Application of adaptive designs in clinical trials research - PowerPoint Presentation

1. Application of adaptive designs in clinical trials researchMunya DimairoResearch Fellow in Medical StatisticsUniversity of Sheffield, UKm.dimairo@sheffield.ac.uk / mdimairo@gmail.com Twitter: @mdimairoCREDO Ethiopia 12-13 July 2017

2. DeclarationsEmployed by the University of SheffieldLead investigator of an Adaptive designs CONSORT Extension (ACE) projectFunded by NIHR and MRC HTMR No conflict of Interest to declare

3. ObjectivesHighlight some limitations of fixed sample size RCTsHow we can overcome some of the limitations concept of adaptive designs some trial adaptation opportunities with case studies (prospective and retrospective) Highlight some considerations – nothing is for freeQuestions and discussion (small group exercise)

4. Some limitations of fixed sample size RCTsImplications of poor ‘success’ rates of investigative treatmentsresearch waste (time and resources)ethicsInefficient in some casesinaccurate design assumptions (Charles et al., 2009; Clark et al., 2013; Vickers, 2003)multiple competing treatments (grant application review experiences)addressing some research questions robustly (population enrichment example)evaluation of new treatments is time consuming Ethical issues/ urgency in decision-making processoutbreaks or emergency care casesexposure to ineffective treatments (interests of the patient vs society)

5. How can we do things differently?Adaptive designs …‘… it’s like taking insurance against unforeseeable events’“… provides pre-planned opportunities to use accumulating trial data to modify aspects of an ongoing trial while preserving its validity and integrity” what is validity? (robust inference) what is integrity? (convincing stakeholders) why pre-planning and what does it mean? (credibility and adequate design evaluation)

6. Adaptation: sample size re-estimation (1)Often inaccurate design assumptions either over estimate or under estimate the required sample sizeNeed to validate design assumptions and react accordingly RATPAC retrospective case study (Goodacre et al., 2011)evaluation of a point-of-care cardiac marker panel in patients presenting to the ED with suspected MIdesign assumptions50% standard care hospital discharge rate5% absolute increase to declare superiority80% power and 5% type I errorplanned sample size N=3130 (1565 per arm)ImplicationsMedian (IQR) of 2271 (2190 to 2298) vs 3130 plannedMedian (IQR) overestimation of 861 (835 to 942)Funding ran out after 2243 participants Funders declined funding extension request for reasonsStudy had already addressed questionsUnnecessary recruitment could have happened Sample size re-estimation could have provided safeguardsNo need to go back to the funder

7. Adaptation: sample size re-estimation (2) CARISA prospective case study (Chaitman et al., 2004)Investigated two doses of ranolazine (750mg or 1000mg) against placebo on exercising capacity of patients with severe chronic anginaPrimary endpoint: treadmill exercise duration at trough (12 hrs after dosage)Design assumptions yielded462 patients to achieve a 90% powerincreased to 577 to account for potential dropoutsWhat happened during the trialBlinded sample size re-estimation after 231 patients (~50% of the planned sample size)Variability found to be markedly higher than assumedSample size increased by 40% to 810 to preserve 90% power823 were recruitedStatistical analysis used for fixed sample size designed RCT appropriate following a blinded sample size re-estimationsignificant clinical improvements in exercise duration were found for both ranolazine doses

8. Adaptation: early trial stopping – group sequential (1)Desire to stop the trial early as soon as we have enough evidence about investigative treatment regarding:Beneficial effects (effectiveness/efficacy or futility) SafetyWhy bother?Accelerates decision-making process, especially in critical care or outbreaksAccelerates evaluation of new therapiesEffective treatment are made accessible to patients quickerFewer patients are exposed to potentially unsafe treatmentsPotential savings in research time and resources Back to RATPAC retrospective case study Emergency care, primary endpoint observed early, and huge sample size plannedRedesign features2 interim analysis at 50% and 70% of the planned sample sizeOptions to stop early for efficacy or futilityStringent/aggressive stopping rule (stop when there is overwhelming evidence at interim)Results Could have stopped early at 50% of planned sample size in favour of PoC treatmentStagewise adjusted results to account for interim analysis and early stopping are consistent with the observedImplications Patients, time and resources ScenarioSample sizeProportion of sample size usedPatients savingsReduction in recruitmentduration (months) Planned fixed sample size313053.5%145610.3Planned GSD334850.0%167411.8Achieved recruitment226374.0%5894.2

9. Adaptation: early trial stopping – group sequential (2)3CPO retrospective case study (Gray et al., 2008)To determine whether noninvasive ventilation (CPAP or NIPPV) against standard of care reduces mortality (within 7 days) in the treatment of patients with acute cardiogenic pulmonary edemaRedesign features 80% power, 15% mortality rate in SOC, 6% reduction, 5% type 1 error2 interim analyses at 50% and 65% Stop for futility only at the 1st interim analysisStop for either futility or efficacy at the 2nd interim analysis Stringent LD (OBF) stopping rulesResults Could have stopped for futility at 65%After 100 000 trial simulations, the:chances of failing to reject H0 if trend continued this way = 99.7%chances of showing superiority in favour of CPAP or NPPV assuming an overwhelming benefits of 6% difference for the remaining 35% of the data = 15.4%Stagewise adjusted results to account for interim analyses and early stopping are consistent with the observed

