Download RSCH410

RSCH410
final
Observational / Descriptive Studies
Characteristics
Adv, disadv
Uses
Analyses
Interpretation
This lec Q’s will be MCQ not as case like in the other 3 studies
Characteristics
Case Reports
Careful and detailed report by one
or more clinicians of the profile of
a single patient
• e.g. previously un-described disease
• e.g. unexpected link between diseases
• e.g. unexpected new therapeutic effect
• e.g. adverse events
o
exposures (i.e. a case report gave the

clue that “oral contraceptives” use increases
of venous thromboembolism.
o

“Luck” in being the first to encounter an
interesting case.

o

Rigor in diagnosis, testing and
documentation of clinical findings
the risk
Strengths
• over one million case reports indexed on
Medline.
• uses language that is familiar to clinicians
and easy to interpret.
• useful reminder about conditions, diagnoses
etc. that are rarely seen in most practices.
• for researchers, case reports generate
hypotheses that can be tested using other
study designs.
Limitations:
• No appropriate comparison group.
• Cannot be used to test for presence of a
valid statistical association.
• Since based on the experience of one
person:
--presence of any risk factor
may be purely coincidental
--Not a true epidemiologic design.
• Tendency to publish reports of strange
conditions that have little relevance to
daily practice.
• Some authors erroneously try to imply
causation, therapeutic benefits, etc.
Case Series
Characteristics
 Experience of a group of patients with a
similar diagnosis.
 Cases may be identified from a single or
multiple sources.
 Generally report on new/unique condition.
 May be only realistic design for rare
disorders
Strengths:
• Useful for hypothesis generation.
• Used as an early means to identify the beginning
or presence of an epidemic.
• Can suggest the emergence of a new disease
(i.e. AIDS).
• Informative for very rare disease with
few established risk factors.
Limitations:
• Lack of an appropriate comparison
group
• Cannot be used to test for presence of a
valid statistical association
• Not a true epidemiologic design.
Cross-sectional studies

