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Novel multiple testing procedures for structured study objectives and families of hypotheses a case study Guenther Mueller-Velten Novartis Pharma AG EMA Workshop on Multiplicity Issues in Clinical Trials London, 16 November 2012


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Novel multiple testing procedures for structured study objectives and families of hypotheses – a case study

Guenther Mueller-Velten

Novartis Pharma AG

EMA Workshop on Multiplicity Issues in Clinical Trials London, 16 November 2012

Acknowledgement: Frank Bretz, Bjoern Holzhauer, Willi Maurer

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Outline

  • Introduction of graphical approaches to multiple testing
  • Case study
  • Background and clinical considerations
  • Resulting multiple testing procedure
  • Interim analysis
  • Summary and conclusions

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Graphical approaches to multiple testing

Motivation

Increasing complexity of confirmatory trial designs

  • Multiple treatment arms, multiple primary and secondary

endpoints, interim analyses

  • Designing a valid multiple testing strategy with desired

properties is a cross-functional exercise and may involve several iterations

  • Clinical, regulatory and business requirements need to be

translated into a statistical testing procedure in a transparent and understandable way

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Graphical approaches to multiple testing

Motivation (cont’d)

Graphical approaches provide a framework to

  • Tailor advanced multiple test procedures to structured

families of hypotheses

  • Visualize complex decision strategies in an efficient and

easily communicable way, and

  • Ensure strong Type I error rate control (Bretz et al., 2009;

Burman et al., 2009)

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Graphical approaches to multiple testing

Heuristics

Notation

  • Null hypotheses H1, . . . , Hk
  • Initial allocation of the significance level α = α1 + . . . + αk.
  • Unadjusted p-values p1, . . . , pk

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Graphical approaches to multiple testing

Heuristics

Notation

  • Null hypotheses H1, . . . , Hk
  • Initial allocation of the significance level α = α1 + . . . + αk.
  • Unadjusted p-values p1, . . . , pk

“α propagation”

If a hypothesis Hi can be rejected at level αi (i.e. pi ≤ αi), reallocate its level αi to the remaining, not yet rejected hypotheses (according to a prefixed rule) and continue testing with the resulting α levels.

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Graphical approaches to multiple testing

Conventions

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Graphical approaches to multiple testing

Conventions

1 Hypotheses H1, . . . , Hk

represented as nodes

H1 H2

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Graphical approaches to multiple testing

Conventions

1 Hypotheses H1, . . . , Hk

represented as nodes

2 Split of significance level α

as weights α1, . . . , αk

H1 H2 H1 H2 α1 = α

2

α2 = α

2

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Graphical approaches to multiple testing

Conventions

1 Hypotheses H1, . . . , Hk

represented as nodes

2 Split of significance level α

as weights α1, . . . , αk

3 “α propagation” through

weighted, directed edges

H1 H2 H1 H2

α 2 α 2

H1 H2

α 2 α 2

1 1

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Case study

Introduction and background

  • Randomized double-blind event driven outcome trial in

stable post myocardial infarction (MI) patients

  • Three doses of a new therapy vs. placebo on top of

standard of care

  • No validated surrogate available for dose-finding prior to

Phase III

  • Primary endpoint is composite of CV death, MI or stroke
  • Two key secondary endpoints targeting additional label

claims, thus included in multiple testing procedure

  • Extended composite endpoint including hospitalization for

unstable angina requiring urgent unplanned revascularizations

  • New onset Type 2 diabetes among patients with

pre-diabetes at baseline

  • Additional multiplicity due to efficacy interim analyses

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Case study

Clinical considerations

  • Primary endpoint is essential to establish efficacy of the

respective dose group. Key secondary objectives target additional label claims for doses that have established efficacy based on the primary endpoint.

  • Successiveness: Do not reject a secondary hypothesis

without having rejected the associated primary hypothesis. (Maurer et al., 2011)

  • Benefit risk considerations: Higher doses potentially more

efficacious and lower doses generally safer

  • Allow testing of lower doses regardless of efficacy at higher

doses.

  • Unequal split of significance level.

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Case study

Structured family of hypotheses

  • Four-armed trial comparing
  • Three dose levels + standard of care
  • Placebo + standard-of-care
  • Three endpoints
  • Primary endpoint: composite of CV death, MI or stroke
  • Two key secondary endpoints

⇒ Nine hypotheses

  • Three doses (low, medium, high)
  • Three endpoints per dose

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Tailored multiple test procedure

high dose medium dose low dose primary

H1 H2 H3

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Tailored multiple test procedure

high dose medium dose low dose primary secondary

H1 H2 H3 H4 H5 H6

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Tailored multiple test procedure

high dose medium dose low dose primary secondary

H1 H2 H3 H4 H5 H6

The nodes for secondary endpoints represent families of two null hypotheses related to the two key secondary endpoints 16

