presentation of Data
• Data once collected should be presented in a
such a way as to be easily understood. The style of
presentation depends, of course, on type of data.
• Data can be presented in as frequency tables,
charts, graphs, etc. Here we would discuss some
of the important means of presentation.
15 September 2013

84. FREQUENCY TABLES
• In a FREQUENCY TABLE data is presented in
a tabular form. It gives the frequency with
which (or the number of times) a particular
value appears in the data.

85. Blood Pressure of patients coming to a tertiary
care hospital OPD
Distribution Frequency Relative Cumulative
Relative
Below 100 6 0.10 0.10
100 – 120 9 0.15 0.25
121 – 140 24 0.40 0.65
141 – 160 15 0.25 0.90
Above 160 6 0.10 1.00

86. Bar charts
• Bar charts are used for nominal or ordinal data.
Years
No.ofcigarettes
Cigarette consumption of persons 18 years of age
or older, United States, 1900 - 1990

87. Pie chart
• Pie charts can also be used to display nominal
or ordinal data.
15 September 2013
Gender distribution

88. Histogram
A histogram depicts a frequency distribution for Continuous
Histogram showing distribution of Age (years)