Choosing the analysis population in non-inferiority studies: per protocol or intent-to-treat

Stat Med. 2006 Apr 15;25(7):1169-81. doi: 10.1002/sim.2244.


For superiority trials, the intent-to-treat population (ITT) is considered the primary analysis population because it tends to avoid the over-optimistic estimates of efficacy that results from a per-protocol (PP) population. However, the roles of the ITT population and PP population in non-inferiority studies are not clearly defined as in superiority trials. In this paper, a simulation study is conducted to systematically investigate the impact of different types of missingness and protocol violations on the conservatism or anticonservatism of analyses based on the ITT and the PP population in non-inferiority trials. We find that conservatism or anticonservatism of the PP or ITT analysis depends on many factors, including the type of protocol deviation and missingness, the treatment trajectory (for longitudinal study) and the method of handling missing data in ITT population. The requirement that non-inferiority be shown for both PP and ITT populations does not necessarily guarantee the validity of a non-inferiority conclusion and a sufficiently powered PP analysis is not necessarily powered for ITT analysis. It is important to assess the potential types and rates of protocol deviation and missingness that might occur in a non-inferiority trial and to obtain some prior knowledge regarding the treatment trajectory of the test treatment versus the active control at the design stage so that a proper analysis plan and appropriate power estimation can be carried out. In general, for the types of protocol violations and missingness considered, we find that hybrid ITT/PP analysis, which excludes non-compliant patients as in the PP analysis and properly addresses the impact of non-trivial missing data as in the MLE-based ITT analysis, is more promising by way of providing reliable non-inferiority tests.

MeSH terms

  • Analysis of Variance
  • Clinical Protocols*
  • Computer Simulation
  • Controlled Clinical Trials as Topic / methods*
  • Data Interpretation, Statistical*
  • Drug Evaluation / methods*
  • Humans
  • Likelihood Functions
  • Models, Statistical*
  • Patient Dropouts
  • Refusal to Participate
  • Research Design*
  • Sensitivity and Specificity
  • Software
  • Treatment Outcome*