Epidemological methods

Epidemiologic Methods:
Simplified
Dr Kundan
Department of Surgical Oncology
Mahavir Cancer Sansthan
Bihar , India

Study Design and Its Strength
of Evidence
1. Systematic review, meta-analysis:
secondary data analysis
2. Randomized Controlled Trials (RCT)
3. Cohort: prospective or retrospective
Quasi experiment
4. Case control: prospective or retrospective
5. Cross sectional
6. Case Reports / Case Series
Strongest
evidence
Weakest
evidence

• Study of the distribution and determinants of
health-related states or events in specified
populations and the application of this study to
control health problems.
John M. Last, Dictionary of Epidemiology
• An example of such applications is the lung
cancer study conducted by Doll and Hill in the
early 1950s, which linked tobacco smoking to an
increased mortality of lung cancer in over 40,000
medical professionals in the United Kingdom.

• Observational studies do not involve the
artificial manipulation of study regimens.
• Descriptive studies focus on the distribution of
diseases with respect to person, place, and
time .

Procedure in descriptive study
• Defining the population to be studied
• Defining the disease under study
• Describing the disease by Time / Place /
Person
• Measurement of disease
• Comparing with Known indices
• Formulation of Hypothesis

Analytical study
• Subject of interest is Individual
• Objective is to test hypothesis

cross-sectional study
• In a cross-sectional study, the information on
various factors is collected from the study
population at a given point in time.
• However, it should be noted that cross-sectional
studies have serious methodological limitations if
the research purpose is etiologic inference.
• Because exposures and disease status are
evaluated simultaneously, it is usually not
possible to know the temporality of events unless
the exposure cannot change over time

Cohort Study
• Cohort studies follow groups of individuals over time to investigate
the causes of disease, establishing links between risk factors and
outcomes.
• Cohort studies prospectively proceed from exposure to outcome.
• Investigators identify groups with and without an exposure of
interest, and then follow the exposed and unexposed groups over
time to determine outcomes.
• As the study is conducted, outcome from subjects in each cohort is
measured and relationships with specific characteristics
determined. If the exposed group has a higher incidence of the
outcome than the unexposed, then the exposure is associated with
an increased risk of the outcome.

Advantages
• Subjects in cohorts can be matched, which limits the
influence of confounding variables
• Standardization of criteria/outcome is possible
• Easier and cheaper than a randomized controlled trial (RCT)
Disadvantages
• Cohorts can be difficult to identify due to confounding
variables
• No randomization, which means that imbalances in patient
characteristics could exist ( selection bias )
• Blinding/masking is difficult
• Outcome of interest could take time to occur

Case Control Study
• A study that compares patients who have a
disease or outcome of interest (cases) with
patients who do not have the disease or outcome
(controls), and looks back retrospectively to
compare how frequently the exposure to a risk
factor is present in each group to determine the
relationship between the risk factor and the
disease.
• Case control studies are also known as
"retrospective studies" and "case-reference
studies."

Advantages
• Good for studying rare conditions or diseases
• Less time needed to conduct the study because the condition or disease
has already occurred
• Lets you simultaneously look at multiple risk factors
• Useful as initial studies to establish an association
• Can answer questions that could not be answered through other study
designs
Disadvantages
• Retrospective studies have more problems with data quality because they
rely on memory and people with a condition will be more motivated to
recall risk factors (also called recall bias).
• Not good for evaluating diagnostic tests because it’s already clear that the
cases have the condition and the controls do not
• It can be difficult to find a suitable control group

Statistics concepts
A simplified approach

MEAN
• Otherwise known as an arithmetic mean, or average.
• It is used when the spread of the data is fairly similar
on each side of the mid point, for example when the
data are “normally distributed”.
If a value (or a number of values) is a lot
smaller or larger than the others, “skewing”
the data, the mean will then not give a good
picture of the typical value.

MEDIAN
• the mid-point
• It is used to represent the average when the
data are not symmetrical, for instance the
“skewed” distribution.

• Using the first example of five patients aged 52,
55, 56, 58 and 59, the median age is 56, the same
as the mean – half the women are older, half are
younger.
• However, in the second example with six patients
aged 52, 55, 56, 58, 59 and 92 years, there are
two “middle” ages, 56 and 58. The median is
halfway between these, i.e. 57 years. This gives a
better idea of the mid-point of this skewed data
than the mean of 62

Box and whisker plot of energy intake of 50 patients
over 24 hours. The ends of the whiskers represent the
maximum and minimum values,

MODE
• The mode is the most common of a set of
events.
• bi-modal distribution

• Measures of dispersion or variability provide
information regarding the relative position of other
data points in the sample.
• Such measures include the following: range, inter-
quartile range, standard deviation, standard error of
the mean (SEM), and the coefficient of variation.
• Range is a simple descriptive measure of variability. It
is calculated by subtracting the lowest observed value
from the highest.
• The most commonly used measures of dispersion
include variance and its related function, standard
deviation,

STANDARD DEVIATION
• SD indicates how much a set of values is spread around the average.
• Standard deviation (SD) is used for data which are “normally
distributed” to provide information on how much the data vary
around their mean.
• SD indicates how much a set of values is spread around the average.
- 1 SD includes 68.2% of the values.
- ±2 SD includes 95.4% of the data.
- ±3 SD includes 99.7%.

