Alec WalkerSeptember 2014

Core Characteristics of Randomized Clinical Trials

Preplanned Analysis

Goals Primary Secondary Strategy for unanticipated resultsStudy size Statistical power Stopping rules

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Preplanned Data Collection

Baseline Characteristics of each participant’s history Concomitant illnesses Diagnostic tests and procedures Medical examinationTreatment Dose, route, frequency, duration, timingEndpoints Symptoms and tests required for diagnosisSafety Adverse outcomes Contemporaneous assessment of causality

Entry Criteria

Treated disease Definition Severity, PrognosisOther health conditions Concomitant diseasesOthers Demographics – age, sex, raceImplicit Criteria Populations served by participating clinical sites See also Informed Consent laterGoals 1o Clarify the comparison 2o Generalizabilty to target population

Comparison of Results in Groups

Groups Anecdotes, however persuasive, are set aside Frequency of outcome is the measure of effect We are examining net effects

Improved as a result of treatment No effect of treatment Deleterious effect of treatment

Comparisons Substitution of another person’s experience for the

impossible “What if?” question. (Counterfactual: What would have been the treated person’s experience if there had been no treatment?)

Informed Consent

Introduced for ethical reasons Patients should be aware that they are participating

in an experiment Actively agree to enter

A subtle selection criterion Language skills Education Trust in the medical care system Inclination to follow directions

Random Assignment of Treatments

“Coin flip” metaphor Mechanical process Assignment not systematically associated with any

patient characteristics At the discretion of the investigator:

Number of compared treatments Allocation ratio Blocking

Effects Expectation of similar outcomes between groups

under the Null Hypothesis Justification for the calculation of p-values

Randomization

Treatment allocation is determined by a process That generates

An expectation of zero correlation between

treatment and predictors of outcome. The Predictors may be

Known or unknown to the experimenter Measured or unmeasured Measured poorly or well

Balance

All characteristics other than treatment are balanced in expectation Measured and unmeasured Predictors and correlates of predictors The intermediate states that later arise from these

Balance

All characteristics other than treatment are balanced in expectation Measured and unmeasured Predictors and correlates of predictors The intermediate states that later arise from these

Unadjusted estimates are unbiased estimates of treatment effect Differences, ratios, more complex functions of Risk, rates, hazards, survival, … Costs, QoL, … Even of dependent happenings, like epidemics

(provided that exposure groups are not intermixed)

Treatment Adherence

Commitment from patients Encouragement from staff Monitoring

Pill counts Blood level

Dedicated Outcome Data Collection

Disease prespecified Expert consensus on diagnosis

Symptoms Signs Diagnostic testing

Recurrent monitoring

Limited Follow-up

Need to get drug to market For chronic conditions, no amount of follow-up will

reproduce ultimate conditions of use Surrogate outcomes

Examples Control of blood pressure or HbA1c Patient-reported outcomes

Desiderata Well established correlates of clinically important Generally not important clinically in themselves Manifest earlier

Real clinical outcomes can be addressed later

N Engl J Med. 2010 Apr 1;362(13):1192-202

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Differential outcome identification?

Protocol provided similar surveillance Scheduled visits Scheduled biopsies

Work-up of symptoms was inevitably differential because the symptomatic outcomes were differential Urinary retention, urinary tract infection, BPH surgery Presumably differences in symptoms that fell short of

study outcomes The protective association with cancer was small and

much smaller than corresponding protective association with noncancer outcomes – the algebra of detection bias works out with plausible assumptions

This same problem will often be part of the mix of uncertainty in observational studies.

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Differential outcome identification?

Years 1 & 2 Years 3 & 4

Treatment | Grade 5-7 8-10 5-7 8-10

Finasteride 417 18 223 12

Placebo 558 17 274 1

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The authors raise the possibility that early detection in the placebo group may have reduced the risk of more advanced prostate cancer in those patients in later years.

What should we consider to be the treatment effect?

Does dutasteride increase the risk of advanced prostate cancer?

Problems Solved, Problems Remaining

Randomization in RCTs provides the gold standard for inference No hypothesis of confounding Frequentist interpretation of measures is supported

by the structure of the trial RCT populations may be atypical

In baseline characteristics In adherence to therapy In care of follow-up

RCT follow-up may be short

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