Interim Analysis in Clinical Trials - Tampereen …

1 1 Interim Analysis in Clinical TrialsProfessor Bikas K Sinha [ ISI, KolkatA ]Courtesy : Dr Gajendra Viswakarma Visiting ScientistIndian Statistical Institute Tezpur Centree-mail: is a Clinical trial?A test of a new intervention or treatment on people for detecting -Tolerability -Safety-Efficacy A Clinical trial is defined as a prospective study comparing the effect and value of intervention (s) against a control in human of Clinical Trials Superiority Non-inferiority EquivalenceIt can be a Phase I, Phase II or Phase III Trial4 Diagrammatical Presentation of Clinical TrialsControl betterTest better-G0 Gequivalencenon-inferiorsuperior 5 Clinical Trial Stages Phase I: Clinical Pharmacology and Toxicity Objective:To determine a safe drug dose for further studies of therapeutic efficacy of the drug Design:Dose-escalation to establish a maximum tolerated dose (MTD) for a new drug Subjects:1-10 normal volunteers or patients with disease6 Clinical Trial Stages Phase II: Initial Clinical Investigation for Treatment Effect Is a fairly small-scale Objective.

2 To get preliminary information on effectiveness and safety of the drug Design:Often single arm (no control group) Subjects:100-500 patients with disease (or depends on Therapeutic Area [TA])7 Clinical Trial Stages Phase III: Full-Scale Evaluation of the Treatment (Comparative Clinical trial): planned experiment on human subjects. To some people the term Clinical trial is synonymous with such a full-scale Phase III trial. Phase III trial is most rigorous and extensive type of scientific Clinical investigation of a new treatment. Objective: To compare efficacy of the new treatment with the standard regimen Design: Randomized Control Subjects: depends on phase II trial patients with disease8 Clinical Trial Stages Phase IV: Post-Marketing After the research program leading to a drug being approved for marketing, there remain substantial inquiries still to be undertaken as regards monitoring for adverse effects and additional large-scale, long-term studies of morbidity and mortality.

3 Objective:To get more information (long-term side effects) Design:no control group Subjects:Patients with disease using the treatment9 The Big PictureDRUG ADRUG B Test So What is Different? Ethics: Experiment involving human subjects brings up new ethical issues Bias: Experiment on intelligent subjects requires new measures of controlWe will also study the additional considerations in Clinical Trials to address the above Analysis Analysis comparing intervention groups at any time before the formal completion of the trial, usually before recruitment is complete. Often used with "stopping rules" so that a trial can be stopped if participants are being put at risk unnecessarily. Timing and frequency of Interim analyses should be specified in the Analyses Interim analyses is a tool to protect the welfare of subjects By stopping enrollment/treatment as soon as a drug is determined to be harmful By stopping enrollment as soon as a drug is determined to be highly beneficial By stopping Trials which will yield little additional useful information (or which have negligible chance of demonstrating efficacy if fully enrolled, given results to date) The associated statistical methods are generally referred to as group sequential methods13 Flowchart of the StudyVisit 6T2T1 Visit 5 End of treatmentVisit 4 Control Visit 1 EnrolmentVisit 2 Visit 315 days to 4 weeks4 weeks4 weeks4 weeksTreatment-free follow upTreatment period4 weeks4 weeksScreeningTest (safe dose determined)

4 Visit 74 weeksRequired Sample size of the study is 330 (each are required 110 subjects)14 Disposition Table on going studyDrug CDrug T1 Drug T2 TotalPatient Screened129 Screening Failure23 Patient Randomized363634106 Study Incomplete + ongoing9+58+510+328+12 Completed Visits 5+2223216615 Mean PASI Change at Visits in Different Treatment PASIDrug ADrug BDrug C16 Some Examples of Why a Trial May Be Terminated Treatments found to be convincingly different Treatments found to be convincingly not different Side effects or toxicities are too severe Data quality is poor Accrual is slow Definitive information becomes available from an outside source making trial unnecessary or unethical Scientific question is no longer important Adherence to treatment is unacceptably low Resources to perform study are lost or diminished Study integrity has been undermined by fraud or misconduct17 Opposing Pressures in Interim AnalysesTo Terminate.

5 Minimize size of trial minimize number of patients on inferior arm costs and economics timeliness of resultsTo Continue: increase precision reduce errors increase power increase ability to look at subgroups gather information on secondary endpoints18 The pitfalls of Interim analysesRCTs [Randomized Clinical Trials ] with Interim sample out the Clinical statistical test of efficacy at pre-planned stages in the Interim until sample size has been reached* *One treatment declared significantly better than the other if we get a p-value less than 5%..19 Statistical Considerations in Interim Analyses Consider a safety/efficacy study (phase II) At this point in time, is there statistical evidence The treatment will not be as efficacious as we would hope/need it to be? The treatment is clearly dangerous/unsafe? The treatment is very efficacious and we should proceed to a comparative trial?