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Tailored multiple test procedure

high dose medium dose low dose primary secondary

H1 H2 H3 H4 H5 H6

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Tailored multiple test procedure

high dose medium dose low dose primary secondary

H1 H2 H3 H4 H5 H6 0.2α 0.4α 0.4α

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Tailored multiple test procedure

high dose medium dose low dose primary secondary

H1 H2 H3 H4 H5 H6 0.2α 0.4α 0.4α

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Tailored multiple test procedure

high dose medium dose low dose primary secondary

H1 H2 H3 H4 H5 H6 0.2α 0.4α 0.4α

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Tailored multiple test procedure

high dose medium dose low dose primary secondary

H1 H2 H3 H4 H5 H6 0.2α 0.4α 0.4α

0.45 0.7 0.25 0.3 0.3 1 21

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Tailored multiple test procedure

high dose medium dose low dose primary secondary

H1 H2 H3 H4 H5 H6 0.2α 0.4α 0.4α

0.45 0.7 0.25 0.3 0.3 1 1 1 1 22

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Tailored multiple test procedure

high dose medium dose low dose primary secondary

H1 H2 H3 H4 H5 H6 0.2α 0.4α 0.4α

0.45 0.7 0.25 0.3 0.3 1 1 1 1 Key secondary endpoints within each dose group will be tested with a weighted Bonferroni-Holm test at the available local significance level 23

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Tailored multiple test procedure

high dose medium dose low dose primary secondary

H1 H2 H3 H4 H5 H6 0.2α 0.4α 0.4α

0.45 0.7 0.25 0.3 0.3 1 1 1 1 Key secondary endpoints within each dose group will be tested with a weighted Bonferroni-Holm test at the available local significance level Improvement of the multiple testing procedure by using weighted Dunnett’s test for all intersection hypotheses that contain at least two of H1, H2 and H3 for the same endpoint 24

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Example rejection sequence

high dose medium dose low dose primary secondary

H1 H2 H3 H4 H5 H6

0.2α 0.4α 0.4α 0.45 0.7 0.25 0.3 0.3 1 1 1 1 25

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Example rejection sequence

high dose medium dose low dose primary secondary

H1 H2 H3 H4 H5 H6

0.06α 0.49α 0.45α 0.7 0.3 1 1 1 1 26

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Example rejection sequence

high dose medium dose low dose primary secondary

H2 H3 H4 H5 H6

0.55α 0.45α 0.7 0.3 1 1 1 27

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Example rejection sequence

high dose medium dose low dose primary secondary

H2 H3 H5 H6

0.165α 0.835α 1 1 0.3 0.7 28

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Case study

Interim analyses: Bonferroni inequality for the repeated hypothesis testing

  • Interim analyses at 50% and 75% information fraction It
  • A fixed Bonferroni split is used, with nominal overall significance levels α*t, t = 1, 2, 3,
  • f 0.01% and 0.04% for the first and second efficacy interim analysis,

leaving 2.45% for the final analysis.

  • At each of the 3 analyses, the graphical testing procedure exploiting correlations

between test statistics will be performed at the respective nominal level α*t , controlling the familywise type I error rate at the overall (one-sided) significance level α = 2.5%.

H1 H2 H9

It 0 50% 75% 100% α*t = 0.01% 0.04% 2.45%

Note: If a primary hypothesis is rejected, the respective secondary hypothesis cannot be tested at the full level α, even if the trial is stopped !

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Summary and conclusions

  • Confirmatory clinical trials are becoming increasingly more complex, often comparing

multiple doses or treatments with a control for several primary and secondary endpoints.

  • The multiple study objectives are reflected by structured families of hypotheses that are

characterized by multiple groups of “parent” primary hypotheses and “descendant” secondary hypotheses.

  • Novel graphical approaches for constructing and visualizing complex multiple test

procedures with a focus on structured families of hypotheses are well suited to facilitate communication in clinical teams and to provide transparent decision strategies.

  • Graphical procedures ensure strong control of the overall Type I error rate across all

primary and secondary hypotheses.

  • Multiple test procedure should be customized based on operating characteristics
  • btained via clinical scenario simulation.
  • Ideally, EMA could provide in its new guidance a harmonized terminology and

framework categorizing study objectives and endpoints with respect to their impact on approval and labeling and respective need for type 1 error control.

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References

  • Bretz, F., Maurer, W., Brannath, W., and Posch, M. (2009) A graphical

approach to sequentially rejective multiple test procedures. Statistics in Medicine 28, 586-604.

  • Burman, C.-F., Sonesson, C. and Guilbaud, O. (2009) A recycling framework

for the construction of Bonferroni-based multiple tests. Statistics in Medicine 28, 739–761.

  • Kordzakhia G. and Dmitrienko A. (2012) Superchain procedures in clinical

trials with multiple objectives. Statist. Med. (early view)

  • Maurer, W., Glimm, E., and Bretz, F. (2011) Multiple and repeated testing of

primary, co-primary and secondary hypotheses. Statistics in Biopharmaceutical Research 3, 336-352.

  • Rohmeyer, K., Klinglmueller, F., Bornkamp, B. (2012) gMCP: Graph Based

Multiple Test Procedures. R package version 0.8-0. URL http://CRAN.R- project.org/package=gMCP

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Back-up

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Case study

Improvement of the multiple testing procedure by using weighted Dunnett‘s test for all intersection hypotheses that contain at least two of H1, H2 and H3 for the same endpoint 0.45 0.63

First key secondary null hypotheses for high dose

0.27

First key secondary null hypotheses for medium dose First Key secondary null hypotheses for low dose

0.27 0.72

0.4 × α 0.2 × α 0.4 × α

ε ε ε 0.25 0.07 0.07 0.13

Primary null hypothesis high dose Primary null hypothesis medium dose Primary null hypothesis low dose Second key secondary null hypotheses for high dose Second key secondary null hypotheses for medium dose Second Key secondary null hypotheses for low dose

0.03 0.08 0.03 1 1 1 1-ε 1-ε 1-ε

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