Dose Finding Designs Incorporating Patient Reported Outcomes Shing M. Lee Columbia University Joint work with Bin Cheng and Xiaoqi Lu

Dose Finding Clinical Trials Goal: To determine the maximum tolerated dose (MTD) Outcome: Dose limiting toxicity (DLT) defined as a grade 3 or higher toxicity according to the NCI CTCAE The NCI CTCAE is provided by clinicians All methods to date only use clinician reported DLT outcomes

Patient Reported Outcomes Increased interest in including patient perspective in clinical trials Patients provide unique perspective Literature suggests under-reporting of symptoms by clinicians Lack of standardize instrument to collect patient reported outcomes PRO-CTCAE

PRO-CTCAE Recently, validated instrument for collecting patient reported symptoms 124 items covering 78 symptoms Similar grading system to the NCI-CTCAE Has been incorporated in dose finding trials as secondary outcomes

Advantages of Incorporating PRO-CTCAE Incorporating patient perspective May reveal unexpected symptoms Doses selected can be tolerated by patients Relevant for new anticancer treatments

Problem Formulation Let Yc be the clinician reported DLT Let Yp be the patient reported DLT Let d1,…, dk be the K doses of interest Let θc be the DLT threshold based on clinicians and θp be the DLT threshold based on patients Traditional MTD is defined as: dc* = argmax (P(Yc = 1|d) ≤ θc)

Marginal Model Approach Define MTD using patient data as: dp* = argmax(P(Yp=1|d )≤ θp) To incorporate both, we can define MTD as: d* = min(dc*, dp*) To estimate this, we can fit two separate CRM*s Estimate model parameters separately Assign the next patient: d [i+1] = argmax {Fc(d,beta1) ≤θc, Fp(d, beta2) ≤θp)} d[n] -> d* almost surely as n -> ∞ * O’Quigley et al. Biometrics 1990

Joint Model Approach Redefine outcome as: Z = 0 if Yc = 0 and Yp = 0 Let θc be the threshold for P(Yc = 1) = P(Z = 2) and φ be the threshold for P(Yc = 1 or Yp = 1) = P(Z ≥ 1) MTD is defined based on multiple constraints d* = min (dc*, dc,p*), where dc,p*=argmax (P(Z ≥ 1 |d) ≤ φ) We can apply the CRM-MC* which has been shown to be consistent * Lee, et al. , Biostatistics 2011, Cheng and Lee JSPI 2015

Simulation Study Assume K=5, N=21 and 39, θc=0.25, θp=0.35, φ=0.50 Using empiric working models Using two stage designs with MLE We compare the performance of the marginal and the joint approach against the CRM

MTD = 3 and Coincides 0.05 0.25 0.45 0.55 P(Yc=1) 0.17 0.18 0.35 0.65 Percentage of Recommendation Dose Level 0.05 0.25 0.45 0.55 P(Yc=1) 0.17 0.18 0.35 0.65 0.80 P(Yp=1) 0.20 0.50 0.90 P(Yc=1 or Yp=1)

MTD = 2 and Clinician 0.05 0.25 0.45 0.55 0.70 P(Yc=1) 0.10 0.15 0.35 Percentage of Recommendation Dose Level 0.05 0.25 0.45 0.55 0.70 P(Yc=1) 0.10 0.15 0.35 0.60 0.75 P(Yp=1) 0.30 0.50 0.65 0.80 P(Yc=1 or Yp=1)

MTD = 3 and Patient 0.05 0.10 0.16 0.25 0.45 P(Yc=1) 0.20 0.35 0.65 Percentage of Recommendation Dose Level 0.05 0.10 0.16 0.25 0.45 P(Yc=1) 0.20 0.35 0.65 0.80 P(Yp=1) 0.30 0.50 0.70 P(Yc=1 or Yp=1)

MTD = 5 and Coincides 0.01 0.02 0.05 0.10 0.25 P(Yc=1) 0.04 0.09 0.17 Percentage of Recommendation Dose Level 0.01 0.02 0.05 0.10 0.25 P(Yc=1) 0.04 0.09 0.17 0.20 0.35 P(Yp=1) 0.50 P(Yc=1 or Yp=1)

MTD = 5 and Coincides (N=39) Percentage of Recommendation Dose Level 0.01 0.02 0.05 0.10 0.25 P(Yc=1) 0.04 0.09 0.17 0.20 0.35 P(Yp=1) 0.50 P(Yc=1 or Yp=1)

Conclusions When the MTD coincides both marginal and joint models perform similar to the CRM, except when the MTD is at highest dose level Both marginal and joint model achieve the goal of finding a dose that satisfies both constraints The marginal model is more conservative than the joint model