6 20 Consider a comparative study (phase III) At this point in time, is there statistical evidence One arm is clearly more effective than the other? One arm is clearly dangerous/unsafe? The two treatments have such similar responses that there is no possibility that we will see a significant difference by the end of the trial?Statistical Considerations in Interim Analyses21 We use Interim statistical analyses to determine the answers to these questions. It is a tricky business: Interim analyses involve relatively few data points inferences can be inexact we increase chance of errors. if Interim results are conveyed to investigators, a bias may be introduced in general, we look for strong evidence in one or another Considerations in Interim Analyses22 Example: ECMO trial Extra-corporeal membrane oxygenation (ECMO) versus standard treatment for newborn infants with persistent pulmonary hypertension.

7 N = 39 infants enrolled in study Trial terminated after Interim Analysis 4/10 deaths in standard therapy arm 0/9 deaths in ECMO arm p = (one-sided) Questions: Is this result sufficient evidence on which to change routine practice? Is the evidence in favor of ECMO very strong?23 Example: ISIS trial The Second International Study of Infarct Survival (ISIS-2) Five week study of streptokinase versus placebo based on 17,187 patients with myocardial infarction. Trial continued until 12% death rate in placebo group death rate in streptokinase group p < Issues: strong evidence in favor of streptokinase was available early on impact would be greater with better precision on death rate, which would not be possible if trial stopped early earlier Trials of streptokinase has similar results, yet little Approaches for Interim AnalysisThree main philosophic approaches Frequentist approach: Multiple Looks Group Sequential Designs Stopping Boundaries Alpha Spending Functions Two Stage Designs Likelihood approach Bayesian approach All differ in their approaches Frequentist (Multiple Looks) is most commonly seen ( but not necessarily the best !)

8 25 RCT (Randomized Clinical Trial with Trt A vs Trt B): Required Sample Size: 200 TRT A100 TRT B100An Example of Multiple Looks: 26 Four Interim looks (50, 100, 150, and 200)TRT A100 TRT B1001st Interim lookP = Example of Multiple Looks: 27 Four Interim looks (50, 100, 150, and 200)TRT A100 TRT B1002nd Interim lookP = Example of Multiple Looks: 28 Four Interim looks (50, 100, 150, and 200)TRT A100 TRT B100P = = = = Example of Multiple Looks: P = Example of Multiple Looks: Consider planning a comparative trial in which two treatments are being compared for efficacy (response rate).H0: p2 = p1H1: p2 > p1 A standard design says that for 80% power and with alpha of , you need about 100 patients per arm based on the assumption p2= , p1= which results in for the difference. So what happens if we find p < before all patients are enrolled ? Why can t we look at the data a few times in the middle of the trial and conclude that one treatment is better if we see p < plots to the right show simulated data where p1= and p2= our trial, looking to find a difference between to , we would not expect to conclude that there is evidence for a , if we look after every 4 patients, we get the scenario where we would stop at 96 patients and conclude that there is a significant of PatientsRisk of we look after every 10 patients, we get the scenario where we would not stop until all 200 patients were observed and would conclude that there is not a significant difference (p = )Number of PatientsRisk of we look after every 40 patients, we get the scenario where we would not stop either.

9 If we wait until the END of the trial (N = 200), then we estimate p1to be and p2to be The p-value for testing that there is a significant difference is of PatientsRisk of we have messed up if we looked early on? Every time we look at the data and consider stopping, we introduce the chance of falsely rejecting the null hypothesis. In other words, every time we look at the data, we have the chance of a type 1 error. If we look at the data multiple times, and we use alpha of as our criterion for significance, then we have a 5% chance of stopping each time. Under the true null hypothesis and just 2 looks at the data, then we approximate the error rates as: Probability stop at first look: Probability stop at second look: * = probability of stopping is of Sample Size on a True Proportion n\p^ 10 0, .45 0, .60.

10 1, .7 .18, .82 .3, .9 20 .02,.38 .1, .5 .18, .62 .28, .72 .38, .82 30 .05, .35 .42, .78 40 .07, .33 .35, .75 50 .09, .31 p^ +/- 2 sqrt{p^(1-p^)/n} .36, .74 100 .12, .28 serve as both-sided .50, .70 200 .15, .25 limits to TRUE p .53, .67 300 .16, .24 .54, .6634 Effect of Sample Size on a True Proportion n\p^ 400 , 500 , 1000 .175, .225 1500 .18, .22 2000 .182, .218 p^ +/- 2 sqrt{p^(1-p^)/n} 3000 .185, .215 serve as both-sided limits 4000 .19, .21 for TRUE p 5000 .19, .2135 Illustrative Examples : Interim AnalysisExample 1. It is desired to carry out an experimentto examine the superiority, or otherwise, of a thera-peutic drug over a standard drug with 5% level and90% power for detection of 10% difference in the proportions cured.