HOW TO CALCULATE STANDARD DEVIATION
• it is not necessary to know how to calculate
the SD.

The Normal (Gaussian) distribution
completely described by two parameters, the mean (m) and
the variance (s2);
• bell-shaped (unimodal);
• symmetrical about its mean;
• shifted to the right if the mean is increased and to the left
if the mean is decreased (assuming constant variance);
• flattened as the variance is increased but becomes more
peaked as the variance is decreased (for a fixed mean).
• Additional properties are that:
• the mean and median of a Normal distribution are
equal;

Evaluating Diagnostic and Screening
Tests

• it is important to consider the validity (i.e. sensitivity and
specificity) as well as the predictive value (i.e. positive and
negative predictive values) of the test.
• Sensitivity is the probability that a person will test positive
given that they have the disease (D+).
• Specificity is the probability that a person will test negative
(T-) given that they do not have the disease (D-).
• Positive predictive value (PPV) is the probability that the
disease is truly present (D+) given that the test result is
positive (T+).
• Negative predictive value (NPV) is the probability that the
disease is truly absent (D-) given that the test result is
negative (T-).

Common Measures of Association
and Statistical Tests

Odds
• The term means a disease or effect happening versus
not happening
• Supposing that 10 out of 100 patients of acute
myocardial infarction would die, the odds are 10 will
die and 90 will live. So, the Odds are 10/90 = 0.11
(Happens/Not Happen)
• Now a medical paper says that there is a new drug ABC
shows benefit in reducing death rate of myocardial
infarction. On being treated with the new drug, only 2
out of 100 acute myocardial infarction cases died. This
means 2 dies and 98 live. So Odds for this new
treatment are 2/98 = 0.02

ODD RATIO
• Odds Ratio = control odds/Treatment Odds =
0.11/0.02 = 5.5
• This means treatment with this new drug
reduces chance of death by 5.5 times

RISK AND RISK RATIO
• Risk is a similar term that means disease or
effect out of the entire population.
In the same example of the heart attack above,
the risk of death is 10 out of 100 (not 10/90 as
in odds) Risk of death in MI 10/100 = 0.10
(Happens/Total) Similarly risk after being
administered ABC has a risk of 2/100 = 0.02

RISK RATIO
• Risk ratio = 0.10/0.02 = 5 (very close to odds ratio)
• In most cases, odds ratio and risk ratio is close
• Now consider,
a mortality of 90% (90 out of 100 die
the odds would be 90/10 (died 10 survived) returning
a value of odds of 9,
while the risk ratio would be 90/100 = 0.9.
So, dichotomy between odds and risk indicate high
event rate in control group and this may corrupt a
study.

Hazard ratio
Hazard ratio (HR) is a measure of an effect of an intervention on an outcome of interest
over time.
Hazard ratio is reported most commonly in time-to-event analysis or survival analysis (i.e.
when we are interested in knowing how long it takes for a particular event/outcome to
occur).
The outcome could be an adverse/negative outcome (e.g. time from treatment/surgery
until death/relapse) or a positive outcome (e.g. time to cure/discharge/conceive/heal or
disease-free survival).
Hazard Ratio (i.e. the ratio of hazards) = Hazard in the intervention group ÷ Hazard in the
control group
Hazard represents the instantaneous event rate, which means the probability that an
individual would experience an event (e.g. death/relapse) at a particular given
point in time after the intervention, assuming that this individual has survived to that
particular point of time without experiencing any event.

• Because Hazard Ratio is a ratio, then when:
HR = 0.5: at any particular time, half as many
patients in the treatment group are
experiencing an event compared to the control
group.
HR = 1: at any particular time, event rates are
the same in both groups,
HR = 2: at any particular time, twice as many
patients in the treatment group are experiencing
an event compared to the control group.

• One of the main differences between risk ratio
and hazard ratio is that risk ratio does not care
about the timing of the event but only about
the occurrence of the event by the end of the
study (i.e. whether they occurred or not: the
total number of events by the end of the
study period).
• In contrast, hazard ratio takes account not
only of the total number of events, but also of
the timing of each event.

