1. JSM 2018
A Case Study on Model Based Meta Analysis (MBMA) for Drug Development Decisions
Guohui Liu, Zhaoyang Teng, Zoe Hua, Neeraj Gupta, Karthik Venkatakrishnan, Richard Labotka

2. Clinical Trial Perform Metrics
12% entering phase 1 were approved by FDA
34% phase 3 studies achieve statistical significance
Average time > 10 years
Average cost: 2.6 billion
Oncology:
40% of clinical trial and 19% of participants
Average cost per patient: $59,500 ( vs $36,500 in all) disease)
Factors associated with late stage oncology study failure
lack of a biomarker-driven strategy
failure to attain proof of concept in phase II
Biopharmaceutical Industry-Sponsored Clinical Trials: Impact on State Economies, 2015
Jardim DL, Groves ES, Breitfeld PP, Kurzrock R. Factors associated with failure of oncology drugs in late-stage clinical development: A systematic review. Cancer Treatment Reviews 52 (2017) 12–21

3. Objectives: Go/No Go decision
Motivation
Objectives: Go/No Go decision
Publications
Phase 2 with ORR
Phase 3 with PFS
Statistical significant target:
HR=0.8 (10 m vs 12.6 m)
Minimal detective value: 12.6 m
Target PFS: 15 m
ORR
Model based meta analysis
PFS
Teng Z, Gupta N, Hua Z, Liu G, Samnotra V, Venkatakrishnan K, Labotka R. Model-Based Meta-Analysis for Multiple Myeloma: A Quantitative Drug-Independent Framework for Efficient Decisions in Oncology Drug Development. Clin Transl Sci (2017) 00, 1–8
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4. Contemporary RRMM Studies
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5. Model-based meta-analysis (MBMA) : Method 1: Linear regression
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6. Method 2: Bayesian Method with Linear Regression
A prior distribution in linear regression to account for the variability
(β0, β1) estimated from linear regression as an empirical Bayesian approach; σ2 follows an inverse Gamma distribution
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7. Method 3: Bayesian Predictive Power
Two parameters of interest:
Assume has the following prior distribution
A small number of patients (N; phase II study) and a small number of events (E; phase III study), can be used to define treatment effect and variance in prior distribution
For example, 8 patients with ORR of 60% from phase II and 2 patients with median PFS of 10 months from phase III
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8. Predict PFS Using ORR: Bayesian Predictive Power
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9. If no significant safety concerns, then –
Summary
If no significant safety concerns, then –
If at least 33 patients (out of 50 patients, ORR=66%) have confirmed Responses (≥PR) then team recommendation is: GO.
If ≤ 32 (out of 50 patients) confirmed responders – No-Go
MBMA is acknowledged as a valuable tool in the drug development process that is widely used for integrating data to enable informed and efficient development decisions.
The results of these analyses demonstrate the feasibility of developing a quantitative drug-independent framework for RRMM to predict PFS based upon an endpoint (ORR) for which data are available earlier.
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