Topics in Clinical Trials (8 ) - 2012

1. Topics in Clinical Trials (8) - 2012 J. Jack Lee, Ph.D. Department of Biostatistics University of Texas M. D. Anderson Cancer Center

2. Monitoring Response Variables • Monitor toxicity: ethical considerations, protect the safety of participants • Monitor the primary endpoint • Shows intervention is clearly better • Early stopping due to efficacy • Shows intervention is harmful • Early stopping due to adverse effect • Shows intervention is unlikely to be beneficial • Early stopping due to futility • No clear indication one way or the other • Continue the trial as planned • Adjust the sample size based on observed effect, etc. • Data monitoring committee (DMC) / Data Safety and Monitoring Board (DSMB)

3. Fundamental Point • During the trial, response variables need to be monitored for early dramatic benefits or potential harmful effects. • Preferably, monitoring should be done by a group of independent investigators. • Although many techniques are available to assist in monitoring, none of them should be used as the sole basis for the decision to stop or continue the trial.

4. Data Monitoring Committee (DMC) • Composition • Why not include the study investigator? • Conflict of interest, lack of objectivity • Knowing the interim result may affect the clinical equipoise and the subsequent trial conduct • Credibility • Independent group of experts in the field including clinicians, statisticians, patient advocates, etc. • Institutional DMC or a special DMC assembled for the trial

5. Responsibility of DMC • Ensure the safety of participants • Ensure the integrity of the trial • monitor the accrual of the trial • Examine the randomization process • Evaluate the compliance status • Make decision to continue or terminate the trial based on all data and information • Oversee the trial on behalf of the sponsor • Provide a service to the regulatory agency such as NCI or FDA

6. Operation of DMC • Meeting schedule • Before or in the early phase of the trial • Correspond to the time of pre-defined interim analyses • End of study • Format and Confidentiality • Open session • All invited: accrual, logistic, data quality, adherence, toxicity, etc. • Closed session • DMC + study statistician • DMC only • Executive session • DMC + study PI

7. Decisions During the Trial • Planned Interim Analysis • Group sequential design • Unplanned Interim Analysis • Group sequential design – alpha spending function • Conditional Power • Stochastic Curtailment • Predictive Power / Predictive Probability • Interim Decisions • Stop the trial due to efficacy • superiority, one way or another • Stop the trial due to futility • Lack of efficacy, no difference between groups • Continue the trial as planned • Sample size re-estimation

8. Conditional Power ApproachExample 1: Compare 2 Antibiotics • Goal: compare the response rate of pipracillin (tx1) vs. clindamycin (tx2) • Design: RCT with 47 pts/arm. 80% power to detect an absolute difference of 25% (85% vs. 60%) with 1-sided a= 5% (2-sided a= 10%) • Interim result: Question: Can the trial be stopped early?

9. Estimation • The 95% CI of P1 – P2 covers 0.25. If the goal of this trial is to detect a 25% difference in response rate, cannot stop the trial due to “no difference.” • CI calculation does not reflect the nature of interim analysis. If use “repeated CI” (e.g. Jennison & Turnbull), the CI will be wider.

10. Conditional Power by B-Value • Lan & Wittes, Biometrics 44:579-585, 1988.

13. Stochastic Curtailment Lan, Simon, Halperin: 82 Comm in Stat.-Seq:1:207-219 Davis, Hardy: 94, J. Clin. Epi 47:1033-1042 • One might stop the trial early and • Reject Ho if cond. Prob P(Z(1)  R| Z(t), Ho) ≥ g • Accept Ho if cond. Prob P(Z(1)  A| Z(t), H1) ≥ g’ • Early termination can be due to efficacy or futility • Overall type I error rate ≤ a/g • Overall type II error rate ≤ b/g’ • With a small number of looks, the error rates will be even less • Boundaries of stochastic curtailment

14. Stochastic Curtailment and Conditional PowerUnder H1:  = 1.960 + 1.282 = 3.242, cannot stop before t=0.64 Stop, Conclude H1 Stop, Conclude Ho Stop, Conclude H1

15. Early Stopping Using Predictive Power • Similar to the conditional power approach but integrate over the possible values of the conditioning parameter Predictive Power =

16. Early Stopping Using Predictive Probability (PP) • At interim, compute the predictive probability, i.e., a positive result at the end of trial if the current trend continues given the current data. • If PP is very high, stop the trial now and declare treatment is working, • If PP is very low, stop the trial now and declare treatment is not working, • Otherwise, continue.

