Package: ppseq 0.2.5

ppseq: Design Clinical Trials using Sequential Predictive Probability Monitoring

Functions are available to calibrate designs over a range of posterior and predictive thresholds, to plot the various design options, and to obtain the operating characteristics of optimal accuracy and optimal efficiency designs.

Authors:Emily C. Zabor [aut, cre], Brian P. Hobbs [aut], Michael J. Kane [aut]

ppseq_0.2.5.tar.gz
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ppseq.pdf |ppseq.html✨
ppseq/json (API)
NEWS

# Install 'ppseq' in R:

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

Bug tracker:https://github.com/zabore/ppseq/issues

Pkgdown site:https://www.emilyzabor.com

Datasets:

one_sample_cal_tbl - Output from a one-sample call to 'calibrate_thresholds'
one_sample_decision_tbl - Output from a one-sample call to 'calc_decision_rules'
two_sample_cal_tbl - Output from a two-sample call to 'calibrate_thresholds'
two_sample_decision_tbl - Output from a two-sample call to 'calc_decision_rules'

On CRAN:

6.32 score 5 stars 28 scripts 246 downloads 8 exports 77 dependencies

Last updated 6 months agofrom:75eac30702. Checks:8 OK. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Feb 02 2025
R-4.5-win	OK	Feb 02 2025
R-4.5-mac	OK	Feb 02 2025
R-4.5-linux	OK	Feb 02 2025
R-4.4-win	OK	Feb 02 2025
R-4.4-mac	OK	Feb 02 2025
R-4.3-win	OK	Feb 02 2025
R-4.3-mac	OK	Feb 02 2025

Exports:calc_decision_rules calc_next calc_posterior calc_predictive calibrate_posterior_threshold calibrate_thresholds optimize_design sim_single_trial

Dependencies:askpass base64enc bslib cachem cli codetools colorspace cpp11 crosstalk curl data.table digest dplyr evaluate fansi farver fastmap fontawesome fs furrr future generics ggplot2 globals glue gtable highr htmltools htmlwidgets httr isoband jquerylib jsonlite knitr labeling later lattice lazyeval lifecycle listenv magrittr MASS Matrix memoise mgcv mime munsell nlme openssl parallelly patchwork pillar pkgconfig plotly promises purrr R6 rappdirs RColorBrewer Rcpp rlang rmarkdown sass scales stringi stringr sys tibble tidyr tidyselect tinytex utf8 vctrs viridisLite withr xfun yaml

One-sample expansion cohort

Emily C. Zabor

Rendered fromone_sample_expansion.Rmdusingknitr::rmarkdownon Feb 02 2025.

Last update: 2022-10-03
Started: 2021-03-02

Two-sample randomized trial

Emily C. Zabor

Rendered fromtwo_sample_randomized.Rmdusingknitr::rmarkdownon Feb 02 2025.

Last update: 2022-10-03
Started: 2021-04-13

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Calculate a decision rule table for interim monitoring of a pre-specified design	calc_decision_rules
Calculate response probability for the next patient	calc_next
Calculate a single posterior probability	calc_posterior
Calculate a single posterior predictive probability	calc_predictive
Calibrate the posterior probability threshold	calibrate_posterior_threshold
Calibrate according to posterior probability threshold and predictive probability threshold with interim futility monitoring	calibrate_thresholds
Evaluate a single dataset for a single pp_threshold and ppp_threshold combination	eval_thresh
Output from a one-sample call to 'calibrate_thresholds'	one_sample_cal_tbl
Output from a one-sample call to 'calc_decision_rules'	one_sample_decision_tbl
Custom optimization method for 'calibrate_thresholds' objects	optimize_design.calibrate_thresholds
Plot method for 'calc_decision_rules' objects	plot.calc_decision_rules
Plot method for 'calibrate_thresholds' objects	plot.calibrate_thresholds
Print method for 'calibrate_thresholds' objects	print.calibrate_thresholds
Simulate a single dataset based on the response probability(ies), the total sample size(s), and the interim look schedule(s)	sim_dat1
Simulate a single trial with posterior probability monitoring	sim_single_trial
Output from a two-sample call to 'calibrate_thresholds'	two_sample_cal_tbl
Output from a two-sample call to 'calc_decision_rules'	two_sample_decision_tbl