Dose-finding designs incorporating toxicity data from multiple treatment cycles and continuous efficacy outcome Sumithra J. Mandrekar Mayo Clinic Invited Session: Alternative Endpoints and Dose Selection Methods in Early-Phase Cancer Trials May 09, 2017 test

Phase I Trials: Characteristics Small sample size, usually in the range of 20-60 patients Extensive Adverse event (AE) data collected: Relationship to treatment (Attribution) When the event occurs (timing of occurrence) Different types of events Multiple Grades

Data collected versus Data used? Binary Endpoint: dose-limiting toxicity (DLT) Ignores the richness of toxicity data Ignores mild or moderate toxicities Cycle 1 data typically used in MTD definition Cancer therapies are rarely given for just one cycle Late-onset toxicities from subsequent cycles

New Toxicity Endpoint Normalized total toxicity profile (nTTP), a continuous toxicity score, from 0 to 1 Incorporates multiple types and grades of AEs Incorporates AE data from multiple cycles of treatment for each patient If a patient experiences a DLT event, he/she will not contribute to the score after that cycle Ezzalfani et al., SIM, 2013

Definition of TTP 2 Euclidean norm of the weights of all toxicities (type and grade) experienced in a cycle for a given patient at a given dose level Ezzalfani et al., SIM, 2013

nTTP Example First, identify the main toxicity types related to the treatment, Example: renal, neurological, and hematological toxicities. Second, obtain a weight matrix for the grade and type of toxicities Example: weight matrix for the 3 toxicity types from grade 0 to 4 5 grades (0-4) 3 types Here max TTP score = 2.34 A patient with grade 1 renal, grade 3 neurological, and grade 2 hematological toxicities will have nTTP = (0.52 + 12 + 0)0.5/2.5 = 0.45.

Repeated Measures Design No intra patient dose escalation Decision rule Dose level that minimizes the Bayesian risk is selected for the dose assignment for the next cohort

Repeated Measures Design Continuous toxicity score, nTTP → Capture the richness of toxicity data Repeated measures model → Capture toxicity data over multiple treatment cycles using a linear mixed model Bayesian framework → Adaptive nature → Small sample size → Utility function/Bayesian Risk Yin et al., SIM, 2016

Knowledge of the past gave the foundation upon which has been built the present, and upon which we predict the future. --Dr. William Mayo, 1928

Simulation Settings Six dose levels Maximum number of treatment cycles: 6 Maximum sample size: 36 Cohort size: 3 Patient accrual process –A new cohort is entered after each cycle. –Cohort’s follow-up ends after completion of 6 treatment cycles. 3+3 design for the first 2 cohorts No skipping of dose levels during dose escalation. 3 types of toxicities considered: renal, neurological, and hematological. 12 scenarios: MTD location (dose levels 2, 3, 4, 5) & time trend (increasing, decreasing, and no trend) the probabilities of observing each grade at each dose level were chosen nonparametrically to satisfy: the probability of observing a grade 0 event decreases with dose; the probability of observing a grade 4 event increases with dose; (3) the probability of observing any fixed grade event across dose levels is unimodal. Yin et al., SIM, 2016

Simulation Scenarios Target nTTP: 0.28

Expected nTTP with Increasing Time Trend

Expected nTTP with Decreasing Time Trend

Efficacy Outcome A continuous variable, from 0 to 1 Assessed at the end of cycle 3 Missing if a patient drops out before cycle 3 Goal: To find efficacious doses that are also safe in phase I design

Joint Model The joint model consists of two sub-models: Linear mixed effect model for longitudinal nTTP scores. Linear model for efficacy outcome. Two sub-models are linked by a random effect Wang et al., (2000)

Efficacy sub-model Association between the two sub models Yu et al., Submitted

Dose Finding Algorithm A multi-stage, adaptive design (three stages) Recruit patients by cohorts Stage 1: Dose escalation using toxicity data ONLY – using the RMD At the end of stage 1, an initial set of allowable doses to investigate based on safety Stage 2: Enter stage 2 after approximately half the number of patients are enrolled into the trial. Update allowable (safe) doses after every cohort, and randomize patients with emphasis towards higher predicted efficacy

Dose Finding Algorithm Stage 3: At the end of the trial, fit the joint model using full data, define efficacious doses and make a final recommendation The design allows for early termination, if all doses are toxic. No skipping of untried dose levels

Results

Results

Results

Conclusions Both designs mimic real world setting: Accounting for patient dropouts due to DLT Missing efficacy data Operating characteristics are acceptable Percentage of correct selection is high across all scenarios Very good overdose control High probability of early termination when all doses are toxic First design to incorporate: Multidimensional toxicity data across multiple cycles Dual endpoint of continuous efficacy and multiple cycles of toxicity endpoint phase I R package on CRAN: https://cran.r-project.org/web/packages/phase1RMD/index.html

Thank you mandrekar.sumithra@mayo.edu Collaborators: Dan Sargent, Vivien Yin and Yu Du