Bayesian Benefit-Risk Assessment - EFSPI

Bayesian Benefit-Risk Assessment

Maria Costa GSK R&D

Disclosure

Maria Costa is an employee and shareholder of GSK

Data presented is based on human research studies funded and

sponsored by GSK

2 June 2015 Maria Costa

Bayesian Benefit-Risk

Outline

1. Motivation

2. GSK’s Approach to Benefit-Risk Assessment

3. Bayesian Joint Modelling of Mixed Outcomes

4. A GSK Case Study

5. Summary



Motivation



Growing industry and regulator awareness that more quantitative

methods can contribute to transparency of benefit-risk assessment

Efficacy and safety signals could be linked via exposure to active drug

Joint modelling of efficacy and safety endpoints enables data driven

quantitative assessment of the benefit-risk profile

Bayesian inference provides a direct framework to build relevant and

intuitive probability statements in the context of benefit-risk

GSK’s Approach to Benefit-Risk Assessment

5

A 3 Step Process...

June 2015 Maria Costa


“The endpoints are important

because…

and we met them or we didn’t…

and this is why the drug will

matter, or why it shouldn’t

continue.”

BENEFIT-RISK PROFILE

Frame Model / Analyse / Graph Conclude

Value Tree Graph, effects table, others…

“Based on this evidence, we believe the benefit outweighs the risk because…”


Separate analysis of benefits and risks

Results presented jointly

6

Graphical Methods



Forest Plot Norton Heatmap

Active Placebo

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 0 1 2 3 4

Weeks

Sub

ject

s

Benefit Only Benefit +AE Neither AE only Withdrew

Benefit and Risk Comparison


Joint analysis of benefits and risks

Results presented jointly

7

More Complex Methods



Utility Index Contour Plots

Bayesian Joint Modelling of Mixed Outcomes

Strength of efficacy and safety signals likely to be linked at subject level via

exposure to active drug:

E.g., antibody drugs for diabetes mellitus, increases in C-peptide (efficacy) and

cytokine releases (safety)

Approach that accounts for observed correlation at subject level between

efficacy and safety signals is desirable

Often efficacy and safety endpoints modelled using different distributions

Further development conditional on magnitude of efficacy and safety effect

8

Motivation



Higher

exposure

Higher efficacy

Higher risk of AE

continuous, count, binary, etc

binary, count, continuous, etc


Option 1: Use generalised linear mixed models

Assume J different observations on same subject (each following some distribution)

For subject i with mean response mui, g(mui) = Xi b + Zi ui , ui ~ N(0, G(Xi))

Random effect ui is shared across all J observations for subject i thus modelling

potential correlation

When gj (.) is not identity function then fixed effects b are conditional on random

effects ui

Monte Carlo integration can be used to obtain marginal population effects

Constraints may be necessary to ensure identifiability for certain distributions

9

Approaches to Linking Mixed Outcomes




Option 2: Use copulas

Copulas - distribution functions used to form new multivariate distributions given set of

marginal distributions of interest (which are preserved)

E.g., H(y1,y2) = C(F(y1), G(y2) | θ), F(.) and G(.) CDF of marginal distribution of rv y1

and y2

C(. , . | θ) is the copula function (e.g., Gaussian CDF)

θ measures association between y1 and y2

Directly obtain marginal population effects for parameters of interest

Choice of copula C(.) may impact results through different dependency

assumptions

Difficult to interpret beyond 3 dimensions (non-unique model definition)

10

Approaches to Linking Mixed Outcomes



A GSK Case Study

Antipsychotic drugs can cause extra pyramidal side effects (EPS)

Dystonia (involuntary muscle contractions)

Akathisia (extreme, uncontrollable restlessness)

Tardive dyskinesia (involuntary, repetitive movements)

...

