Continuous Safety Monitoring in Large Phase I Cancer Clinical Trials with Multiple Expansion Cohorts

Transcription

1 Continuous Safety Monitoring in Large Phase I Cancer Clinical Trials with Multiple Expansion Cohorts Masha Kocherginsky, PhD 1 Theodore Karrison, PhD 2 1 Northwestern University and 2 The University of Chicago (USA) May 8, / 32

2 Introduction Phase I trials with multiple dose expansions cohorts (DECs) Total N in the tens or hundreds Typical design features: Treat patients at a single dose (MTD) during expansion Safety monitoring is ad hoc and usually within a DEC Safety assessed within a DEC and information is not formally shared between DEC s Ad hoc DEC design and sample size We propose: across-dec (pooled) continuous safety monitoring using sequential probability ratio test (SPRT) 2 / 32

3 Traditional Phase I trials Phase 1a: Dose escalation (e.g. 3+3 ; typical n = 15 to 18) Phase 1b: Dose expansion ( 1 cohort; typical n = 15 or more per DEC) Most common design is 3+3, or 3+3 followed by dose expansion at MTD Model-based dose escalation: Continuous Reassessment Method (CRM), O Quigley et al (1990) Modifications to CRM (e.g. see Le Tourneau, JNCI 2009) 3 / 32

4 Schema Dose expansion cohorts DEC j, j = 1,..., J Different diseases or disease subgroups Dose escalation (e.g. "3+3") DEC 1 DEC 2... DEC J 4 / 32

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6 Dahlberg et al (JNCI, 2014) Dahlberg et al (JNCI, 2014) Reviewed Phase I trials opened within Dana-Farber/Harvard Cancer Center from Average sample size increased from 33.8 to 73.1 In 2011 (n=60 trials): 26.7% enrolled into 3 cohorts during dose expansion 60% of studies provided no statistical justification for the sample size 91.7% of trials stated response as an objective 6 / 32

7 Cancer Letter et al (Oct 7, 2016) Cancer Letter et al (Oct 7, 2016) PD-L1 and PD-L2 immunotherapy Phase I trials: 35 trials enrolling 200 patients 6 trials enrolling 500 patients 7 / 32

8 Example: JAVELIN trial (NCT ) International multicenter Phase I trial of Avelumab 3+3 followed by 16 DEC s (different diseases) Estimated total enrollment: N = 1,706 Phase 1a (Heery et al, The Lancet, 2017) Planned 3+3 with 4 dose levels; n=18 in dose escalation Actually enrolled 53 patients in dose-level cohort expansions to provide additional safety and PK Phase 1b (Gulley et al, The Lancet, 2017) NSCLC cohort enrolled 184 subjects Dose expansion (Kelly et al, ASCO abstract, 2015) interim analysis of 480 patients in 9 dose expansion cohorts Little detail on design or sample size justification in these publications 8 / 32

9 Recent Developments in design of Phase I trials with Dose Expansion Cohort 9 / 32

10 Goals of Phase I Trials Dose escalation: Identify the dose to study in Phase II trials (RP2D) Evaluate safety of this dose Dose expansion: Further evaluate safety of RP2D (usually the MTD) Preliminary assessment of efficacy at this dose DEC can also be used to refine the RP2D (MTD) based on: toxicity efficacy 10 / 32

11 Iasonos and O Quigley Journal of Clinical Oncology (2013) Discuss MTD re-estimation approaches after the trial is completed Considered fixed DEC sample size: n = 10 in DEC (total n = 16 at MTD) n = 4 in DEC (total n = 10 at MTD) Reanalysis based on O Quigley s paper Retrospective analysis of sequential dose finding schemes (Biometrics, 2005) 11 / 32

12 Boonstra et al (JNCI 2015) Evaluated the probability of selecting true MTD Compared different Phase I designs: DEC CRM S CRMS + DEC CRML CRM S - during dose escalation only; CRM L - during both escalation and expansion Re-estimate MTD after trial completion (O Quigley, 2005) Found that adding a DEC is beneficial DEC is better than CRM S + DEC is better than CRM S CRM L is only sometimes better than CRM S + DEC 12 / 32

13 Iasonos and O Quigley, Statistics in Biopharmaceutical Research (2016) Dose escalation: estimate MTD (d m ) using 3+3 or CRM Don t assume that MTD after dose escalation is the true MTD Dose expansion (single DEC): re-estimate probability of toxicity ˆR(d j ) at each dose level d j using CRM randomly assign subjects to dm and either d m 1 or d m+1 based on ˆR(d j ) θ θ is the acceptable (or target) toxicity rate, e.g. 30% 13 / 32

14 Iasonos and O Quigley, Statistics in Biopharmaceutical Research (2016) Continuously monitoring DEC using sequential probability ratio test (SPRT) based either on efficacy alone both efficacy and toxicity SPRT bounds can be determined for pre-specified up front for α and β Use SPRT bounds as stopping rules for efficacy 14 / 32

