helen looker mph course october 21 st 2014. understanding clinical trials a b super drug wonder drug
TRANSCRIPT
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Helen LookerMPH Course
October 21st 2014
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Understanding Understanding Clinical TrialsClinical Trials
A B
SuperDrug
WonderDrug
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Why clinical trials
Design features
Main problems
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quart of cider
3 servings elixir of vitriol
6 spoonfuls of vinegar
3 portions of nutmeg
2 oranges, 1 lemon
Experimental treatments (per day)
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Limitations of theory
Previous disasters
Spontaneous improvements
Importance of small effects
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Intervention Impact of intervention
Sleep baby on its front SIDS frequency increased 200%
Anti-oxidants to reduce mortality
5% increase in mortality
Juvenile delinquents exposed to prison – scared straight
Increase in offending rates
School based driver education
Increase in accident rates
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Service organisation
Quality of direct care
Ancillary care
Initial severity
Co-morbidity
Adherence
Social support
Lifestyle
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Drugs
Surgery
Type of management
Public health interventions
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Phase I clinical pharmacology
Phase II initial clinical assessment
Phase III rigorous testing
Phase IV post-marketing surveillance
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Trials are experiments on people
Must be real doubt (clinical equipoise)
Obtain informed consent
Preserve clinical freedom
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Historical controls◦health care has moved on◦same diagnostic criteria??
Concurrent control◦why were some not selected
Randomized controls◦ new treatment◦ placebo/ conventional treatment
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Streptomycin and Pulmonary TB
Intervention Control
Recruited 55 52
Dead at 6 months
7%
27%
Published 1948
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Like tossing a coin
Avoids choosing
Permits fair comparison◦ two groups the same at baseline
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two groups the same at baseline◦ group A: 1x , 4 x , 2 x
◦ group B: 1x , 4 x , 2 x
A A A A A AB BB B B A B B
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Factors which might influence outcome
◦ Illness severity at entry
◦Current treatment
◦Disease duration
◦Relevant previous medical history
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New treatment
Control
% advanced disease
47
49
% married 65 70
mean age (years)
64.2
48.9
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Severe
Moderate
Moderate
A
B
A
B
A
B
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When powerful predictor of outcome
Construct groups low to high
Randomise within groups
Achieve balance on the predictor
Keep it simple
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Inclusion criteria◦ likely to benefit from treatment
definitely has the disease patient is likely to respond
◦unlikely to be harmed no known adverse reactions/ contraindications
Aim◦well defined, homogeneous group◦ increased likelihood of detecting an effect ◦smaller, cheaper trials
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Exclusion criteria◦clear preference for intervention or
control by patient or doctor
◦??? patient unlikely to adhere to treatment complete the follow-up
◦many other factors
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Entry Criteria
◦Diastolic BP 90-109
◦Age 35-64
◦Men and women
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Common exclusion criteria
◦Comorbidities
◦Recently cardiovascular disease
◦Age: 50+ yrs, some 85+ years
◦Severe hypertension
◦Dementia
◦Depression
◦Substance abuseVan Spall et al 2007Uijen et al 2007
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Compared to primary care patients with CVD, trial participants were:
◦Younger
◦More men
◦Lower risk CVD
◦Fewer with history of CVD
Uijen et al 2007
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Death rateTrial participant 3.6%
Eligible, not enrolled
7.1%
Not eligible 11.4%
Steg et al 2007
Global Registry of Acute Coronary Events
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Systematic reviews
% of ineligible patients (median)
Asthma 94%
COPD 95%
Wound healing 85%
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Well-defined
Easily delivered
Same for all patients
Prior evidence of effect
Measurable outcome
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Drug
Surgery
Psychotherapy
Counselling
Complex intervention
Difficulty of standardising
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Death
Symptoms Quality of life
Clinical measurement
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Clinically relevant
Easily measured
Accurately measured◦ measured in the same way for intervention and control
groups
Specified in trial protocols◦ primary out identified◦ others called secondary outcomes
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Single blind Vs.
Double blind
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Increase in effect size
Poor randomisation 41%
Not double blind 17%
Schultz et al 1995
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Healthy ones may emigrate
Sick ones may be admitted to hospital
Rule of thumb◦ less than 20% lost◦similar losses for intervention and control
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Specify treatment
Define study group
Random allocation
Blinded outcome assessment
Fair interpretation
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Like tossing a coin
Avoids choosing
Protects against unknown confounding
Permits fair comparison
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Too few patients
Performance bias
Losing patients
Flawed analysis/interpretation
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Expected effect size◦ The bigger the effect you are trying to measure the fewer
people needed
How certain do you need to be that a detected difference is true?◦ Typically aim for a significance criteria of 0.05 (ie if you
find a difference between groups you have a 95% confidence that the difference is true)
Power to detect a difference when there is a true difference ?◦ Typically select a range of 80-90%
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Systematic differences in the care provided to the participants in the comparison groups other than the intervention under investigation.
Usually intervention get more attention than controls
Example in psychotherapy ◦ intervention group may get more attention or more
drugs than controls◦ Example in diabetes
◦ Increased clinical contact improves diabetes control
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Why do patients drop out?
Treatment side-effects
Lack of desired improvement
Too much time/effort to continue
Maintained contact with study
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Analyse by intention to treat◦ change in treatment may be related to
efficacy ◦ exclusion will lead to bias
Beware sub-group analysis◦ for every 20 groups explored one will be
spuriously significant
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◦ 30% reported severity of RA
◦ Randomisation described in 11%
◦ 8% of reported double blind were not
◦ 6% nominated main outcome measure
◦ 1% report sample size calculation
◦ 42% made doubtful/invalid statements
Bero and Rennie 1996
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857 significance tests
48 were significant
43 expected by chance
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What You Should KnowWhat You Should Know New treatments need careful
assessment
The RCT is study design of choice randomisation enables fair comparison controls for confounding: known and unknown also need fair outcome assessment
Problems often occur unequal at baseline, lack of blinding , loss to follow-up, poor outcome measurement, flawed analysis
Papers may mislead
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◦ Who is being studied
◦ What treatments are being used
◦ Are treatment groups comparable at baseline
◦ Are sensible outcome measures used
◦ Are outcomes assessed blind
◦ How many patients dropped out
◦ What do the results really mean