CONFIDENCE INTERVALS
• A range (interval) in which we can be fairly sure
(confident) that the “true value” lies.
• Standard deviation and confidence intervals – what is
the difference?
Standard deviation tells us about the variability (spread)
in a sample.
The CI tells us the range in which the true value (the
mean if the sample were infinitely large) is likely to be.

Confidence interval (CI)
• The confidence interval indicates the level of uncertainty
around the measure of effect (precision of the effect
estimate) which in this case is expressed as an OR.
• Confidence intervals are used because a study recruits only
a small sample of the overall population so by having an
upper and lower confidence limit we can infer that the
true population effect lies between these two points.
• Most studies report the 95% confidence interval (95%CI).
• If the confidence interval crosses 1 e.g. 95%CI 0.9-1.1 this
implies there is no difference between arms of the study.

• z*–values for Various Confidence Levels
• Confidence Level z*-value
80% 1.28
90% 1.645
95% 1.96
98% 2.33
99% 2.58

Confidence interval = 0.83 (CI 0.88–0.98) -
The upper value is below 1. So, it is statistically
significant. However, clinical significance of a value is considered
only when it is below 0.85. So, in this case, it is statistically
significant, but clinically not significant
Confidence interval = 0.78 (CI 0.76–0.97)
Here the upper value is below 1, while the lower
one is below 0.85. Hence, it is statistically significant and may be
clinically significant
Confidence interval = 0.78 (CI 0.57–1.12)
Here the value wraps around 1, hence statistically
not significant. However, since the lower value is below 0.85, it may
still be clinically significant. In such cases, a repeat trial is indicated.
Confidence interval = 0.59 (CI 0.43 – 0.76)
In this case, both the upper and lower values are below
0.85, so it is statistically as well as clinically significant.

Measure of Disease Frequency
1. Cumulative Incidence (Incidence, Risk, I, R)=
Number of new case over a time period
Population at risk at the outset
- Indicates the risk for the disease to occur in population at risk over a time
period. Value from 0 to 1.
2. Incidence Density (Incidence Rate, ID, IR)=
Number of new case over a time period
Person time at risk
Indicates the velocity (speed) of the disease to occur in population over a time
period. Value from 0 to infinity
3. Prevalence (Point Prevalence):
Number of new and old cases at a point of time
Population
Indicates burden of disease. Value from 0 to 1.

P VALUES
• The P value gives the probability of any observed
difference having happened by chance.
• The P value gives the probability that the null
hypothesis is true.
• Null hypothesis - There is no significant
difference between specified populations, any
observed difference being due to sampling or
experimental error.
• The lower the P value, the less likely it is that the
difference happened by chance and so the higher
the significance of the finding.

• P = 0.05 is usually classed as “significant”
• P = 0.01 as “highly significant”
• P = 0.001 as “very highly significant”

Parametric statistics
A- One sample
• 1. Single Sample z Test
• 2. Single-Sample t Test
B- Two or more independent samples
• 1. t Test for Two Independent Samples
• 2. One Way Analysis of Variance (ANOVA)
• 3. Analysis of Covariance (ANCOVA)
C- Two or More Dependent Samples
1. t Test for Two Dependent Samples
2. Single Factor Within-Subjects ANOVA

Ordinal (Rank Order) Data -
Nonparametric Tests – Median Tests
• A. One Sample
• 1. Wilcoxon Signed-Ranks Test
B. Two or more independent samples
• 1. Mann-Whitney U Test
• 2. Kruskal-Wallis One-Way Analysis of Variance by
Ranks
C. Two or more dependent samples
• 1. Wilcoxon Matched Pairs Signed Ranks Test
• 2. Binomial Sign Test for Two Dependent Samples
• 3. Friedman Two-Way Analysis of Variance by Ranks

Introduction to Clinical Trials

Types of Clinical Research
1. Case Reports
Anecdotal  Problem
2. Observational
a. Case Control/Retrospective (lung cancer)
b. Cross Sectional
c. Prospective / Cohart
 Risk Factor Associations
3. Drug Development
(Phase 0, Phase I, & Phase II)
 Dose and activity
4. Experimental (Clinical Trial) Phase III
 “Effect”

Phases of Clinical Trials (Cancer)
Phase 0 - Preclinical
•Preclinical animal studies
•Looking for dose-response
Phase I
•Seeking maximum tolerated dose (MTD)
•Patients usually failed other alternatives
Phase II
•Estimate of drug activity
•Decide if drug warrants further testing (Phase III)
•Estimate of serious toxicities

Phase III
• Provide effectiveness of drug or therapy
• Various designs
– No control
– Historical control
– Concurrent
– Randomized
• Testing for treatment effect
Phase IV
• Long term post Phase III follow-up
• Concern for safety
Phases of Clinical Trials (Cancer) [2]

Phase I Design
Typical/Standard Design
• Based on tradition, not so much on statistical theory
• Dose escalation to reach maximum tolerated dose
(MTD)
• Dose escalation often based on Fibonacci Series
1, 1 , 2, 3 , 5 , 8 , 13 . . . .

