Package: MRTSampleSizeBinary 0.1.2

Tianchen Qian

MRTSampleSizeBinary: Sample Size Calculator for MRT with Binary Outcomes

Provides a sample size calculator for micro-randomized trials (MRTs) with binary outcomes based on Cohn et al. (2023) <doi:10.1002/sim.9748>. Also provides a power calculator when the sample size is input by the user.

Authors:Eliot Wong-Toi, Thabat Dahdoul, Tianchen Qian

MRTSampleSizeBinary_0.1.2.tar.gz
MRTSampleSizeBinary_0.1.2.tar.gz(r-4.5-noble)MRTSampleSizeBinary_0.1.2.tar.gz(r-4.4-noble)
MRTSampleSizeBinary_0.1.2.tgz(r-4.4-emscripten)MRTSampleSizeBinary_0.1.2.tgz(r-4.3-emscripten)
MRTSampleSizeBinary.pdf |MRTSampleSizeBinary.html✨
MRTSampleSizeBinary/json (API)
NEWS

# Install 'MRTSampleSizeBinary' in R:

install.packages('MRTSampleSizeBinary', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

alpha_1 - Vector that defines the success probability null curve.
beta_1 - Vector that defines the MEE under the alternative hypothesis.
f_t_1 - A matrix defining the MEE under the alternative hypothesis.
g_t_1 - A matrix defining the success probability null curve.
m_matrix_1 - An example matrix for "bread" of sandwich estimator of variance.
p_t_1 - A vector of randomization probabilities for each time point.
sigma_matrix_1 - An example matrix for "meat" of sandwich estimator of variance.
tau_t_1 - Vector that holds the average availability at each time point.

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

9 exports 0.00 score 28 dependencies 36 scripts 302 downloads

Last updated 10 months agofrom:838952648e. Checks:OK: 2. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Aug 30 2024
R-4.5-linux	OK	Aug 30 2024

Exports:compute_m_sigma compute_ncp is_full_column_rank max_samp min_samp mrt_binary_power mrt_binary_ss power_summary power_vs_n_plot

Dependencies:cli colorspace fansi farver ggplot2 glue gtable isoband labeling lattice lifecycle magrittr MASS Matrix mgcv munsell nlme pillar pkgconfig R6 RColorBrewer rlang scales tibble utf8 vctrs viridisLite withr

Sample Size Calculator for Micro-Randomized Trials with Binary Outcomes

Eliot Wong-Toi, Thabat Dahdoul, Tianchen Qian

Rendered fromsample-size-calcualtor-for-mrt.Rmdusingknitr::rmarkdownon Aug 30 2024.

Last update: 2023-12-04
Started: 2021-03-04

Citation

Readme and manuals

Help Manual

Help page	Topics
Vector that defines the success probability null curve.	alpha_1
Vector that defines the MEE under the alternative hypothesis.	beta_1
Computes "M" and "Sigma" matrices for the sandwich estimator of variance-covariance matrix.	compute_m_sigma
Computes the non-centrality parameter for an F distributed random variable in the context of a MRT with binary outcome.	compute_ncp
A matrix defining the MEE under the alternative hypothesis.	f_t_1
A matrix defining the success probability null curve.	g_t_1
Check if a matrix is full column rank.	is_full_column_rank
An example matrix for "bread" of sandwich estimator of variance.	m_matrix_1
Returns default maximum sample size to end power_vs_n_plot().	max_samp
Compute minimum sample size.	min_samp
Calculate power for binary outcome MRT	mrt_binary_power
Calculate sample size for binary outcome MRT	mrt_binary_ss
A vector of randomization probabilities for each time point.	p_t_1
Calculate sample size at a range of power levels.	power_summary
Returns a plot of power vs sample size in the context of a binary outcome MRT. See the vignette for more details.	power_vs_n_plot
An example matrix for "meat" of sandwich estimator of variance.	sigma_matrix_1
Vector that holds the average availability at each time point.	tau_t_1