Pharmacovigilance

1. TYPES OF RANDOMIZATION
Guided by
Dr. Jigna S. Shah
Presented by
Mr. Bharat Kumar
17MPH202
Department of Pharmacology, Institute of
Pharmacy,
Nirma university, Ahmedabad.

2. Randomization
A method based on chance alone by which study
participants are assigned to a treatment group.
Randomization minimizes the differences among
groups by equally distributing people with particular
characteristics among all the trial arms. The
researchers do not know which treatment is better.

4. Need of Randomization
If, at the end of a clinical trial, a difference in outcomes
occurs between two treatment groups (say, intervention
and control) possible explanations for this difference
would include:
1. the intervention exhibits a real effect;
2. the outcome difference is solely due to chance
3. there is a systematic difference (or bias) between
the groups due to factors other than the intervention.
Randomization aims to obviate the third possibility.

5. Criteria for randomization
1. Unpredictability
• Each participant has the same chance of receiving any
of the interventions.
• Allocation is carried out using a chance mechanism so
that neither the participant nor the investigator will
know in advance which will be assigned.
2. Balance
• Treatment groups are of a similar size & constitution,
groups are alike in all important aspects and only differ
in the intervention each group receives
3. Simplicity
• Easy for investigator/staff to implement

6. Forms of Randomization
• Simple Randomization
• Block Randomization
• Stratified Block Randomization
• Dynamic (adaptive) random allocation

7. Simple Randomization
• Randomization based on a single sequence of random
assignments
• basic method of simple randomization is flipping a coin
• Computer generated sequence
• For example, with two treatment groups (control versus
treatment), the side of the coin (i.e., heads - control, tails
- treatment) determines the assignment of each subject.

8. Simple Randomization
Randomization approach is simple and easy to
implement in a clinical research.
In large clinical research, simple randomization can be
trusted to generate similar numbers of subjects among
groups. However, randomization results could be
problematic in relatively small sample size clinical
research, resulting in an unequal number of participants
among groups.

9. Block Randomization
• The block randomization method is designed to
randomize subjects into groups that result in equal sample
sizes
• used to ensure a balance in sample size across groups
over time
• Used for small studies to maintain reasonably good
balance among groups
• In a two group design, Blocks having equal numbers of
As and Bs (A = intervention and B = control, for
example) are used, with the order of treatments within the
block being randomly permuted

10. Illustration
With a block size of 4 for two groups(A,B), there are 6
possible permutations and they can be coded as:
1=AABB, 2=ABAB, 3=ABBA, 4=BAAB, 5=BABA,
6=BBAA
Each number in the random number sequence in turn
selects the next block, determining the next four
participant allocations (ignoring numbers 0,7,8 and 9).
e.g., The sequence 67126814…. will produce BBAA
AABB ABAB BBAAAABB BAAB.

11. Stratified Block Randomization
• The stratified randomization method addresses the need to
control and balance the influence of covariates
• This method can be used to achieve balance among groups
in terms of subjects’ baseline characteristics (covariates).
• Typical examples of such factors are age group, severity of
condition, and treatment centre. Stratification simply
means having separate block randomisation schemes for
each combination of characteristics (‘stratum’)
• For example, in a study where you expect treatment effect
to differ with age and sex you may have four strata: male
over 65, male under 65, female over 65 and female under
65

12. Dynamic (adaptive) random
allocation
Simple and block randomization methods are defined, and
allocation sequences set up, before the start of the trial.
In contrast, dynamic randomization methods allocate patients
to treatment group by checking the allocation of similar
patients already randomized, and allocating the next treatment
group "live" to best balance the treatment groups across all
stratification variables. Biased coin randomization and
minimisation are two such methods.

13. Inappropriate randomisation
methods
• Assigning patients alternately to treatment group is
not random assignment
• Assigning the first half of the population to one
group is not random assignment
• Assignments by methods based on patient
characteristics such as date of birth, order of entry
into the clinic or day of clinic attendance, are not
reliably random

14. Bias
Bias is said to have occurred if the results observed
reflect other factors in addition to (or even instead of) the
effect of the treatment:
Some potential sources of bias:
• Selection bias
• Performance bias
• Detection bias
• Laboratory bias
• Sample size bias

15. Selection bias
Selection bias is concerned with two main issues:
• systematic differences arise between the sampling
population and the sample drawn and,
• systematic differences arise between treatment groups
at the outset of the trial, usually due to individuals
either tampering with or predicting the allocation
sequence.

