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How to Interpret Research Evidence

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Title: How to Interpret Research Evidence


1
How to Interpret Research Evidence
  • EBM Workshop
  • September.2007
  • Aaron Tejani
  • aaron.tejani_at_fraserhealth.ca

2
Declaration
  • Paid by
  • Fraser Health 80
  • Therapeutics Initiative, UBC 20
  • No perceived or actual conflict of interest with
    the pharmaceutical industry in the last 4 years

3
What do these guys have in common?
4
Questions?
  • The wise man doesnt give the right answers, he
    poses the right questions
  • - Claude Levi-Strauss

5
Identifying Misleading Claims
  • Cautionary Tales in the Clinical Interpretation
    of Therapeutic Trial Reports.
  • Scott et al. IntMedJ200535611-21
  • Cautionary tales in the interpretation of
    systematic reviews of therapy trials
  • Scott et al. IntMedJ 200636587599
  • Users Guide to Detecting Misleading Claims in
    Clinical Trial Reports.
  • Montori VM, Jaeschke R et al. BMJ20043291093-96

6
Evidence Based Medicine
  • Definition
  • The integration of best research evidence with
    clinical expertise and patient values.
  • When these three elements are integrated,
    clinicians and patients form a diagnostic and
    therapeutic alliance with optimized clinical
    outcomes and quality of life.
  • David Sackett and colleagues
  • EBM is NOT purely academic or financial exercise
  • Its implementation has major clinical
    implications that can save lives and prevent harm

7
Critical Appraisal
  • Definition
  • A method of assessing and interpreting the
    evidence by systematically considering its
  • Validity
  • Results
  • Relevance
  • Essential part of evidence-based clinical
    practice
  • Required to determine BEST evidence

8
Different Forms of Evidence
  • Systematic review
  • A rigorous, systematic process to identify,
    synthesis and evaluate the available literature
  • Can be used to change practice by implementing
    the best available literature
  • Meta-analysis
  • It is an extension of a well done systematic
    review, which provides a quantitative estimate of
    the net benefit aggregated over the included
    studies

9
Different Forms of Evidence
  • Clinical Trials
  • Randomized controlled trial (RCT)
  • Minimize bias
  • Cohort
  • Useful for topics with known health risks
  • Harms of smoking
  • Effects of drugs in pregnancy

10
Different Forms of Evidence
  • Case series or case control
  • Useful for identifying areas that require further
    investigation
  • Help identify adverse effects
  • Expert opinion
  • When other forms of data are not available

11
Hierarchies of Evidence
  • I-1 Systematic review of several double-blind
  • randomized control trials
  • I-2 One or more large double-blind randomized
  • control trials
  • II-1 One or more well conducted cohort
  • studies
  • II-2 One or more well-conducted case-control
  • studies
  • III Expert committee sitting in review, peer
  • leader opinion
  • IV Personal experience

There are many different types of grading
systems
12
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13
The Numbers
TI letter 16 1996
14
Real Clinical Scenarios
TI letter 16 1996
15
Absolute Risk Reduction (ARR)
  • Absolute Risk Reduction illustrates
  • The actual decrease from control to treatment in
    terms of effect
  • The absolute change
  • Example
  • Absolute risk reduction for the use of beta
    blockers post MI
  • 3.9

16
Absolute Risk Reduction
  • Calculating Absolute Risk Reduction
  • ARR X-Y
  • xcontrol event rate
  • ytreatment event rate
  • Example
  • Treatment cancer rate4
  • Control cancer rate8
  • ARR8-44 in cancer with treatment compared to
    control

17
Number Needed to Treat (NNT)
  • Tool to place results into humanistic terms
  • Number needed to treat (NNT)
  • Benefit of therapy
  • Number needed to harm (NNH)
  • Harm of therapy

18
Number Needed to Treat
  • Calculation of Number Needed to Treat (NNT)
  • NNT 100/ARR()
  • Previous exmaple
  • ARR4, NNT100/425
  • Treat 25 people to prevent one cancer
  • Calculation of Number Needed To Harm
  • NNH 100/ARI()

19
NNH
  • Example
  • Treatment GI bleed rate10
  • Control GI bleed rate5
  • ARI10-55 increased risk with treatment for a
    GI bleed
  • NNH100/520
  • 1 GI bleed will occur for every 20 people treated

20
Important
  • Only calculate ARR/ARI/NNT/NNH if the result is
    statistically significant!!

21
Relative Risk Reduction (RR)
  • Definition
  • Difference between the control and treatment
    usually in terms of reducing the chances

22
Relative Risk Reduction
  • Calculating Relative risk (RR)
  • X is control group
  • Y is treatment group
  • RR Y/X
  • Calculating Relative Risk Reduction (RRR)
  • X is control
  • Y is treatment
  • RRR CER-EER/CER
  • Previous example8-4/450 relative reduction in
    cancer with treatment

23
Types of Outcomes
  • Dichotomous/categorical
  • Yes or No
  • Possible to calculate absolute risk and NNT
  • Continuous
  • Blood pressure
  • Rating scales
  • In order to calculate AR and NNT/NNH, a
    clinically relevant change must be clearly
    defined a priori

24
Confidence Intervals
  • If the trial is repeated an infinite number of
    times, the results will fall within this range
    95 of the time
  • If p0.05
  • 95 certain that difference found is within the
    stated range, 5 likelihood it is due to chance
  • if p0.05
  • Helps to determine how precise the results are
  • Narrow versus wide confidence interval
  • Point estimate

