SampleSize.CondPoisson: Sample size calculation for the continuous sequential...

Description Usage Arguments Details Value Acknowledgements See also Author(s) References Examples

View source: R/SampleSize.CondPoisson.R

Description

The function SampleSize.CondPoisson obtains the required sample size (length of surveillance) needed to guarantee a desired statistical power for a pre-specified relative risk, when doing continuous sequential CMaxSPRT, using a Wald-type upper boundary, which is flat with respect to the likelihood ratio function.

Usage

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SampleSize.CondPoisson(cc,D=0,M=1,alpha=0.05,power=0.9,RR=2)

Arguments

cc

The total number of observed adverse events in the historical data. There is no default.

D

The minium number for the ratio P_k/V before the null hypothesis can be rejected. The default value is D=0. A delayed start with D>0 is recommended to avoid signaling very early on such that very little information would be available to judge whether the signal is more likely to be a true signal or chance finding.

M

The minimum number of events needed before the null hypothesis can be rejected. The default value is M=1. A delayed start with M>1 is recommended to avoid signaling very early on such that very little information would be available to judge whether the signal is more likely to be a true signal or chance finding.

alpha

The significance level. It must be in the range (0,0.5]. The default value is alpha=0.05.

power

The target vector of overall statistical powers to detect an increased relative risk (RR). The default value is power=0.90.

RR

The target vector of relative risks to be detected with the requested vector of statistical powers. The default value is RR=2.

Details

When using the CMaxSPRT and the CV.CondPoisson function to conduct continuous sequential analysis for Poisson data and limited historical data, the null hypothesis is rejected when the log likelihood ratio exceeds the predetermined critical value calculated by CV.CondPoisson. The sequential analysis ends without rejecting the null hypothesis when a predetermined upper limit on the sample size is reached, expressed either in terms of the ratio of the cumulative person-time in the surveillance population divided by the total cumulative person-time in historical data (StopType="Tal"), or in terms of the observed number of events in the surveillance population (StopType="Cases"). For example, the sequential analysis may end as soon as the sample size is such that the cumulative person-time in the surveillance population is twice the cumulative person-time in historical data, or there are 50 observed events in the surveillance population. The function SampleSize.CondPoisson calculates what the upper limit on the sample size (length of surveillance) that is required for the continuous CMaxSPRT to achieve the desired statistical power for a pre-specified relative risk RR. It outputs the upper limit on sample size for both definitions of the surveillance length, one expressed in terms of the ratio of the cumulative person-time in the surveillance population divided by the total cumulative person-time in historical data (T), and the other one expressed in terms of the observed number of events in the surveillance population (K). To save computing time, the liberal computation approach proposed by Silva et al. (2016) is used in SampleSize.CondPoisson to find the solution.

Value

SampleSize_by_RR_Power

A table containing the main performance measures associated to the required samples sizes, expressed in the scale of the number of events in the surveillance period, for each combination of RR and power.

Acknowledgements

Development of the SampleSize.CondPoisson function was funded by:
- National Institute of General Medical Sciences, NIH, USA, through grant number R01GM108999 (v2.0.1, v2.0.2). - Foundation for Research Support of Minas Gerais State (FAPEMIG), MG, Brazil, through the grant Demanda Universal.

See also

CV.CondPoisson: calculating the critical value for continuous CMaxSPRT.
Performance.CondPoisson: calculating the statistical power, expected time to signal and expected time of analysis for continuous CMaxSPRT.

Author(s)

Ivair Ramos Silva, Lingling Li

References

Li L, Kulldorff M. (2010). A conditional maximized sequential probability ratio test for pharmacovigilance. Statistics in Medicine, 30;29(2):284-95.

Silva IR, Li L, Kulldorff M. (2016). Exact Conditional Sequential Testing for Poisson Data. Working paper.

Examples

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# Sample size required to obtain a power of 90%, for a relative risk of 1.5,
# no delay for starting the surveilla# nce (D=0), under an alpha level of 5%,
# with 5 events in the historical data.

# res<- SampleSize.CondPoisson(cc=50,D=0,M=1,alpha=0.05,power=0.9,RR=2)

# If we write:
# res
# then we get:

# $K
# [1] 66

# $Tal
# [1] 0.7890625

# $cv
# [1] 3.626436

Sequential documentation built on May 29, 2017, 5:40 p.m.

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