Typical Scheme
1. Enter 3 patients at a given dose
2. If no toxicity, go to next dosage and repeat step 1
3. a. If 1 patient has serious toxicity, add 3 more
patients at that does (go to 4)
b. If 2/3 have serious toxicity, consider MTD
4. a. If 2 or more of 6 patient shave toxicity, MTD
reached (perhaps)
b. If 1 of 6 has toxicity, increase dose and go back
to step 1

Standard Phase I Design
• Designed to find dose where 1/3 of
patients experience dose limiting toxicity
(DLT)
• Standard escalation design tends to
underestimate target dose

Dose-response curves
used in simulations (1)

Dose-response curves
used in simulations (2)

Summary of Designs
A. “Standard”
– Observe group of 3 patients
– No toxicity increase dose
– Any toxicity  observe 3 or more
• One toxicity out of 6  increase dose
• Two or more toxicity  stop
B. “1 Up, 1 Down”
– Observe single patients
– No toxicity  increase dose
– Toxicity  decrease dose

Summary of Designs
C. “2 Up, 1 Down”
– Observe single patients
– No toxicity in two consecutive  increase dose
– Toxicity  decrease dose
D. “Extended Standard”
– Observe groups of 3 patients
– One toxicity  dose unchanged
– Two or three toxicity  decrease dose

Summary of Designs
E. “2 Up, 2 Down”
– Observe groups of 2 patients
– One toxicity  dose unchanged
– Both toxicity  decrease dose
B, C, D, E - fixed sample sizes ranging from 12
to 32 patients
Can speed up process to get to target dose
range

Phase II Design
• Goal
– Screen for therapeutic activity
– Further evaluate toxicity
– Test using MTD from Phase I
– If drug passes screen, test further

Phase II Design
• Design of Gehan
– No control (?)
– Two stage (double sampling)
– Goal is to reject ineffective drugs ASAP
Decision I: Drug is unlikely to be effective in  x%
of patients
Decision II: Drug could be effective
in  x% of patients

Phase II Design
• Typical Gehan Design
– Let x% = 20%
– That is, want to check if drug likely to work in at
least 20% of patients
1. Enter 14 patients
2. If 0/14 responses, stop and
declare true drug response 20%
3. If 1+/14 responses, add 15-40
more patients
4. Estimate response rate

Phase II Design (Why 14 failures?)
• Compute probability of consecutive failures
• If drug  20% effective, there would be ~95.6%
chance of at least one success
• If 0/14 success observed, reject drug
Patient Prob
1 0.8
2 0.64 (0.8 x 0.8)
3 0.512 (0.8 x 0.8 x 0.8)
--- ---
8 0.16
--- ---
14 0.044

Phase II Trials
• Many – most cancer Phase II trials follow this design
• Many other diseases could – there seems to be no
standard non-cancer Phase II design
• Might also randomize patients into multiple arms
each with a different dose – can then get a dose
response curve

• The foundation for the design of controlled experiments established for
agricultural experiments
• The need for control groups in clinical studies recognized, but not widely
accepted until 1950s
• No comparison groups needed when results dramatic:
– Penicillin for pneumococcal pneumonia
– Rabies vaccine
• Use of proper control group necessary due to:
– Natural history of most diseases
– Variability of a patient's response to intervention
Phase III Introduction

Phase III Design- Basic consideration
• Objective – Primary / Secondary
• Target Population
• Inclusion and exclusion criteria
• Selection of controls
• Run-in /Lead in period

Phase III Design
• There are several types of trial designs:
– Non-randomised controlled trial
– Randomised controlled trial
• Parallel group
• Cross-over
Single or double blind
Superiority or non-inferiority trial

Non-randomised controlled trials
• Participants are allocated into treatment and
control groups by the investigator.
• Controls used in non-randomised trials:
– Concurrent controls: participants matched
according to demographics.
– Historical controls: all participants receive the
medicine being studied; the results are either
compared to the patient's history (for example a
patient living with a chronic illness) or a previous
study control group.

