Clinical trials design 1

Question 1

The ideal clinical trial:

Answer 1

Determines the safety and efficacy of a new drug in humans

• Clinically meaningful
– Represents a meaningful advance in healthcare/ patient outcomes (changes survival or symptoms not just biomarker)
• Reliable
– Results can be trusted, reproducible if trial is repeated
• Valid
– Internal – observed differences can be correctly attributed to the intervention - the extent to which the observed results represent the truth in the population we are studying and, thus, are not due to methodological errors
– External – trial results can be generalized to the real-world population

Question 2

Clinical trial design

Answer 2

clinically meaningul
reliability
internal validity
external validity

Question 3

Clinically-meaningful

Answer 3

Research question, primary outcome measure

Question 4

Reliability

Answer 4

Study design, conduct, standard operating procedures, measurement techniques

Question 5

Internal validity

Answer 5

– Bias/systematic errors
– Confounding
– Random error

Question 6

External validity

Answer 6

Participant diversity – inclusion and exclusion criteria

– Intervention – feasibility (in routine practice), acceptability (to patients and practitioners

Question 7

Superiority trials

Answer 7

– Test whether new drug is better than comparator e.g. placebo or current best treatment

Most clinical trials
– Treatment advance, comparison with placebo or existing best practice

null hypothesis - treatment is not better or same as comparator

Question 8

Equivalence trials

Answer 8

Test whether a new drug is the same as an existing treatment
• Clinical equivalence – clinical outcomes
• Bioequivalence – PK parameters e.g. blood concentration or receptor occupancy

Treatment change, comparison with original e.g.
• Change in drug delivery or manufacture e.g. modified release, new delivery method
• Generic drugs or biosimilars

null hypothesis - treatment is not similar to comparator

Question 9

• Non-inferiority trials

Answer 9

null hypothesis - treatment is worse than comparator

Test whether a new treatment is no worse than an existing treatment

compare new drugs to current effective treatments

Question 10

one sides test

Answer 10

tests for the possibilty of a relationship in ONE direction
provides more power to detect an effect
risk of missing an effecr in other direction

Question 11

Two sided test

Answer 11

tests for the possibility of a relationship in both directions

Question 12

1 sided or 2 sided?

Answer 12

superiority and equivalence 2 sided test

non inferiority 1 sided

Question 13

Bias

Answer 13

Systematic distortion of the results of a clinical study away from the truth
– Caused by inadequacies in design, conduct or analysis of the trial or publication of its results
– Reduces validity of study results

Question 14

selection bias

Answer 14

systematic differce in way subjects are erolled/treatments allocated

can be controlled]by randomisation
or concealment of treatment allocation

Question 15

bias in study management

Answer 15

treatment groups are not handled equally

controlled by standardisation of study procedures

Question 16

observer bias

Answer 16

participants/investigators influences by knowledge of assigned drug

controlled by blinding

Question 17

introduced by exclusionsa fter randomisation

Answer 17

missing data due to participant dropouts

controlled by analysis design

Question 18

publication bias

Answer 18

positive trial outcomes more likely to be published

controlled by trial registration

Question 19

types of bias

Answer 19

selection
bias in study management 
observer bias
introduced by exclusions after randomisation
publicaiton bias

Question 20

Intention to treat vs per protocol analysis

Answer 20

ITT: Preserves randomisation
minimises bias 
deals with dropouts
mirrors real life
practical
might miss a real effect 

Per protocol
determines maximum potential effectiveness of treatment
dont have to worry about drop outs

Question 21

missing data

Answer 21

missing data: patients miss visit or outcomes not measured

Question 22

types of missing data

Answer 22

missing completely at random (maybe questionnaire got lost - nothing to do with person)
missing at random (to do with the person)
missing not at random (due to drop outs)

Question 23

impact of missing data

Answer 23

loss of power

introduction of bias

Question 24

Options fo handling missing data

Answer 24

analyse complete cases only
PRO - if data are missing at random analysis can be unbiased
CON - introduces bias if missing data are non-random
loss of power

analyse all available data
pros - more power can produce unbiased analysis

last obervsation carried forward
pro - simple and easy to implement
cons - assumption that measurement stay the same is flawed - severe bias

multiple imputation
missing data replaced by value predicted form statistical model
pro - ubiased if appropriate model used

Question 25

Confounder

Answer 25

predicts the outcome and is associated with treatment but is NOT a consequence of the treatment

Question 26

Minimising confounders

Answer 26

Randomisation
– Everyone has an equal chance of being allocated to each group
• Balances confounders between groups

• Stratification
– Randomisation is within subgroups of key confounders
• Increases balance of key confounders between groups

• Minimisation
– Dynamic method that assigns participants to treatment groups in an order that
minimises overall imbalance between groups for a number of selected
confounding factors

Question 27

Random error

Answer 27

Even studies with ideal study design, conducted to minimise bias and confounding can still give a result due to random error rather than a treatment effect

– Type 1 error
• Rejecting the null hypothesis when it is true, false positive result
– Type 2 error
• Failing to reject the null hypothesis when it is false, false negative result

Question 28

Sample

Answer 28

a set of observations from population

Question 29

population

Answer 29

complete set of possible observations

Question 30

Sample size

Answer 30

Number of people to be enrolled in the trial or study

• Optimal sample size for clinical studies:

– Big enough to:
• Minimise type 2 error
• Represent the population – so results are generalisable

– Small enough to:
• Be feasible and achievable
• Minimise risk to participants, cost and waste

• Needs to be specified in the clinical trial protocol BEFORE the study starts
– Reduces bias in analysis and interpreting results
• e.g. bias of stopping study when p<0.05 has been reached!!

Question 31

Factors determining the sample size

Answer 31

Expected measure of the primary outcome and variance in control group
2. Smallest treatment effect or benefit we are trying to detect
3. The significance level at which we will reject the null hypothesis
4. The study power with which we will find a significant difference if it
exists
5. The design of the study
– Parallel group, cross-over etc
– Superiority, equivalence, non-inferiority
6. The expected dropout rate of participants in the study

Question 32

primary outcome measure

Answer 32

• Most important study outcome, addresses main aim of the study
• Used to decide on overall result of study/support regulatory action
• Selection is a balance between what is most important and what is most likely to demonstrate a change/benefit

• Using a primary outcome measure reduces risk of error
– Outcome of study determined by single analysis
• Reduces the risk of type 1 error from multiple comparisons
– Primary outcome used to estimate sample size
• Reduces the risk of type 2 error from too small study

Question 33

Type 1 error

Answer 33

rejecteing null hypothesis when it is correct
false positive
alpha set to 0.05

Question 34

type 2 error

Answer 34

failing to reject null hypothesis when it is false
set by b 20% (0.2)
study power (ability to find a significant result is one exists) is 100-b so 80%

Question 35

Sample size - other things to consider

Answer 35

Ethical implications
• Resources e.g.
– Patients
– Investigators
– Centres
• Time
• Costs