2019 Statistical Practice in Cancer Conference Operating Characteristics For Clinical Trial Design J. Jack Lee, Ph.D. Kenneth R. Hess, Ph.D. Department of Biostatistics 1
Outline Hess : 5 min: Introduction and overview 20 min: Dose finding phase I OCs and BOIN design Lee 5 min: Shiny Applications 20 min: Toxicity and efficacy monitoring for single arm phase II studies Both: 10 min: Q and A 2
Trial Design Operating Characteristics OCs elucidate how a design performs under various scenarios For simple randomized trials, OCs = power curves For single arm studies, appropriate OCs depend on design Fundamental part of trial design (calibration) Should be planned and executed carefully Metrics should be chosen appropriately Generally based on computer simulations Important to “stress test” designs OCs should be included in protocol 3
Phase I Dose-finding in Oncology Goals: Assess DLTs and Estimate MTD/RP2D Assume toxicity and efficacy increase with dose Start with lower dose and escalate sequentially Typically use cohorts of 3 patients Dose finding based on observed DLTs 4
3+3 Design (1950s) Classical design, still used in > 90% of phase I studies Simple and transparent Treat patients in groups of 3 Escalate if 0 DLTs in 3 pts Expand with 3 more patients if 1 DLT in 3 pts Stop if > 1 pts with DLT in 3 or 6 pts Total N determined by # of pts with DLTs 5
Start at minimum 3+3 Design dose level Treat 3 patients at this dose level >1 of 3 0 of 3 Go to next higher # with DLT MTD exceeded dose level 1 of 3 No Maximum dose Treat 3 patients level? at this dose level Yes MTD not found >1 of 6 # with DLT MTD exceeded 1 of 6
Newer Design Options mCRM – modified continual reassessment method – Fits probability model for DLT-dose curve ( O’Quigley , 1990) mTPI – modified toxicity probability interval – Fits probability model to maximize chance of selecting dose with DLT rate in interval around target (Ji, 2010) BOIN – similar to mTPI but better (Liu, 2015) mTPI2 – revision to mTPI to fix flaw (Guo, 2017) Keyboard – same as mTPI2 mCRM = model-based; others = model-assisted
Obstacles to Use of Newer Designs Simplicity and transparency of 3+3 design Difficulty explaining model-based designs Difficulty implementing model-based designs Difficulty accepting simulation results as proof 8
Computer Simulation of Trials DLT = binary event; binomial distribution Specify scenarios of true DLT rates E.g., dose level 1: 10%, 2: 25%, 3: 45% Generate random data for sequences of 3 pts Follow algorithm until MTD is found Repeat 10,000 times Generate metrics
6 Simulated Trials for DLT rate = 10%, 25%, 45% Exp # Results MTD # Pts 0/3, 1/3+1/3 Level 1 9 1 0/3, 1/3+0/3, 2/3 Level 2 12 2 0/3, 3/3 Level 1 6 3 2/3 Exceeded 3 4 1/3+0/3, 0/3, 2/3 Level 2 12 5 0/3, 0/3, 1/3 + 2/3 Level 2 12 6
Results from 1,000 3+3 Simulated Trials Dose Level 1 2 3 4 5 DLT 10% 20% 30% 40% 55% Probability 27% 35% 20% 7% 1% Selection Probability Mean # Pts 4.4 4.6 3.3 1.6 0.4 MTD below dose level 1: 9% MTD not reached: 0.1%
Phase I Study OCs Use variety of scenarios with MTD at different dose levels Include scenarios where MTD between dose levels Include scenarios where MTD well below first dose level Include scenarios where MTD well beyond last dose level When possible also include randomly generated scenarios 12
Model-assisted Designs Easier to understand that model-based designs More transparent that model-based designs Easier to generate trial designs in practice Easier to implement when running trials Easy to use, online Shiny applications available 13
Bayesian Optimal Interval (BOIN) Design New model-assisted design Described in Yuan Y, et. al. 2016 CCR 22:4291 Any DLT rate target can be specified Maximum N is specified Easy to implement Table to guide dose-escalation
BOIN creates three distinct probability regions and looks to see into which region the observed data fall. λ d λ 𝑓
BOIN Design Parameters Target DLT rate, φ λ𝑓 and λ𝑒 are predetermined boundaries based on φ Maximum number of patients overall Maximum number of patients at any dose level Cohort size Dose elimination threshold; default = 0.95 Ref: S Liu, Y Yuan JRSSC 2015 64:507 16
Start at the lowest dose BOIN Design Treat a patient or a cohort of patients Yes Stop the trial and Reach the maximum Select the MTD sample size? No ≤ λ𝑓 ≥ λ𝑒 Compute DLT rate at current dose Within ( λ𝑓, λ𝑒 ) Retain the current De-escalate dose Escalate the dose dose
BOIN Dose Escalation Rules BOIN Table of Number of Patients 3 6 9 12 15 18 21 24 27 30 Decision 0 E E E E E E E E E E 1 S E E E E E E E E E 2 D S E E E E E E E E Rules for 3 DU D S S E E E E E E 4 DU D S S E E E E E Changing 5 DU DU D S S E E E E Number of DLTs 6 DU DU D D S S S E E 7 DU DU D D S S S E Dose Levels 8 DU DU DU D D S S S 9 DU DU DU DU D D S S Based on 10 DU DU DU DU D D S 11 DU DU DU DU DU D D 12 DU DU DU DU DU DU D Observed 13 DU DU DU DU DU D 14 DU DU DU DU DU DU 15 DU DU DU DU DU DU Toxicity Data E: Escalate to the next higher dose; S: Stay at the same dose; D: De-escalate to the previous lower dose; DU: De-escalate to the previous lower dose and the current dose will never be used again in the trial
Design Performance Comparison Metric 3+3 BOIN mCRM Prob. Correct Select MTD 33% 49% 49% Pts treated at MTD (%) 26% 31% 34% % selected dose <16% DLT 40% 25% 20% % selected dose >33% DLT 8% 12% 15% Risk of treating < 6 at MTD 43% 28% 28% From: H Zhou et al, CCR 2018 (target DLT rate = 25%)
Conclusions OCs are essential in phase I study design 3+3 has obvious statistical short-comings Need to communicate these problems better Need to explain simulations better BOIN design: better than 3+3; easier than CRM Free software for BOIN – includes protocol template 20
Thank you for your attention! 21
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