m2 medical epidemiology
DESCRIPTION
Clinical trials. M2 Medical Epidemiology. Clinical Trials. A clinical trial is A cohort study A prospective study An interventional study An experiment A controlled study. The Structure of a Clinical Trial. Various Aspects Are Standardized and Protocol-based. Subject selection - PowerPoint PPT PresentationTRANSCRIPT
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M2 Medical Epidemiology
Clinical trials
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Clinical Trials
A clinical trial is
A cohort study A prospective study An interventional study An experiment A controlled study
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The Structure of a Clinical Trial
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Various Aspects Are Standardized and Protocol-based
Subject selection Subject assignment H & P data Therapeutic intervention Lab calibration Outcome evaluation
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Subject Selection
Adequate number of subjects Adequate number of expected
endpoints Easy to follow-up Willing to participate (give consent) Eligibility (criteria)
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•Efficacy Versus Effectiveness
Internal Validity (validity) versus External Validity (generalizability)
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Types of Control Groups
Historical Contemporaneous Concurrent Randomized
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Allocating Treatment
If Concurrent Controls are best, what is the best way of assigning patients to receive a new treatment or serve as controls.
Complete (Simple) randomization Restricted randomization
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Complete Randomization
Patients assigned by Identical chance process (but not necessarily in equal numbers)
Mechanics Insures process fairness Does not insure balance, especially in
small studies.Therefore, may still need statistical adjustment
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Randomization Contd.
Has nothing to do with sampling bias. Randomization (random allocation) versus
random sample. Does NOT deal with “chance” as a possible
explanation of the difference. To the contrary. Can be used to create groups of unequal
size. Baseline characteristics (table 1).
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Allocation Concealment
The allocation sequence is concealed from those enrolling participants until assignment is complete.
Prevents enrollers from (subconsciously or otherwise) influencing which participants are assigned to a particular treatment group.
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Allocation Concealment
To assess concealment, raters of trials look for:
Central (phone) randomization Sequentially numbered, opaque, sealed
envelopes Sealed envelopes from a closed bag Numbered or coded bottles or
containers
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Restricted Randomization
Stratification Blocking (Permuted Block
Design) Minimization (Dynamic
Balancing)
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Stratified Randomization
When an important prognostic factor (risk factor for the outcome being studied) exists.
Subjects are stratified according to that factor prior to randomization.
To the benefits of complete randomization adds assurance of balance on factors used to form strata.
May still need adjustment on other factors.
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Scheme of stratified randomization
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Blocking
Ensures close balance of the numbers in each group at all times during trial.
Example: For every six patients 3 will be allocated.
Problem If block size is discovered. Remedy: more blinding, varying block size,
larger blocks. Basic, Stratified,Randomized (random-sized)
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Minimization (Dynamic Balancing)
Ensures balance of several factors . No list in advance. First patient is truly
randomly allocated. For each subsequent patient the treatment
allocation is identified which minimizes the imbalance. Then a choice is made at random with weighting in favor of it.
Has to be done away from enrollment point and enrollers blind to process.
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Variations in Prognosis by Hospital
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Problems With Contemporaneous Comparisons
Regional population differences. Regional practice differences. Diagnostic variations. Referral pattern biases. Variations in data collections.
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1 Year Mortality in Trials of Medical Vs. Surgical Treatment of Coronary Artery Disease
Historical controls 6 trials 9,290 patients
Medical 14.2 %Surgical 7.0 %Randomized controls 9 trials 18,861 patients
Medical 6.6 %Surgical 7.6 %
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Why Do Controls in a Randomized Trial Do So Well ?!
Volunteerism Eligibility Placebo effect Hawthorne effect Regression towards the mean
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Volunteerism
People who agree to participate in clinical trials are an “elite” group of patients with extremely good prognosis
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Eligibility
Patients have to meet stringent eligibility criteria before randomization, or they would be excluded
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Placebo Effect
Placebo can do just about anything (prolong life, cure cancer).
Placebo can also cause side effects. Placebo effect is very useful in medicine
but in epidemiology it causes problems, so we try to equalize it between the 2 groups.
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Hawthorne Effect
Hawthorne works of the Western Electric Co. Chicago, IL
People who know they are being studied modify their behavior and do better than the average patient
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Regression Towards the Mean
Weather game Individuals with initially abnormal results tend
on average to have more normal (closer to the mean) results later.
Lab tests, BP etc. Recheck before randomization. Run-in
period. Sophomore slump, medical school, Airforce
landing feedback.
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Why Does Prognosis Improve Over Time ?
1. Initial reports come from referral centers
2. Publicity brings in more patients
3. Physicians’ awareness increases diagnosis
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4. Development of a Diagnostic Test Allows diagnosis of atypical cases. Is an incentive for physicians. It’s more
challenging to diagnose difficult (atypical cases)
Physicians with zero diagnostic skill can now diagnose this disease.