16. Performance Bias
Performance bias is the tendency for participants to
change their behavior or responses to questions because
they are aware of being in a trial and of their treatment
allocation.

17. Detection Bias
Detection bias is concerned primarily with blinding.
In this case, however, the concern is about those
collecting the data, not those providing it.
The Cochrane Handbook warns that if “outcome
assessors are aware of assignments, bias could be
introduced into assessments of outcome, depending
on who measures the outcomes.”

18. Laboratory Bias
• the knowledge of which treatment the patient
received may affect the way in which the test is run
or interpreted, or be retested.
• although this is most severe with subjectively graded
results (pathology slides, photographs, ECG, etc.),
this can also be a problem with "objective tests" such
as laboratory assays which may be run subtly
differently by the technician.

19. Sample size bias
Sample size calculations are a deceptively simple tool to
minimize bias – an insufficiently large sample size can lead
to imprecise estimation, biasing the results downward.

20. Blinding
Blinding is a procedure in which one or more parties in a
trial are kept unaware of which treatment arms participants
have been assigned to, in other words, which treatment was
received. Blinding is an important aspect of any trial done in
order to avoid and prevent conscious or unconscious bias in
the design and execution of a clinical trial.
Blinding (also called masking or concealment of
treatment) is intended to avoid bias caused by subjective
judgment in reporting, evaluation, data processing, and
analysis due to knowledge of treatment.

21. Types of Blinding
• Unblinded or open label : All parties are aware of the
treatment the participant receives
• Single blind or single-masked : Only the participant is
unaware of the treatment they receive
• Double blind or double-masked : The participant and the
clinicians / data collectors are unaware of the treatment the
participant receives
• Triple blind : Participant, clinicians / data collectors and
outcome adjudicators / data analysts are all unaware of the
treatment the participant receives

22. Open Label Studies
Open Label clinical trials do not attempt to disguise the new drug
or treatment, meaning that no standard treatment or placebo is
utilized. This leans towards bias, as both the patient and the
physician are aware of which groups are receiving what type of
treatment.
These may be useful for
• pilot studies
• dose ranging studies
However, even these applications may be substantially biased by
knowledge of the treatment given and may result in
• toxicity over (or under) reported
• efficacy over estimated

23. Single Blind Studies
• The participants in the clinical trial do not know if they are
receiving the placebo or the real treatment.
• This is done to reduce the risk of errors, since some
participants might produce spurious results if they know that
they are taking the placebo or medication.
• In this model, the experimenter monitoring the participants
knows which individuals received the placebo and which
ones got the treatment under examination.

24. Double Blind Studies
• When both the subjects and the investigators are kept from knowing
who is assigned to which treatment, the experiment is called “double
blind"
• This is considered to be the superior model of clinical research since it
eliminates outcomes that are produced due to placebo effect, as well
as observer bias by the experimenter. The fact that the experimenter
does not know which group received the placebo or the experimental
drug means that the risk of conscious and unconscious observer bias is
reduced, making the study more accurate.

25. Triple Blinding
The treatment or intervention is unknown to
(a) the research participant,
(b) the individual(s) who administer the treatment or
intervention, and
(c) the individual(s) who assess the outcomes.
The terms blind and masking are synonymous; both terms
describe methods that heslp to ensure that individuals do not
know which treatment or intervention is being administered.
The purpose of triple-blinding procedures is to reduce
assessment bias and to increase the accuracy and objectivity of
clinical outcomes.

26. Reference
• “An overview of randomization techniques: An unbiased assessment of
outcome in clinical research” J Hum Reprod Sci. 2011 Jan-Apr; 4(1): 8–11,
doi: 10.4103/0974-1208.82352
• “Designing a research project: randomised controlled
trials and their principles” - J M Kendall
• Triple-Blind Study , In: Encyclopedia of Research Design - Neil J. Salkind,
DOI: http://dx.doi.org/10.4135/9781412961288.n471
• “Risk and evidence of bias in randomized controlled trials” – Alex Eble
and Peter Boone, june 2012
• https://www.biopharmainstitute.com/faq/what-is-the-difference-between-
single-blind-and-double-blind-clinical-trials
• “Randomization in clinical trial, recruitment , inclusion and exclusion
criteria” – Dr Urmila M. Aswar , pune
• “The concept of randomization and blinding in clinical trials” – Suraj P
Anand