18. Frequentist Approach

19. Bayesian Approach =

20. Example 2: Predictive Probability in Breast Cancer Buzdar AU et al, JCO 23, 3676-3685, 2005 • Disease: HER-2 (+) Breast Cancer • Agent: A: 4 cycles of paclitaxel + 4 cycles of fluorouracil, epirubicin, and cyclophosphamide B: same as A + weekly trastuzumab for 24 weeks • Statistical Design: phase II • Sample Size: 164 (maximum) • Primary Endpoint: pathological complete response (pCR) • Method: Standard two-stage design with Predictive Probability Interim Monitoring by DSMB • Results: After 34 pts completed therapy, DSMB stopped the trial. With 34 Patients Arm CR rates A 4 / 16 (25%) B 12/ 18 (67%) With 42 Patients Arm CR rates A 5 / 19 (26%) B 15/ 23 (65%) P=0.016

23. Review of Randomized Phase II Trials Lee and Feng, JCO 23, 4450-4457, 2005 3% 56%

24. Comparing Fixed vs. Random P • Assume we observed x/n successes. • What is the probability of observing X/N successes in the future? • Assuming the estimated P is • fixed • random • What are the differences? • Which one do you prefer?

29. Available Software • East (www.cytel.com) • ADDPLAN (www.addplan.org) • PEST http://www.rdg.ac.uk/mps/mps_home/software/pest4/pest4.htm • PASS http://www.ncss.com/passsequence.html • S+SeqTrial http://www.statsci.com/products/seqtrial/default.asp • GLUMIP for internal pilot study http://www.soph.uab.edu/coffey • M.D. Anderson (biostatistics.mdanderson.org)

30. PEST 4 (Planning and Evaluation of Sequential Trials) • This package includes facilities for: • A range of designs to detect: superior efficacytreatment equivalence or non-inferiorityfutilitysafety concerns • Flexible interim monitoring information-based to guarantee power • Test statistics calculated within PEST 4 and displayed with the design boundaries • Adjustment for prognostic factors using stratification or covariate adjustment • Final analysis providing valid p-values, estimates and confidence intervals • Simulation for illustration and exploration of accuracy

31. Derry FA et al. Efficacy and safety of oral sildenafil (Viagra) in men with erectile dysfunction caused by spinal cord injury. Neurology. 1998 Dec;51(6):1629-33. Figure 1. Single triangular sequential design shows original and adjusted continuation regions. Z is a measure of the observed advantage of sildenafil versus placebo and its expected value is the log odds ratio ([THETA]) multiplied by the information (V) collected up to that point. V is the information collected up to the interim analysis point about [THETA], as contained in the current value of Z. The expected value of the variance of Z is V. The arrow marks the critical point along the bottom boundary. To the left of this point sildenafil is significantly worse than placebo; to the right of this point there is evidence of no difference.

33. GROUP SEQUENTIAL TESTS in PASS • Group Sequential Tests of MeansThis module calculates sample size and power for group sequential designs used to compare two treatment means. The program allows you to vary the number and times of interim tests, the type of alpha spending function, and the test boundaries. It also gives you complete flexibility in solving for power, significance level, sample size, or effect size. The results are displayed in both numeric reports and informative graphics. Group Sequential Tests of ProportionsThis module calculates sample size and power for group sequential designs used to compare two proportions. The program allows you to vary the number and times of interim tests, the type of alpha spending function, and the test boundaries. It also gives you complete flexibility in solving for power, significance level, sample size, or effect size. The results are displayed in both numeric reports and informative graphics. Group Sequential Tests of Survival CurvesThis module calculates sample size and power for group sequential designs used to compare two survival curves. The program allows you to vary the number and times of interim tests, the type of alpha spending function, and the test boundaries. It also gives you complete flexibility in solving for power, significance level, sample size, or effect size. The results are displayed in both numeric reports and informative graphics.

