Intro Clinical Trails

  • May 2020
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© 2003, Greenfield Research

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introduction to Statistical aspects of

clinical trials

© 2003, Greenfield Research

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© 2003, Greenfield Research

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New Clinical Entity (NCE) In-vitro: combinatorial chemistry; pharmacogenetics; effects First Often studies first studies inregistration man, in patients. healthy Attempts volunteers. to prove efficacy; dose After Large or scale during studies tousually prove to discover drug suitable more for about registration. safety, efficacy including, metabolites, toxicity, drug-drug interactions; Pharmacokinetics, finding; tolerability pharmacodynamics, basic tolerabilityidentify in Control different group: populations. placebo or Many proven lacktherapy. control group. surrogate markers.

10 to 20 years

Phase two

Pre-clinical Phase three phase Phase one concept Phase four © 2003, Greenfield Research

profit 4

Clinical trials To determine whether or not there are differences between the effects of treatments Treatments A and B purpose

A

design

trial

data

difficulties

B

? A

=

B

Analysis method knowledge

•Previous studies •publications •data bases •theory

© 2003, Greenfield Research

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Difficulties and ethics: •

Patients • • • • • •



Data • • • • •

• © 2003, Greenfield Research

availability inclusion and exclusion criteria willingness to participate presentation rates compliance how many? measures of effects adverse effects influence of other factors random variation of effects between people bias • allocation bias • assessment bias • analysis

Cost 6

Assume everything else controlled except for random variation of effects between people

Consider: • a single continuous quantitative variable and its presentation distribution • random allocation of patients to two treatments • posterior (after treatment) distributions: are they they same or different for the two treatments, as measured by their means and standard deviations?

© 2003, Greenfield Research

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FEV1: • prior (presenting) distribution:

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• posterior (after treatment) distributions:

• mean = 1.70

• treatment A mean = 1.75

• sd = 0.30

• treatment B mean = 1.80

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Two approaches to analysis: 1.

Compare means of the two posterior distributions.

2.

Compare the means of the changes for all the patients in the two treatment arms.

Statistical tests are needed for these comparisons Statistical tests help us to decide how many patients will be needed

© 2003, Greenfield Research

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Form of a test statistic:

What you want to test Variability of what you want to test Need to understand: • Probability distributions, means and standard deviations • Means and standard deviations of sums of variables • Sampling distributions of means, standard errors of means • Central limit theorem © 2003, Greenfield Research

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Is treatment A better than treatment B?

© 2003, Greenfield Research

Treatment A

Treatment B

total

Recovered

17

8

25

No better

3

12

15

total

20

20

40

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If treatment A is better than treatment B . . .

Treatment A

Treatment B

total

Recovered

17

8

25

No better

3

12

15

total

20

20

40

Why did these eight recover? Were they younger, stronger, or in a better condition than those who did not recover? © 2003, Greenfield Research

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If treatment A is better than treatment B . . .

Treatment A

Treatment B

total

Recovered

17

8

25

No better

3

12

15

total

20

20

40

Why didn’t these three recover? Were they older, more feeble, or in a worse condition than those who did? © 2003, Greenfield Research

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Perhaps treatment A is not better than treatment B? So . . .

Treatment A

Treatment B

total

Recovered

17

8

25

No better

3

12

15

total

20

20

40

Could there have been an allocation bias?

© 2003, Greenfield Research

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Perhaps treatment A is not better than treatment B? So . . .

Treatment A

Treatment B

total

Recovered

17

8

25

No better

3

12

15

total

20

20

40

Could there have been an assessment bias?

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Perhaps treatment A is not better than treatment B? So . . . Treatment A

Treatment B

total

Recovered

17

8

25

No better

3

12

15

total

20

20

40

Could the results have occurred by chance?

© 2003, Greenfield Research

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Pharmacogenetics:

How differences in genes can influence responses to drugs

© 2003, Greenfield Research

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Example of pharmacogenetics

codeine

placebo

Treatment A

Treatment B

total

Recovered

17

8

25

No better

3

12

15

total

20

20

40

Perhaps these 3 cannot respond to codeine © 2003, Greenfield Research

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codeine

loss of

morphine

methyl group enzyme:

cyp2d6

Gene encoding cyp2d6 is on chromosome 22

It is altered in ten percent of people

© 2003, Greenfield Research

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The efficiency and effectiveness and cost of a clinical trial depend on:

• • • • • • • • © 2003, Greenfield Research

response to each treatment influence of other factors such as age, gender or life style number of patients how patients are selected for the trial how patients are allocated to treatments type of trial:

parallel or crossover

compliance of patients to treatments how data are recorded, analysed and interpreted 20

Type of trial: • Parallel • Cross-over

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Sequential



Group sequential



Factorial 21

Parallel Designs POPULATION Assessment Eligible and willing subjects Allocation Treatment A

Treatment B

Assessment Data analysis

© 2003, Greenfield Research

arm 1

arm 2

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Two-period Cross-over designs POPULATION Assessment Eligible and willing subjects Allocation

First period

Treatment A

Treatment B

Assessment

Second period

Treatment B

Treatment A

Assessment Data analysis

© 2003, Greenfield Research

arm 1

arm 2

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Allocation Random • Simple randomisation •

Weighted randomisation

• Block randomisation • Sequential randomisation Semi-random • Minimisation Non-random • Open © 2003, Greenfield Research

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Sample size

number of patients in a trial

based either on independent samples t-test or on comparison of proportions

based either on a

paired t-test

or on comparison of paired proportions

© 2003, Greenfield Research

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Sample size

number of patients in a trial

alpha value:

probability of a type one error : of rejecting the null hypothesis when it is actually true 0.05 or 0.01

power:

probability (80% or 90%) of getting a statistically significant difference if the true difference between treatments is of a given size (clinically significant difference) adjust for expected dropouts

© 2003, Greenfield Research

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Exclusions: patients presenting with the condition being studied must be assessed for suitable inclusion: they may be excluded if, for example: • • • • •

over-weight over or under age pregnant exhibiting what may be an adverse response on interacting medication

Exclusion must be decided before allocation to treatment © 2003, Greenfield Research

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Intention to treat After a patient has been entered into a trial, after allocation to treatment, that patient’s data must be included in the analysis even if the patient has dropped out of the trial before completion of treatment or if there are any missing values. If ‘intention to treat’ analysis is being applied, missing values of a variable after drop out will be replaced by the last values recorded. © 2003, Greenfield Research

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