| Title: | Calculates Continuous Time Likelihood Ratio Weights Assuming Multiplicative Intensity Models and 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. 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. |
| Authors: | Kjetil Røysland [ths], Pål Christie Ryalen [aut, cre]
|
| Maintainer: | Pål Christie Ryalen <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 0.1.0 |
| Built: | 2025-01-29 02:51:56 UTC |
| Source: | https://github.com/cran/ahw |
Removes ties from by adding noise at tied times
addNoiseAtEventTimes(fr, id, from, to)addNoiseAtEventTimes(fr, id, from, to)
fr |
Data on long format |
id |
Name of column in |
from |
Name of the variable that contains the name of start state for each at-risk interval |
to |
Name of column with stop time of the at risk period |
Longitudinally refined data.table of the input data.table fr with random noise added for tiebreaking.
Pål Christie Ryalen <[email protected]>
library(data.table) data(fr1) fr1 <- as.data.table(fr1) fr1 <- addNoiseAtEventTimes(fr1) head(fr1)library(data.table) data(fr1) fr1 <- as.data.table(fr1) fr1 <- addNoiseAtEventTimes(fr1) head(fr1)
Estimates continuous time weights for performing causal survival analysis. For instance, weighted Nelson-Aalen or Kaplan-Meier estimates can be given a causal interpretation.
aalen.predict
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.
makeContWeights( faFit, cfaFit, dataFr, atRiskState, eventState, startTimeName, stopTimeName, startStatusName, endStatusName, idName, b, weightRange = c(0, 10), willPlotWeights = TRUE, withLeftLim = FALSE )makeContWeights( faFit, cfaFit, dataFr, atRiskState, eventState, startTimeName, stopTimeName, startStatusName, endStatusName, idName, b, weightRange = c(0, 10), willPlotWeights = TRUE, withLeftLim = FALSE )
faFit |
The |
cfaFit |
The |
dataFr |
|
atRiskState |
At risk state for the event of interest |
eventState |
State for the event of interest |
startTimeName |
Name of column with start time of the at risk period |
stopTimeName |
Name of column with stop time of the at risk period |
startStatusName |
Name of the column that contains the starting state for each interval |
endStatusName |
Name of the column that contains the end state for each interval |
idName |
Name of column in |
b |
Smoothing bandwidth parameter |
weightRange |
Truncates weights outside this interval |
willPlotWeights |
Plot indicator |
withLeftLim |
Calculates left limit at jump if desired |
Longitudinally refined data.table of the initial dataFr with weights column added.
Pål Christie Ryalen <[email protected]>
https://arxiv.org/abs/1802.01946
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")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")
vec with last non-NA valueAssumes first element is non-NA
naReplace(vec)naReplace(vec)
vec |
Vector of any type |
numeric vector with each NA entry replaced with the last previous non-NA entry.
Can be replaced by link[zoo]{na.locf0}
Pål Christie Ryalen <[email protected]>
naReplace(c(1, 2, 3, NA, NA, 4)) naReplace(c("text", NA, NA))naReplace(c(1, 2, 3, NA, NA, 4)) naReplace(c("text", NA, NA))
Plotting all the individual weight trajectories in fr, along with the mean. Plots weights for assessment.
plotContWeights( fr, stopTimeName = "to", startStatusName = "from.state", endStatusName = "to.state", idName = "id" )plotContWeights( fr, stopTimeName = "to", startStatusName = "from.state", endStatusName = "to.state", idName = "id" )
fr |
Data with weight column |
stopTimeName |
Name of column with stop time of the at risk period |
startStatusName |
Name of the variable that contains the name of start state for each at-risk interval |
endStatusName |
Name of the variable that contains the name of end state for each at-risk interval |
idName |
Name of column in |
No return value.
Pål Christie Ryalen <[email protected]>
Refines dataFr so that each individual at risk get a row for each of the provided event times
refineTable(dataFr, atRiskState, eventState)refineTable(dataFr, atRiskState, eventState)
dataFr |
data.frame or data.table on long format |
atRiskState |
At risk state(s) |
eventState |
Observed event times |
data.table
Pål Christie Ryalen <[email protected]>
predict.aalen
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.
weightPredict(fPred, cfPred, wtFrame, ids, eventTimes, eventIds, b)weightPredict(fPred, cfPred, wtFrame, ids, eventTimes, eventIds, b)
fPred |
|
cfPred |
|
wtFrame |
|
ids |
All individuals in the data set |
eventTimes |
Observed event times |
eventIds |
Individuals that experience the event |
b |
Smoothing parameter |
data.table
Pål Christie Ryalen <[email protected]>