Allows diagnosis of non-cases (false positives)
Allows population based studies
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Prognosis Improves Over Time. Contd.
5. Publicity that a disease is very common relieves clinician from worrying that they may be overdiagnosing it.
6. Placebo effect increases over time. Why?
7. Safer treatment (laparoscopic cholecystectomy) lowers the threshold for diagnosing “symptomatic gall stones”
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Stage Migration BiasWill Rogers Effect
8.Improved staging tests cause an apparent improvement of prognosis in every stage.
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Severity
# of Pts. # of
Deaths Case-Fatality
Rate
Staging Mild 10 1 10% 10 2 20%
I 15%
10 3 30% 10 4 40%
II 35%
10 5 50% Severe 10 6 60%
III 55%
Stage Migration BiasWill Rogers Effect
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Severity# ofPts.
# ofDeaths
Case-FatalityRate Old Staging New Staging
Mild 10 1 10% I 10%10 2 20%
I 15%
10 3 30%II 25%
10 4 40%II 35%
10 5 50%Severe 10 6 60%
III 55%III 50%
Stage Migration BiasWill Rogers Effect
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“BEFORE-AFTER” STUDYMortality by severity level
Grade 200controls
200CCUcases
Mild 20% 2%
Severe 50%
32%
27%
19%
Shock 100% 88%
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“BEFORE-AFTER” STUDYSeverity distribution
Grade 200 controls 200CCU cases
Mild 121 (60 %) 56 (28)
Severe 72 (36%) 119 (59%)
Shock 7 (4%) 25 (13%)
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Will Rogers Effect
Will Rogers: “ When Okies moved from Oklahoma to California, they improved the IQ in both states”
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Exclusion Criteria
Excluded patients are “ineligible” So Why the separate category?
More informative Usually very large number. Usually underestimates, real number
even bigger. Why?
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Exclusion Criteria
Usually there is a table
What to watch for Patient preference Clinician preference no reason given
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Studies within a Clinical Trial
Study of ASA in MI to prevent long term mortality
Can study other predictors of mortality. For example smokers versus non smokers (adjusting for ASA)
Can study predictors of other outcomes for which we have data. For example onset of CHF. Again study exposed versus unexposed (to LVH for example) adjusting for ASA (Why?)
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Studies within a Clinical Trial
Can study if another drug is associated with the main outcome or secondary outcomes.
Beware bias (non randomized cohort). If exposure info not available on whole
population (e.g. serum samples) then:Nested Case-control.Case-cohort.
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Loss to follow up
Differential vs. Random
• Compare their baseline variables with the rest of the subjects.
• Chase a subgroup.
• Worst case scenario
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Objectives of Subgroup Analysis
Support the main finding Check the consistency of main finding Address specific concerns re efficacy or
safety in specific subgroup Generate hypotheses for future studies
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Inappropriate Uses of Subgroup Analysis Rescue a negative trial Rescue a harmful trial Data dredging: find interesting results
without a prespecified plan or hypothesis
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To Avoid Inappropriate Uses of Subgroup Analysis Prespecify analysis plan Prespecify hypotheses to be tested
based on prior evidence Plan adequate power in the subgroups Avoid the previous pitfalls.
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Problems with Subgroup Analysis
1. Low power
2. Multiplicity
3. Test for interaction
4. Comparability of the treatment groups maybe compromized
5. Over interpretation
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Blinding
PATIENTS Single blind. CLINICAL THERAPISTS usually double
blind. CLINICAL EVALUATORS. Have to
specify. Subjective vs. objective assessment
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Drop ins and Drop outs
Define. Typical case Other
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Subjects who drop out of study or change treatment. But available for outcome assessment. Intention to treat analysis Once randomized always analyzed Why ?
