On the Use of Co-data in Clinical Trials

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May 3, 2019 - We propose Tailored exchangeability model based on meta analytic ... At design stage of current trial, perform meta-analysis of co-data.

On the Use of Co-data in Clinical Trials Satrajit Roychoudhury Pfizer Inc. 3rd Annual Boston Pharmaceutical Symposium, Cambridge, MA May 3, 2019

Pfizer Confidential

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Acknowledgement • Joint work with: – Beat Neuenschwander and Heinz Schmidli Novartis Pharma AG

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Co-data Approaches in Clinical Trial • Two recent developments – look at the data frequently  adaptive trials – look at more data  trials with historical data • In this talk: – we follow the maxim: more data lead to better decisions – we extend the historical data framework to co-data: any relevant complementary/contextual data

– co-data can be historical or concurrent • Prospective planning and proper statistical methodology is the key Pfizer Confidential

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Historical Co-Data • your (the actual) trial • + 3 trials with historical co-data

• trial 3 is ongoing...

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Historical and Concurrent Co-Data • your (the actual trial • + 3 trials with historical co-data... • trial 3 is ongoing...

• ...trial 3 is ongoing... • and trial 4 hasn’t started yet •  concurrent co-data

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Statistical Methodology: Hierarchical model • Most clinical literature uses two extreme models • No pooling: Separate inference for each tumor type (stratified analysis) - “Low power for small sample size situations” • Complete pooling: grouping in the data is irrelevant, i.e. imposing restriction that all tumor type effects are same – “optimistic borrowing”

• Bayesian hierarchical modeling is a specific methodology may be used to combine information of different strata. • Exchangeable/Hierarchical model lies between these two extreme cases – “Shrinkage”: the estimates are pulled towards a common mean Pfizer Confidential

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Notable Work • Full exchangeability of strata parameters is the key assumption for Bayesian hierarchical model discussed in literature: – Thall et al. 2003, Chugh et al. 2009, Berry et. al 2013

• General class of nonparametric priors (random partitioning, Polya tree priors etc.) were discussed by – Leon-Novelo 2013, Mueller and Mitra 2013

• We propose Tailored exchangeability model based on meta analytic approaches – borrowing information across similar strata, while avoiding too optimistic borrowing for extreme strata – Neuenschwander, Roychoudhury and Schmidli 2016 Pfizer Confidential

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Meta-Analytic (MA) Approaches • Two MA approaches – Meta-Analytic-Predictive (MAP) is prospective At design stage of current trial, perform meta-analysis of co-data and obtain distribution of * MAP Prior: * | Y1,...YC -

Combine MAP prior with current trial data Y* (Bayesian analysis)

• Meta-Analytic-Combined (MAC) is retrospective - Perform a meta-analysis of all co-data and current trial data

- Parameter of interest: the parameter in the actual trial * | Y1,...YC,Y* Pfizer Confidential

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A Meta-Analytic Framework for Co-Data

Y1

Y2

YJ

Y*

1

2

J

*

G() – data (sampling) model

Yj | j ~ F(j)

– parameter model

1,..., J ,* |  ~ G() :Exchangeability

– too restrictive if relevance of co-data differs – Possible Extension: adjustment with covariates Pfizer Confidential

Partial exchangeabilty 9

Flexible Meta-analytic Approach for Co-data

Y1

Y2

YJ

Y*

1

2

J

*

G(g(J))

G(*)

G(g(1))

G(g(2))

• Extension: j ~ G(g(j)) g(j)  {1,...,G}, j = 1,...J,* Differential discounting • For example: – G=2 for observational and randomized co-data – note: the larger G, the less information for between-trial sd 1,.., G Pfizer Confidential

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A Robust Meta-analytic Approach for Co-data

p1 G(g(1))

Y1

Y2

YJ

Y*

1

2

J

*

1- p1 H1

p2 G(g(2))

1- p2 H2

pJ G(g(J))

1- pJ

p*

1- p*

HJ

G( g*)

