Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling 'rhs(lhs)'.

Run Standard CIF Analysis

Description

Given a prepped data list from prep_data_cif , run the standard CIF analysis.

Usage

do_cif_analysis(prepped, tau = 15)

Arguments

Details

- RMLT1 uses parameters (a,b,c) = (0,1,0) for recovery/discharge analysis. - RMLT2 uses (a,b,c) = (0,0,1) for death analysis.

Value

A list with formatted results for RMLT1 and RMLT2.

Run Weighted CIF Analysis

Description

Given the list from prep_data_weighted_cif , run Weighted RMLT1 (recovery/discharge) and Weighted RMLT2 (death) at a user-specified time horizon tau.

Usage

do_weighted_cif_analysis(prepped, tau)

Arguments

Details

- Weighted RMLT1 uses eta=1 for recovery/discharge analysis. - Weighted RMLT2 uses eta=2 for death analysis.

Value

A list with formatted results for WRMLT1 and WRMLT2.

Longitudinal Severity Scores Dataset

Description

Repeated measurements of ordinal severity scores over time for the same patients in the main_df dataset, with treatment-specific trajectory patterns.

Usage

data(long_df)

Format

A data frame with variable rows per patient:

PersonID

Patient identifier matching ID in main_df (character)

OrdinalScore

Severity score on 1-8 scale (numeric)

RelativeDay

Study day (numeric) starting from day 0 (baseline)

Details

Measurements are taken at scheduled visits: days 0 (baseline), 1, 3, 5, 7, 10, 14, 18, 21, 25, 28. The trajectory follows treatment-specific probabilities: treatment patients have 45 and 15 worsening probability, creating realistic differential clinical progression patterns.

Source

Simulated data using treatment-specific random walk with boundaries

Examples

data(long_df)
data(main_df)
head(long_df)
# See data for first patient
subset(long_df, PersonID == "Patient_001")
# Compare average scores by treatment
long_df %>%
 dplyr::left_join(main_df[,c("ID","Treatment")], by=c("PersonID"="ID")) %>%
 dplyr::group_by(Treatment) %>%
 dplyr::summarise(mean_score = mean(OrdinalScore))

Main Competing Risks Dataset Simulated clinical trial data with competing risks survival outcomes. This dataset follows the structure of Adaptive COVID-19 Treatment Trials (ACTT) with built-in treatment effects for demonstration purposes.

Description

Main Competing Risks Dataset Simulated clinical trial data with competing risks survival outcomes. This dataset follows the structure of Adaptive COVID-19 Treatment Trials (ACTT) with built-in treatment effects for demonstration purposes.

Usage

data(main_df)

Format

A data frame with 150 rows and 7 variables:

ID

Patient identifier (character)

TimeToRecovery

Time to recovery event in days (numeric)

TimeToDeath

Time to death event in days (numeric)

RecoveryCensoringIndicator

Recovery censoring indicator (0=event observed, 1=censored)

DeathCensoringIndicator

Death censoring indicator (0=event observed, 1=censored)

BaselineScore

Baseline severity score, range 4-7 (numeric)

Treatment

Treatment arm indicator (0=control, 1=treatment)

Details

This is a simulated dataset created for demonstration purposes with realistic treatment effects built in: treatment group has ×ばつ faster recovery times and×ばつ improved survival compared to control. The data represents a clinical trial with competing risks where patients can either recover or die, with administrative censoring at 30 days.

Source

Simulated data based on Weibull distributions with treatment-specific parameters

Examples

data(main_df)
head(main_df)
summary(main_df)
# Compare outcomes by treatment
tapply(main_df$TimeToRecovery, main_df$Treatment, summary)
tapply(main_df$TimeToDeath, main_df$Treatment, summary)

Prepare Data for Standard CIF

Description

Cleans and prepares a single dataset for standard (competing risks) CIF analysis.

Usage

prep_data_cif(
 data,
 ID = "USUBJID",
 TimeToRecovery = "TTRECOV",
 TimeToDeath = "TTDEATH",
 Recov_Censoring = "RECCNSR",
 Death_Censoring = "DTHCNSR",
 Treatment = "trt"
)

Arguments

Value

A list with:

• data.w: The processed data frame with columns cn, etime, estatus, etype2, Treatment.

• Treatment: Subset of data.w where Treatment==1.

• Control: Subset of data.w where Treatment==0.

Prepare Data for Weighted CIF (Legacy Wrapper)

Description

Convenience wrapper that mirrors the original Part II data preparation workflow for weighted restricted mean analyses. The function now delegates to prep_data_weighted_cif2 to provide consistent checks and support for arbitrary ordinal state definitions.

Usage

prep_data_weighted_cif(
 data_main,
 data_long,
 wID_main = "USUBJID",
 wTimeToRecovery_main = "TTRECOV",
 wTimeToDeath_main = "TTDEATH",
 wRecov_Censoring_main = "RECCNSR",
 wDeath_Censoring_main = "DTHCNSR",
 wBaselineScore_main = "ordscr_bs",
 wTreatment_main = "trt",
 wID_long = "USUBJID",
 wADY_long = "ADYC",
 wScore_long = "ORDSCOR",
 wStates_death = c(4, 5, 6, 7),
 wWeights_death = c(2, 1.5, 1, 0.5),
 wStates_discharge = c(4, 5, 6, 7),
 wWeights_discharge = c(0.5, 1, 1.5, 2)
)

Arguments

Value

See prep_data_weighted_cif2 .

Prepare Data for Weighted CIF

Description

Prepares merged competing-risks and longitudinal severity data for weighted restricted mean analyses. The routine removes patients with zero follow-up or missing baseline severity, handles discharge-to-die cases, merges the longitudinal trajectory, and computes user-specified weighted time summaries for death-focused and discharge-focused analyses.

Usage

prep_data_weighted_cif2(
 data_main,
 data_long,
 wID_main = "USUBJID",
 wTimeToRecovery_main = "TTRECOV",
 wTimeToDeath_main = "TTDEATH",
 wRecov_Censoring_main = "RECCNSR",
 wDeath_Censoring_main = "DTHCNSR",
 wBaselineScore_main = "ordscr_bs",
 wTreatment_main = "trt",
 wID_long = "USUBJID",
 wADY_long = "ADYC",
 wScore_long = "ORDSCOR",
 wStates_death = c(4, 5, 6, 7),
 wWeights_death = c(2, 1.5, 1, 0.5),
 wStates_discharge = c(4, 5, 6, 7),
 wWeights_discharge = c(0.5, 1, 1.5, 2)
)

Arguments

Value

A list containing:

• data.ws.death and data.ws.discharge: Full merged datasets with an added wU column for (death) or (discharge).

• Treatment.death and Control.death: Subsets for weighted WRMLT2 (death-focused).

• Treatment.discharge and Control.discharge: Subsets for weighted WRMLT1 (recovery-focused).