25
CI Humour Break
  • No, you cant leave the room

26
P-Values
  • Determines if results are true or could be due to
    chance (Type 1 error, alpha)
  • P value of 0.05 means that there is a 5
    probability that the results are due to chance
  • P-value of 0.01 means that there is a 1
    probability that the results are due to chance
  • Two tail or one tail tests
  • Specifies direction of difference
  • i.e 2-sided, can see differences in positive or
    negative direction
  • P value needs to be adjusted for multiple
    comparisons

27
Power
  • Power
  • Need to have enough people in the study to have
    enough power to determine if a difference
    actually occurred
  • If no difference seen, then need to consider the
    sample size/power calculation
  • If a difference is seen, power is not an issue

28
Alpha
  • Choosing your Alpha
  • What you are willing to accept
  • 5 or 1 probability that difference is due to
    chance
  • Once you pick your alpha you CANNOT change it
    post hoc
  • Alpha relates to p-value and is used to calculate
    sample size

29
Risk, Odds and Hazards
Risk
Odds
Hazards
30
Risk Ratio (relative risk)
  • If 24 skiers
  • 6 fall
  • The risk of falling is 25 (6/24)

31
Odds
  • If 24 skiers
  • 6 fall
  • 18 do not fall
  • Odds is 6/18 or 1/3
  • The chances of falling were 3 to 1 against
  • 3 times more likely not to fall than to fall

32
Odds Ratio
  • Odds of one treatment versus odds of the other
    treatment
  • If 24 skiers
  • 6 fall
  • If 24 snowboarders
  • 12 fall
  • Odds ratio 0.25/0.5
  • 50
  • Half as likely to fall if you are skiing as
    compared to snowboarding

33
Continuous Outcomes
  • E.g. blood pressure, rating scales, etc
  • Important points
  • Make sure scale is valid
  • Need to know what a clinically relevant change
    in the scale is
  • Need to understand what the scale is measuring
  • i.e linear, non-linear, Likert-type scale

34
Dont get hung up with STATS!!
  • The appropriate statistical test is not as
    important as methods and outcomes
  • DO NOT PANIC
  • More important aspects of critical appraisal
  • Clearly defined methods
  • Reasonable and clinically significant outcomes
    and measurements

35
Randomization
36
Define 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.
  • www.medterms.com/script/main/art.asp?articlekey38
    700

37
What Characteristics Are Equally Distributed?
38
The Benefits of Randomization
  • Everyone has an equal chance of getting assigned
    treatment or control
  • MOST IMPORTANT
  • Groups are divided equally for known and unknown
    characteristics
  • This ensures
  • Differences in outcome are likely only due to
    differences in assigned treatment
  • If randomization was deemed successful

39
Users Guides Statement
  • The beauty of randomization is that it assures,
    if sample size is sufficiently large, that both
    known and unknown determinants of outcome are
    evenly distributed between treatment and control
    groups.

http//www.cche.net/usersguides/therapy.asp
40
Was Randomization Effective?
  • Look at Baseline characteristics to see if they
    were balanced
  • NOTE This does not account for unknown
    characteristics but if numbers are large and
    knowns are balanced
  • Can assume unknowns are as well
  • p-values when comparing baseline characteristics
  • Dont worry about them
  • If you think the differences may have an effect
    then they might
  • Doesnt matter is they are statistically
    significant differences

41
Users Guides Statement
  • The issue here is not whether there are
    statistically significant differences in known
    prognostic factors between treatment groups (in a
    randomized trial one knows in advance that any
    differences that did occur happened by chance)
    but rather the magnitude of these differences.

http//www.cche.net/usersguides/therapy.asp
42
NAC for Prevention of RF in Cardiac Surgery
43
If baseline characteristics are different
  • All is not lost
  • Authors can do analyses adjusting for differences
  • Should clearly state that this was done and how
  • If adjusted and unadjusted analyses show similar
    results
  • You can be more confident of the findings

44
Not RandomizedSo What?
  • Non-randomised studies overestimate treatment
    effect by 41 with inadequate method, 30 with
    unclear method
  • JAMA 1995 273 408-12.
  • Completely different result between randomized
    and non-randomized studies
  • BritJAnaesth1996 77 798-803.

45
Red and Yellow Lollipops!!
Red Yellow
Male
Female
Born Jan-June
Watched the news last night
Plays an instrument
Has a pet
46
Allocation Concealment (AA)
47
Example
  • GP in his office with 2 elderly gentlemen in his
    waiting room
  • He knows one guy well and this man has had
    terrible luck with his health
  • He is enrolling men into a clinical trial
  • He knows that the next person is going to get Box
    A and the one after that gets Box B
  • Last month he enrolled a man who got Box A and
    has not improved rather he has gotten worse

48
What is AA?
  • Is it blinding?
  • Can it always be done?
  • Can blinding always be done?
  • What is selection bias?
  • Example

49
What is AA?
  • Definition
  • shields those involved in a trial from knowing
    upcoming assignments. Without this protection,
    investigators and patients have been known to
    change who gets the next assignment, making the
    comparison groups less equivalent
  • Purpose?
  • To reduce selection bias

Evid.Based Med. 2000536-38
50
What is AA?
  • If AA is not successful what design feature is
    compromised and why?
  • if the investigator or clinician (or the
    patient) is able to identify the impending
    treatment allocation and is able to influence the
    enrolment (or selection) of participating
    patients, the value of randomisation is
    compromised.
  • May lead to imbalances in prognostic factors
    between groups

