Interim analyses provide opportunities to evaluate accumulating evidence during a trial, enabling ethical decisions about whether to continue, modify, or terminate the study based on observed data. These analyses serve multiple purposes: detecting overwhelming efficacy that would make continued randomization to placebo unethical, identifying futility when the treatment is unlikely to demonstrate benefit even with complete enrollment, and monitoring safety signals that might require study termination or modification. The timing, number, and methodology of interim analyses must be pre-specified to maintain trial integrity.

The statistical challenges of interim analyses arise from repeated testing of accumulating data. Each look at the data provides an opportunity to declare success, and without adjustment, the probability of a false-positive finding increases with each interim analysis. Statistical methods such as group sequential designs address this multiplicity by adjusting significance thresholds for each analysis to maintain the overall Type I error rate at the desired level. Spending functions, such as O'Brien-Fleming or Pocock boundaries, define how the total alpha is allocated across planned analyses.

Data Safety Monitoring Boards (DSMBs) or Data Monitoring Committees (DMCs) typically oversee interim analyses to preserve trial integrity and blinding. These independent committees receive unblinded results and assess whether predefined stopping boundaries have been crossed or whether other findings warrant study modification or termination. The sponsor and investigators remain blinded to interim results unless the DSMB recommends unblinding. This separation ensures that trial conduct is not influenced by accumulating results while enabling appropriate response to emerging evidence.