search for: jointmodel

...2) { : missing value where TRUE/FALSE needed." However if I include the term "exp(logrna)" in the fixed effects, the joint model works fine, but this is not what I want! This is where I really need help, what is happening? Charles Charles Graham wrote > I am trying to use the jointModel function in the JM package to fit a > simple joint model to longitudinal and survival data. > I have come accross a range of errors when trying different things and > just can't seem to get around them all. > > The code I use is as follows: > fitLME = lme(cd4~trt+time, random...

Dear Graham. Any updates regarding your message about JointModel error messages? I am encountering similar errors. Thank you. Havi [[alternative HTML version deleted]]

Hi all: I am a user of "JM" package. Here's the problem of "sample size". The warning is: Error in jointModel(fitLME, fitSURV_death, timeVar = "time", method = "piecewise-PH-GH") : sample sizes in the longitudinal and event processes differ. According to the suggestion of "missing data",I use the same data set(data_JM) without any missing value. fitLME <- lme(CD4 ~ t...

...nonrandom dropout. New features include: * for all joint models fitted by JM there is now the option to use a pseudo adaptive Gauss-Hermite rule. This is much faster than the default option and produces results of equal or better quality. It can be invoked via the 'method' argument of jointModel() by specifying "aGH" instead of "GH", e.g., 'method = "piecewise-PH-aGH"' instead of 'method = "piecewise-PH-GH"'. * function rocJM() has been added that estimates time-dependent sensitivity and specificity (and the corresponding time-depe...

...nonrandom dropout. New features include: * for all joint models fitted by JM there is now the option to use a pseudo adaptive Gauss-Hermite rule. This is much faster than the default option and produces results of equal or better quality. It can be invoked via the 'method' argument of jointModel() by specifying "aGH" instead of "GH", e.g., 'method = "piecewise-PH-aGH"' instead of 'method = "piecewise-PH-GH"'. * function rocJM() has been added that estimates time-dependent sensitivity and specificity (and the corresponding time-depe...

Dear R-users, I'd like to announce the release of the new package JM (JM_0.1-0 available from CRAN) for the joint modelling of longitudinal and time-to-event data. The package has a single model-fitting function called jointModel(), which accepts as main arguments a linear mixed effects object fit returned by function lme() of package nlme, and a survival object fit returned by either function coxph() or function survreg() of package survival. In addition, the method argument of jointModel() specifies the type of the s...

Dear R-users, I'd like to announce the release of the new package JM (JM_0.1-0 available from CRAN) for the joint modelling of longitudinal and time-to-event data. The package has a single model-fitting function called jointModel(), which accepts as main arguments a linear mixed effects object fit returned by function lme() of package nlme, and a survival object fit returned by either function coxph() or function survreg() of package survival. In addition, the method argument of jointModel() specifies the type of the s...

...wo settings. First, when focus is in the survival outcome and we wish to account for the effect of an endogenous (aka internal) time-dependent covariate measured with error. Second, when focus is in the longitudinal outcome and we wish to correct for nonrandom dropout. New features include: * jointModel() with option "spline-PH-aGH" for the 'method' argument can now also handle competing risks settings. * jointModel() with option "spline-PH-aGH" for the 'method' argument can now also handle exogenous time-dependent covariates, using the (start, stop] notatio...

...wo settings. First, when focus is in the survival outcome and we wish to account for the effect of an endogenous (aka internal) time-dependent covariate measured with error. Second, when focus is in the longitudinal outcome and we wish to correct for nonrandom dropout. New features include: * jointModel() with option "spline-PH-aGH" for the 'method' argument can now also handle competing risks settings. * jointModel() with option "spline-PH-aGH" for the 'method' argument can now also handle exogenous time-dependent covariates, using the (start, stop] notatio...

...cept one thing about the label of the axises. I tried to label both x and y axises using ylab and xlab options. But the plot only gives variable variables. The labels did not show up. Below is the code I used. Any comments are appreciated! Thanks. library(gamlss) enable2r=read.csv("D:\\lzg\\jointmodel\\enable2r.csv",header=T) enable2r$ID<-factor(enable2r$ID) par.plot(factpal~timetodeath2,data=enable2r,sub=ID,ylim=c(45,184),ylab='FACIT-PAL',xlab='Time to death',color=FALSE,lwd=1) -- View this message in context: http://r.789695.n4.nabble.com/par-plot-for-repeated-measure...

...ocus is in the longitudinal outcome and we wish to correct for nonrandom dropout. New features include: * a relative risk model with a piecewise-constant baseline risk function is now available for the event outcome, using option 'piecewise-PH-GH' in the 'method' argument of jointModel(). * several types of residuals are supported for the longitudinal and time-to-event outcomes. Moreover, for the longitudinal outcome there is also the option to compute multiple-imputation-based residuals, as described in Rizopoulos, Verbeke and Molenberghs (Biometrics 2009, to appear)....

...knots positions are the same across strata -- this can be changed by either directly specifying the knots or by setting the control argument 'equal.strata.knots' to FALSE. * the new function wald.strata() can be used to test for equality of the spline coefficients among strata. * jointModel() has now the extra argument 'lag' that allows for lagged effects in the time-dependent covariate represented by the linear mixed model. * method = "ph-GH" that fits a relative risk with an unspecified baseline risk function has been renamed to method = "Cox-PH-GH&quot...

...ocus is in the longitudinal outcome and we wish to correct for nonrandom dropout. New features include: * a relative risk model with a piecewise-constant baseline risk function is now available for the event outcome, using option 'piecewise-PH-GH' in the 'method' argument of jointModel(). * several types of residuals are supported for the longitudinal and time-to-event outcomes. Moreover, for the longitudinal outcome there is also the option to compute multiple-imputation-based residuals, as described in Rizopoulos, Verbeke and Molenberghs (Biometrics 2009, to appear)....

...knots positions are the same across strata -- this can be changed by either directly specifying the knots or by setting the control argument 'equal.strata.knots' to FALSE. * the new function wald.strata() can be used to test for equality of the spline coefficients among strata. * jointModel() has now the extra argument 'lag' that allows for lagged effects in the time-dependent covariate represented by the linear mixed model. * method = "ph-GH" that fits a relative risk with an unspecified baseline risk function has been renamed to method = "Cox-PH-GH&quot...

Dear all, I have some trouble using the "id"-argument with aftreg (accelerated failure time regression analysis from the eha library). As far as I understand it, the id argument is used to group individuals together if there are time-varying covariates and the data is arranged in counting process style. Unfortunately, i cannot figure out how to use the "id"-argument. The