MJA 2005182(2)87-89
51
What is good AA?
  • Opaque-sealed envelopes
  • Pharmacy-controlled allocations
  • Coded identical medication containers
  • Telephone or web-based central randomization

52
Who Cares if AA Wasnt Done?
  • Those trials that report inadequate methods of AA
  • Report 30 larger effect sizes compared to
    trials that use sound methods of AA
  • Those trials that do not mention AA
  • Report 40 larger effect sizes compared to
    trials that reported AA

JAMA 199527340812.
53
What do you do if AA isnt done well?
  • If you meta-analyze trials
  • Do a sensitivity analysis of trials with good AA
    versus no AA
  • E.g.Atypical Coverage in patients with CAP

54
What do you do if AA isnt done?
  • If there is no meta-analysis
  • Assume the effect size may be exaggerated and
    base decisions on conservative estimate of effect
    size
  • i.e look at the conservative end of the
    confidence interval
  • Mortality reduction 6 (95 CI 1 to 8)
  • May conclude reduction is probably between 1 and
    5
  • CAUTION This is not scientific! This is my
    attempt at common sense

55
Reporting
  • Do not assume that AA was not done if not
    reported
  • Can ask the authors if they did or not
  • Only 9-15 of trials adequately report these
    methods

56
Bad reporting
57
Good reporting
58
MJA 2005182(2)87-89
59
Blinding (Masking)
60
Definitions
  • Single blind
  • Either clinician OR patient is unaware of
    assigned treatment
  • Double blind
  • Both clinician and patient are unaware of
    assigned treatment
  • Triple blind
  • Clinician, patient, and people who adjudicate
    outcomes are unaware of treatment assignment

61
A question
  • What is double-dummy?

62
The purpose of blinding
  • Attempts to minimize
  • Reporting bias
  • E.g.
  • Assessment bias
  • E.g.
  • Concomitant treatment bias
  • E.g.

63
How can you tell if blinding is broken?
  • If the authors test for success of blinding
  • Blinding may be broken when
  • One treatment has a particular side effect that
    would give it away
  • E.g. infusion site reactions
  • Look at ADR table and see if this may be occurring

64
Testing the success of blinding
  • Some would argue that success of blinding testing
    is not reliable
  • (Sackett Int J Epi 200736665-666)
  • These tests only tell us about the hunches
    people have
  • It is better to measure the effects of lost
    blinding
  • Co-intervention (get the study drug by other
    means)
  • Contamination (controls get open label treatment
    with another drug)
  • Reporting bias (e.g.study drug people down-play
    symptoms)

65
What is the consequence of broken blinding?
  • Studies with poor/absent blinding tend to over
    estimate treatment effects by 17
  • JAMA 1995273408-12.

66
What to do if blinding is broken?
  • Assume the effect size is an over-estimate
  • Look to the conservative end of the confidence
    interval
  • Mortality reduction 6 (95 CI 1 to 8)
  • May conclude reduction is probably between 1 and
    5
  • CAUTION This is not scientific! This is my
    attempt at common sense

67
How do you interpret open-label trials?
  • If there is a meta-analysis
  • Do a sensitivity analysis on blinded versus
    un-blinded trials and see if the effect size
    changes
  • If there is no meta-analysis
  • Assess whether blinding was possible
  • Interpret findings carefully knowing they could
    be an overestimation of an effect
  • Especially if blinding was possible

68
MJA 2005182(2)87-89
69
Intention-to-treat Analysis
  • Very Important!!
  • Definition
  • Analyze participants into the groups to which
    they were randomized
  • Even if they did not take assigned treatment,
    dropped out early, or did not follow protocol
    exactly

70
Intention-to-treat Analysis
71
Loss to Follow Up
  • Each trials should report the number of people
    lost and how many per group
  • Usual assumption
  • Nothing bad happened to people that were lost
  • General rule
  • gt20 of randomized population lostanalysis
    becomes unreliable
  • Consider worse case scenario and see if this
    changes the result

72
A Primer on the Interpretation ofSubgroup
Analyses (SA) in Clinical Trials
73
George Bernard Shaw
  • Beware of false knowledge, it is more dangerous
    than ignorance.

74
Different?
75
What is a Subgroup Analysis?
  • Drug A versus Drug B are equal with respect to
    mortality in patients with coronary artery
    disease
  • You want to see if this finding is the same in
    men versus women
  • Or do Diabetics have a mortality benefit as
    compared to non-Diabetics?

76
Why is SA So Common in Trials?
  • Basically to see if different types of patients
    respond differently to the same treatment
  • If there is an overall benefit of treatment
  • See if there is more or less benefit in a
    subgroup
  • If there is no overall effect
  • See if there is an effect in at least a certain
    type of patient

77
What Questions Should SA Attempt to Answer?
  • At what stage of disease is treatment most
    effective?
  • How are the risks and benefits of treatment
    related to co-morbidity?
  • What time after an event is treatment most
    effective?

