Research Methodology 1Introduction to Community Health II

Kriengkrai Srithanaviboonchai MD, MPHDepartment of Community Medicine

15 Sep 2017

Research design

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Basic AppliedBasic Research is driven by a scientist's curiosity or interest in a scientific question. The main motivation is to expand man's knowledge, not to create or invent something. There is no obvious commercial value to the discoveries that result from basic research.

Applied research is designed to solve practical problems of the modern world, rather than to acquire knowledge for knowledge's sake. One might say that the goal of the applied scientist is to improve the human condition.

Research design

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Qualitative Quantitative

Research design

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Qualitative QuantitativeTopic Human behavior Nature

Variable Reason, opinion Heart rate, cure/not cure

Data Text, story Number, count

Data collection Focus group, observation Measurement

Sample size Small Large

Research design

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Qualitative Quantitative+

Mixed method

Quantitative research design

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Alter the events under study?

yes no

Experiment Observational study

Make measurements on more than one occasion?

yes no

Longitudinal study Cross-sectional study

Observational research

The researcher does not interfere, just observes and measures what happens in the nature

Can be divided into descriptive and analytic (more details later)

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Experimental research

Other names: intervention study, clinical trial The researcher gives intervention (exposure,

independent variable) Must have comparison(s) Always analytic

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Observational research

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Descriptive Analytic Describe distribution/rate of

diseases (or conditions) in terms of person, time, and place

No comparison or hypothesis

Compare data between variables Has hypothesis, comparison

Observational research

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Descriptive Analytic

Incidence Prevalence Cross-sectional Case-control Cohort

Cross-sectional study

Other name: survey The researcher measures exposure and disease at

the particular point in time If prevalence of disease is higher in exposed

individuals then the exposure may be the cause of disease

Pros: quick, cheap, yields prevalence Cons: not good in explaining causation

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Cross-sectional designThe present

Risk factor;

Disease

Risk factor;

No disease

No risk factor;

Disease

No risk factor;

No diseasePopulation

1. Select a sample from the population

2. Measure predictor and outcome variables at the same time

Sample

Contingency (2 by 2) Table

Disease No disease

Exposed a b a + b

Non-exposed

c d c + d

a + c b + d a+b+c+d

Relative risk (RR)

Other name: Risk ratio Relative measure of association Ratio of risk of having disease in exposed group

and risk of having disease in non-exposed group

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RR = Risk of having disease in exposed

Risk of having disease in unexposed

Prevalence ratio (PR)

Special type of RR for cross-sectional study Ratio of prevalence of disease in exposed group

and prevalence of disease in non-exposed group

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PR = Prevalence of disease in exposed

Prevalence of disease in unexposed

PR in cross-sectional study

Disease No disease

Exposed a b a + b

Non-exposed

c d c + d

a + c b + d a+b+c+d

=a / (a + b)

c / (c + d)

PR = Prevalence of disease in exposed

Prevalence of disease in non-exposed

Cohort design

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Prospective Retrospective

Prospective cohort study

Other names: follow-up study, longitudinal study Observe healthy subjects for a period of time If incidence of disease is higher in exposed

individuals then the exposure may be the cause of disease

Pros: good in explaining the causation, yields incidence,

Cons: expensive, take long time, need large sample size

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Prospective cohort design

1. Select a sample from the population

2. Measure predictor variables

3. Follow-up the cohort

4. Measure outcome variables

Population

Sample

DiseaseNo

disease

DiseaseNo

diseaseRisk factor present

Risk factor absent

The presentThe future

Retrospective cohort study

Other name: historical cohort Assemble of cohort and exposure measurements

were done in the past The researcher measures or acquires incidence

data at present time Pros: quick, cheap Cons: availability and quality of data depend on the

others

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Retrospective cohort design

1. Identify a cohort that has been assembled in the past

2. Collect data on predictor variables (measured in the past)

3. Follow-up the cohort

4. Collect data on outcome variables (measured in present)

Population

Sample

DiseaseNo

disease

DiseaseNo

diseaseRisk factor present

Risk factor absent

The pastThe present

Incidence rate ratio (IRR)

Special type of RR for cohort study Ratio of incidence of disease in exposed group and

incidence of disease in non-exposed group

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IRR = Incidence of disease in exposed

Incidence of disease in unexposed

IRR in cohort study

Disease No disease

Exposed a b a + b

Un-

exposedc d c + d

a + c b + d a+b+c+d

IRR =Incidence of having disease in exposed

Incidence of having disease in non-exposed

=a / (a + b)

c / (c + d)

Case-control study

Other name: retrospective study Start with a group of patient (case) and then find

another group who are not sick (control) Ask both group about exposure to potential risk

factor in the past If case exposed more than control then the potential

risk factor may be the cause of disease Pros: quick, efficient, good for rare diseases Cons: prone to bias, no prevalence and incidence

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Case-control design

Population

with disease

(cases)

DiseaseSample

of cases

Much large

population

without

disease

(controls)

No disease

Sample

of controls

Risk

Factor

present

Risk

Factor

absent

Risk

Factor

present

Risk

Factor

absent

The presentThe past

1. Select a sample from a population of people with the disease (cases)

2. Select a sample from a population at risk that is free of the disease (controls)

3. Measure predictor variables

Probability and Odds

Probabilities: proportion of chance of having interesting events out of all possibilities

