1. 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
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2. Phase I Trials: Characteristics
Small sample size, usually in the range of patients
Extensive Adverse event (AE) data collected:
Relationship to treatment (Attribution)
When the event occurs (timing of occurrence)
Different types of events
Multiple Grades

3. 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

4. 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

5. 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

6. 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 = ( )0.5/2.5 = 0.45.

7. 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

8. 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

9. 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

10. 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

11. Simulation Scenarios
Target nTTP: 0.28

12. Expected nTTP with Increasing Time Trend

13. Expected nTTP with Decreasing Time Trend

15. 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

16. 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)

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

18. 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

19. 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

20. Results

21. Results

22. Results

23. 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:

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