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16 SPRT, Wald (1945) Hypotheses: H0 : p = p 0 vs H 1 : p = p 1 Cumulative sum of log likelihood ratio (Λ i ) after i observations: Si = S i 1 + logλ i, where S 0 = 0 Stopping rule: a < S i < b: continue monitoring S i a: accept H 0 S i b: accept H 1 For pre-specified α and β, a log β 1 β 1 α and b log α SPRT vs. O Brien-Fleming: don t need to pre-specify the sample size 16 / 32

17 SPRT, Wald (1945) Use SPRT package in R Example: p0 = 0.2, p 1 = 0.5, α = 0.1, β = 0.1 True p = 0.5 Toxicity counts # DLT's Subjects 17 / 32

18 Other Continuous Sequential Tests Other methods that consider composite hypotheses Maximized SPRT (Kulldorff et al, Sequential Analysis, 2011) Sequential Generalized Likelihood Ratio test (SGLR) Frequent application in post-marketing vaccine safety trials Shih et al (Statistics in Medicine, 2010) compared performance of these tests, and SPRT appears to have better performance under most scenarios in single arm trials 18 / 32

19 Continuos safety monitoring across multiple DEC s 19 / 32

20 Recall Dose escalation (e.g. "3+3") DEC 1 DEC 2... DEC J 20 / 32

21 Continuous toxicity monitoring Dose escalation: select MTD (e.g. 3+3 ) Assume: equal probability of toxicity in all DEC s Use SPRT to monitor toxicity across multiple cohorts (pooled monitoring) Stop when cross upper boundary Sample size for each DEC j : fixed sample size for each DECj e.g. based on exact binomial probability (single stage design) 21 / 32

22 Simulation setup Compare 3 methods: decj: continuous monitoring (SPRT) within each DEC j, stop when the first DEC j crosses upper boundary overall: continuous monitoring (SPRT) across all DEC s binp: single stage design within each DECj, stop when the first DEC j has > R/N toxicities 22 / 32

23 Simulation setup Acceptable toxicity rate is θ = 0.30 Set up SPRT boundary H0 : p 0 = 0.3 vs H 1 : p 1 = 0.5 H0 : p 0 = 0.2 vs H 1 : p 1 = 0.4 Set Type I and Type II error rate as α = 0.10 and β = 0.10 Same boundary for within- and across-dec monitoring Generate data with true probability of toxicity p tox = {0.1, 0.2, 0.3, 0.4, 0.5, 0.6} 23 / 32

24 Probability of stopping p tox p0 p1 nj r type dec overall binp dec overall binp Inflated Type I error in both within-dec monitoring approaches ( 0.30 under H 0 with nominal α = 0.10) Within-cohort monitoring multiple comparisons Overall DEC monitoring maintains Type I error rate 24 / 32

25 Probability of stopping Across-DEC monitoring H 0 : p = 0.3 vs. H 1 : p = 0.5 boundaries: 66.6% probability of early stopping when p = 0.4 H 0 : p = 0.2 vs. H 1 : p = 0.4 boundaries: 9.9% probability of early stopping when p = % power when p = θ = / 32

26 Average sample size p tox p0 p1 nj r type dec overall binp dec overall binp Larger for across-dec monitoring when p < p 1 Smaller for across-dec monitoring when p p 1 binp has larger overall sample size than either continuous monitoring approach 26 / 32

27 Bonferroni α = α/j for within-cohort decj monitoring Use α = 0.10/5 = 0.02 for the SPRT boundary when monitoring within DEC j Probability of stopping p tox p0 p1 nj r type dec overall binp dec overall binp Type I error is now / 32

28 Conclusions Large Phase I trials with multiple dose expansion cohorts are common (n > 100) Typically a dose escalation followed by multiple dose expansion cohorts Active research area, including MTD re-estimation randomizing patients to several dose levels during dose expansion continuous monitoring (e.g. SPRT) primarily for efficacy 28 / 32

29 Conclusions We propose continuous monitoring for toxicity across multiple DEC s Generate toxicity bounds based on SPRT upfront DEC sample size based on a single stage design Compare 3 early stopping approaches: stop trial when the first of DECj s crosses an upper boundary stop trial when overall toxicity count crosses an upper boundary stop trial when the first DECj exceeds rejection number 29 / 32

30 Conclusions Across-DEC monitoring maintains Type I error rate, and has smaller average N than within-dec continuous monitoring Within-DEC monitoring has inflated Type I error rate due to multiple comparisons Adjusting within-dec boundaries using Bonferroni s doesn t work Single-stage design has worse properties than either of the continuous monitoring approaches Continuous monitorig for toxicity across DEC s using SPRT is a practical approach to safety monitoring in large multi-cohort Phase I trials 30 / 32

31 Future work Consider alternative designs for DEC (e.g. Simon s two-stage design for efficacy) Explore other sequential tests Following Iasonos and O Quigley, randomize patients to d m+1 or d m 1 if cross upper or lower boundary 31 / 32

32 THANK YOU! 32 / 32