search for: relapse

Hello R-community, It's been a week now that I am struggling with the implementation of a cox model in R. I have 80 cancer patients, so 80 time measurements and 80 relapse or no measurements (respective to censor, 1 if relapsed over the examined period, 0 if not). My microarray data contain around 18000 genes. So I have the expressions of 18000 genes in each of the 80 tumors (matrix 80*18000). I would like to build a cox model in order to retrieve the most significan...

Dear friends: I'm interested to make a stacked plot of cumulative incidence. that's, the cuminc model is fitted [fit=cuminc(time, relapse)] and cumulative incidence is in place. I'd like to stack the cuminc plots (relapse of luekemia and death free from leukemia, for example) , then the constituent ratio of leukemia relapse and treatment related mortality is very clear. Can you give me some directions? Yours Ping Zhang...

Hello everyone, I am a very new user of R and I have a query about the cuminc function in the package cmprsk. In particular I would like to verify that I am interpreting the output correctly when we have a stratification variable. Hypothetical example: group : fair hair, dark hair fstatus: 1=Relapse, 2=TRM, 0=censored strata: sex (M or F) Our data would be split into: Fair, male, relapse Dark,male, relapse Fair, female, relapse Dark, female, relapse Fair, male, TRM Dark,male, TRM Fair, female, TRM Dark, female, TRM Fair, male, censored Dark,male, censored Fair, female, censored Dark, f...

...], a[, Event]) ~ strata(a[,Prediction]), data = a), times=c(0,1,2,3,4,5)) I have studied two kind of events (disease-free survival and metastasis free survival), please see the results below. At year 5, in group C2 I have one more patient with an event when looking at DFS (13) than when looking at relapse (12). However, the probability is higher when looking at DFS (0.23) than relapse (0.18), which I cannot understand as I have one more event. Can anyone explain or point to documentation explaining this? Is it due to the fact that there was no more event between year 4 and 5 when looking at DFS?...

Hello everyone,   I am a very new user of R and I have a query about the cuminc function in the package cmprsk. In particular I would like to verify that I am interpreting the output correctly when we have a stratification variable.   Hypothetical example:   group : fair hair, dark hair fstatus: 1=Relapse, 2=TRM, 0=censored strata: sex (M or F)   Our data would be split into:   Fair, male, relapse Dark,male, relapse Fair, female, relapse Dark, female, relapse   Fair, male, TRM Dark,male, TRM Fair, female, TRM Dark, female, TRM   Fair, male, censored Dark,male, censored Fair, female, censored Dark, f...

Dear R users, I d like to assess the effect of "treatment" covariate on a disease relapse risk with the package cmprsk. However, the effect of this covariate on survival is time-dependent (assessed with cox.zph): no significant effect during the first year of follow-up, then after 1 year a favorable effect is observed on survival (step function might be the correct way to say that ?)....

Hello, I have a problem regarding calculation of Cumulative Incidence Function. The event of interest is failure of bone-marrow transplantation, which may occur due to relapse or death in remission. The data set that I have consists of- lifetime variable, two indicator variables-one for relapse and one for death in remission, and the other variables are donor type (having 3 categories), disease type(having 3 categories), disease stage(having 3 categories) and karnofsky s...

...of e+80 values (for many genes included). I am using the "prediction" method from the ROCR package which takes as arguments the calculated scores and the true class scores. I really don't know what to compare my values with, because the only data that I have available are the time to relapse or last follow-up (months) and the relapse score (1=TRUE, 0=FALSE) of the patients. I have never performed ROC analysis before and I am a bit lost... Any help with this is really very welcome! Thank you all, Eleni [[alternative HTML version deleted]]

...legend.labs <- c("Cluster1", "Cluster2", "Cluster3", "Cluster4") } ggsurvplot(cluster, data = inputfile, risk.table = F, palette = palette, ylim=c(0,1),ggtheme = theme_bw(),xlab="Relapse Free Suvival (Days)", main = "Survival curve",pval = TRUE,font.x = 16,font.y = 16, font.tickslab = 14,font.legend =c(14,"plain","black"), legend = "bottom", legend.title = "Tree Cluster",...

Dear R users, I d like to assess the effect of "treatment" covariate on a disease relapse risk with the package cmprsk. However, the effect of this covariate on survival is time-dependent (assessed with cox.zph): no significant effect during the first year of follow-up, then after 1 year a favorable effect is observed on survival (step function might be the correct way to say that ?)....

...quot;blue") legend.labs <- c("Cluster1", "Cluster2", "Cluster3", "Cluster4") } fig <-ggsurvplot(cluster, data = inputfile, risk.table = F, palette = palette, ylim=c(0,1),ggtheme = theme_bw(),xlab="Relapse Free Suvival (Days)", main = "Survival curve",pval = TRUE,font.x = 16,font.y = 16, font.tickslab = 14,font.legend =c(14,"plain","black"), legend = "bottom", legend.title = "Tree Cluster",...

