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

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, )

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.

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• What is the problem?

• Why have you chosen that subject?

• Why do you start?

• Why is it important?

Introduction (Concept)

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

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Synonyms

• Methodology

• Patients and Methods

• Volunteers and Methods

• Subjects and Methods

Materials & Methods

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• How study is designed?

• How study will be carried out?

• How data will be collected?

• How data will be analyzed?

Materials & Methods

Material & MethodsSettingDurationStudy DesignSample SizeSampling TechniqueInclusion CriteriaExclusion CriteriaData Collection ProcedureData Analysis

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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)

• 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….

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Study Designs

Types of Epidemiological Studies

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Non ExperimentalObservational Studies

Experimental/Interventional Studies

Population Based

IndividualBased

Descriptive(Health Survey)

Analytic(EcologicalStudy)

DescriptiveCase reportsCase series

Analytic

RandomizedControl trial or(Clinical trial)

Non-randomizedQuasi-

ExperimentalField trial

Community Trial

Cross-sectional studyOr Prevalence study

Cohort study or Follow-up study

Case-control studyOr Case-reference

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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

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Comparative or AnalyticalStudies

• 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.

TypesObservationalExperimental

• 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

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Case Report

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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.

• 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

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Case Report

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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

• A detailed report by a physician of an unusual disease in a single person.Population: unknownSelect patient: (case report)or patients (case series) with disease of interestAssessment: Describe clinical findingsAnalysis: Radiographs, lab reports, etcInterpretation: Special features of this diseaseExample: “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

Cross-sectional Study

• Data collected at a single point in time

• Describes associations

• Prevalence

• Burden of Disease A “Snapshot”

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.

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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

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.

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

Cohort studies

longitudinal Prospective studiesForward looking studyIncidence study

starts with people free of disease assesses exposure at “baseline”assesses disease status at “follow-up”

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Cohort Studies

Disease No Disease

StudyPopulation

Exposed Non-exposed

No DiseaseDisease

Exposure isself selected

Follow throughtime

Relative Risk (RR)

It is the “ratio of incidence of disease among exposed to incidence of disease among non- exposed”

Incidence among exposedRelative Risk = ----------------------------------

Incidence among not exposed

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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

2 x 2 Tablesa = number who are exposed and have the outcomeb = number who are exposed and do not have the outcomec = number who are not exposed and have the outcomed = number who are not exposed and do not have the outcome

*****************************************************a + b = total number who are exposedc + d = total number who are not exposeda + c = total number who have the outcomeb + d = total number who do not have the outcomea + b + c + d = total study population

a bc d

OutcomeYes No

YesExposure

No

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

OutcomeYes No Total

YesExposure

No

a + bc + d

Risk among the exposed

Risk amongthe unexposed

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 / 33RR = = = 6.67

c / (c + d) 7 / 67

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

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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

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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

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Cohort Study – Prospective

Unexposed (controls)

Exposed (cases) With outcome

Without outcome

With outcomeWithout outcome

Onset of study

Direction of study

Cohort selected for study

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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

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

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• 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

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• 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

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The Framingham Study

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

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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.

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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

Case-Control Studies

Had Exposure No Exposure

StudyPopulation

Cases Controls

No ExposureHad Exposure

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

dbca

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

Interpretation

Both the RR and OR are interpreted as follows:

= 1 - indicates no association

> 1 - indicates a positive association

< 1 - indicates a negative association

Odds Ratio ExampleAutism

MMR Vaccine? Yes No

Total

Yes 130 115 245

No 120 135 255

Total 250 250 500

a x d 130 x 135OR = = = 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.

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?

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

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Problems with Case Control StudiesCoffee-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.

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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.

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Advantages

1. only realistic study design for uncovering etiology in rare diseases2. important in understanding new diseases3. commonly used in outbreak investigation4. useful if induction period is long5. relatively inexpensive

Disadvantages 1. Susceptible to bias if not carefully designed

(and matched)2. Especially susceptible to exposure misclassification3. Especially susceptible to recall bias4. Restricted to single outcome5. Incidence rates not usually calculable6. Cannot assess effects of matching variables

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.

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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) .

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

SamplingA sample is a sub set of the population, with all its inherent qualities. Inferences about the populationcan 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 thewhole 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.

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

Steps in Sampling1. 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.

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.

TWO MAJOR TYPES OF SAMPLING PROCEDURES:

PROBABILITY Each element has the same chance of being included in the sample like:

1. Simple random2. Systematic3. Cluster4. Stratified

NON-PROBABILITY There is no assurance that each element will have the same chance of being included in the sample:

1. Consecutive2. Convenience3. Purposive

Convenience

TYPES OF SAMPLING METHODS

Sampling

Non-Probability Sampling

Consecutive Purposive

Probability Sampling

Simple Random

Systematic

Stratified

Cluster

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

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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.

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

STRATIFIED SAMPLINGOne 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.

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, SchoolsOne can select (randomly) only a few cluster,

number all the units within it and draw either:

1. A random sample or2. A systematic sample

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.

CONSECUTIVECONSECUTIVE

CONVENIENCECONVENIENCE

PURPOSIVEPURPOSIVE

Three major types of nonprobability sampling are

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.

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.

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

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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

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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.

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INTRODUCTION TO BIOSTATISTICS

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

•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

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IT ALL STARTS WITH DATA

•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.April 12, 2023

WHAT IS A VARIABLE

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

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Dependent Variable

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

Categorical(Qualitative)

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Numerical(Quantitative)

Nominal Ordinal Discrete Continuous

VARIABLE / DATA CLASSIFICATION

•The characteristic which can’t be expressed numerically like sex, ethnicity , healing etc.•Types

– Nominal

– Ordinal

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CATEGORICAL / QUALITATIVE VARIABLES

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.

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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.

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

Bar charts

•Bar charts are used for nominal or ordinal data.

Years

No.

of

ciga

rett

es

Cigarette consumption of persons 18 years of age or older, United States, 1900 - 1990

Pie chart•Pie charts can also be used to display nominal or ordinal data.

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Gender distribution

Histogram

A histogram depicts a frequency distribution for ContinuousHistogram showing distribution of Age (years)