Package 'jointCompRisk'

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

prepped	A list returned by prep_data_cif(), containing Treatment and Control.
tau	Numeric, time horizon (e.g. 15 or 29).

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.

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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

prepped	A list returned by prep_data_weighted_cif().
tau	Numeric time horizon (e.g., 15 or 29).

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.

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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))

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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 1.5× faster recovery times and 1.8× 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)

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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

data	A data frame with columns for ID, time to recovery, time to death, recovery censor, death censor, and treatment indicator.
ID	Name of the patient ID column. Default is "USUBJID".
TimeToRecovery	Name of the time-to-recovery column. Default "TTRECOV".
TimeToDeath	Name of the time-to-death column. Default "TTDEATH".
Recov_Censoring	Name of the recovery-censor column. Default "RECCNSR" (0=event,1=censor).
Death_Censoring	Name of the death-censor column. Default "DTHCNSR" (0=event,1=censor).
Treatment	Name of the treatment indicator column (0=control,1=treatment). Default "trt".

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.

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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

data_main	A data.frame with ID, TTR, TTD, RECCNSR, DTHCNSR, baseline score, trt, etc.
data_long	A data.frame with repeated clinical scores over time (e.g. ADYC, ORDSCOR).
wID_main	Name of the patient ID column in the main dataset (default "USUBJID").
wTimeToRecovery_main	Name of the time-to-recovery column (default "TTRECOV").
wTimeToDeath_main	Name of the time-to-death column (default "TTDEATH").
wRecov_Censoring_main	Name of the recovery-censor column (default "RECCNSR").
wDeath_Censoring_main	Name of the death-censor column (default "DTHCNSR").
wBaselineScore_main	Name of the baseline ordinal column (default "ordscr_bs").
wTreatment_main	Name of the treatment indicator column (0=control,1=treatment). Default "trt".
wID_long	Name of the patient ID column in the long dataset (default "USUBJID").
wADY_long	Name of the day-since-treatment column in the long dataset (default "ADYC").
wScore_long	Name of the ordinal score column in the long dataset (default "ORDSCOR").
wStates_death	Vector of ordinal states for death weighting (default c(4,5,6,7)).
wWeights_death	Numeric weights, same length as wStates_death (default c(2,1.5,1,0.5)).
wStates_discharge	Vector of states for discharge weighting (default c(4,5,6,7)).
wWeights_discharge	Numeric weights, same length as wStates_discharge (default c(0.5,1,1.5,2)).

Value

See prep_data_weighted_cif2.

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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

data_main	A data.frame with ID, TTR, TTD, RECCNSR, DTHCNSR, baseline score, trt, etc.
data_long	A data.frame with repeated clinical scores over time (e.g. ADYC, ORDSCOR).
wID_main	Name of the patient ID column in the main dataset (default "USUBJID").
wTimeToRecovery_main	Name of the time-to-recovery column (default "TTRECOV").
wTimeToDeath_main	Name of the time-to-death column (default "TTDEATH").
wRecov_Censoring_main	Name of the recovery-censor column (default "RECCNSR").
wDeath_Censoring_main	Name of the death-censor column (default "DTHCNSR").
wBaselineScore_main	Name of the baseline ordinal column (default "ordscr_bs").
wTreatment_main	Name of the treatment indicator column (0=control,1=treatment). Default "trt".
wID_long	Name of the patient ID column in the long dataset (default "USUBJID").
wADY_long	Name of the day-since-treatment column in the long dataset (default "ADYC").
wScore_long	Name of the ordinal score column in the long dataset (default "ORDSCOR").
wStates_death	Vector of ordinal states for death weighting (default c(4,5,6,7)).
wWeights_death	Numeric weights, same length as wStates_death (default c(2,1.5,1,0.5)).
wStates_discharge	Vector of states for discharge weighting (default c(4,5,6,7)).
wWeights_discharge	Numeric weights, same length as wStates_discharge (default c(0.5,1,1.5,2)).

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).

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