# Pseudo Value Regression for the Extra Length-of-Stay ```r closPseudo(data, state.names, tra, cens.name, s = 0, formula, na.action, aw = FALSE, ratio = FALSE, ncores = 1, trick_ties = FALSE) ``` ## Arguments - `data`: data.frame of the form data.frame(id,from,to,time) or (id,from,to,entry,exit) - **id:**: patient id - **from:**: the state from where the transition occurs - **to:**: the state to which a transition occurs - **time:**: time when a transition occurs - **entry:**: entry time in a state - **exit:**: exit time from a state - `state.names`: A vector of characters giving the states names. - `tra`: A quadratic matrix of logical values describing the possible transitions within the multistate model. - `cens.name`: A character giving the code for censored observations in the column 'to' of `data`. If there is no censored observations in your data, put 'NULL'. - `s`: Starting value for computing the transition probabilities. - `formula`: A formula with the covariates at the right of a `~` operator. Leave the left part empty. - `na.action`: A function which indicates what should happen when the data contain 'NA's. The default is set by the 'na.action' setting of 'options', and is 'na.fail' if that is unset. The 'factory-fresh' default is 'na.omit'. - `aw`: Logical. Whether to compute the expected change of LOS using alternative weighting. Default is `FALSE`. - `ratio`: Logical. Compute the ratio of the expected length-of-stay given instermediate event status instead of a difference. Default value is `FALSE` - `ncores`: Number of cores used if doing parallel computation using the `parallel` package - `trick_ties`: If `TRUE`, pseudo values are computed only one per subject sharing the same entry, exit times / transition types. ## Details The function calculates the pseudo-observations for the extra length-of-stay for each individual. These pseudo-observations can then be used to fit a direct regression model using generalized estimating equation (e.g., package `geepack`). Computation of the pseudo-observations can be parallelised using the `mclapply` function of the `parallel` package. See argument `ncores`. Recent versions of R have changed the `data.frame` function, where the default for the `stringsAsFactors` argument from `TRUE` to `FALSE`. `etm` currently depends on the states being factors, so that the user should use `data.frame(..., stringsAsFactors=TRUE)`. ## Returns An object of class `closPseudo` with the following components: - **pseudoData**: a data.frame containing `id`, computed pseudo values (see details) and the covariates as specified in the formula - **theta**: Estimates of excess LoS in the whole sample - **aw**: like in the function call - **call**: Function call ## References Andersen, P.K, Klein, J.P, Rosthoj, S. (2003). Generalised linear models for correlated pseudo-observations, with applications to multi-state models. **Biometrika**, 90(1):15--27. ## Author(s) Arthur Allignol arthur.allignol@gmail.com ## See Also `mclapply`, `clos` ## Examples ```r if(require("kmi", quietly = TRUE)) { ## data in kmi package data(icu.pneu) my.icu.pneu <- icu.pneu my.icu.pneu <- my.icu.pneu[order(my.icu.pneu$id, my.icu.pneu$start), ] masque <- diff(my.icu.pneu$id) my.icu.pneu$from <- 0 my.icu.pneu$from[c(1, masque) == 0] <- 1 my.icu.pneu$to2 <- my.icu.pneu$event my.icu.pneu$to2[my.icu.pneu$status == 0] <- "cens" my.icu.pneu$to2[c(masque, 1) == 0] <- 1 my.icu.pneu$to <- ifelse(my.icu.pneu$to2 %in% c(2, 3), 2, my.icu.pneu$to2) my.icu.pneu <- my.icu.pneu[, c("id", "start", "stop", "from", "to", "to2", "age", "sex")] names(my.icu.pneu)[c(2, 3)] <- c("entry", "exit") ## computation of the pseudo-observations ## Not run: ps.icu.pneu <- closPseudo(my.icu.pneu, c("0", "1", "2"), tra_ill(), "cens", formula = ~ sex + age) ## regression model using geepack require(geepack) fit <- geeglm(ps.e.phi ~ sex + age, id = id, data = ps.icu.pneu$pseudoData, family = gaussian) summary(fit) ## End(Not run) } else { print("This example requires the kmi package") } ```

closPseudo function