Table 1 Summary of the effect of NPI on clinical development by NPI strength and recommendations for each scenario. For each recommendation, a (non-exhaustive) list of specific risk mitigation measures is suggested.

Weak (leading to disease burden change similar as year-to-year fluctuations)

Assessment of clinical benefit is difficult with low number of events

Reinforce and underline clinical significance of the demonstrated effect

• Select population/endpoints where a smaller (absolute) effect on RTI prophylaxis is still clinically meaningful (characterized by small minimally important difference). One example is to focus on prophylaxis of viral infection induced wheezing or asthma exacerbations, see^70,71, rather than upper RTI (mostly common cold) in the general population

• Comprehensive reporting of rates, relative, and absolute benefit

• Include secondary endpoints that add a diversified and multifaceted view to the clinical significance for assessors of the trial results (e.g., symptom-free days as RTI duration related endpoint)

• Seek regulator’s feedback on the study protocol and statistical analysis plan with respect to clinical benefit assessment

Medium (leading to substantially lower disease burden; magnitude of change with respect to average exceeds year-to-year fluctuations)

Reduced post-hoc power with fixed sample size and less available patients that suffer from fixed minimum number of episodes

Mitigate loss of power through sample size adjustment, adaptive trial design, and statistical analysis tailored to rare events

• Multi-center trials with access to a larger patient pool can facilitate recruitment of larger sample sizes under difficult conditions

• Use Model Informed Drug Development (MIDD) to leverage the totality of evidence for an optimal trial design and extrapolation^72,73

• Primary endpoint analysis based on event rate ratio (ERR) and accounting for excess zeros, e.g., zero-inflated negative binomial regression (ZINB) in frame of generalized linear models (GLM)^74,75

• Use trial monitoring and (Bayesian) adaptive trial design⁷⁶ especially sample size reestimation (increasing the sample size based on interim data analysis)⁷⁷, group sequential designs⁷⁸ (trials can be stopped early once significant results are obtained, or the trial can be stopped for futility)

• Seek regulator’s feedback on any modeling and simulation methods applied (e.g., FDA’s MIDD pilot program)⁷⁹, for complex innovative trial design and the statistical analysis (e.g., FDA’s complex innovative trial design pilot program⁸⁰)

Strong = lockdown (leading to attenuation of seasonal epidemic)

High risk of insufficient sample size and severe recruitment issues

Change the development plan

• Change development timeline

• Conduct observational study to assess the effect of NPI, see e.g., ref. ⁸¹

• Prioritize retrospective analyses (see ref. ⁸² for an example in case of OM-85).

• Perform exploratory modeling studies