Type:	Package
Title:	Calculates Continuous Time Likelihood Ratio Weights Assuming Multiplicative Intensity Models and Additive Hazard Models
Version:	0.1.0
Depends:	R (≥ 3.5.0)
Imports:	methods, timereg, plyr, data.table (≥ 1.10.4)
LazyData:	Yes
Description:	Estimates continuous time weights for performing causal survival analysis. For instance, weighted Nelson-Aalen or Kaplan-Meier estimates can be given a causal interpretation. See Ryalen, Stensrud, and Røysland (2019) <doi:10.1007/s10985-019-09468-y> and Ryalen (2019) https://www.duo.uio.no/handle/10852/70353 for theory and examples.
License:	GPL (≥ 3)
Encoding:	UTF-8
RoxygenNote:	7.2.3
Suggests:	testthat (≥ 3.0.0), survival
Config/testthat/edition:	3
NeedsCompilation:	yes
Packaged:	2022-12-22 11:10:00 UTC; paalcry
Author:	Kjetil Røysland [ths], Pål Christie Ryalen [aut, cre]
Maintainer:	Pål Christie Ryalen <p.c.ryalen@medisin.uio.no>
Repository:	CRAN
Date/Publication:	2022-12-22 20:40:05 UTC

Tmat

Description

Tmat

Tie removal

Description

Removes ties from by adding noise at tied times

Usage

addNoiseAtEventTimes(fr, id, from, to)

Arguments

Value

Longitudinally refined data.table of the input data.table fr with random noise added for tiebreaking.

Author(s)

Pål Christie Ryalen <p.c.ryalen@medisin.uio.no>

Examples

library(data.table)
data(fr1)
fr1 <- as.data.table(fr1)
fr1 <- addNoiseAtEventTimes(fr1)
head(fr1)

Re-weighting Point Processes Based on Additive Hazard Models

Description

Estimates continuous time weights for performing causal survival analysis. For instance, weighted Nelson-Aalen or Kaplan-Meier estimates can be given a causal interpretation.

fFrame

Description

fFrame

fr1

Description

fr1

Continuous time weight estimation based on aalen.predict

Description

Refines data longitudinally in order to do estimate parameters(e.g. Nelson-Aalen or Kaplan-Meier) using continuous time weights. The weights can be assessed using the plot option.

Usage

makeContWeights(
  faFit,
  cfaFit,
  dataFr,
  atRiskState,
  eventState,
  startTimeName,
  stopTimeName,
  startStatusName,
  endStatusName,
  idName,
  b,
  weightRange = c(0, 10),
  willPlotWeights = TRUE,
  withLeftLim = FALSE
)

Arguments

Value

Longitudinally refined data.table of the initial dataFr with weights column added.

Author(s)

Pål Christie Ryalen <p.c.ryalen@medisin.uio.no>

References

https://arxiv.org/abs/1802.01946

Examples

library(data.table)
library(timereg)

# fr1 is a longitudinal data set with subjects that are diagnosed at time 0, and may
# be treated as time evolves. Subjects can die before receiving treatment

# The method assumes there are no tied event times in the observed data. Although there are no
# tied event times in fr1, we use the function addNoiseAtEventTimes() for illustration here
fr1 <- as.data.table(fr1)
fr1 <- addNoiseAtEventTimes(fr1)

# Time to treatment and death are confounded by the baseline variable L. We want to
# mimic a scenario where time to treatment is randomized (and does not depend on L):
fr1_diag <- fr1[fr1$from.state == "diag", ]
fFit <- aalen(
  Surv(from, to, to.state == "treat") ~ 1 + L, data = fr1_diag, n.sim = 50L,
  robust = 0
)
cfFit <- aalen(
  Surv(from, to, to.state == "treat") ~ 1, data = fr1_diag, n.sim = 50L,
  robust = 0
)

# We calculate and plot the weights
frame1 <- makeContWeights(fFit, cfFit, fr1, "diag", "treat", "from", "to",
  "from.state", "to.state", "id",
  b = 0.4,
  weightRange = c(0, 5)
)

# We fit a weighted model for the outcome. A is a treatment indicator (A=1 means treated).
a1 <- aalen(
  Surv(from, to, to.state == "death") ~ 1 + A, data = frame1,
  weights = frame1$weights, n.sim = 50L, robust = 0
)

# We plot the A coefficient from the weighted regression,
# and compare with the true hypothetical coefficient
plot(a1$cum[, c(1, 3)],
  type = "s", ylim = c(-1.2, 0.5), xlim = c(0, 5),
  main = "Weighted additive hazard treatment coefficient"
)
lines(Tmat, col = 2)
legend("bottomleft", c("weighted estimate", "hypothetical treatment coef"),
  lty = 1, col = c(1, 2), bty = "n"
)

# Next we consider an example with dependent censoring.
# Subjects are censored depending on a baseline variable u. We wish to mimic the
# cumulative hazard for death we would have seen if the censoring were independent.

faFit <- aalen(
  Surv(from, to, to.state == "Censored") ~ 1 + u, data = fFrame, n.sim = 50L,
  robust = 0
)
cfaFit <- aalen(
  Surv(from, to, to.state == "Censored") ~ 1, data = fFrame, n.sim = 50L,
  robust = 0
)

frame <- makeContWeights(
  faFit, cfaFit, fFrame, "Alive", "Censored", "from", "to", "from.state",
  "to.state", "id", 100
)

fMod <- aalen(
  Surv(from, to, to.state == "Dead") ~ 1, data = fFrame, n.sim = 50L,
  robust = 0
)
wMod <- aalen(
  Surv(from, to, to.state == "Dead") ~ 1, data = frame, weights = frame$weights,
  n.sim = 50L, robust = 0
)

plot(fMod$cum, type = "s", main = "Nelson-Aalen for death", ylab = "")
lines(wMod$cum, type = "s", col = "red")
legend("topleft", c("factual", "weighted factual"), lty = 1, col = c(1, "red"), bty = "n")

Replaces NA-values in vec with last non-NA value

Description

Assumes first element is non-NA

Usage

naReplace(vec)

Arguments

Value

numeric vector with each NA entry replaced with the last previous non-NA entry.

Note

Can be replaced by link[zoo]{na.locf0}

Author(s)

Pål Christie Ryalen <p.c.ryalen@medisin.uio.no>

Examples

naReplace(c(1, 2, 3, NA, NA, 4))
naReplace(c("text", NA, NA))

Plots mean and individual weight trajectories

Description

Plotting all the individual weight trajectories in fr, along with the mean. Plots weights for assessment.

Usage

plotContWeights(
  fr,
  stopTimeName = "to",
  startStatusName = "from.state",
  endStatusName = "to.state",
  idName = "id"
)

Arguments

Value

No return value.

Author(s)

Pål Christie Ryalen <p.c.ryalen@medisin.uio.no>

Expands data.table

Description

Refines dataFr so that each individual at risk get a row for each of the provided event times

Usage

refineTable(dataFr, atRiskState, eventState)

Arguments

Value

data.table

Author(s)

Pål Christie Ryalen <p.c.ryalen@medisin.uio.no>

Continuous time weight estimation based on predict.aalen

Description

Extracts cumulative hazard estimates for each individual. Each individual receives a weight process evaluated at pre-specified time points. The weight process is estimated as a cumulative product involving estimated cumulative hazard increments, and a hazard ratio estimated using a smoothing parameter b.

Usage

weightPredict(fPred, cfPred, wtFrame, ids, eventTimes, eventIds, b)

Arguments

Value

data.table

Author(s)

Pål Christie Ryalen <p.c.ryalen@medisin.uio.no>