...are: data.matrix2[1:3,1:5] GSM36777 GSM36778 GSM36779 GSM36780 GSM36781 [1,] 1.009274 1.0740659 1.048540 1.015946 1.022650 [2,] 1.007992 0.8768410 0.962442 1.111742 1.121150 [3,] 0.981853 0.9606492 1.024987 1.053302 1.063408 I also have the time in which each patient-sample is examined for relapse. This information is in vector y, which has length 286, and is declared in months. Indicatively: y[1:5] [1] 101 118 9 106 37 Finally, I have a variable censored, which is 1 if the patient has relapsed when examined at the examined time and 0 if not. Indicatively: censored[1:5] [1] 0 0 1 0 1 I...

....labs <- c("Cluster1", "Cluster2", "Cluster3", "Cluster4") > } > > ggsurvplot(cluster, data = inputfile, risk.table = F, > palette = palette, > ylim=c(0,1),ggtheme = theme_bw(),xlab="Relapse Free Suvival (Days)", > main = "Survival curve",pval = TRUE,font.x = 16,font.y = 16, > font.tickslab = 14,font.legend =c(14,"plain","black"), > legend = "bottom", > legend.title = "Tre...

Hello R users, I am trying to run a cox model for the prediction of relapse of 80 cancer tumors, taking into account the expression of 17000 genes. The data are large and I retrieve an error: "Cannot allocate vector of 2.4 Mb". I increase the memory.limit to 4000 (which is the largest supported by my computer) but I still retrieve the error because of other big v...

...d.labs <- c("Cluster1", "Cluster2", "Cluster3", "Cluster4") > } > > > fig <-ggsurvplot(cluster, data = inputfile, risk.table = F, > palette = palette, > ylim=c(0,1),ggtheme = theme_bw(),xlab="Relapse Free Suvival > (Days)", > main = "Survival curve",pval = TRUE,font.x = 16,font.y = 16, > font.tickslab = 14,font.legend =c(14,"plain","black"), > legend = "bottom", > legend.title = &quo...

...;, "blue") legend.labs <- c("Cluster1", "Cluster2", "Cluster3", "Cluster4") } fig <-ggsurvplot(cluster, data = inputfile, risk.table = F, palette = palette, ylim=c(0,1),ggtheme = theme_bw(),xlab="Relapse Free Suvival (Days)", main = "Survival curve",pval = TRUE,font.x = 16,font.y = 16, font.tickslab = 14,font.legend =c(14,"plain","black"), legend = "bottom", legend.title = "Tree Cluster",...

I'm trying to categorize a continuous variable (yes, I know that's horrible, but I'm trying to reproduce some exercises from a textbook) and don't really know an efficient way to do this. I have a data frame that looks like: surv_time relapse sex log_WBC rx 1 35 0 1 1.45 0 2 34 0 1 1.47 0 3 32 0 1 2.20 0 4 32 0 1 2.53 0 And I'm trying to categorize log_WBC into: (0-2.30) = "low" (2.31-3.00)= "medium" (>3.00) = "high" I...

...g package as you suggested (R2.7.1 on XP Pro). here is my script based on the example from timereg for a fine & gray model in which relt = time to event, rels = status 0/1/2 2=competing, 1=event of interest, 0=censored random = covariate I want to test library(timereg) rel<-read.csv("relapse2.csv", header = TRUE, sep = ",", quote="\"", dec=".", fill = TRUE, comment.char="") names(rel) > names(rel) [1] "upn" "rels" "relt" "random" times<-rel$relt[rel$rels==1] fg1<-comp.risk(Surv(...

...days, OV_Had_a_Recurrence_CODE) ~ clusters, data = inputfile) ggsurvplot(cluster2, data = inputfile, risk.table = F, palette = c("red", "black"), ylim=c(0,1), ggtheme = theme_bw(), xlab="Relapse Free Suvival (Days)", main = "Survival curve", pval = TRUE, font.x = 16, font.y = 16, font.tickslab = 14, font.legend =c(14,"plain","black"), legend = "bottom",...

...f genes for correlation with patient survival. I found out that the coxph function is appropriate for doing this since it works with continuous variables such as Affy mRNA expression values. I applied the following code: cp <- coxph(Surv(t.rfs, !e.rfs) ~ ex, pData(eset.n0)) #t.rfs: time to relapse, status (0=alive,1=dead), ex: expression value (continuous) The results I get look sensible but I would appreciate any advice on the correctness and also any suggestions for any (better) alternative methods. Best wishes