# Continuous time weight estimation based on `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. ```r makeContWeights( faFit, cfaFit, dataFr, atRiskState, eventState, startTimeName, stopTimeName, startStatusName, endStatusName, idName, b, weightRange = c(0, 10), willPlotWeights = TRUE, withLeftLim = FALSE ) ``` ## Arguments - `faFit`: The `aalen` fit for the factual hazard - `cfaFit`: The `aalen` fit for the hypothetical hazard - `dataFr`: `data.frame` or `data.table` on long format - `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 `dataFr` that identifies individuals - `b`: Smoothing bandwidth parameter - `weightRange`: Truncates weights outside this interval - `willPlotWeights`: Plot indicator - `withLeftLim`: Calculates left limit at jump if desired ## Returns Longitudinally refined `data.table` of the initial `dataFr` with `weights` column added. ## Examples ```r 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") ``` ## References [https://arxiv.org/abs/1802.01946](https://arxiv.org/abs/1802.01946) ## Author(s) Pål Christie Ryalen

makeContWeights function