surveys exposures and disease status at a
single point in time (a cross-section of the
population)
Characteristics
 Often used to study conditions that are
relatively frequent with long duration of
expression (nonfatal, chronic conditions).
 It measures prevalence, not incidence of
disease.
 Not suitable for studying rare or highly fatal
diseases or a disease with short duration of
expression.
Strengths:
•
Provides prevalence estimates of exposure and
disease for a well-defined population.
•
Easier to perform than studies that require follow-up
(hence relatively inexpensive).
•
Can evaluate multiple risk (and protective) factors and
health outcomes at the same point in time.
May identify groups of persons at high or low risk of
disease
•
•
Can be used to generate hypotheses about
associations between predictive factors and disease
outcomes
Limitations :
•
Prevalent rather than incident (new) cases
are used – the exposure could be associated
with survival after disease occurrence,
rather than development of the disease
•
Temporal sequence between exposure
and disease cannot be established
i.e. Which came first, chicken or the egg?
• Usually don’t know when disease
occurred.
• Rare events: a problem
Ecologic studies
• Characteristics
Measures that represent characteristics of entire
populations are used to describe disease and
to postulate causal associations.
• Measure of interest is correlation between
exposure rates and disease rates among
different groups.
•
Strengths:
•
Cheap, quick, and simple (generally make
use of secondary data)
Limitations:
•
Cannot link exposure-disease relationship
at the individual level
•
Uses average exposure levels rather than
actual levels of exposure
•
Inability to control for confounding factors
Summary
• Observational studies are the starting point.
• Case Reports, Case Series and Cross-sectional
studies are useful for generation of hypotheses.
• Cross-sectional studies:
-provide prevalence estimates of
exposure
and disease
-may identify groups of persons at high or
low risk of disease
Case control , cohort, clinical trials
•
•
•
•
•
Characteristics
Adv, disadv
Uses
Analyses
Interpretation
MCQ’s on Case scenario like the examples in the lecs
Analysis ( odd ratio or risk factors )
‫البسط والمقام‬
Example
Cases: case control
Follow up : cohort
Effect or drug : clinical trails
Memorize the examples
Case - Control Studies
Definition:
It is a type of observational analytic
epidemiologic investigation in which
subjects are selected on the basis of
whether they do or do not have the
particular disease under study.
Importance:
• The most frequently undertaken analytical
epidemiological studies
• The only practical approach for identifying risk
factors for rare diseases
• They are best suited to the study of diseases
for which medical care is sought, such as
cancers or hip fracture
Design :
• At baseline:
• Selection of cases (disease) and controls (no disease) based
on disease status
• Exposure status is unknown
• Retrospective design – lacks temporality!
Selecting Cases:
• Study cases should be representative of all
cases
• The study need not include all cases in the
population
Incident cases are preferable to prevalent
cases for reducing
(a) recall bias and
(b) over-representation of cases of long
duration
Sources of cases and controls
CASES
All cases diagnosed in
the community
All cases diagnosed in a
sample of the population
CONTROLS
Sample of general
population
Non-cases in a sample of
the population
All cases diagnosed in
all hospitals
Sample of patients in all
hospitals who do not have
the disease
All cases diagnosed in a
single hospital
Sample of patients in the
same hospital who do not
have the disease
Any of the above
methods
Spouses, siblings or
associates of cases
Analysis: Odds Ratio (OR)
• A ratio that measures the odds of exposure for cases
compared to controls
• Odds of exposure = number exposed  number
unexposed
• OR Numerator: Odds of exposure for cases
• OR Denominator: Odds of exposure for controls
Analysis: Odds Ratio (OR)
Disease Status
Exposure
Status
Smoker
Nonsmoker
CHD cases No CHD
(Controls)
(Cases)
112 a b 176
88
Total
Odds Ratio = a
c
b
=
d
200
ad
bc
=
c d
224
400
112 x 224
176 x 88
= 1.62
Interpreting the Odds Ratio
OR<1
Odds of exposure
Odds
for cases are less
comparison
than the odds of
between cases
exposure for
and controls
controls
Exposure as a
risk factor for
the disease?
Exposure
reduces
disease risk
(Protective
factor)
OR=1
OR>1
Odds of
exposure are
equal among
cases and
controls
Odds of exposure
for cases are
greater than the
odds of exposure
for controls
Exposure is not
a risk factor nor
a protective
factor
Exposure
increases
disease risk
(Risk factor)
Analysis:Odds Ratio (OR)
Possible Sources of Bias and Error
• Information on the potential risk factor
(exposure) may not be available:
- either from records
- or the study subjects’ memories
• Cases may search for a cause for their
disease and thereby be more likely to report
an exposure than controls (recall bias)
• The investigator may be unable to determine
with certainty whether the suspected agent
caused the disease or whether the
occurrence of the disease caused the person
to be exposed to the agent
Advantages:
• Quick and easy to complete, cost effective
• Most efficient design for rare diseases
• Usually requires a smaller study population than a
cohort study
• Several exposures can be studied.
Disadvantages:
• Uncertainty of exposure-disease time
relationship
• Inability to provide a direct estimate of risk
• Not suitable for studying rare exposures
• Subject to biases (recall & selection bias)
Summary
Case-control study:
• Observational, analytic study.
• Most frequently undertaken analytical studies
• Quick and easy to complete, cost effective
• Most efficient design for rare diseases
• Subjects are selected on the basis of presence or
absence disease under study
• Odds Ratio (OR)
Cohort study
Definition:
A cohort is a group of people who share a
common characteristic or experience
Cohort study: An observational, longitudinal,
analytic epidemiologic study in which a
particular outcome, such as death from a heart
attack, is compared in groups of people who are
alike in most ways but differ by a certain
characteristic, such as smoking ( EXPOSURE )
Characteristics :




A “cohort” is a group of people, referred to as
“disease-free population” or “population at risk”
A survey is first carried out to exclude prevalent
cases from the cohort
A period of "follow-up“ is specified, for possible
new cases' occurrence
We know the exposure status, looking for the
disease status
Types:
Two types are recognized:
 Prospective (longitudinal): forward in
time follow-up study

Retrospective (historical): backward in
time study (depends on records:
medical / employment). This is the type
preferred under occupational settings.
Advantages:





No / little temporal ambiguity (suggests
cause-effect relationship)
Calculation of incidence rates
Suitable for rare exposures
Factors associated with selection cannot
influence disease status and hence the
results.
Several outcomes can be studied, after
follow-up starts.
Disadvantages (of prospective):
Expensive
 Time-consuming
 May be impractical
 Loss to follow-up may affect sample-size

INDICATIONS (uses): ??
When there is good evidence of exposure
and disease.
 When exposure is rare but incidence of
disease is higher among exposed
 When follow-up is easy, cohort is stable
 When ample funds are available

Problems during Follow-up :






Follow-up of a large group.
Limited resources.
Time scarcity.
Paucity of trained personnel
Attrition
Ethical concerns
Attrition Reduction :





Obtaining an informed consent.
Recording commitment to continue and
cooperate in the study.
Tracing LOST subjects.
Considering Information of lost subjects at
the time of analysis
Keeping non-response at a low level to
improve the validity.
Analysis

