Missing data is an integral part of clinical trials and its analysis. This study discusses the downsides of having missing values in clinical data, traditional methods used to resolve this issue and some techniques which can be implemented to remedy the same.

The data used for the study is simulated from Theophylline data from Pinheiro and Bates (1995). The simulated data measures the Theophylline drug concentration in the body of 10 Subjects over 24 hours. There are three cases considered with increasing number of randomly created missing values for the Concentration variable. Subsets are created to fit linear and quadratic linear subject and population models. The fitted models are compared using Sums of Squares of Imputation and Imputed R2. These comparative techniques indicate that replacing the missing values in clinical data with appropriate estimates, while maintaining the authenticity of the data, is feasible.

Library of Congress Subject Headings

Clinical trials--Statistical methods; Clinical trials--Data processing; Missing observations (Statistics); Mathematical statistics; Linear models (Statistics); Population--Mathematical models

Publication Date


Document Type


Student Type


Degree Name

Applied Statistics (MS)

Department, Program, or Center

School of Mathematical Sciences (COS)


Robert Parody


RIT – Main Campus

Plan Codes