H*

• Robustification: j ~ pj G(g(j)) + (1- pj) Hj : g(j)  {1,...,G}, j = 1,...J,*

• Allows for nonexchangeable parameters to add robustness Pfizer Confidential

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Prior distributions for  • Since the number of trials (J) is usually small, priors matter • Recommendations (Spiegelhalter 2004, Gelman 2006) – use priors that put most of their probability mass on plausible values – example: log-odds scale,   2, half-normal priors with scale 1 and 0.5 •  ~ Half-Normal(1): (0.03,2.24)95% very small to very large heterogeneity •  ~ Half-Normal(0.5): (0.01,1.12)95% very small to large heterogeneity

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Weight of Co-Data: Effective Sample Sizes • Various variance-based approximations to express amount of information of the prior or posterior distribution as an equivalent effective sample size (ESS) – Malec (2001), Pennello (2008), Morita (2008), N et al (2010) – Two-variances approach • analysis of interest for *: variance var* and unknown ESS* • simpler analysis: variance var0 and known (!) ESS0; e.g. complete pooling • assumption: sample sizes approximately proportional to precisions

ESS* = ESS0  ( var0 / var* )

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Example: Basket Trial of Imatinib 186 subjects with 40 different malignancies with known genomic MOA of imatinib target kinases

KIT, PDGFRA, or PDGFRB

Synovial Sarcoma (SS)

Aggressive Fibromatosis (AF)

Dermatofibrosarcoma Protuberans (DP)

1/16 (6%)

2/20 (10%)

10/12 (83%)

Aggressive systemic mastocytosis (ASM) 1/5 (20%)

Primary endpoint: ORR

Hypereosinophilic syndrome (HD)

Myeloproliferative disorder (MD)

6/14 (43%)

4/7 (58%)

Blumenthal. Innovative trial designs to accelerate the availability of highly effective anti-cancer therapies: an FDA perspective, AACR 2014

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Stratified and Pooled Analysis

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MAC and Robust MAC Model • Data: nj = Number of patients and rj = Number of responder for strata j • Likelihood/sampling model: rj ~ Bin( nj, πj ) • Model: θj = log(πj / 1- πj) For each stratum j two possibilities are considered: – With probability pj : θj ~ N(,2) – With probability 1- pj : θj ~ N(mJ, vJ ) – pj = 0 => MAC or HM

– For this example we assume pj = 0.5 Pfizer Confidential

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MAC and Robust MAC Analysis

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Example 1: Phase III Interim Analyses •

Two phase III trials A and B running in parallel – endpoint: survival – 379 events (n): =2.5%, 90% power for log-hazard ratio A = log(0.75)

– interim analysis when at least 150 deaths occurred in both trials



Two historical trials – a small proof-of-concept trial, and a randomized phase II trial



Interim decisions – based on probability of success (PoS): stop phase III trial if PoS < 10% (e.g.)



Co-data analysis with the standard NNHM Yj | j ~ N(j, 4/nj ), 1,..., J,* |, ~ N( , 2 ),  ~ N(0,4),  ~ HN(0.5) Pfizer Confidential

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Stratified Analyses: Estimates and Probability of Success (PoS) ▪ ...

▪ PoS calculation requires two components • parameter uncertainty at interim: posterior of j • conditional power, for example for trial 3, n=379, n I=162, =2

• PoS is then the expected (over posterior) conditional power Pfizer Confidential

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Co-data Analyses: Estimates and Probability of Success (PoS)

Co-data analysis • improves precisions for log-hazard ratios • PoS do not change much Pfizer Confidential

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Effective Sample Sizes (ESS)

▪ Co-data analysis: • improves precisions for log-hazard ratios • ESS is  60% larger compared to stratified analyses

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Probability of Regulatory Success ▪ Successful regulatory submission requires both Phase III trials to be positive:

▪ Probability of regulatory success (PoRS) PoRS = Analysis

PoS of Trial A

PoS of Trial B

PoRS

Full exchangeability

0.51

0.65

0.36

Differential heterogeneity

0.49

0.64

0.34

Exchangeability-nonexchangeability mixture (50-50)

0.49

0.65

0.34

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Phase I Combination Trials •

Combination therapies are now popular in Oncology



Phase I Oncology Trial objectives: – Safety and tolerability of patients

– Find maximum tolerable dose (MTD) or recommended phase II dose – data: binary dose-limiting toxicity (DLT) data



There is no longer one MTD but a many – critical to determine the MTD boundary and the set of acceptable doses.