Calculation of incidence rates among exposed
and non exposed groups

Estimation of risk
ANALYSIS
Disease Status
Exposure
Status
Yes
No
Total
Incidence
rate
Yes
No
a
b
a+b
d
b+d
c+d
c
a+c
Total
N
Study
cohort
Comparison
cohort
a
Incidence among exposed = a+b
c
Incidence among unexposed = c+d
ANALYSIS
Estimation of risk
Relative Risk (RR)
incidence of disease among exposed
_____________________________
RR =
Incidence of disease among non-exposed
a/a+b
_________
RR =
c/c+d
ANALYSIS
Estimation of risk
Attributable Risk (AR):
AR =
Incidence of
Incidence of
disease among
disease among
exposed
non exposed
_______________________________
Incidence of disease among exposed
AR =
a/a+b – c/c+d
_______________
a/a+b
Smoking
Lung cancer
Total
Smoking
YES
NO
Total
YES
70
6930
7000
NO
3
2997
3000
Total
73
9927
10000
Calculate RR and AR for above data
Incidence of lung cancer among smokers
70/7000 = 10 per 1000
 Incidence of lung cancer among non-smokers
3/3000 = 1 per thousand
RR = 10 / 1 = 10
(lung cancer is 10 times more common among smokers than non
smokers)
AR = 10 – 1 / 10 X 100
= 90 %
(90% of the cases of lung cancer among smokers are attributed to
their habit of smoking)

The Ideal Cohort :
An ideal cohort should be:
 Stable.
 Cooperative.
 Committed
 Well-informed
Research Methodology
STUDY DESIGNS
Experimental studies
Clinical trials
Definition:
A planned experiment on humans.
The setting is usually a health institutions
and it usually involves patients.
Rationale:
Before a new treatment method is made
available to the public it must be studied
and tested for safety and effectiveness.
Research Methodology
STUDY DESIGNS
Experimental studies
Clinical trials
Types of Trials
Treatment
 Prevention
 Diagnostic
 Screening
 Quality of Life

Research Methodology
STUDY DESIGNS
Experimental studies
Clinical trials
 Analyze study data and interpret the results
• Analysis follows study protocol
• Type of analysis is dictated by
goals and objectives
• Minimize bias and uncertainty
 incomplete data
 terminology
 attrition
 specified analyses
 statistical analytic plan
Research Methodology
STUDY DESIGNS
Experimental studies
Clinical trials
Steps of clinical trial
 Analyze study data and interpret the results
Relative Risk(RR):
outcome
Group
Intervention
Control
Total
RR=
Positive
Negative
a
c
b
d
Experimental Event Rate (EER)
-------------------------------Control Event Rate (CER )
Total
a+b
c+d
Research Methodology
STUDY DESIGNS
Experimental studies
Clinical trials
Steps of clinical trial
 Analyze study data and interpret the results
Relative Risk(RR):
outcome
Group
Ligation
Sclerotherapy
Total
RR=
Death
Survival
18
32
46
33
Experimental Event Rate (EER)
-------------------------------Control Event Rate (CER )
Total
64
65
Research Methodology
STUDY DESIGNS
Experimental studies
Clinical trials
Steps of clinical trial
 Analyze study data and interpret the results
Relative Risk(RR):
RR=
Experimental Event Rate (EER)
-------------------------------Control Event Rate (CER )
EER = a/(a+b) =18/64
Outcome
CER = c/(c+d) =32/65
18/64
RR= ---------- = 0.57
32/65
Statistical power
Group
Dea Surv Total
th
ival
46
64
Ligation 18
33
65
Scleroth 32
erapy
Research Methodology
STUDY DESIGNS
Experimental studies
Clinical trials
Women’s Health Initiative
RQ: Does calcium plus vitamin D reduce risk of
fractures in postmenopausal women?
Design: Randomized trial
Subjects: 36,282 PM women enrolled in WHI
Intervention: 1 gm calcium + 400 IU vitamin D
Outcome: clinical fractures
Adherence at end of trial 60% and about 60% of
placebo group was taking calcium
Research Methodology
STUDY DESIGNS
Experimental studies
Clinical trials
Follow-up
RQ: Does diet and exercise reduce risk of
developing type 2 diabetes in persons with
glucose intolerance?
Design: Randomized trial
Subjects: 2500 with glucose intolerance
Intervention: low fat weight loss diet and
moderate intensity aerobic exercise
outcome = development of frank diabetes
Research Methodology
STUDY DESIGNS
Experimental studies
Clinical trials
Diet and Exercise to Prevent Diabetes in
Persons with Glucose Intolerance
DM
No DM
D&E
65
560
No D& E
125
500
625
625
1250
190
1060
RR = .5; p = .001
Research Methodology
STUDY DESIGNS
Experimental studies
Clinical trials
Conclusions &
Take Home Message