78
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79
Example CAPRIE Trial Lancet 1996 348 132939
  • Clopidogrel versus ASA for reducing ischemic
    events in patients at risk
  • Patients with MI, Stroke, or PAD history
  • DBRCT
  • Clopidogrel 75mg po daily ngt30,000
  • ASA 325 po daily
  • Duration 1-3 years
  • Primary ischemic stroke, MI, vascular death

80
Example CAPRIE Trial Lancet 1996 348 132939
81
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82
Problems with SA
  • 1. Multiple testing
  • The more tests you do, the greater the
    probability of finding a difference (that is
    really due to chance)
  • Need to correct P value for multiple comparisons
  • Crude way
  • 0.05/ tests

Cook DI et al. MJA 2004180(3)289-91
83
Adjusting for Multiplicity
  • Knumber of independent tests performed
  • p is the smallest p-value calculated
  • Corrected p 1-(1-p)k
  • The adjusted p-value can then be compared to the
    traditional p0.05 as being statistically
    significant or not

84
Adjusting for Multiplicity
  • Example CHARISMA
  • ClopidogrelASA vs ASA alone in patients with CVD
  • NOTE No benefit in overall population
  • Symptomatic vs asymptomatic

N Engl J Med 20063541706-17.
85
Adjusting for Multiplicity
  • Example CHARISMA

N Engl J Med 20063541706-17.
86
Adjusting for Multiplicity
  • Example CHARISMA
  • 12 subgroups
  • Unadjusted p0.046
  • Adjusted p-value for symp vs asymp
  • 1-(1-0.046)120.43

NEJM 20063541706-17
NEJM 20063541667-1669
87
Probability of False Positives
e.g. 4 subgroups anayzed Approximately 15-20
chance of false-positiveinstead of a 5 chance
(i.e p0.05)
NEJM 20063541667-1669
88
Problems with SA
Lancet 1996 348 132939
  • 1. Multiple testing
  • Using the CAPRIE example
  • 3 subgroups 3 tests for the primary outcome
  • 0.05 / 3 0.0167 is the adjusted p-value
  • E.g. for any of the subgroups would have to have
    plt0.0167 to be SS or adjusted p0.008 1-(1-p)k

89
Problems with SA
  • 2. Lack of statistical power for SA
  • Most studies powered for the whole population
    only
  • Studies usually do not have power to detect
    differences in subgroup
  • If a difference is seen, there is enough power
    but it may be a false positive
  • If no difference is seen in a subgroup it may be
    due to actual lack of power

90
Caution
  • When there is no overall effect
  • Subgroups will show an effect 7-21 of the time
    that arent real
  • When there is an overall effect
  • Subgroups wont show a difference 41-66 of the
    time

Health Technol Assess 2001 5 156.
91
Tests for Interaction
  • A test for heterogeneity of treatment effect
  • The appropriate statistical test
  • Does not test the magnitude of difference between
    subgroups and the overall population
  • Does test to see if the subgroup effect is
    different form the overall effect but says
    nothing of by how much

92
Tests for Interaction
  • E.g. CAPRIE

Lancet 1996 348 132939
93
Tests for Interaction
  • E.g. CHARISMA

Lancet 1996 348 132939
94
Problems with SA
  • 3. Subgroups are not truly randomized
  • Randomization works to balance known and unknown
    factors in the overall population
  • Subgroups are NOT truly randomized unless
    randomization was
  • Stratified
  • This allows SA to be based on pre-randomization
    characteristics

95
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96
Problems with SA
  • 3. Subgroups are not truly randomized
  • E.g. TNT trial

NEJM 2005352(March 08)
97
Problems with SA
  • 3. Subgroups are not truly randomized
  • E.g. TNT trial

NEJM 2005352(March 08)
98
Problems with SA
  • 3. Subgroups are not truly randomized
  • E.g. TNT trial

NEJM 2005352(March 08)
99
Problems with SA
  • 3. Subgroups are not truly randomized
  • E.g. TNT trial

NEJM 2005352(March 08)
100
Problems with SA
  • 3. Subgroups are not truly randomized
  • E.g. TNT trial

NEJM 2005352(March 08)
101
Problems with SA
  • 3. Subgroups are not truly randomized
  • E.g. TNT trial
  • Achieving a certain LDL level is a
    post-randomization phenomenon
  • No test for interaction were done/reported on the
    effect in patients who achieved lower vs higher
    LDL levels
  • Should have randomized to titrating to LDLlt2.5
    and LDLlt2.0
  • This trial was a high vs low dose

NEJM 2005352(March 08)
102
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103
Problems with SA
  • 4. The play of chance
  • Even properly done SA can yield significant tests
    of interaction by chance alone
  • Especially when the subgroup effect is not
    plausible or it is unanticipated

CHARISMA Lancet 1996 348 132939
104
Replication is the Solution
  • Due to lack of power and high probability of
    false-positives
  • Subgroup differences should be hypothesis
    generating
  • The findings should be tested in a trial designed
    for this purpose

105
Replication is the Solution
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107
Checklist for Good SAs
  • 1. Subgroups should be
  • Based on pre-randomization characteristics and
    stratified accordingly
  • YES
  • Based on intent-to-treat population
  • To maintain the benefits of randomization
  • YES

108
Checklist for Good SAs
  • 2. Must be pre-defined
  • And have a plausible biological reason for
    choosing the subgroup
  • YES
  • Should justify the direction of the expected
    difference in the subgroup
  • NO

109
Checklist for Good SAs
  • 3. Reporting
  • All numerators and denominators should be
    reported
  • YES
  • of planned subgroups
  • YES
  • Address the issue of multiple testing
  • NO

110
Checklist for Good SAs
  • 4. Statistical Analysis
  • Need to do tests for interaction (heterogeneity
    of effect between overall population and
    subgroup)
  • A significant interaction test tells you
  • The subgroup effect is different than overall
    effect
  • DOES NOT tell you anything about the magnitude of
    difference

111
Checklist for Good SAs
  • 5. Interpretation of findings
  • The overall effect should be stressed
  • Due to the high risk of false-positive findings
    in subgroups
  • Not unusual to find a SS difference in a subgroup
    when NSS overall finding
  • YES, the overall effect was stressed

112
In General
  • Focus on overall results
  • Use results in subgroups only if
  • Significant interaction test (heterogeneity)
  • You still want to explore possible reasons for
    interaction
  • i.e make sure there is a biological reason for
    the subgroup difference
  • Has this difference been found in other studies?

