The following approaches can be considered to reduce the estimated sample size in clinical trials:

- Objective
- Choose another objective, e.g.
- Reduce objectives of study, e.g. phase 2 instead of phase 3, fewer primary/key secondary endpoints, descriptive instead of inferential
- Change target concept, e.g. physical instead of psychological QOL

- Operating characteristics:
- Relax type 1 error
- Relax type 2 error
- Perform literature review and if expected treatment is uncertain use most optimistic parameter values (risky!)

- Choose another objective, e.g.
- Design
- Select a more homogeneous target population (ie lower measurement error)
- Increase treatment effect, e.g.
- higher dose of test drug
- control with less effect (e.g., placebo instead of active control)

- Extend observation period (assuming increasing effect over time)
- Reuse participants, e.g. cross-over design
- Add repeated measurements, ie add more visits
- Apply group-sequential designs
- Apply sample size reestimation
- Relax definition of protocol deviations
- Select endpoint with lower measurement error
- Replacing measure with another with higher reliability/sensitivity to change, e.g. introduce more levels on response scale, VAS instead of rating scale
- Aggregating multiple measure into an endpoint, e.g. multi-item summary score, composite endpoint, multivariate endpoint (cf MANOVA, Principal Component)
- Continuous endpoint instead of binary (responder) endpoint

- Statistical analysis
- Use parametric instead of non-parametric model
- Use most distribution with best goodness-of-fit
- Reduce parameters (df) in statistical model
- Impute missing values (ie assume all patients are completers after imputation in sample size estimation, ie no dropout)
- Repeated measures models (MMRM)
- Include covariates (unexplained variation -> explained variation), e.g.
- Standard (demographics, patient characteristics, disease characteristics)
- Endpoint at baseline
- Prognostic variables of placebo effect (cf Cognivia)
- Prognostic variables of missingness

- Select multiple testing procedure to optimize power
- Bayesian analysis using external data (RWE) or expert elicitation