Overall risk assessment is key – Model based approaches: summarize the risk at each dose pair – actual decisions use additional information (e.g. efficacy, PK, biomarkers, later cycle AE) to select “best” dose pair(s) for next cohort Pfizer Confidential

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Practical Model based Approach for Combination Studies • Parsimony – small number of parameters due to small number of tested dose combinations

• Interpretability – easily interpretable parameters for • single agent 1 toxicity

• single agent 2 toxicity • interaction

• Continuity – if the dose of one compound is 0, the model simplifies to the single-agent model Pfizer Confidential

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Escalation with Overdose Criterion (EWOC)

Dose escalation happens when the following condition is satisfied: • Pr(ij > 0.33 | data) < 25% Pfizer Confidential

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Co-data for Phase I Combination Trial (1/3) – two historical singleagent trials: A for agent 1, ongoing B for agent 2

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Co-data for Phase I Combination Trial (2/3)

– after 3 cohorts of actual trial AB: concurrent co-data from trial A

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Co-data for Phase I Combination Trial (3/3)

– at end of AB trial: co-data from IIT combination trial Pfizer Confidential

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Phase I Trial for Combination Treatment in Cancer \ log(odds( 1, d ))  log(1 )  1 log(d1 ) 1

log(odds( 2 , d ))  log( 2 )   2 log(d 2 ) 2

0  12 ,d

1

odds( 12 , d

1

,d 2

,d 2

  1, d   1, d   1, d  2 , d 1

2

0 )  odds( 12 ,d

1

1

,d 2

2

) exp(d1 d 2 )

(1 , 1 ,  2 ,  2  0) – (note: reference/scaling doses dropped in formulas) – if no dose-dependent interaction desired: simply use exp() – typically  > 0, but not necessarily Pfizer Confidential

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Robust Co-Data Model for Drug Combination Studies • Let us assume θ1j = (log 𝛼1𝑗 , log 𝛽1𝑗 ) and θ2j = (log 𝛼2𝑗 , log 𝛽2𝑗 )

• 𝜃1𝑗 ~

𝑝1𝑗 𝐵𝑉𝑁 𝜇1 , Γ1 + 1 − 𝑝1𝑗 𝐵𝑉𝑁 𝑚1𝑗 , S1𝑗

• 𝜃2𝑗 ~

𝑝2𝑗 𝐵𝑉𝑁 𝜇2 , Γ2 + 1 − 𝑝2𝑗 𝐵𝑉𝑁 𝑚2𝑗 , S2𝑗

• 𝜂𝑗 ~

𝑝𝜂𝑗 𝑁(𝜇𝜂 , 𝜏𝜂2 )

2 + (1 − 𝑝𝜂𝑗 )𝑁(𝑚𝜂𝑗 , 𝑆𝜂𝑗 )

Exchangeable part Pfizer Confidential

Non-exchangeable part 30

Risk-Benefit Plot

Mean, 95%-int

DLT rate interval probabilities (0-0.16) (0.16-0.33) (0.33-1)

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Co-data Analysis Analysis with historical co-data only

Analysis with all the co-data

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Effective sample sizes Information gain from co-data: effective sample sizes (ESS)

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Use of Co-data: Planned vs Unplanned • Clear specification of statistical analysis method before trial begins • Proper choice of evidence is necessary – prior to start of trial

– choice must be “science” based not “result” based – avoiding publication bias

– inter-disciplinary collaboration

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Conclusion • Making better use of data - which includes co-data - is one contribution to innovation in medical product development • Many applications with co-data – pediatric trials (adult data), non-inferiority trials (placebo, active control data), health-technology assessments, basket trials

• Methodology (meta-analytic) fairly well developed • Co-data use: mainly for early phase trials or trial adaptations – what about using co-data for primary analysis in confirmatory trials? – not commonly used, but the mindset changes... – recent example in epilepsy (historical controls) Katz (2006), French (2010), Wechsler (2014) Pfizer Confidential

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Thank You

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