search for: albumin

Hello, I probably have a syntax error in trying to generate an expected survival curve from a weighted cox model, but I can't see it. I used the help sample code to generate a weighted model, with the addition of a "weights=albumin" argument (I only chose albumin because it had no missing values, not because of any real relevance). Below are my code with the resulting error messages. Thanks in advance! > pfit <- coxph(Surv(time,status>0) ~ trt + log(bili) + log(protime) + age + + + platelet, data=pbc +...

Dear Sir, I am doing some work on a population of patients. About half of them are admitted into hospital with albumin levels less than 33. The other half have albumin levels greater than 33, so I stratify them into 2 groups, x and y respectively. I suspect that the average length of stay in hospital for the group of patients (x) with albumin levels less than 33 is greater than those with albumin levels g...

...ox test using a dataset from: http://faculty.washington.edu/~heagerty/Books/Biostatistics/chapter16.html (Primary Biliary Cirrhosis data link at top of the page), I'm using the following code: --------------- start of code library(survival) liver <- scan("liver2.txt",list(age=0,albumin=0,alkphos=0,ascites=0,bili=0, cholest=0,edema=0,edmadj=0,hepmeg=0,obstime=0,platelet=0,protime=0, sex=0,sgot=0,spiders=0,stage=0,status=0,treatmnt=0, triglyc=0,urinecu=0)) fit<-coxph(Surv(obstime,status)~bili+edmadj+albumin+protime+age,data=liver) summary(fit) -----------...

...col.names=c('id', 'time', 'status', 'trt', 'age', 'sex', 'ascites', 'hepato', 'spiders', 'edema', 'bili', 'chol', 'albumin', 'copper', 'alk.phos', 'ast', 'trig', 'platelet', 'protime', 'stage'), na.strings='.') pbc$age <- pbc$age/365.25 newfit <- coxph(Surv(time...

...pbc data set. I just want to apply simple cox regression in the data set. I am a beginner in R but I don't think I am doing anything wrong. I have the book of Fleming and Harrington 1990. I perform cox regression by typing: out<- coxph(Surv(times/365,status)~log(bili)+log(proth)+edema+log(albumin)+age) out Call: coxph(formula = Surv(times/365, status) ~ log(bili) + log(proth) +     edema + log(albumin) + age)                 coef exp(coef) se(coef)     z       p log(bili)     0.8636    2.3716  0.08294 10.41 0.0e+00 log(proth)    2.3868   10.8791  0.76851  3.11 1.9e-03 edema         0.8...

I need a picture like this: http://rwiki.sciviews.org/doku.php?id=graph_gallery:graph38 http://rwiki.sciviews.org/doku.php?id=graph_gallery:graph38 but when I try compile it require("hexbin") data(NHANES)# pretty large data set! good <- !(is.na(NHANES$Albumin) | is.na(NHANES$Transferin)) NH.vars <- NHANES[good, c("Age","Sex","Albumin","Transferin")] # extract dependent variables and find ranges for global binning x <- NH.vars[,"Albumin"] rx <- range(x) y <- NH.vars[,"Transferin"]...

...group[id>=250 & id<300] <- 6 group[id>=300] <- 7 I estimate the following model, using the Pbc data (with time-varying covariates) from Therneau and Grambsch's book: fitf <- coxph(Surv(start,stop,event==2) ~ age + log(bili) + log(protime) + log(albumin) + edema + frailty(group), na.action=na.exclude, data=Pbcseq) Then I obtain: > fitf[10] $frail [1] 0.06273372 0.16192093 0.10050877 0.37716999 -0.20853156 -0.71887977 [7] -0.10922275 And: > fitf[11] $fvar [1] 0.03631634 0.03261972 0.03750465 0.04459689 0.0511...

...approx(tt, ss, xout=times, method='constant', f=0)$y } describe.survival <- function(y) { km <- kmsurv(y, c(2,5)) c('2 Year'=km[1], '5 Year'=km[2], 'Mean, y'=sum(y[,1])/sum(y[,2])) } S <- with(pbc, Surv(fu.days/365.25, status)) s1 <- summary(S ~ age + albumin + ascites + bili + drug + edema + chol, fun=describe.survival, data=pbc) for(w in 1:2) { if(w==1) setpdf(f1a,sublines=1,h=5.25) else setpdf(f1b,sublines=1,h=5) plot(s1, which=if(w==1)1:2 else 3, cex.labels=.7, cex.group.labels=.7*1.15, subtitles=T, main='', pch=if(w==2) 16 else c('2...

Dear colleagues, I hope someone can help me with my problem. I have fitted a cox model with the following syntax: # cox01def <-cph(Surv(TEVENT,EVENT) ~ ifelse(AGE>50, (AGE-50)^2,0) + BMI + # HDL+DIABETES +HISTCAR2 + log(CREAT)+ as.factor(ALBUMIN)+STENOSIS+IMT,data # = XC, x=T, y=T, surv=T) *1 Furthermore I have estimated my beta's also with a Lasso method - Coxpath ( from Glmpath pckage) , namely: # LASSOdata <- list(x=cox01$x, time=XC$TEVENT, status=XC$EVENT) # summary(LASSOdata) # LASSOpath <- coxpath(data=SMARTdata) My p...

...get a p-value = 0. :confused: Can this be true? Why aren?t there decimal points? Is there a way to find out the exact p-value? Here is the output: ---------------------------------------------------------------------------------------------------- Call: coxph(formula = Surv(start, stop, status) ~ Albumin_gproL, data = dial, na.action = na.omit, method = "breslow") n=22449 (31 observations deleted due to missingness) coef exp(coef) se(coef) z p Albumin_gproL -0.157 0.855 0.0172 -9.16 0 exp(coef) exp(-coef)...

Mª Luz, si el comando de Carlos te devuelve 0 es que no hay NA's. Da igual que tus columnas sean caracteres o número. Lo que intuyo es que tienes celdas con la frase "NA" que interpretas como NA. Mandanos el summary de tu data.table y lo vemos... ----- Mensaje original ----- De: "MªLuz Morales" <mlzmrls en gmail.com> Para: "Carlos J. Gil Bellosta"

# Dear list, # I am an R-beginner and # spent the last days looking for a method to insert tables produced # with R into a word document. I thought about SPPS: copy a table from # an SPO-file and paste it into a word document # (if needed do some formatting with that table). # Annother idea was, to produce a TEX-file, # insert it and make it a word-table. # I found the following libraries, which

...t-Type: text/plain; charset="us-ascii" Dear colleagues, I hope someone can help me with my problem. I have fitted a cox model with the following syntax: # cox01def <-cph(Surv(TEVENT,EVENT) ~ ifelse(AGE>50, (AGE-50)^2,0) + BMI + # HDL+DIABETES +HISTCAR2 + log(CREAT)+ as.factor(ALBUMIN)+STENOSIS+IMT,data # = XC, x=T, y=T, surv=T) *1 Furthermore I have estimated my beta's also with a Lasso method - Coxpath ( from Glmpath pckage) , namely: # LASSOdata <- list(x=cox01$x, time=XC$TEVENT, status=XC$EVENT) # summary(LASSOdata) # LASSOpath <- coxpath(data=SMARTdata) My p...