1. Change in therapy may be related to outcome or eligibility
2. To get the full benefit of randomization
3. Effectiveness versus efficacy
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Five-Year Mortality in Coronary Drug Project
ADHERENCE CLOFIBRATE
Total 18.2
> 80% 15.0
< 80% 24.6
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Five-Year Mortality in Coronary Drug Project
ADHERENCE CLOFIBRATE PLACEBO
Total 18.2 19.4
> 80% 15.0 15.1
< 80% 24.6 28.2(p<5X10-16)
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Five-Year Mortality in Coronary Drug Project
Baselinecholesterol
Cholesterolchange
Clofibrate Placebo
Fall 17.2 20.7Total group
Rise 22.2 19.7
Fall 16.0 21.2<250
Rise 25.5 18.7
Fall 18.1 20.2>250
Rise 15.5 21.3
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Screening Mammography
Population Incidence of breast cancer/1000 woman years
Not offered screening 2.03
Screened 2.20
Refused screening 1.80
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A COHORT STUDY OF RECURRENT MI BY PARTICIPATION IN A GRADUATED EXERCISE PROGRAM FOLLOWING
INTITIAL MI
RECURRENT MI
YES NO TOTAL
PARTICIPATION IN GRADUATED EXERCISE PROGRAM
YES 7 59 66
NO 18 46 64
TOTAL 25 105 130
RELATIVE RISK = 0.38
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A RANDOMIZED CLINICAL TRIAL OF ENDURANCE TRAINING FOR PREVENTION OF RECURRENT MI
RECURRENT MI
YES NO TOTAL
ENDURANCE TRAINING YES 28 359 387
No 21 345 366
TOTAL 49 704 753
RELATIVE RISK = 1.26
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Alternatives to “Intention to Treat” Analysis
1. “Per Protocol” analysis.
How is it done?
Problems.
2. “As Treated” analysis.
How?
Problems.
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“Per Protocol” analysis.
Censoring data after subjects become non-adherent
1. Preserves randomization
2. Stops counting events (when? “Carry-over” effect)
3. Reduced power
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“As Treated” analysis
Change the treatment arm of the subject as he/she changes exposure
1. The follow-up time and the events will be assigned to current exposure
2. Retain all events.3. Randomization violated.4. Have to assign “lag-time” (latency) and
Carry-over time.
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CLINICAL TRIALS JARGON
Consecutive patients (versus a random sample) Baseline characteristics of patients (to see if
randomization worked) Number of subjects and average duration of
follow-up (versus patient years)
Interim analysis, problems Cumulative incidence (versus incidence density)
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Jargon contd.
Relative risk (Odds Ratio, or Hazard Ratio) (hopefully <1)is: rate of outcome in a drug group rate of outcome in a placebo group
Relative risk reduction (similar to attributable risk %). But here it is 1-RR.
Absolute difference in risk (similar to AR, very important, sometimes not reported
Relative risk reduction versus absolute difference in risk
Number needed to treat 1/ADR
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Jadad Scale
Asked judges to make a large list of items. Tested the items on 13 study reports. Excluded items that had very high or very low
“endorsement” rate (items found in every study and those not found in any study).
So the instrument is discriminating but intentionally excludes very important items (for example definition of subjects, presence of control group).
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Jadad Scale
Final list has 3 items with maximum score of 5
Randomization and concealment (0-2 points)
Double blinding (0-2 points) Description of withdrawals and
dropouts (numbers and reasons) (0-1 point)
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Randomization (and concealment) 1 point if described as randomized. Add a second point if method was
described and is adequate. Take away first point (back to zero) if
method was described and was inadequate.
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Double blind
1 point if described as double blind. Add a second point if method was
described and is adequate (both participant and assessor could not identify the intervention)
Take away first point (back to zero) if method was described and was inadequate.
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Delphi scale
By repeated rounds reached consensus
9 items1. Randomization2. Allocation concealment3. Were the groups similar at baseline
regarding most important prognostic factors (Table 1)
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Delphi scale cont
4. Eligibility criteria specified
5. Outcome assessor blind
6. Care provider blind
7. Patient blind
8. Point measures and measures of variability for primary outcome
9. Intention-to- treat
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Descriptions of “Trials” 34% relative decrease in the incidence of MI. The
decrease is statistically significant. The 95% CI ranges from 55% relative decrease to a 9% relative decrease.
1.4% decrease in …. (2.5% versus 3.9%). The decrease is statistically significant. The 95% confidence interval ranges from a 2.5% decrease to a ..
77 persons must be treated for an average of just over 5 years to prevent 1 MI.
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Ratings of Trials (-5=harmful,+5=very effective)
Mean Rating
RR 3.36
AR 2.84
NNT 1.05
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Ethical Issues
When is it unethical to randomize ? When Do you stop a trial?
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DSMB
Data Safety Monitoring Board Early Termination rules O’Brien Fleming
1. Early vs. late
2. Benefit vs. harm (blinding?)
3. Multiplicity
4. Rules. Scenarios.
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WHI DSMB Scenario 8
Suppose there is significant increase in breast cancer (which is the primary adverse event).
The Global Index (first of 8 adverse events) is supportive of harm.
Nothing else is significant.
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WHI DSMB Scenario 8
Primary outcomes Continue Total Mortality Continue Combination
Weighted, Unweighted, Bayesian Cont. Primary +Global index significant Cont. Primary +Global index supportive Cont. Primary adverse event + GI supportive
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WHI DSMB Scenario 8
12 Members All voted to stop. Primary adverse event + GI supportive