113
Practical Application
  • So for CAPRIE
  • Could use the results to treat PAD patients with
    Clopidogrel but not stroke or MI patients
  • Might want to study the effect of clopidogrel in
    MI patients

114
Subgroup Analyses References
  • Lagakos SW. NEJM 2006354(16)1667-9
  • Cook DI et al. MJA 2004180(3)289-91
  • Simes RJ et al. MJA 2004180(5)467-9
  • Rothwell P. Lancet 200536517686

115
Demographics
116
Composite Outcome (CO) Interpretation
117
What is a Composite Outcome?
  • Example 1
  • TIME Trial (Lancet 2001 358 951957)
  • Invasive therapy versus medical management for
    symptomatic coronary artery disease
  • Death, non-fatal MI, admission for ACS
  • The primary endpoint was analysed by intention
    to treat as a composite endpoint, and all
    components separately as secondary endpoints.

118
What is a Composite Outcome?
  • Example 2
  • CAPRIE Trial (Lancet 1996 348 132939)
  • Clopidogrel versus ASA and ischemic events in
    patients at risk
  • The first occurrence of ischaemic stroke,
    myocardial infarction, or vascular death.
  • No stated/planned assessment of individual
    components of the composite

119
What is a Composite Outcome?
  • Example 3
  • ValHeFT Trial (N Engl J Med 20013451667-75.)
  • Added Valsartan versus standard therapy for CHF
  • Mortality and the combined end point of mortality
    and morbidity
  • defined as cardiac arrest with resuscitation,
    hospitalization for heart failure, or
    administration of intravenous inotropic or
    vasodilator drugs for four hours or more with
    hospitalization.

120
Benefit of Composite Outcomes
  • Statistical Efficiency
  • Improved medical care has led to low event rates
    (e.g. fewer MIs, strokes, etc)
  • Need large trials with long follow up to
    demonstrate differences between treatments
  • Not feasible for many researchers
  • COs allow researchers to show differences in
    smaller trials with shorter durations

121
What do you think?
  • Death / MI/ Admission for ACS??
  • Death / MI / Stroke??
  • Cardiac arrest with resuscitation,
    hospitalization for heart failure, or IV
    inotropic or vasodilator drugs for four hours or
    more without hospitalization??

122
The Primary Question About CO
  • Can I use the analysis of the composite outcome
    comparison between treatment and control as the
    basis for a decision?
  • E.g. If the CO is lower for Intervention X
    compared to control would I prescribe X?
  • OR
  • If the CO is lower for X do I need to look at the
    analysis of the components before I make a
    decision?

123
Checklist for COs1,2
  • The benefit of using a CO is realized if
  • ? Individual components are of equal importance
  • ? Effects of intervention on components will be
    similar (i.e occur at similar frequency)
  • ? The more important components should not be
    negatively affected by the intervention
  • ? Counting rules are appropriate and individual
    component data is presented

124
1. Individual Components are of Equal Importance
  • Example1TIME Trial
  • Death, non-fatal MI, admission for ACS
  • Could argue that death and MI are more important
    to patients than admission for ACS
  • It then becomes important to know
  • Which part is driving the reduction in the CO??
    If it is admission due to ACS then invasive
    treatment may not be worth it for a patient

Invasive Med
125
2. Components Occur at Similar Frequency
  • Example1TIME Trial
  • Death, non-fatal MI, admission for ACS
  • Easy to see that Death and MI occur much less
    than Admission for ACS
  • In this case the using the CO result when making
    decisions could be problematic
  • The total CO event rate is primarily driven by
    admissions and not the outcomes that may hold
    greater importance to a patient

126
3. Important Components are Not Negatively
Affected
  • Example1TIME Trial
  • Death, non-fatal MI, admission for ACS
  • The MOST important outcome numerically occurs
    more frequently in the Invasive treatment group
    but this is NSS

127
4. Counting Rules are Appropriate and Component
Data is presented
  • Example1TIME Trial
  • Death, non-fatal MI, admission for ACS
  • YESComponent data is presentedbut was the
    counting proper?
  • They dont tell us if it is first occurrrence
    of or if individual component rates are for
    entire study
  • What if you had an MI and then a month later you
    were admitted for ACS?
  • Were both events accounted for?

