m2 medical epidemiology

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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