Abstract
Randomized controlled trials (RCTs) in surgery have been impeded by concerns that
improvements in the technical performance of a new technique over time (a "learning curve") may distort comparisons. The statistical assessment of learning
curves in trials has received little attention. In this paper, we discuss what a learning curve effect is, the factors which effect it, how to display it, and how to incorporate the learning effect into the trial analysis. Bayesian hierarchical models are proposed to adjust the trial results for the existence of a learning curve effect. The implications for trial evaluation and data collection are considered.
improvements in the technical performance of a new technique over time (a "learning curve") may distort comparisons. The statistical assessment of learning
curves in trials has received little attention. In this paper, we discuss what a learning curve effect is, the factors which effect it, how to display it, and how to incorporate the learning effect into the trial analysis. Bayesian hierarchical models are proposed to adjust the trial results for the existence of a learning curve effect. The implications for trial evaluation and data collection are considered.
| Original language | English |
|---|---|
| Pages (from-to) | 421-427 |
| Number of pages | 7 |
| Journal | Clinical Trials |
| Volume | 1 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - Oct 2004 |
Bibliographical note
AcknowledgementsWe wish to thank the steering committee of the Ipswich Childbirth study for access to their data. This project was funded by a Medical Research Council (MRC) PhD studentship through the MRC Health Services Research Collaboration. The Health Services Research Unit receives core funding from the Chief Scientists Office of the Scottish Executive Health Department. The views expressed in this paper are those of the authors.