Randomised controlled trials
• Participants are randomly allocated between
treatment and control groups.
• Randomisation removes potential for bias.
• There are different types of randomised trial
designs:
1. Factorial design trials
2. Withdrawal trials
3. Parallel group trials
4. Cross-over trials

Comparisons
• In a clinical trial design, there are a number of different
types of comparisons that can be included:
– Superiority comparison trials demonstrate that the
investigational medicine is better than the control.
– Equivalence comparison trials demonstrate that the
endpoint measure is similar (no worse, no better) to the
control.
– Non-inferiority comparison trials demonstrate that the
investigational medicine is not worse than the control.
– Dose-response relationship trials demonstrate various
dose parameters including starting dose and maximum
dos.

Randomisation in clinical trials
• Randomisation is the process of assigning a trial
participant randomly (by chance) to treatment or
control groups.
• Different tools are used to randomise (closed
envelopes, computer sequences, random
numbers).
• There are two components to randomisation:
a) the generation of a random sequence
b) Implementation of the random sequence, ideally in a
way so that participants are not aware of the
sequence.

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Purpose of Control Group
• To allow discrimination of patient outcomes
caused by test treatment from those caused
by other factors
– Natural progression of disease
– Observer/patient expectations
– Other treatment
• Fair comparisons
– Necessary to be informative

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Choice of Control Group
• Goals of Controlled Clinical Trials
• Types of Control Groups
• Significance of Control Group
• Assay Sensitivity

Considerations in Choice of Control
Group
• Available standard therapies
• Adequacy of the control evidence for the
chosen design
• Ethical considerations

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Significance of Control Group
• Inference drawn from the trial
• Ethical acceptability of the trial
• Degree to which bias is minimized
• Type of subjects
• Kind of endpoints that can be studied
• Credibility of the results
• Acceptability of the results by regulatory authorities
• Other features of the trial, its conduct, and
interpretation

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Type of Controls
• External
– Historical
– Concurrent, not randomized
• Internal and concurrent
– No treatment
– Placebo
– Dose-response
– Active (Positive) control
• Multiple
– Both an Active and Placebo
– Multiple doses of test drug and of an active control

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Use of Placebo Control
• The “placebo effect” is well documented
• Could be
– No treatment + placebo
– Standard care + placebo
• Matched placebos are necessary so patients and
investigators cannot decode the treatment assignment
• E.g. Vitamin C trial for common cold
– Placebo was used, but was distinguishable
– Many on placebo dropped out of study
– Those who knew they were on vitamin C reported
fewer cold symptoms and duration than those on
vitamin who didn't know

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Concurrent Controls
• Not randomized
• Patients compared, treated by different
strategies, same period
• Advantage
– Eliminate time trend
– Data of comparable quality
• Disadvantage
– Selection Bias
– Treatment groups not comparable
• Covariance analysis not adequate

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Biases in Concurrent Control Study
• Types
– Magnitude of effects
– False positive
• Sources
• Patient selection
– Referral patterns
– Refusals
– Different eligibility criteria
• Experimental environment
– Diagnosis/staging
– Supportive care
– Evaluation methods
– Data quality

Goals of Controlled
Clinical Trials
• Superiority Trials
– A controlled trial may demonstrate efficacy of
the test treatment by showing that it is
superior to the control
• No treatment
• Best standard of care

Goals of Controlled
Clinical Trials (2)
• Non-Inferiority Trials
– Controlled trial may demonstrate efficacy by showing the
test treatment to be similar in efficacy to a known
effective treatment
• The active control had to be effective under the conditions
of the trials
• New treatment cannot be worse by a pre-specified amount
• New treatment may not be better than the standard but
may have other advantages
– Cost
– Toxicity
– Invasiveness

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Advantages of Randomized
Control Clinical Trial
1. Randomization "tends" to produce comparable groups
Design Sources of Imbalance
Randomized Chance
Concurrent Chance & Selection Bias
(Non-randomized)
Historical Chance, Selection Bias,
(Non-randomized)& Time Bias
2. Randomization produces valid statistical tests
Reference: Byar et al (1976) NEJM

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Disadvantages of Randomized Control
Clinical Trial
1. Generalizable Results?
– Subjects may not represent general patient
population – volunteer effect
2. Recruitment
– Twice as many new patients
3. Acceptability of Randomization Process
– Some physicians will refuse
– Some patients will refuse
4. Administrative Complexity

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Ethics of Randomization (1)
• Statistician/clinical trialist must sell benefits of randomization
• Ethics MD should do what he thinks is best for his patient
– Two MD's might ethically treat same patient quite differently
• Chalmers & Shaw (1970) Annals New York Academy of Science
1. If MD "knows" best treatment, should not participate in trial
2. If in doubt, randomization gives each patient equal chance to
receive one of therapies (i.e. best)
3. More ethical way of practicing medicine

