Handling Missing Data in Clinical Trials: An Overview
- PDF / 699,930 Bytes
- 9 Pages / 504 x 719.759 pts Page_size
- 82 Downloads / 215 Views
0092-8615/2000 Copyright 0 2000 Drug Information Association Inc.
Printed in h e USA. All rights reserved.
HANDLING MISSING DATA IN CLINICAL TRIALS: AN OVERVIEW WILLIAMR. MYERS,PHD Senior Statistician, Department of Biometrics and Statistical Sciences, Procter and Gamble Pharmaceuticals, Cincinnati, Ohio
A major problem in the analysis of clinical trials is missing data caused b y patients dropping out of the study before completion. This problem can result in biased treatment comparisons and also impact the overall statistical power of the study. This paper discusses some basic issues about missing data as well as potential “watch outs. ” The topic of missing data is ofren not a major concern until it is time for data collection and dnta analysis. This paper provides potential design considerations that should be considered in order to mitigate patients from dropping out of a clinical study. In addition, the concept of the missing-data mechanism is discussed. Five general strategies of handling missing data are presented: I . Complete-case analysis, 2. “Weighting methods, ” 3. Imputation methods, 4. Analyzing data as incomplete, and 5. “Other” methods. Within each strategy, several methods are presented along with advantages and disadvantages. Also briefly discussed is how the International Conference on Harmonization (ICH) addresses the issue of missing data. Finally, several of the methods that are illustrated in the paper are compared using a simulated data set. Key Words: Clinical trials; Missing data; Dropouts; Imputation methods; Missing-data mechanism
INTRODUCTION A PRIMARY CONCERN when conducting a clinical trial is that patients will drop out (or withdraw) before study completion. The reason for withdrawal may be study-related (eg, adverse event, death, unpleasant study procedures, lack of improvement) or unrelated to the study (eg, moving away, unrelated disease). This problem is especially prevalent in clinical trials where a slow-acting treatment or a drug that may be intolerant is being investigated. Dropouts in clinical
Presented at the DIA 35’ Annual Meeting, June 27July 1, 1999, Baltimore, Maryland. Reprint address: William R. Myers, PhD, Department of Biometrics and Statistical Sciences, Pmter and Gamble Pharmaceuticals, 8700 Mason Montgomery Road, P.O. Box 47. Mason, OH 45040-9462.
trials can produce biased treatment comparisons and reduce the overall statistical power. This paper will focus on the case where missing data occur as a result of patients dropping out of the study. More specifically, it focuses on the case in which a patient’s missing response at assessment time r implies it will be missing at all subsequent times. This is termed a rnonorone pattern of unit-level missing data (1). An example where missing data deviate from the aforementioned pattern is in the case of health related quality-of-life research, where a patient does not answer an item (or question) within a questionnaire, but does not necessarily drop out of the clinical trial. There are numerous issues one must con
Data Loading...