128
TIME Trial Conclusion
  • Do not base decisions on analysis of CO rates
    alone
  • Need to look at the details of the components

129
Example 2 CAPRIE TrialIschemic
stroke/MI/Vascular Death
  • 1. Components are of equal importance
  • Yes, most would agree that they are all
    clinically important outcomes
  • In that case looking at the overall result could
    be used to make a decision
  • Be careful
  • All cause death is not part of the composite
  • Assumes clopidogrel wont effect non-vascular
    death negatively
  • Need to consider the rest of the checklist

130
Example 2 CAPRIE TrialIschemic
stroke/MI/Vascular Death
  • 2. Occur at similar frequencies
  • ???
  • NFMI occur less frequently than vascular deaths
    and strokes

131
Example 2 CAPRIE TrialIschemic
stroke/MI/Vascular Death
  • 3. Important outcomes are not negatively affected
  • None of the components were negatively affected

132
Example 2 CAPRIE TrialIschemic
stroke/MI/Vascular Death
  • 4. Counting rules appropriate and component data
    presented
  • Component data presented BUT
  • Not sure if counting rules were OK
  • The main table reports first occurrence of
    rates
  • So it is possible that a patient died at some
    point after a stroke and this death was not
    counted

133
CAPRIE Trial Conclusion
  • Could use CO analysis as the basis for a decision
  • The only issue is the lower MI event rates versus
    other components but this is debatable

134
Example3 ValHeFT Trial
  • Focus on counting rules only
  • Appropriate for DEATH
  • E.g. the total deaths are greater than death
    (as first event)

135
Use the CO Analysis if
  • ? Individual components are of equal importance
  • ? Effects of intervention on components will be
    similar (i.e occur at similar frequency)
  • ? The more important components should not be
    negatively affected by the intervention
  • ? Counting rules are appropriate and individual
    component data is presented

136
Composite Outcome References
  • Kleist P. Applied Clinical Trials 2006 (May
    Issue)
  • Montori VM et al. BMJ 2005330594-596

137
Superiority, Non-inferiority, and Equivalence
Trials.
  • Aaron Tejani
  • April 18. 2007

138
The Main Purpose of Each
  • Superiority
  • Is one treatment better than another?
  • Non-inferiority
  • Is one agent no worse than a standard therapy
    (based on a pre-defined no worse margin)?
  • Equivalence
  • Is one agent no worse or no better than a
    standard therapy (based on pre-defined limits of
    no worse or better)?

139
Superiority Trials
  • E.g. GUSTO III
  • Designed to show that RPA would lower mortality
    more than TPA in MI patients
  • RPA 7.47 vs TPA 7.24
  • Risk difference 0.23 (2-sided 95CI, -0.66 to
    1.10 percent).
  • Incorrect conclusion No difference in mortality
  • Correct conclusion not sure what the difference
    in mortality is

140
Non-inferiority Trials
  • E.g. St. Johns Wort vs Paroxetine
  • Designed to show that SJW was no worse than
    paroxetine at decreasing HamD scores

141
Non-inferiority Trials
  • E.g. St. Johns Wort vs Paroxetine
  • Key points
  • Defined the no worse margin a priori
  • Basically they were saying that
  • E.g. If paroxetine reduced HamD by 15 points then
  • Then the worse case end of the confidence
    interval for the difference between SJW and
    paroxetine would have to be no more than 2.5
    points

142
Non-inferiority Trials
  • E.g. St. Johns Wort vs Paroxetine
  • Paroxetine HamD decrease 11.4
  • SJW HamD decrease 14.4
  • The difference is 3 points more with SJW
  • The range of the difference is 1.5 to 4.0
  • The worse case is only a 1.5 difference
  • This worse case is better than a -2.5 difference
    (the defined margin)
  • Hence non-inferiority is proven

143
Non-inferiority Trials
  • E.g. St. Johns Wort vs Paroxetine
  • Hypothetically non-inferiority would not have
    been proven if
  • Paroxetine decrease was 15
  • SJW decrease was 13
  • The difference was -2 (range -4 to -1)
  • The worse case for the difference is -4 points
  • This is lower than the margin on -2.5 points
  • Hence SJW would be considered not non-inferior
    BUT need to look at per protocol analysis

144
Non-inferiority Trials
  • Per protocol and Intention to treat should be
    done for non-inferiority trials
  • Per protocol
  • Randomization benefit lost hence many differences
    between treatment groups
  • Analyzing only those that follow protocol so some
    randomized people are censored
  • As a result it becomes harder to prove one agent
    is no worse than another because there are now
    more confounding variables
  • Authors should always see similar findings in
    both analyses to support a non-inferiority claim
  • E.g. St. Johns Wort vs Paroxetine
  • The worse case was a 0.7 point difference hence
    non-inferiority was proven

145
Equivalence Trials
  • 3 month vs 6 month follow up of BP patients by
    GPs
  • Designed to show that the difference between 3
    and 6 month visit would be less than 10, either
    better or worse (plus/minus 5mmHg)

146
Equivalence Trials
  • 3 month vs 6 month follow up of BP patients by
    GPs
  • Equivalence was proven for 6 month visits vs 3
    month visits

147
Is Sample Size Different?
  • Sample size needs to be larger for a
    non-inferiority trial
  • Harder to show differences within small range
  • Need more people to be that precise
  • I.e. expecting small differences
  • Small differences will only surface with large
    numbers of patients

148
Can You Switch?
  • If you prove non-inferiority can you then
    conclude superiority?
  • Yes, as the sample size for needed for
    superiority would be met by a non-inferiority
    trial
  • As long as it is a pre-specified analysis
  • P-value needs to be re-calculated
  • Clinical relevance of superiority needs to be
    thought of