38. East • East is software for the design, simulation and monitoring of clinical trials using group sequential and adaptive methodologies. East allows investigators to easily design superiority, futility only, and non-inferiority trials, for all endpoints, with complete confidence that the type-1 error and power of the study will be protected. East's simulation capability aids clinicians and statisticians in understanding the trade-offs between trial designs so that designs can be compared and investigators can choose the best one. And East's interim monitoring module will perform all the necessary calculations for exact inference at an interim analysis. • By designing, simulating and monitoring clinical trials using East, investigators can take advantage of flexible approaches that will allow them to identify futile trials and terminate them, fast-track effective therapies, and salvage underpowered studies by performing sample-size reassessment, without jeopardizing the statistical integrity of the trial.

41. S+SeqTrial • S+SEQTRIAL offers a complete computing environment for applying group sequential methods, including: • A fully object-oriented language with specialized objects (such as design objects, boundary objects, and hypothesis objects) • and methods (such as operating characteristics and power curve plots); • Easy comparative plots of boundaries, power curves, average sample number (ASN) curves, and stopping probabilities; • User-selected scales for boundaries: sample mean, z-statistic, fixed sample p-value, partial sum, error spending, Bayesian posterior mean, and conditional and predictive probabilities;

42. Stopping Rule Computation • The unified family of group sequential designs, which includes all common group sequential designs: Pocock (1977), O’Brien & Fleming (1979), Whitehead triangular and double triangular (Whitehead & Stratton, 1983), Wang & Tsiatis (1987), Emerson & Fleming (1989), and Pampallona & Tsiatis (1994); • A new generalized family of designs. S+SEQTRIAL includes a unified parameterization for designs, which facilitates design selection, and includes designs based on stochastic curtailment, conditional power and predictive approaches; • Applications including normal, Binomial, Poisson, survival, one-sample and two-sample; One-sided, two-sided, and equivalence hypothesis tests, as well as new hybrid tests; • Specification of the error spending functions of Lan & DeMets (1989) and Pampallona, Tsiatis, & Kim (1993); • Arbitrary boundaries allowed on different scales: sample mean, z-statistic, fixed sample p-value, partial sum, error spending, Bayesian posterior mean, and conditional and predictive probabilities; Exact boundaries computed using numerical integration.

44. Meta-Analysis • Cochrane data: randomized trials before 1980 of cortico-steroid therapy in premature labor and its effect on neonatal death • What is the overall conclusion of the effect of corticosteroid in reducing neonatal death by combining the data from all trials?

45. Models: Assume Yi is the measure of treatment effect (e.g., log(OR) for Trial i) • Fixed-effect model: A common, fixed q Random-effect model: Random qi from each trial

46. Estimation: Fixed Effect Model • Frequentist’s method • When is assumed known • Bayesian method

47. Fixed Effects: Mantel-Haenszel Method windows(record=T) library(rmeta) data(cochrane) steroid.MH <- meta.MH(n.trt, n.ctrl, ev.trt, ev.ctrl,names=name, data=cochrane) summary(steroid.MH) Fixed effects ( Mantel-Haenszel ) meta-analysis Call: meta.MH(ntrt = n.trt, nctrl = n.ctrl, ptrt = ev.trt, pctrl = ev.ctrl, names = name, data = cochrane) ------------------------------------ OR (lower 95% upper) Auckland 0.58 0.38 0.89 Block 0.16 0.02 1.45 Doran 0.25 0.07 0.81 Gamsu 0.70 0.34 1.45 Morrison 0.35 0.09 1.41 Papageorgiou 0.14 0.02 1.16 Tauesch 1.02 0.37 2.77 ------------------------------------ Mantel-Haenszel OR =0.53 95% CI ( 0.39,0.73 ) Test for heterogeneity: X^2( 6 ) = 6.9 ( p-value 0.3303 )

48. Fixed Effects: Mantel-Haenszel Method Forest Plot

49. Funnel Plot

50. Estimation: Random Effect Model • Frequentist’s method • When is assumed known • When is unknown: Restricted MLE (REML)