  Objectives: Statistical methods for signal detection of adverse drug
reactions in electronics health records (EHRs) are not usually provided with
information about optimal p-values and indicative sample sizes to achieve
sufficient power. Sauzet \& Cornelius (2022) have proposed test for signal
detection based on the hazard functions of Weibull type distributions (WSP
tests) which make use of the time-to-event information available in EHRs. We
investigate optimal p-values for these methods and sample sizes needed to reach
certain test powers.
  Study design and setting: We perform a simulation study with a range of
scenarios for sample size, rate of event due (ADRs) and not due to the drug and
a random time after prescription at which ADRs occurs. Based on the area under
the curve, we obtain optimal p-values of the different WSP tests for the
implementation in a hypothesis free signal detection setting. We also obtain
approximate sample sizes required to reach a power of 80 or 90\%.
  Results: The dWSP (double WSP) and the pWSP-dPWSP (combination of power WSP
and dWSP) provide similar results and we recommend using a p-value of 0.01.
With this p-values the sample sizes needed for a power of 80\% starts at 30
events for an ADR rate of 0.01 and a background rate of 0.01. For a background
rate of 0.05 and an ADR rate equal to a 20\% increase of the background rate
the number of events required is 300.
  Conclusion: For the implementation of WSP type test for signal detection in a
hypothesis free setting a p-values of 0.01 is recommended. The number of
observations and events required to achieve a correct power of detection is
commensurable to the size of typical EHR data.