Clinical trials often yield important results that
affect health and well being
Must follow guidelines & protocol
Must ensure well-being of participant
Clinical trials are susceptible to human error
either on part of investigator or patient
Rank high in the ladder of causality
Know RF for DM, HTN, Breast ,cervical cancer
Non-Communicable Disease
Diabetes
Top 7 Risk Factors
for Type 2 Diabetes
1. Obesity
2. Sedentary Lifestyle
3. Unhealthy Eating Habits
4. Family History and Genetics
5. Age
6. Hypertension and High Cholesterol
7. History of Gestational Diabetes
Non-Communicable Disease
Hypertension
Primary Hypertension
Risk Factors
Age
Alcohol
Cigarette Smoking
Diabetes Mellitus
Elevated serum lipids
Excess Na+ in diet
Gender
Family History
Obesity
Sedentary Lifestyle
Stress
Non-Communicable Disease
Ca Cervix
Risk Factors of Ca Cervix







Human papilloma virus infection
Smoking
Immunosuppression
Chlamydia infection
Diet
Overweight
Oral contraceptives
 Multiple full-term pregnancies
 Young age at the first full-term pregnancy
 Poverty
 Family history of cervical cancer
Non-Communicable Disease
Ca Breast
Risk Factors of Ca Breast
Reproductive Risk Factors
•
•
•
•
•
•
Early menarche.
Late menopause.
Being older at the birth of the first child.
Nulliparous.
Not breastfeeding.
Long-term use of hormone-replacement therapy.
Other Risk Factors
•
•
•
•
•
Age.
Family history of breast cancer
Being overweight (after menopause).
Alcohol (more than one drink a day).
Inactivity.
CHP400:
Community Health Program-lI
SCREENING
Know Validity (sensitivity- specificity)
Reliability
Predictive values
SCREENING
Suitable Screening Test:
• Validity:(Sensitivity, Specificity)
• Reliability:(repeatability/precision)
• Yield (performance): Predictive values of
the test.
SCREENING
Validity of Screening Test:
How good is the screening test compared
with the confirmatory diagnostic test
(Gold Standard test)?

The test will correctly classify a
diseased person as likely to have the
condition (“sensitivity”).

The test will correctly classify a nondiseased person as unlikely to have the
condition (“specificity”).
SCREENING
Validity of Screening Test:
Screening test compared to gold standard
Gold standard
Screening test
Positive
Positive
(TP)
Negative
(FN)
Total
TD
Negative
a b
c d
Total
(FP)
PS
(TN)
NS
TH
GT
SCREENING
Validity of Screening Test:
True Disease Status
+
-
+
a
b
-
c
d
Sensitivity: The probability of testing
positive if the disease is truly present
Sensitivity = a / (a + c)
SCREENING
Validity of Screening Test:
True Disease Status
+
-
+
a
b
-
c
d
Specificity: The probability of screening
negative if the disease is truly absent
Specificity = d / (b + d)
SCREENING
Validity of Screening Test:
Disease
D
No D
Test
90 a b 5
10 c d 95
105
100
200
100
95
Sensitivity: a / (a + c) = 90/100 =90%
Specificity: d / (b + d) = 95/100 =95%
Prevalence of disease =(a+c)/(a+b+c+d)
=100/200 =50%
SCREENING
Reliability of Screening Test:
RELIABILITY (Reproducibility) Precision:
The extent to which the screening test will
produce the same (or very similar) results
each time it is administered (repeated).
--- A test must be reliable before it can be valid.
SCREENING
Yield(Performance) of Screening Test:
Yield is the amount of previously unrecognized
disease that is diagnosed and brought to
treatment as a result of screening.
It is measured by:

Predictive Value Positive (PV+)

Predictive Value Negative (PV-)
SCREENING
Yield(Performance) of Screening Test:
True Disease Status
+
Results of +
Screening
Test
-
a
b
c
d
Predictive value positive (PV+): The probability that a
person actually has the disease given that he or she
tests positive. i.e. The ability to predict the presence of
disease from test results.
PV+ = a / (a + b)
SCREENING
Yield(Performance) of Screening Test:
True Disease Status
+
Results of +
Screening
Test
-
a
b
c
d
Predictive value negative (PV-): The probability that a
person is truly disease free given that he or she
tests negative. i.e. The ability to predict the absence
of disease from test results.
PV= d / (c + d)
SCREENING
Yield(Performance) of Screening Test:
Disease
D
No D
Test
19 a b 99
1 c d 1881
1882
20
2000
1980
Calculate:
• PV+ =19/118=16%
• PV-= 1881/1882=99.95%
118
SCREENING
Yield(Performance) of Screening Test:
Disease
D
No D
Test
57 a b 2
3 c d 38
59
60
100
Calculate:
• PV+ =57/59=96.6%
• PV-= 38/41=93%
40
41