Book description
It is quite common in a randomized clinical trial (RCT) to encounter
patients who do not comply with their assigned treatment. Since
noncompliance often occurs non-randomly, the commonly-used approaches,
including both the as-treated (AT) and as-protocol (AP) analysis, and
the intent-to-treat (ITT) (or as-randomized) analysis, are all well
known to possibly produce a biased inference of the treatment efficacy.
This book provides a systematic and organized approach to analyzing
data for RCTs with noncompliance under the most frequently-encountered
situations. These include parallel sampling, stratified sampling,
cluster sampling, parallel sampling with subsequent missing outcomes,
and a series of dependent Bernoulli sampling for repeated
measurements. The author provides a comprehensive approach by using
contingency tables to illustrate the latent probability structure of
observed data. Using real-life examples, computer-simulated data and
exercises in each chapter, the book illustrates the underlying theory
in an accessible, and easy to understand way.
Key features:
- Consort-flow diagrams and numerical examples are used to
illustrate the bias of commonly used approaches, such as, AT
analysis, AP analysis and ITT analysis for a RCT with noncompliance.
- Real-life examples are used throughout the book to explain the
practical usefulness of test procedures and estimators.
- Each chapter is self-contained, allowing the book to be used as
a reference source.
- Includes SAS programs which can be easily modified in
calculating the required sample size.
Biostatisticians, clinicians, researchers and data analysts working
in pharmaceutical industries will benefit from this book. This text
can also be used as supplemental material for a course focusing on
clinical statistics or experimental trials in epidemiology, psychology
and sociology.