Odds: ratio of chance of having interesting events over chance of having non-interesting events

Throwing a dice:- Probability of getting 1 = 1/6 - Odds of getting 1 = (1/6) / (5/6)

= 1/5

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Probabilities and odds

Odds1

OddsobabilityPr;

obabilityPr1

obabilityPrOdds

Probability Odds0.001 0.001

0.01 0.010

0.02 0.020

0.05 0.053

0.10 0.111

0.20 0.250

0.25 0.333

0.50 1.00

0.80 4.00

0.90 9.00

0.98 49.00

0.99 99.00

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 0.1 0.2 0.3 0.4 0.5

Probability

Od

ds

Odds ratio (OR)

Since there is no prevalence or incidence in case-control study.

It is not possible to calculate RR. OR is used instead in case-control study

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OR =Odds of exposed in a case

Odds of exposed in a control

Odds Ratio (OR)

Case Control

Exposed a b a + b

Un-

exposedc d c + d

a + c b + d a+b+c+d

OR =Odds of exposed in a case

Odds of exposed in a control

= a/c b/d = (a)(d)

(b)(c)

RR and OR

No unit Could be ranged from 0 to Same ways of interpretation

1 = No association>1 = Positive association (exposure is a risk

factor)<1 = Inverse association (exposure is a protective

factor)30

Quantitative research design

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Observational Experimental

Experimental research (Clinical trial)

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Prevention trial Therapeutic trial

Field trial Community

Prevention (primary prevention) trial

Experiment to find if an intervention helps risk of developing disease in healthy people

Field trial (Individual prevention trial): unit of measurement is individual

Community prevention trial: unit of measurement is group of people / community

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Dr. Jonas Salk and Polio vaccine (1955)

Patients in iron lungs during 1952 epidemic

Therapeutic (Secondary prevention) trial

Experiment in patients who get sick with the disease of interest

Intervention could be new medication, surgery, etc. To see if the intervention help improve clinical course

or outcomes of treatment

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Phases of clinical trial

animal/in vitro

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Post marketing surveillance

2 3

Clinical study

Healthy volunteers10 -100

Pharmacokinetic/dynamic

Initial safety

Patients100-1,000

EfficacyFurther safety study

Patients1,000-10,000

Confirm efficacyRegister if success

RCT

Symbols use to explain the study designs

O Observation, Measure

X Intervention, Program

N No randomization

R Randomization

Studies without comparison

X O

•No control group

•Hard to conclude whether the improvement is the resultof the intervention

•Case study, case series

Studies with comparison: Historical Controls

X O

•Use patients and treatment in the past as control

•Differences may cause from better data collection, otherfactors that has changed through time

•Can be used in uniformly fatal disease (Rabies)

X O

TimelinePresentPast

InterventionControl

Studies with comparison: non-randomized design

•Simultaneous control group

•non-randomized, assignment system is predictable

•The results may be confound by other factors

N X ON O

Quasi experiment

Studies with comparison: Randomized controlled trial (RCT)

•Simultaneous control group

•Assignment system is unpredictable (randomized)

•No confounding by other factors if random properlyand sample size is large enough

R X OR O

•Current gold standard of clinical trial design

Randomized controlled trial (RCT)

Current gold standard of clinical study design Most phase III clinical trial are RCT Best design to explain cause and effect between

variables Characteristics

- Randomization- Control group comparison- Blinding

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Randomized controlled trial (RCT)

Intervention

Control

Bad

Outc.

Bad

Outc.

Good

Outc.

Good

Outc.

Population

Sample

Randomize

The present The future

1. Select a sample from the population

2. Measure baseline variables

3. Randomize

4. Apply interventions (one should be a blinded placebo, if possible)

5. Follow up the cohorts

6. Measure outcome variables

Other clinical trial designs Run-in design Factorial design Crossover design Natural experiments

Run-in design Ask potential participants to take placebo for a period

of time (2-3 weeks) Invite only people who show are really commit, show

good compliance, and has discipline into the study Increase proportion of participants who complete the

study Improve efficiency of the study

Special designs: Factorial Study two treatments in

one study The mode of actions and

outcomes have to be independent

Effective and cost saving Burden of side effects in

‘cell a’

BothA and B

(cell a)

B only

(cell b)

A only

(cell c)

NeitherA nor B

(cell d)

+

+

-

-

Treatment A

TreatmentB

Special designs: Factorial

BothA and B

(cell a)

B only

(cell b)

A only

(cell c)

NeitherA nor B

(cell d)

+

+

-

-

Treatment A

TreatmentB

Hypothesis 1(compare cell a + bwith cell c + d)

Hypothesis 2(compare cell a + cwith cell b + d)

Special designs: Crossover

Participants serve as self-control Subjects are randomized to be in treatment or control

first After being observed for a certain period of time, the

patients are switched to the other therapy There must be enough of a washout period if there is a

carryover effect

Special designs: Natural experiment The researcher does not assign the exposure The intervention was done by the others Most textbooks don’t define this as real experiment

Clinical

studies of

atomic bomb victims

Limitations of Experimental studies

Expensive Take long time Ethical issues Not suitable for rare disease Difficulty in studying

- Rare events- Outcomes in distant future