• Byar et al. (1976) NEJM
1. RCT  honest admission best is not
known!
2. RCT is best method to find out!
3. Reduces risk of being on inferior
treatment
4. Reduces risk for future patients

• Classic Example -
Reference: Silverman (1977) Scientific Amer
1. High dose oxygen to premature infants was
common practice
2. Suspicion about frequency of blindness
3. RCT showed high dose cause of blindness

Comparing Treatments
• Fundamental principle
• Groups must be alike in all important aspects and only differ in the
treatment each group receives
• In practical terms, “comparable treatment groups” means
“alike on the average”
• Randomization
• Each patient has the same chance of receiving any of the
treatments under study
• Allocation of treatments to participants is carried out using a
chance mechanism so that neither the patient nor the physician
know in advance which therapy will be assigned
• Blinding
• Avoidance of psychological influence
• Fair evaluation of outcomes

Randomized Phase III Experimental
Designs
Assume:
• Patients enrolled in trial have satisfied eligibility criteria and
have given consent
• Balanced randomization: each treatment group will be
assigned an equal number of patients
Issue
• Different experimental designs can be used to answer
different therapeutic questions

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Parallel Design
Screen
Trt A
Randomize -
Trt B
• H0: A vs. B
• Advantage
– Simple, General Use
– Valid Comparison
• Disadvantage
– Few Questions/Study

Advantages
• Can be applied to almost any disease
• Any number of groups can be run simultaneously
• Groups can be in separate locations
Challenges
• Homogenisation of the groups (Especially where
different geographical locations are used)

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Withdrawl Study
I Trt A
Trt A -
II Not Trt A
• H0: How long should TRT A continue?
• Advantage
–Easy Access to Subjects
–Show continued Tx Beneficial
• Disadvantage
–Selected Population
–Different Disease Stage

Cross Over Design
H0: A vs. B
Scheme
Period
Group I II
AB 1 TRT A TRT B
BA 2 TRT B TRT A
• Advantage
– Each patient their own control
– Smaller sample size
• Disadvantage
– Not useful for acute disease
– Disease must be stable
– Assumes no period carry over
– If carryover, have a study half sized
(Period I A vs. Period I B)

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Factorial Design
• Schema
Factor I
Placebo Trt B
Factor II
Placebo N/4 N/4
Trt A N/4 N/4
B vs. Placebo
A vs. Placebo

Factorial Design
• Advantages
– Two studies for one
– Discover interactions
• Disadvantages
– Test of main effect assumes no interaction
– Often inadequate power to test for interaction
– Compliance
• Examples
– Physicians' Health Study (PHS) NEJM 321(3):129-135, 1989.
– Final report on the aspirin component
– Canadian Cooperative Stroke Study (1978) NEJM p. 53

Superiority vs.
Non-Inferiority Trials
Superiority Design: Show that new treatment is
better than the control or standard (maybe a
placebo)
Non-inferiority: Show that the new treatment
a) Is not worse that the standard by more than some
margin
b) Would have beaten placebo if a placebo arm had been
included (regulatory)

Superiority vs Noninferiority
1.0
( )
( )
( )
.8 1.25
Benefit Harm
RR

 
Better Worse
RR
Active Control
Placebo
Harm
Non-significant
Benefit
( )
( )
( )
1.0
Standard
Plbo
Worse
Non-Inferior
Better Modified from Fleming, 1990
X
X
X
X
X
X

Equivalence/Non-inferiority Trial
• Trial with active (positive) controls
• The question is whether new (easier or cheaper) treatment is as good as
the current treatment
• Must specify margin of “equivalence” or non-inferiority
• Can't statistically prove equivalency -- only show that difference is less
than something with specified probability
• Historical evidence of sensitivity to treatment
• Sample size issues are crucial
• Small sample size, leading to low power and subsequently lack of
significant difference, does not imply “equivalence”

542-03-#111
Difference in Events
Test Drug – Standard Drug

542-03-#112
Active Control Design
1.0
( )
( )
( )
.8 1.25
Benefit Harm
RR

 
Better Worse
RR
Active Control
Placebo
Harm
Non-significant
Benefit
( )
( )
( )
1.0
Standard
Plbo
Worse
Non-Inferior
Better
Modified from Fleming, 1990
X
X
X
X
X
X

Non-Inferiority Challenges (1)
• Requires high quality trial
• Poor execution favors non-inferiority
• Requires strong control; weak control favors
non-inferiority

Non-Inferiority Challenges (2)
• Treatment margin somewhat arbitrary
• Imputed Trt vs. Plbo effect
– Uses historical control concept
– Imputed estimate not very robust

