Understanding and anticipating lag-time bias in cost-effectiveness studies: the role of time in cost-effectiveness analysis

Int J Technol Assess Health Care. 2014 Dec;30(6):608-11. doi: 10.1017/S0266462314000762.


Background: Timely provision of information on the cost-effectiveness of innovations in health care becomes more and more important, resulting in increasing pressure on researchers to provide proof of cost-effectiveness in a short time frame. However, most of these innovations require considerable time and effort to optimally implement leading to a biased "steady state" cost-effectiveness outcome. As decision makers in health care predominantly have a short-term focus, the discrepancy between short-term study outcomes and long-term cost-effectiveness may very well lead to misguided decisions about the adoption of innovations in health care.

Methods: Factors such as learning effects, capacity constraints, and delayed time to benefit are all related to a short-run timeframe and result in inefficiencies during the implementation of an innovation. These factors and the mechanisms by which they influence the cost-effectiveness outcome are explained for three different types of healthcare innovations.

Results: As standard cost-effectiveness analysis assumes costs and effects to behave constant and representative for an innovation's entire economic lifetime, resulting cost-effectiveness outcomes might give a biased, and often overly pessimistic, reflection of the actual cost-effectiveness of an innovation. This is further amplified by the fact that short-run inefficiencies are most prevalent and impactful during an innovation's earliest stage of operation.

Conclusions: This study advocates to carefully take into account the different factors contributing to lag-time bias in the design and analysis of cost-effectiveness studies, and to communicate potential biases due to short-run inefficiencies to all stakeholders involved in the decision making process.

Keywords: Bias.

MeSH terms

  • Bias*
  • Cost-Benefit Analysis / methods*
  • Humans
  • Learning
  • Technology Assessment, Biomedical / economics*
  • Technology Assessment, Biomedical / methods*
  • Time Factors