Skip to contents
-
apply_stopping_rule_z()
- Apply group sequential stopping rules to test statistics
-
asymptotic_information_difference_means()
asymptotic_information_difference_proportions()
asymptotic_information_risk_difference()
asymptotic_information_relative_risk()
- Approximate information from a random samples of a given size for continuous and binary outcomes
-
asymptotic_information_logrank()
- Approximate information from a random samples of a given size for log hazard ratio
-
asymptotic_information_mann_whitney_fm()
- Approximate information from a random samples of a given size for ordinal outcomes
-
boot_control()
boot_control_testing()
- Parameters for using boot::boot() in Non-Sequential Analyses
-
calculate_covariance()
- Compute bootstrap covariance of estimates
-
calculate_estimate()
- Compute an estimate from a wrapper function
-
compute_bootstrap_serial()
compute_bootstrap_parallel()
- Compute bootstrap from a vector of IDs
-
correct_one_sided_gsd()
- Correct one-sided trialDesignGroupSequential object
-
count_outcomes()
- Count outcome events from prepared study data
-
count_outcomes_at_time_t()
- Reconstruct the count of events at a given study time during a study
-
data_at_time_t()
- Reconstruct data available at an earlier point in a study
-
dr_joffe()
dr_joffe_fit()
- Doubly Robust Estimation Using Joffe's Doubly Robust Weighted Least Squares (DR-WLS) Estimator
-
estimate_information()
- Estimate the observed information level
-
example_1
- Example 1: Simulated Trial with a Single, Continuous Outcome
-
example_1_final
- Example 1: Simulated Trial with a Single, Continuous Outcome - Final Analysis
-
example_1_ia_1
- Example 1: Simulated Trial with a Single, Continuous Outcome - Interim Analysis 1
-
example_1_ia_2
- Example 1: Simulated Trial with a Single, Continuous Outcome - Interim Analysis 2
-
hr_events()
hr_power()
hr_alpha()
hr_minimal()
hr_design()
- Calculate minimum hazard ratio, number of required events, power, or Type I error
-
impute_covariates_mean_mode()
- Impute missing baseline covariates using mean/mode imputation
-
information_to_n_continuous_1_to_1()
information_to_n_binary_1_to_1()
information_to_events_log_hr()
- Convert information level into an approximate sample size requirement for a one-stage design
-
information_trajectory()
- Reconstruct a trajectory of information accrual
-
initialize_monitored_design()
- Initialize an Information Monitoring Design
-
monitored_analysis()
- Perform pre-specified analyses for an interim monitored trial design
-
monitored_analysis_control()
monitored_analysis_control_testing()
- Control arguments for performing information monitored analyses
-
monitored_design_checks()
- Check consistency of a
monitored_design
object
-
mw_from_pmfs()
- Title Compute the Mann-Whitney estimand from Probability Mass Functions
-
orthogonalize_estimates()
- Orthogonalize estimates and covariance matrix
-
plot_outcome_counts()
- Produce a cumulative plot of study events
-
prepare_monitored_study_data()
- Prepare monitored study data for monitoring and analysis
-
relabel_by_id()
- Create relabeled dataset from a list of resampled IDs
-
required_information_single_stage()
required_information_mw_single_stage()
- Determine the information level required for a one-stage, fixed sample size design
-
required_information_sequential()
- Adjust Information for Interim Monitoring
-
rr_n_per_arm()
rr_power()
rr_alpha()
rr_minimal()
rr_design()
- Calculate relative risk, sample size, power, or Type I error
-
sim_colon_cancer
- sim_colon_cancer: Processed Moerton Colon Cancer Data
-
speffsurv_impart()
- Wrapper for speff2trial::speffsurv: See speff2trial::speffSurv
-
standardization()
standardization_tx_formula()
standardization_tx_stratified()
standardization_correction()
- Compute standardization (i.e. G-Computation) estimator
-
test_data
- test_data: A dataset used for testing package functions