Compound X novel antipsychotic - antagonist dopamine D2/D3 and 5HT2 receptors

However, D2/D3 receptor antagonism is associated with EPS

Clinical trial to evaluate safety and efficacy of compound X in acute schizophrenia

Placebo and active comparator (Olanzapine®)

In this talk, focus on compound X 120mg (54 subjects) vs placebo (52 subjects)

11

Compound X in Adults With Schizophrenia



Development of compound X was stopped due to preclinical finding of phospholipidosis

risk of suicide

A GSK Case Study

12

The Value Tree



Symptoms Decrease in PANSS TS

Nervous System

Disorders / EPS Tremor

Dystonia

Weight Gain

Individual Risks Glucose Intolerance /

Insulin Resistance

High Dyslipidemia

Benefits

Risks

Benefit-Risk Balance

Akathisia

Identified Benefit

or Risk Category

Identified benefit/

risk Outcome

AIMS Scale Barnes Akathisia Scale Simpsons Angus Scale

Dyskinesia

A GSK Case Study

PANSS Total Score (TS) = Positive and Negative Syndrome Scale measures

symptom severity in patients with schizophrenia

Highest possible score is 210 = most severe measure of schizophrenia

Lowest possible score is 30 = subject not suffering from schizophrenia

Change from baseline (CFB) in PANSS TS at week 6 ~ N(µ, σ2)

Efficacy threshold for further development: treatment difference (TD) = -8 pt, but

clinically meaningful TD is ~ -15§

Adverse events (AEs) associated with Nervous System Disorders

IAE = 1 if subject has an AE, 0 otherwise, IAE ~ Bernoulli(p), p = Prob(AE)

Safety threshold for further development : odds ratio (OR) = 1.5, huge level of unmet

need, large number co-morbidities, top 5 causes of disability in individuals < 25*

Non-informative priors assumed for all parameters in the model

Covariates in the model include age, baseline PANSS TS and treatment

13

Efficacy and Safety Endpoints and Prior Distributions



§ Hermes et al, 2012

* Nasrallah et al, 2013

A GSK Case Study

14

Exploratory Analysis



Correlation between efficacy and safety outcomes:

CorrX = -0.18, and CorrPlacebo = -0.15 All the subjects who had a nervous system

AE and took drug X had an improvement

from baseline in PANSS TS by week 6

- Improvement in score associated with AEs?

A GSK Case Study

How much evidence exists from the data to support the Benefit-Risk profile:

15

Estimated Joint Posterior Distribution of Treatment Difference and Odds Ratio



Parameter Median (95% Crd.I.)

TD -7.01 (-15.91, 1.90)

OR 2.65 (0.76, 12.39)

CorrX -0.10 (-0.38, 0)

CorrPlacebo -0.077 (-0.30, 0)

Safety Threshold: OR = 1.5

Efficacy Threshold: TD = -8

Prob(+ BR Profile) = 7%

Joint Posterior Median

TD < - 8pt AND OR < 1.5?

A GSK Case Study

16

Benefit-Risk Evaluation and Decision-Making



Higher probabilities for lower

benefit thresholds and/or

higher risk thresholds

How much evidence (probability) exists to support a chosen Benefit-Risk profile?

A GSK Case Study

A new patient, aged 50, is diagnosed with schizophrenia (baseline

PANSS score of 119) and the GP is considering whether to prescribe

compound X...

...will compound X be effective, do nothing, or harm this patient?

We can predict both the efficacy and safety responses for this new

subject given what we have learned from the study data

Predicted CFB in PANSS TS after 6 weeks = -19.22 (95% CI [-58.46, 19.91])

Predicted probability of AE of nervous system disorder = 0.30

17

Predicting Response and AE event for a “New” Subject



A GSK Case Study

Use predictive distribution for CFB PANSS TS to make predictive probabilistic

statements around particular levels of efficacy

18

Predicting Response and AE event for a “New” Subject



Prob (Observed CFB PANSS TS at week 6 < 0 |

NAA Data) = 84%

Prob (Observed CFB PANSS TS at week 6 < -15 |

NAA Data) = 58%

Although this patient has a high probability of

benefitting from drug X, it has just over 50%

chance of achieving a clinically meaningful

improvement, whilst having a 30% chance of

experiencing an AE

The GP may still choose to prescribe drug X

if no alternative therapies are available

Summary

Benefit-Risk assessment is both qualitative and quantitative

Bayesian inference based on joint models of mixed outcomes is a powerful tool

for Benefit-Risk assessment

Explore dependency between benefit and risk thresholds for decision-making

Joint (and conditional) probabilistic statements

Predicting responses for a new subject conditional on what was learned from study data

Benefit-Risk profile is a combination of two different quantities:

Set of thresholds for efficacy and safety – define Benefit-Risk profile

Level of evidence (posterior probability) to support Benefit-Risk profile



Acknowledgements

Thomas Drury (Integral Statistics Limited)

GSK:

Susan Duke

Colleen Russell

John Krauss



Thank you

Bayesian Benefit-Risk Assessment - EFSPI

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