149
Can You Switch?
  • If you prove non-inferiority can you then
    conclude superiority?
  • E.g. SJW vs paroxetine
  • SJW decreased HamD more than paroxetine
  • They pre-specified that they would do this
  • BUT they di not calculate a new p-value

150
Non-inferiority Margin
  • This is the most important thing to look at
  • Needs to be chosen ahead of time
  • Needs to be based on statistical and clinical
    reasoning
  • Should be derived from the benefit seen with
    standard therapy over placebo

151
Non-inferiority Margin
  • Should be derived from the benefit seen with
    standard therapy over placebo
  • E.g Drug A is standard
  • Reduces MIs by 2 vs placebo (95CI 1-4)
  • Drug B is new and is being studied vs Drug A in a
    non-inferiority trial
  • The margin should be set as no worse than 1
    mortality difference with B vs A
  • 1 comes from the worse case of the 95CI versus
    placebo of standard therapy
  • Should not be a margin of 2 (this doesnt take
    into account the uncertainity in the benefit of
    Drug A vs placebo)

152
Checklist
  • Is this equivalence or non-inferiority?
  • Is there a margin pre-specified?
  • Is the margin appropriately justified by authors
    or is it arbitrary?
  • Did they do a per protocol and an ITT analysis?
  • Did they say they would look at superiority ahead
    of time and was a p-value re-calculated?

153
Remember
  • If no statistially significant difference seen in
    a superiority trial
  • DO NOT conclude absence of a difference
  • Conclude there is absence of evidence of a
    difference

154
Checklist contd
  • If they claimed non-inferiority after they
    couldnt show superiority did they check to see
    if they had enough power to do so?
  • They would need more people to conclude
    non-inferiority (hence probably under-powered) or
    have enrolled more than needed for superiority so
    the seocnd analysis would be OK?

155
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156
Surrogate Outcomes
  • Outcomes that are substitutes for measures of how
    a person functions, feels, or if they survive
  • Which are surrogates?
  • BP
  • LDL cholesterol
  • HbA1C
  • Stroke

157
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158
What is a Serious Adverse Event?
  • An adverse event is any undesirable experience
    associated with the use of a medical product in a
    patient. The event is SERIOUS and should be
    reported when the patient outcome is
  • Death
  • Report if the patient's death is suspected as
    being a direct outcome of the adverse event.
  • Life-Threatening
  • Report if the patient was at substantial risk of
    dying at the time of the adverse event or it is
    suspected that the use or continued use of the
    product would result in the patient's death.
  • Examples Pacemaker failure gastrointestinal
    hemorrhage bone marrow suppression infusion
    pump failure which permits uncontrolled free flow
    resulting in excessive drug dosing.
  • Hospitalization (initial or prolonged)
  • Report if admission to the hospital or
    prolongation of a hospital stay results because
    of the adverse event.
  • Examples Anaphylaxis pseudomembranous colitis
    or bleeding causing or prolonging
    hospitalization.

159
What is a Serious Adverse Event?
  • Disability
  • Report if the adverse event resulted in a
    significant, persistent, or permanent change,
    impairment, damage or disruption in the patient's
    body function/structure, physical activities or
    quality of life.
  • Examples Cerebrovascular accident due to
    drug-induced hypercoagulability toxicity
    peripheral neuropathy.
  • Congenital Anomaly
  • Report if there are suspicions that exposure to a
    medical product prior to conception or during
    pregnancy resulted in an adverse outcome in the
    child.
  • Examples Vaginal cancer in female offspring from
    diethylstilbestrol during pregnancy malformation
    in the offspring caused by thalidomide.
  • Requires Intervention to Prevent Permanent
    Impairment or Damage
  • Report if you suspect that the use of a medical
    product may result in a condition which required
    medical or surgical intervention to preclude
    permanent impairment or damage to a patient.
  • Examples Acetaminophen overdose-induced
    hepatotoxicity requiring treatment with
    acetylcysteine to prevent permanent damage burns
    from radiation equipment requiring drug therapy
    breakage of a screw requiring replacement of
    hardware to prevent malunion of a fractured long
    bone.

160
SAE Reporting
  • Required in all clinical trials
  • Problem
  • Not sure which outcomes are included in SAE
    totals
  • Not always reported in publications

161
Example Finasteride for BPH
TI Letter 58 Jan-Mar 2006
162
Example Finasteride for BPH
TI Letter 58 Jan-Mar 2006
163
e.g. Lumiracoxib (TARGET Trial)
Lancet 2004 364 66574
164
e.g. Lumiracoxib (TARGET Trial)GI Adverse Events
Lancet 2004 364 66574
165
e.g. Lumiracoxib (TARGET Trial)CV Adverse Events
Lancet 2004 364 66574
166
e.g. Lumiracoxib (TARGET Trial)SAE Data Not
Reported in Published Trial
  • Total number of patients with SAEs both
    substudies pooled
  • Lumiracoxib 588 (6), NSAIDs 566 (6)
  • Total number of patients with SAEs ibuprofen
    sub-study
  • Lumiracoxib 297 (7), Ibuprofen 272 (6)
  • Total number of patients with SAEs naproxen
    sub-study
  • Lumiracoxib 291 (6), Naproxen 294 (6)
  • Regardless of drug attribution

