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sampsizeval  

Sample Size for Validation of Risk Models with Binary Outcomes
View on CRAN: Click here


Download and install sampsizeval package within the R console
Install from CRAN:
install.packages("sampsizeval")

Install from Github:
library("remotes")
install_github("cran/sampsizeval")

Install by package version:
library("remotes")
install_version("sampsizeval", "1.0.0.0")



Attach the package and use:
library("sampsizeval")
Maintained by
Menelaos Pavlou
[Scholar Profile | Author Map]
First Published: 2021-05-28
Latest Update: 2021-05-28
Description:
Estimation of the required sample size to validate a risk model for binary outcomes, based on the sample size equations proposed by Pavlou et al. (2021) . For precision-based sample size calculations, the user is required to enter the anticipated values of the C-statistic and outcome prevalence, which can be obtained from a previous study. The user also needs to specify the required precision (standard error) for the C-statistic, the calibration slope and the calibration in the large. The calculations are valid under the assumption of marginal normality for the distribution of the linear predictor.
How to cite:
Menelaos Pavlou (2021). sampsizeval: Sample Size for Validation of Risk Models with Binary Outcomes. R package version 1.0.0.0, https://cran.r-project.org/web/packages/sampsizeval. Accessed 10 Apr. 2025.
Previous versions and publish date:
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Complete documentation for sampsizeval
Functions, R codes and Examples using the sampsizeval R package
Some associated functions: sampsizeval . 
Some associated R codes: actual_values.R . package-sampsizeval.R . sampsizeval.R . size_c_app.R . size_c_ni.R . size_cl_app.R . size_cl_ni.R . size_cs_app.R . size_cs_ni.R . tune_mu_sigma.R .  Full sampsizeval package functions and examples
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