Non-Inferiority Methodology
a) Comparison: New Treatment vs. Standard RRa
b) Estimate of standard vs. placebo RRb
(based on literature)
c) Imputed effect of New Trt vs. placebo (RRc)
RRc = RRa x RRb

Assay Sensitivity
• Ability to distinguish an effective treatment from a less
effective or ineffective treatment
• Different implications of lack of assay sensitivity
– Superiority trials
• Failing to show that the test treatment is superior
• Thus failing to lead to a conclusion of efficacy
– Non-inferiority trials
• Finding an ineffective treatment to be non-inferior
• Thus leading to an erroneous conclusion of efficacy

Assay Sensitivity in
Non-Inferiority Trials
• More critical
• Historical evidence of sensitivity to Trt effects
• Appropriate trial conduct
– The design of the non-inferiority trial be similar to that of
previous trials used to determine historical evidence of
sensitivity to Trt effects
– Conduct of the study is similar to the previous trials
– An acceptable margin of non-inferiority be defined, taking into
account the historical data
– The trial be conducted with high quality controls

542-03-#118
Sequential Design
• Continue to randomize subjects until H0 is either
rejected or “accepted”
• A large statistical literature for classical sequential
designs
• Developed for industrial setting
• Modified for clinical trials
(e.g. Armitage 1975, Sequential Medical Trials)

Classical Sequential Design (1)
• Continue to randomize subjects until H0 is either rejected or “accepted”
• Classic

Net
Trt
Effect
100 200 300
No. of Paired Observations
Trt Worse
Continue
Accept H0
Trt Better
Continue
-20
0
20

Classical Sequential Design (2)
• Assumptions
– Acute Response
– Paired Subjects
– Continuous Testing
• Not widely used
• Modified for group sequential designs

Therapeutic vs. Prevention Trials
• Prevention Trials
– Primary - Prevent disease
– Secondary - Prevent recurrence
• Therapeutic Trials
– Treat disease
• Basic fundamentals apply equally
• Some differences exist
– Complexity
– Recruitment Strategies
– Compliance
– Length of Follow-up
– Size

542-03-#122
Confounding Bias
• Suppose you are interested in the effects of
a treatment T upon an outcome O in the
presence of a predictor P
• Randomization takes care of bias due to
factors P before treatment
• Blinding takes care of bias due to factors P
after treatment

Blinding or Masking (1)
• Assures that subjects are similar with regard
to post-treatment variables that could affect
outcomes
• Minimizes the potential biases resulting from
differences in management, treatment, or
assessment of patients, or interpretation of
results
• Avoids subjective assessment and decisions
by knowing treatment assignment

Blinding or Masking (2)
• No Blind
– All patients know treatment
• Single Blind
– Patient does not know treatment
• Double Blind
– Neither patient nor health care provider know treatment
• Triple Blind
– Patient, physician and statistician/monitors do not know
treatment
• Double blind recommended when possible

Masking or Blinding (3)
• Keeping the identity of treatment assignments masked for:
1. Subject
2. Investigator, treatment team or evaluator
3. Evaluation teams
• Purpose of masking: bias reduction
• Each group masked eliminates a different source of bias
• Masking is most useful when there is a subjective
component to treatment or evaluation

Feasibility of Masking
• Ethics: The double-masking procedure should not result in any harm or
undue risk to a patient
• Practicality: It may be impossible to mask some treatments
• Avoidance of bias: Masked studies require extra effort (manufacturing
look-alike pills, setting up coding systems, etc.)
• Compromise: Sometimes partial masking, e.g., independent masked
evaluators, can be sufficient to reduce bias in treatment comparison
• Although masked trials require extra effort, sometimes they are the only
way to obtain an objective answer to a clinical question

Reasons for Subject Masking
• Those on “no-treatment” or standard treatment may be discouraged
or drop out of the study
• Those on the new drug may exhibit a “placebo” effect, i.e., the new
drug may appear better when it is actually not
• Subject reporting and cooperation may be biased depending on how
the subject feels about the treatment

Reasons for
Treatment Team Masking
• Treatment decisions can be biased by knowledge of the treatment,
especially if the treatment team has preconceived ideas about either
treatment
• Dose modifications
• Intensity of patient examination
• Need for additional treatment
• Influence on patient attitude through enthusiasm
(or not) shown regarding the treatment

Reasons for Evaluator
(Third Party) Masking
• If endpoint is subjective, evaluator bias will lead to recording more
favorable responses on the preferred treatment
• Even supposedly “hard” endpoints often require clinical judgment,
e.g., blood pressure, MI

Reasons for Monitoring Committee
Masking
• Treatments can be objectively evaluated
• Recommendations to stop the trial for “ethical” reasons will not be
based on personal biases
• Sometimes, however, triple-mask studies are hard to justify for
reasons of safety and ethics
• A policy not recommended, not required by FDA

