The Hardest Problem There Is

Assessing and Communicating Heterogeneity of Treatment Effects (HTE)

for Patient Subpopulations: Challenges and Opportunities

Subgroup Identification

The Hardest Problem There Is

Stephen J. Ruberg, PhD President

Analytix Thinking, LLC AnalytixThinking@

3/6/2018

ANALYTIX THINKING, LLC (C) 2018

1

Outline

Clinical Trials Observational Data What We Can Do Now

3/6/2018

ANALYTIX THINKING, LLC (C) 2018

2

Clinical Trials

Treatment versus Control

Population of Interest

Treatment Effect = 10%

X47 < 42

Treatment Effect = 30%

Subgroup of Interest

(50% of Population)

X11 < 5

3/6/2018

ANALYTIX THINKING, LLC (C) 2018

3

X47 < 42

Clinical Trials

Treatment versus Control

Population of Interest

Effect = 10%

Complexity

Size of Subpop'n 10%, 25%, 50%

Effect = 30%

Subgroup of Interest

(50% of Population)

X11 < 5

3/6/2018

ANALYTIX THINKING, LLC (C) 2018

4

X47 < 42

Clinical Trials

Treatment versus Control

Population of Interest

Effect = 10%

Complexity

Differential Effect 0%, 5%, 15%, 25%, 50%

Effect = 30%

Subgroup of Interest

(50% of Population)

X11 < 5

3/6/2018

ANALYTIX THINKING, LLC (C) 2018

5

X47 < 42

Clinical Trials

Treatment versus Control

Population of Interest

Effect = 10%

Effect = 30%

Subgroup of Interest

(50% of Population)

Complexity

Number of biomarkers in the dataset X1, X2, X3 ... X110

10, 25, 75, 250, 500

X11 < 5

3/6/2018

ANALYTIX THINKING, LLC (C) 2018

6

X47 < 42

Clinical Trials

Treatment versus Control

Population of Interest

Effect = 10%

Complexity

Number of biomarkers defining subpop'n

1, 2

Effect = 30%

Subgroup of Interest

(50% of Population)

X11 < 5

3/6/2018

ANALYTIX THINKING, LLC (C) 2018

7

X47 < 42

Clinical Trials

Treatment versus Control

Population of Interest

Effect = 10% Effect = 20%

Complexity

Nature of biomarkers effect

1, 2

Effect = 30%

Effect = 40%

Effect = 50%

X11 < 5

3/6/2018

ANALYTIX THINKING, LLC (C) 2018

8

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