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PY367 Licensing a new drug > Understanding clinical trial data > Flashcards

Understanding clinical trial data Flashcards

1
Q

how do you demonstrate evidence of whether the product works

A
  1. whether they have taken a lead activity molecule
  2. made structural modifications to achieve best possible PK and PD properties
  3. formulated it into an elegant and acceptable medicine
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2
Q

how do you demonstrate efficacy

A
  1. have to demonstrate that new product produces desired effect
  2. provides the means for a therapeutic intervention that will benefit a patient
    - including tolerability and acceptability
  3. evidence has to be capable of being verified
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3
Q

what does an ARR of 0.08 mean

A

indicates the reduction of risk by 8%
- eg. patients is 8% less likely to have a stroke

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4
Q

what is meant by numbers needed to treat

A

the number of patients who would have to receive treatment for 1 of them to benefit

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5
Q

what evidence does clinical trials require

A

evidence requires a comparison between a test group and a control group
- control group can receive a placebo or another form of treatment

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6
Q

what is a placebo

A

an inert substance or dosage form which is identical in appearance, flavour and odour to the active substance or dosage form
- used as a negative control in a bioassay or clinical study

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7
Q

when would the use of a placebo not be used

A

ethics
- not treating certain conditions would be unethical
- hypertension, cancer, pain

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8
Q

what does statistical analysis involve

A
  • requires a comparison between 2 groups
  • uses calculations to establish whether the behaviour of 2 groups is different
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9
Q

what is a null hypothesis

A

the claim that there is no effect in the population

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10
Q

what is the statistical conclusion

A

the outcome of the trial should disprove the null hypothesis

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11
Q

how is a clinical trial set up

A
  1. recruit a number of patients with a condition
  2. divide them into 2 groups- test group and control
  3. compare outcomes at the end of the trial
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12
Q

how can trials be flawed

A
  1. statistical errors
    - hypothesis
    - statistical set up
    - analysis of data
  2. bias
    - setting up the groups (selection bias)
    - observational bias
    - confounding factors
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13
Q

what is the central assumption made on results

A

the outcomes from a trial will be applicable to the treatment population as a whole

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14
Q

how is the trial population identified

A
  1. must mirror the treatment population
  2. need to have criteria in place to match the trial population to treatment population
  3. nature of condition
    - inclusion and exclusion criteria
  4. demographics
    - age, gender, ethnicity
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15
Q

how is the inclusion criteria identified

A
  1. identify the condition to be treated precisely
  2. use clinical diagnosis and standardised measurement scales
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16
Q

how is the exclusion criteria identified

A
  1. exclude those for who the treatment wouldn’t apply
  2. identify factors that might interfere with results
    - comorbidities
    - other medications
    - factors affecting adherence to protocol
17
Q

what is a normal distribution

A
  1. a population, when measured against a criterion, will usually show some level of variance
    - this variance is likely to show a distribution around an average value
18
Q

how is the normal distribution identified in a clinical trial

A
  1. trial population must mirror treatment population in all criteria
  2. trial population is divided in 2 groups
19
Q

how is randomisation ensured in a clinical trial

A
  1. for the outcome to be true, the test group must be the same as the control group in all respects
  2. can use random number generator
  3. should be done by an independent person not involved in the trial
20
Q

what is meant by blind and double blind

A
  1. the operator shouldn’t know what treatment an individual has had
    - highlights importance of placebo
  2. double blind- neither the operators or the patients know the treatment
21
Q

what is a cross over trial

A
  1. half way through the trial, the test and control groups are swapped over
    - requires measurements all the way through
    - trial usually runs for longer period of time
    - trials can be stopped if it becomes clear that one treatment is significantly better than the other
  2. applicable to certain conditions- pain relief, insomnia
22
Q

what is the power calculation

A
  1. trial must be designed to be capable of disproving the null hypothesis
  2. needs to be a large enough size to show that outcomes for the 2 comparison groups are statistically different from each other
  3. large enough to eliminate play of chance
23
Q

what is the power of a trial determined by

A
  1. what kind of statistical test is being performed
  2. sample size- larger the size, larger the power
  3. size of experimental effects
  4. level of error in experimental measurements
24
Q

what is intention to treat

A
  1. includes all randomised patients in the groups to which they were assigned
  2. a high drop out rate compromises the power of trial and could hide confounding factor
  3. ITT reduces risk of bias in analysis of data
25
Q

what is on treatment analysis

A
  1. seen in older clinical trials
  2. analyse data from patients who complete the trial
    - drop outs not accounted for
26
Q

how is consistency achieved in data collection

A

all participants must be treated in the same way
- data collected according to same protocol and at same times
- deviation can lead to bias

27
Q

how is precision achieved

A

data analysis should give statistically meaningful indications of the precision obtained
- confidence intervals
- coefficient of variance

28
Q

describe the impact of the outcome of a trial

A
  1. outcome should relate to the overall health benefit of the therapy
    - RRR
    - ARR
    -NNT
29
Q

how can adverse effects be measured

A

numbers needed to harm- number of patients who would have to receive the treatment for 1 of them to experience an adverse event

PY367 Licensing a new drug (9 decks)

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