Package index • impart

All functions

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