Enhanced quantitative decision making reducing the likelihood of incorrect decisions
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Enhanced Quantitative Decision Making - Reducing the likelihood of incorrect decisions. Mike K. Smith, Jonathan French, (Pfizer) Ken Kowalski, (A2PG) Wayne Ewy (formerly Pfizer, retired). Six Components of Model-Based Drug Development*. PK/PD & Disease Models. Trial Performance Metrics.

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Enhanced Quantitative Decision Making - Reducing the likelihood of incorrect decisions

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Enhanced quantitative decision making reducing the likelihood of incorrect decisions

Enhanced Quantitative Decision Making - Reducing the likelihood of incorrect decisions

Mike K. Smith, Jonathan French, (Pfizer)

Ken Kowalski, (A2PG)

Wayne Ewy (formerly Pfizer, retired).


Six components of model based drug development

Six Components ofModel-Based Drug Development*

PK/PD & Disease Models

Trial Performance Metrics

Model-Based Drug Development

Competitor Info. & Meta-Analysis

Decision Criteria

Design & Trial Execution Models

Data Analysis Model

* Lalonde et al, Clin Pharm & Ther, 2007; 82: pp21-32


Quantitative decision criteria

Quantitative Decision Criteria

  • “I’ll know it when I see it…”

  • “Evidence of an effect…”

  • “Reasonable efficacy and safety tradeoffs”

  • WRONG!!!


Quantitative decision criteria1

Quantitative Decision Criteria

  • 2 points improvement over placebo.

    • Better.

    • At least it’s quantitative

  • How sure do you want to be?

    • Mean 2 points?

    • Lower CI 2 points?

    • Mean 2 points and lower CI > 0?


P criteria data

P(Criteria|Data)

  • Not just P(… | Data)

    • Data

    • Prior data, model assumptions, parameter uncertainties

    • Trial design

    • Dropouts, imputation methods etc.

    • Data analytic method


Truth vs trial

Truth vs Trial

  • For a given set of model parameters / assumptions there will be a “true” outcome against the decision criteria.

    • What is the chance of achieving 2 points improvement given current information?

    • For a given set of parameters we will know whether we achieve 2 points improvement or not.

  • Then for this same set of parameters, apply design, dropout / imputation models, analytic technique and assess decision criteria.


Truth vs trial formally

Truth vs. Trial - Formally

  •  is the true (unknown) treatment effect

    • =f(, , ) is specified for a given set of model assumptions

      •  vector of fixed effects parameters

      •  covariance matrix for between-unit (subject or study) random effects

      •  covariance matrix for within-unit (subject or study) random effects


Truth vs trial formally1

Truth vs. Trial - Formally

  • Define quantitative decision rule under truth () and data-analytic results (T), e.g.,

    • Truth:Go if TV,No Go if <TV

    • Data:Go if TTV,No Go if T<TV

      • Note TV denotes the Target Value

      • Note T could be a point estimate or confidence limit on estimate/prediction of 


Operating characteristics

Operating Characteristics

Trial Go

Trial No Go

Total

“True” No Go

“True” Go

Total

P(correct)

PTS

P(Go)


Example

Example

  • Comparing SC-75416 with ibuprofen in dental pain.

    • Published in Kowalski, K.G, et al. “Modeling and Simulation to Support Dose Selection and Clinical Development of SC-75416, a Selective COX-2 Inhibitor for the Treatment of Acute and Chronic Pain”.

  • Decision criteria based on 3 point difference from ibuprofen in TOTPAR6 endpoint.


Example1

PTS = P(  3) = 67.2

Mean = 3.27, SD=0.60

Obs Mean = 3.3

Example

From Kowalski et al: A model-based framework for quantitative decision-making in drug development

Presentation at ACOP, Tuscon, AZ 2008.


Example2

Example

P(correct) = 70.72%

P(Go) = 61.90%

PTS = 67.20%

17

From Kowalski et al: A model-based framework for quantitative decision-making in drug development

Presentation at ACOP, Tuscon, AZ 2008.


Nominal values for ocs

“Nominal” values for OCs

  • P(Correct) can be fixed at >=80%

  • PTS for initiating a new trial depends on quadrant, portfolio, stage of development.

    • Perhaps minimal “dignity level” for starting a trial.

  • Fixing these two implies P(False GO) and P(False NO GO) must float, depend on P(Correct) and PTS.

    • Driven by decision criteria.

  • E.g. For P(Correct) = 80%, P(Incorrect) = 20%, spent across P(False GO), P(False NO GO).


Iterate optimise

Iterate / Optimise

If the operating characteristics “don’t look good”…

Change the data analytic model

Change the design constraints (↑ n /group)

Change the data-analytic decision criteria for the trial.

If we fix one or more of the above (e.g. n /group) then there is limited other things that can improve OCs.

Change the data analytic model, change data-analytic decision criteria for the trial.


The components may change over time

The components may change over time

“Truth” model / prior will be refined over time.

P(“True” Go given current knowledge / model) changes.

Decision criteria may change.

Commercial viability changes. [This may change both our compound target criteria – truth decision rule, as well as the data-analytic decision rule]

Acceptable level of confidence for Trial Go decision changes. [This applies only to data-analytic decision rule]


Final remarks 1

Final Remarks (1)

  • Greater collaboration required among kineticists/modelers, statisticians and clinicians

  • Kineticists/modelers:

    • Explicit and transparent about the assumptions and limitations of their PK/PD and disease models

    • Think strategically about how model will be used to influence internal decision-making

    • Avoid excessive use of NONMEM-jargon and write reports to broader audience

    • Calibrate models against data-derived (non-model-based) statistics of interest


Final remarks 2

Final Remarks (2)

  • Statisticians:

    • Embrace assumption-rich nonlinear models for decision-making especially in early clinical development

    • Avoid “Phase 3” mentality when designing Phase 2 studies…relying on empirical (assumption-poor) models to make decisions in early clinical development can be costly

  • Clinicians:

    • Quantitatively define clinically relevant effects and commercial targets

    • Explicitly and quantitatively defined decision rules


Bibliography

Bibliography

  • Kowalski, K.G., Ewy, W., Hutmacher, M.M., Miller, R., and Krishnaswami, S. “Model-Based Drug Development – A New Paradigm for Efficient Drug Development”. Biopharmaceutical Report 2007;15:2-22.

  • Lalonde, R.L., et al. “Model-Based Drug Development”. Clin Pharm Ther 2007;82:21-32.

  • Kowalski, K.G., Olson, S., Remmers, A.E., and Hutmacher, M.M. “Modeling and Simulation to Support Dose Selection and Clinical Development of SC-75416, a Selective COX-2 Inhibitor for the Treatment of Acute and Chronic Pain”. Clin Pharm Ther, 2008; 83: 857-866.

  • Kowalski, K.G., French, J.L., Smith, M.K., Hutmacher, M.M. “A model-based framework for quantitative decision making in drug development”. Presentation at ACOP, Tuscon, AZ. 2008. http://tucson2008.go-acop.org/pdfs/8-Kowalski_FINAL.pdf


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