10. Adaptation: multi-arm multi-stage (1)RationaleTo investigate multiple competing interventions in a single trialTo save patients, resources and time in the long run compared to a sequence of two-arm parallel group trialsAccelerate decision-making process Number of variations of the designsEither phase 2 or 3 only Phase 2/3 combined in one trials (aka seamless adaptive design – see next slide)TAILoR prospective case study (Pushpakom et al., 2015)Investigates 3 doses of telmisartan (20mg, 40mg, and 80mg) against control in reducing insulin resistance in HIV patients on combination ART Primary endpoint – change in 24 weeks insulin resistance from baselinePlanned with one interim analysisPhase 2 dose-selection (treatment selection)Interim results and implemented trial adaptations Interim analysis conducted at 50% of the planned 336 patients20mg and 40mg were dropped for futility Malawi ongoing case study (Mr Augustine Choko)Welcome Trust FundedA Phase II adaptive multi-arm multi-stage cluster randomised trial randomisingantenatal clinic days to six different trial arms. Pregnant women accessing ANCin urban Malawi for the first time will be recruited into either the standard of care arm (invitation letter to the male partner offering HIV testing) or one of five intervention arms offering oral HIV self-test kits.

11. Adaptation: multi-arm multi-stage (2) Seamless 2/3 adaptive designEither operational or inferential in natureComparator is phase depended (new comparator introduced)Options to drop arms in phase 2 if unsafe or ineffective Options to drop arms in phase 3 if ineffectiveHypothetical scheme

12. Adaptation: response-adaptive randomisation RationaleAllocate more patients to promising treatment during the trial Balancing the interests of patients within the trial and society Most appealing for critical care/outbreaks (such as oncology, ebola, etc)RAR prospective case study (Giles et al., 2003)To assess troxacitabine-based regimes as induction therapy in patients aged ≥50 years with untreated, adverse karyotype, acute myeloid leukemia Bayesian response-adaptive randomisation Started with equal randomisation; IA (1): TA(1): TI (1)Allocation probability to IA (usual care) was held constant (2/3) if 3 arms are in the trialPrimary endpoint: complete remission that occurred within 49 days of starting treatmentP(randomising to TI) ~ 7% after 24 patients (TI dropped)P(randomising to TA) <4% after 34 patients (TA stopped)Study stopped after 34 patients (<50% of the planned 75)Final success (complete remission) rates:10/18 (55%) for the IA arm3/11 (27%) for the TA arm0/5 (0%) for the TI arm70% probability that TA was inferior to IAOnly 5% probability that TA would have a 20% higher complete remission rate than IA

13. Conclusions Well conducted adaptive designs can address shortcomings of fixed sample designed RCTsNothing is for free so there are challenges depending on trial adaptations considered – adequate planning and careful consideration is requiredNot all potential trial adaptations have been considered here .. . It’s often a good idea to take insurance against unforeseeable events when conducting trials …

14. References Chaitman et al. (2004), “Effects of Ranolazine With Atenolol, Amlodipine, or Diltiazem on Exercise Tolerance and Angina Frequency in Patients With Severe Chronic Angina: A Randomized Controlled Trial”, JAMA, American Medical Association, Vol. 291 No. 3, p. 309.Charles et al (2009), “Reporting of sample size calculation in randomised controlled trials: review.”, BMJ (Clinical Research Ed.), Vol. 338 No. may12_1, p. b1732.Clark et al (2013), “Sample size determinations in original research protocols for randomised clinical trials submitted to UK research ethics committees: review.”, BMJ (Clinical Research Ed.), Vol. 346 No. mar21_1, p. f1135.Giles et al. (2003), “Adaptive Randomized Study of Idarubicin and Cytarabine Versus Troxacitabine and Cytarabine Versus Troxacitabine and Idarubicin in Untreated Patients 50 Years or Older With Adverse Karyotype Acute Myeloid Leukemia”, Journal of Clinical Oncology, Vol. 21 No. 9, pp. 1722–1727.Goodacre et al (2011), “The Randomised Assessment of Treatment using Panel Assay of Cardiac Markers (RATPAC) trial: a randomised controlled trial of point-of-care cardiac markers in the emergency department.”, Heart (British Cardiac Society), Vol. 97 No. 3, pp. 190–6.Gray et al (2008), “Noninvasive ventilation in acute cardiogenic pulmonary edema.”, The New England Journal of Medicine, Vol. 359 No. 2, pp. 142–51.Pushpakom et al. (2015), “Telmisartan and Insulin Resistance in HIV (TAILoR): protocol for a dose-ranging phase II randomised open-labelled trial of telmisartan as a strategy for the reduction of insulin resistance in HIV-positive individuals on combination antiretroviral therapy.”, BMJ Open, BMJ Publishing Group, Vol. 5 No. 10, p. e009566.Vickers (2003), “Underpowering in randomized trials reporting a sample size calculation”, Journal of Clinical Epidemiology, Vol. 56 No. 8, pp. 717–720.

15. AcknowledgementPhilip Pallmann on behalf of MRC HTMR ADWG

16. Discussion task ….Think about a research problem you may have which could be addressed by an adaptive trial and discuss the following within your group: What trial adaptations could be considered or you are interested in considering?What motivates you to consider such adaptations?What is the primary endpoint and is it suitable for an adaptive design?What are the other practical considerations?OR A research problem you have conducted using a different trial design but given what you know now it could have been adaptive and discuss:What trial adaptations could have been considered and motivations behind those adaptations?What is the primary endpoint and is it suitable for an adaptive design?What are the other practical considerations?Questions