Design Summary
• Design used must fit goals of trial
• RCT minimizes bias
• Superiority vs. Non-Inferiority trial
challenges
• Use blinding when feasible

What Is GCP?
Good Clinical Practice (GCP) is defined as a
‘standard for the design, conduct, performance,
monitoring, auditing, recording, analyses and
reporting of clinical trials that provides
assurance that the data and reported results
are credible and accurate, and that the rights,
integrity and confidentiality of trial subjects are
protected’

Good Clinical Practice Guidelines
• Are mainly focused on the protection of human
rights in clinical trial.
• Provide assurance of the safety of the newly
developed compounds.
• Provide standards on how clinical trials should be
conducted.
• Define the roles and responsibilities of clinical
sponsors, clinical research investigators, Clinical
Research Associates, and monitors.

Good Clinical Practice Guidelines
(Continued)
• GCPs are generally accepted, international best practices for
conducting clinical trials and device studies
– They are defined as an international ethical and
scientific standard for designing, conducting,
recording and reporting trials that involve the
participation of human subjects
– Compliance with GCPs provide public assurance
that the rights and safety of participants in human
subject research are protected and that the data
that arises from the study is credible

The Core of the Consolidated GCP
Guidance
1 Clinical trials should be conducted in accordance with the ethical principles that
have their origin in the Declaration of Helsinki, and that are consistent with GCP
and the applicable regulatory requirements
2 Before a trial is initiated, foreseeable risks and inconveniences should be
weighed against the anticipated benefit for the individual trial subject and
society. A trial should be initiated and continued only if the anticipated benefits
justify the risks
3 The rights, safety, and well-being of the trial subjects are the most important
considerations and should prevail over interests of science and society
4 The available non clinical and clinical information on an investigational product
should be adequate to support the proposed clinical trial
5 Clinical trials should be scientifically sound, and described in a clear, detailed
protocol
6 A trial should be conducted in compliance with the protocol that has received
prior institutional review board (IRB)/independent ethics committee (IEC)
approval/favorable opinion
7 The medical care given to, and medical decisions made on behalf of, subjects
should always be the responsibility of a qualified physician or, when appropriate,
of a qualified dentist

Thirteen principles of GCP Guidance
8 Each individual involved in conducting a trial should be qualified by
education, training, and experience to perform his or her respective
tasks
9 Freely given informed consent should be obtained from every subject
prior to clinical trial participation
10 All clinical trial information should be recorded, handled, and stored in a
way that allows its accurate reporting, interpretation, and verification
11 The confidentiality of records that could identify subjects should be
protected, respecting the privacy and confidentiality rules in accordance
with the applicable regulatory requirements
12 Investigational products should be manufactured, handled, and stored in
accordance with applicable good manufacturing practice (GMP). They
should be used in accordance with the approved protocol
13 Systems with procedures that assure the quality of every aspect of the
trial should be implemented

History of Good Clinical Practice
• Prior to an actual set of guidelines to follow for
good clinical practice, clinical studies were
dangerous and could result in serous disease, or
possibly death
• The Nuremburg Code of 1947
– Experiments performed in germany during WWII opened the eyes of the world for guidance
for clinical testing on humans.
– The code did set ethical guidelines, but it lacked legislation to back it up.
• Declaration of Helsinki
• In 1964, the World Medical Association established recommendations guiding
medical doctors in biomedical research involving human subjects. These
guidelines influenced national legislation, but there was no set standard
between nations

History of Good Clinical Practice
(Continued)
• The formation of the International Conference on Harmonization
(ICH) led to the creation of the Consolidated Guidance on GCP
– The ICH consisted of the governments of the
United States, EU and Japan coming together to
develop common regulations for the
pharmaceutical markets among member countries

Mission of the GCP Program
 The Good Clinical Practice Program is the focal point
within FDA regarding issues in human research trials
regulated by FDA. The Good Clinical Practice Program:
 Coordinates FDA policies
 Contributes to leadership and direction through
participation in FDA's Human Subject
Protection/Bioresearch Monitoring Council
 Coordinates FDA's Bioresearch Monitoring program with
respect to clinical trials, working together with FDA's Office
of Regulatory Affairs (ORA)
 Contributes to international Good Clinical Practice
harmonization activities
 Plans and conducts training and outreach programs

Under GCP, the FDA Requires That
People be Informed:
• The study involves research of an unproven drug, the
purpose of the research
• How long the participant will be expected to participate
in the study
• What will happen in the study
• Possible risks/benefits to the participant
• Participation is voluntary and that participants can quit
the study at any time without penalty or loss of
benefits to which they are otherwise entitled.

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