Data received via personal communication with Dr.
Hawkey, March 2007
167
TNT SAE Data Request
  • From John C LaRosa mailtoJohn.LaRosa_at_downstate.
    edu Sent Tuesday, August 14, 2007 153 PMTo
    Tejani, AaronSubject Re TNT Serious adverse
    event data requestDavid When you return, can
    you have someone provde me with answers? Thanks
    John
  • "Tejani, Aaron" ltAaron.Tejani_at_fraserhealth.cagt
    08/14/2007 0427 PM Toltjclarosa_at_downstate.edugt
    ccSubjectTNT Serious adverse event data
    requestDr. La Rosa We are reviewing the TNT
    trial with our pharmacy students and were
    wondering if you were able to answer 2 questions
  • 1. Could you provide us with the serious adverse
    event (SAE) rates in both groups? 2. Were the
    components of the composite outcome considered
    and counted as SAEs? E.g were Mis included in the
    SAE totals?
  • Many thanks,
  • Aaron

168
Sample Size and Study Power
  • What it takes to calculate sample size
  • Relation of sample size to the primary outcome
  • Issues related to secondary outcomes, subgroup
    differences, etc

169
Components of a Sample Size Calculation
  • Power
  • The ability to detect a difference that truly
    exists
  • Type II error (beta) missing a difference that
    exists i.e insufficient power
  • E.g 80 power means a 20 chance of missing a
    true difference
  • Power 1 - beta

170
Components of a Sample Size Calculation
  • Level of significance
  • An alpha level must be chosen
  • Alpha relates to Type I error
  • Type I error detecting an effect when none
    exists
  • Chosen alpha becomes your p-value
  • E.g. alpha0.05 then p of less than 0.05 is
    significant
  • What does p-value ( 0.0X) than what we choose
    tell us?
  • Treatment effect found is due to chance X of
    the time

171
Components of a Sample Size Calculation
  • Underlying population event rate
  • Look at previous studies to get this
  • E.g TARGET Trial
  • Lumiracoxib versus naproxen
  • What is the expected rate of GI complications
    with Naproxen

172
Components of a Sample Size Calculation
  • Size of treatment effect
  • This should be the minimal clinically important
    difference
  • This should be justified

173
Components of a Sample Size Calculation
  • Adjusted sample size requirement for lack of
    compliance
  • Achievable treatment effect (which is a component
    of the sample size calculation) is dependent on
    compliance to treatment
  • E.g trial with 100 people per arm if 100
    compliance
  • If only 80 compliance, need 280 per arm

174
Checklist
MJA 2002157256-7.
175
General Comments
  • In calculating a sample size a balance is
    required between risk of a Type I error versus a
    Type II error
  • Sample size and all assumptions apply to only the
    primary endpoint
  • Likely that Type I and II errors will occur for
    anything other than the primary endpoint, even
    with subgroups
  • i.e. be aware that false negatives and false
    positives likely to occur

176
Remember
  • Torture numbers and they will confess to
    anything.
  • Gregg Easterbrook

177
Definition of PICO
  • Four components of PICO
  • Used to formulate a clinical question before you
    read the trial
  • Patient or problem
  • Description of patient or target disorder
  • Intervention
  • Could include exposure, diagnostic test,
    prognostic factor, therapy or patients
    perception
  • Comparison intervention
  • Relevant most often when looking at therapy
    questions
  • Outcome
  • Clinical outcome of interest to you and your
    patient
  • DOES NOT HAVE TO BE WHAT THE AUTHORS MEASURED!

178
Arch Int Med 2001134657-62
179
CASP RCT Checklist
180
Users Guide
181
Appraising Systematic Reviews and Meta-analyses
182
First 2 Screening Questions
  • 1. Did the review ask a focused question?
  • PICO?
  • Recommend thinking of your PCIo first before
    reading the review
  • 2. Did the review include the right type of
    study?
  • If it was therapy did they look at RCTs only?

183
Finding Studies to Include
  • Should search at least these databases
  • Medline/Pubmed
  • EMBASE
  • Cochranes CENTRAL database of RCTs
  • No language restrictions
  • Should also search
  • Reference lists, experts, conference proceedings,
    unpublished studies

184
Assessing Quality of Studies
  • What are quality indicators of clinical trials?
  • If you measure quality need to do something with
    the information
  • Do sensitivity analyses

185
Extracting data
  • Need at least 2 people extracting data
  • Need a standard data extraction form
  • Why?
  • To avoid transcription errors

186
Was it appropriate to Meta-analyze?
  • Does it make sense to combine data from included
    trials?
  • Was heterogeneity assessed?
  • If found were reasons for this explored?
  • If found was random effects model used to analyze
    data?

187
Forest Plots
188
Final Notes on Systematic Reviews
  • Systematic, reproducible, defendable
  • Only as good as the trials included
  • Main questions to ask
  • Did they attempt to get all the trials?
  • Did they compile the important information and
    critically appraise each trial that was included?
  • Should be the first place you go to answer
    clinical questions

189
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190
CASP SR Checklist
191
Users Guide for Overviews
192
Questions or Comments
In dwelling, live close to the ground. In
thinking, keep to the simple. In conflict, be
fair and generous. In governing, don't try to
control. In work, do what you enjoy. In family
life, be completely present. When you are content
to be simply yourself and don't compare or
compete, everybody will respect you.
Tao Te ChingVerse 8
193
Acknowledgements
  • Therapeutic Initiative Group
  • CASP
  • Critical Appraisal Skills Program (CASP)
  • JAMA Users Guides to Evidence-based practice
  • Bandolier
  • Fraser Health Research
  • Susan and Rosa!
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