similar to: cohort sampling

Now that we have case cohort model , we have 1000 people and 50 cases Let the first 10 cases occur at the same time second 10 " third 10 " fourth 10 " fifth 10 " How easy is it to randomly sample 50 different cohort controls for each group? That is: randomly sample 50 cohort

I tried to compare if cch() and coxph() can generate same result for same case cohort data Use the standard data in cch(): nwtco Since in cch contains the cohort size=4028, while ccoh.data size =1154 after selection, but coxph does not contain info of cohort size=4028. The rough estimate between coxph() and cch() is same, but the lower and upper CI and P-value are a little different. Can we

Hi. I've a dataframe with about 46000 women with about 500 cases (cancers). I want to define a case-cohort sampling scheme, matching by age and hospital centre. Is there anyone who has already written a code for that? It should be something similar to the stcacoh macro in Stata. TIA, Stefano -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list

--------- begin included message --------- I tried to compare if cch() and coxph() can generate same result for same case cohort data Use the standard data in cch(): nwtco Since in cch contains the cohort size=4028, while ccoh.data size =1154 after selection, but coxph does not contain info of cohort size=4028. The rough estimate between coxph() and cch() is same, but the lower and upper CI

I am designing an interlaboratory validation study for a presence/absence alternative method test kit vs. a presence/absence reference method test kit. There will be 10 laboratories conducting tests using both methods. In each laboratory, there will be 5 specimens tested, each of the 5 specimens twice by both methods (alternative, standard). The total number of data are 10 x 5 x 4 = 200.

[Corrected the model formula to include "method".] I am designing an interlaboratory validation study for a presence/absence alternative method test kit vs. a presence/absence reference method test kit. There will be 10 laboratories conducting tests using both methods. In each laboratory, there will be 5 specimens tested, each of the 5 specimens twice by both methods (alternative,

Jin Wang had an error. My original note specified a variable that was 1 for subjects NOT in the subcohort, so the correct coxph call is coxph(Surv(edrel, rel) ~ stage + histol + age + offset(-100*(subcohort==0)) + cluster(seqno), data =ccoh.data) This gives the same coefficients as the cch example, along with the infinitesimal jackknife or "robust" variance estimate.

I have a study best described as a retrospective case-cohort design: the cases were all the events in a given time span surveyed, and the controls (event-free during the follow-up period) were selected in 2:1 ratio (2 controls per case). The sampling frequency for the controls was about 0.27, so I used a weight vector consisting of 1 for cases and 1/0.27 for controls for coxph to adjust

Hi All, I am in the middle of doing an analysis of a Case-Cohort design. I had three questions about the analysis: a) Does any one know of some public code for developing the patient risk sets (indexed by failure time) or is there a better way to organize the data? b) I was planning to use the Barlow weighting method. Has this or any other weighting method (Prentice, Self-Prentice) been

Hi, I ran the example on pp. 799-800 from Machael Crawley's "The R Book" using package survival v. 2.36-5, R 2.13.0 and RStudio 0.94.83. The model is a Cox's Proportional Hazards model. The result was quite different compared to the R Book. I have compared my code to the code in the book but can not find any differences in the function call. My results are attached as well as a

Hello: So i've fit a hazard function to a set of data using kmfit<-survfit(Surv(int, event)~factor(cohort)) this factor variable, "cohort" has four levels so naturally the strata variable has 4 values. I can use this data to estimate the hazard rate haz<-n.event/n.risk and calculate the cumulative hazard function by H<--log(haz) Now, I would like to plot this

Dear wizaRds, I have a dataset to analyse which is causing me problems. It is a sample of parents in schools. First we had a population table of the schools in the country in question divided into five regions, and in each region we have an urban/rural split. The population Ns in these ten cells are known. Then three schools were drawn from each cell according to the Lahirie method, i.e with

Dear R-list, I think it's really a newbie question but ... I try to model age-period-cohort models with polynoms in each effect. I have for each level of age and period some cases -k- and persons-years -py-. Models are A_xP_y: log(k_age,period/py_age,period)=f(polynom(x),age)+f(polynom(y),period). For exemple I try to adjust an A2P3 :

----- begin included message In case cohort study, we can fit proportional hazard regression model to case-cohort data. In R, the function is cch() in Survival package Now I am working on case cohort analysis with time dependent covariates using cch() of "Survival" R package. I wonder if cch() provide this utility or not? The cch() manual does not say if time dependent covariate is

Just trying to understand how geom_abline works with facets in ggplot. By way of example, I have a dataset of student test scores. These are in a data table dt with 4 columns: student: unique student ID cohort: grouping factor for students (A, B, . H) subject: subject of the test (English, Math, Science) score: the test score for that student in that subject The goal is to compare

Well, I would suggest using the code already in place in the survival package. Here is my code for your problem. I'm using a copy of the larynx data as found from the web resources for the Klein and Moeschberger book. larynx <- read.table("larynx.dat", skip=12, col.names=c("stage", "time", "age", "year",

Hello, I'm using the package survey, version 3.22-2 in R 2.11.1, on windows XP, but this isn't a platform issue. I am trying to calculate some regression and probability proportional to size (pps) estimates for a dataset I have. The data has 2 strata, weights, and cluster IDs. However, I can't find a way to use multiple strata in the svydesign function. Currently, I'm ditching

Dear all, I have to select 122 stratified random samples from a population of >3900 cells. I have 41 strata and I have to draw a different number of samples from them(between 2 and 8). I have tried to apply the funtion strata following the instruction in the manual: strata(dataframe, stratanames=NULL, size, method=c("srswor"), pik,description=TRUE) but I get the error

Hi - I am using the survfit() function to produce Kaplan-Meier survival curves for several different groups. survfit (Surv() ~ cohort, data=d) Everything works fine, but I'd like to do something different. I have a 12 month survival curve (base) and I also have two survival curves based on some experiment that only go out to 3 months. What I would like to do is take the 3 month realized

Hello everybody I don't understand how I'm supposed to use svydesign caracteristics to explain to R that my sampling design is the following one Data base = tab1 here are the five first rows of the database (nrow = 11792) num esp Quarters Totcat Totshp Totgt Tbtpos fpc1 Totanim Id_An 10 2045 G