Alec Walker September 2014 Core Characteristics of Randomized Clinical Trials

Preplanned Analysis Goals  Primary  Secondary  Strategy for unanticipated results Study size  Statistical power  Stopping rules 2

Preplanned Data Collection Baseline  Characteristics of each participant’s history  Concomitant illnesses  Diagnostic tests and procedures  Medical examination Treatment  Dose, route, frequency, duration, timing Endpoints  Symptoms and tests required for diagnosis Safety  Adverse outcomes  Contemporaneous assessment of causality

Entry Criteria Treated disease  Definition  Severity, Prognosis Other health conditions  Concomitant diseases Others  Demographics – age, sex, race Implicit Criteria  Populations served by participating clinical sites  See also Informed Consent later Goals  1 o Clarify the comparison  2 o 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 Apr 1;362(13):

Balance

Delta = Treatment Effect

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. 18

Differential outcome identification? Years 1 & 2Years 3 & 4 Treatment | Grade Finasteride Placebo 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 20