Defining and Analyzing Patient-centric Endpoints Based on COAs and Digital Technologies (original) (raw)

Chapter 1

Chapter 1

From objectives to endpoints

This chapter offers guidance on how to define patient-relevant COA endpoints that will answer meaningful research objectives in clinical trials.

Chapter 2

Chapter 2

From endpoints to estimands

This chapter provides a gentle introduction to the estimand framework (in lay terms), and its role in planning and analyzing clinical trial endpoints using COAs, with an emphasis on accounting for intercurrent events (ICEs).

Chapter 3

Chapter 3

From estimands to estimators: Analyzing continuous endpoints

This chapter outlines how to best analyze continuous COA endpoints using traditional and emerging statistical approaches, while accounting for ICEs.

Chapter 4

Chapter 4

From estimands to estimators: Analyzing binary endpoints

This chapter outlines how to best analyze binary COA endpoints using traditional and emerging statistical approaches, while accounting for ICEs.

Chapter 5

Chapter 5

From estimands to estimators: Analyzing time-to-event endpoints

This chapter outlines how to best analyze time-to-event COA endpoints using traditional and emerging statistical approaches, while accounting for ICEs.

Chapter 6

SPECIAL CHAPTER

Creating multi-item scale scores using COA data

This special chapter discusses approaches to scoring COA instruments.

Chapter 7

Chapter 6

Diary data and intensively collected digital data

This chapter outlines how to obtain more granular insights on patients’ experience by collecting and analyzing diary data and other intensive longitudinal data (ILD).

Chapter 8

Chapter 7

On tolerability endpoints

This chapter discusses the emerging role of COA data to assess tolerability from the quantitative point of view, and the specific analytical considerations.

Chapter 9

Chapter 8

Structural equation framework: Further understanding COA data

This final chapter synthesizes alternative approaches to analyzing COA data which can offer additional insight and aid interpretation (e.g., by understanding how different aspects measured by COAs impact each other).