similar to: Measure of separation for survival data

Hi, I'm trying the brlr function in a penalized logistic regression function. However, I am not sure why I am encountering errors. I hope to seek your advice here. (output below) Thank you! Your help is truly appreciated. Min-Han #No error here, the glm seems to work fine >

Dear Sir/Ma, I Adelabu.A.A, one of the R-users from Nigeria. When am running a coxph command the below error was generated, and have try some idea but not going through. kindly please assist: > cox1 <- coxph(Surv(tmonth,status) ~ sex + age + marital + sumassure, X) Warning message: In fitter(X, Y, strats, offset, init, control, weights = weights, : Ran out of iterations and did not

Hello everyone, I am searching for a covariate selection procedure in a cox model formulated as a counting process. I use intervals, my formula looks like coxph(Surv(start,stop,status)~ x1+x2+...+cluster(id),robust=T) where id is a country code (I study occurence of civil wars from 1962 to 1997). I'd like something not based on p-values, since they have several flaws for this purpose. I turned

Dear all, in order to estimate transition-specific probabilities in a multi-state model i applied the 'probtrans()' function from the 'mstate'-package. Now, i am at loss with the following message (see attached example): Warning message: In probtrans(msf.0, predt = 0) : Negative diagonal elements of (I+dA); the estimate may not be meaningful. I am not very familiar with matrix

I have posted an update to the GAM package. Note that this package implements gam() as described in the "White" S book (Statistical models in S). In particular, you can fit models with lo() terms (local regression) and/or s() terms (smoothing splines), mixed in, of course, with any terms appropriate for glms. A number of bugs in version 0.92 have been fixed; notably 1) some problems

I have posted an update to the GAM package. Note that this package implements gam() as described in the "White" S book (Statistical models in S). In particular, you can fit models with lo() terms (local regression) and/or s() terms (smoothing splines), mixed in, of course, with any terms appropriate for glms. A number of bugs in version 0.92 have been fixed; notably 1) some problems

Hi, I was reading a paper published in JCO "Prediction of risk of distant recurrence using 21-gene recurrence score in node-negative and node-positive postmenopausal patients with breast cancer treated with anastrozole or tamoxifen: a TransATAC study" (ICO 2010 28: 1829). The author uses a method to estimate the 9-year risk of distant recurrence as a function of continuous recurrence

I'm trying to estimate a cox proportional hazards regression for repeated events (in gap time) with time varying covariates. The dataset consists of just around 6000 observations (lines) (110 events). The (stylized) data look as follows: unit dur0 dur1 eventn event ongoing x 1 0 1 0 0 0 32.23 1 1 2 0 1 1 35.34 1

System: R 2.3.1 on a Windows XP computer. I am validating several cancer prognostic models that have been published with a large independent dataset. Some of the models report a probability of survival at a specified timepoint, usually at 5 and 10 years. Others report only the linear predictor of the Cox model. I have used Harrell's c index for censored data (rcorr.cens) as a measure of

The coxph function in R is not working for me when I use a continuous predictor in the model. Specifically, it fails to converge, even when bumping up the number of max iterations or setting reasonable initial values. The estimated Hazard ratio from the model is incorrect (verified by an AFT model). I've isolated it to the "x1" variable in the example below, which is log-normally

Hello, My name is Lisa and I'm a statistician at Princess Margare Hospital. I wonder if there is any function in R that calculate the Normal rankits based on Blom's approximation? Thank you very much Lisa Wang Msc. Princess Margaret hospital Toronto, Ca

Dear R-list readers: Royston described the effect of sample size on the p-value obtained from the Shapiro-Francia test (Estimating departure from normality. Stat Med 1991;10:1283-93). He developed two indices from the Shapiro-Francia test (i) V' - an index of departure from normality and (ii) v' - a plot of the cumulative squared residuals. He mentioned the availability (in

I am trying to develop a prognostic model using logistic regression. I built a full , approximate models with the use of penalization - design package. Also, I tried Chi-square criteria, step-down techniques. Used BS for model validation. The main purpose is to develop a predictive model for future patient population. One of the strong predictor pertains to the study design and would not

Dear All, I am trying to calculate a 95% confidence interval for the difference in two c statistics (or equivalently D statistics). In Stata I gather that this can be done using the lincom command. Is there anything similar in R? As you can see below I have two datasets (that are actually two independent subsets of the same data) and the respective c statistics for the variables in both cases.

Dear All, Apologies if you have a seen a question like this from me before. I am hoping that if I re-word my question more carefully someone may be able to offer more help than the last time I asked something similar. I am using R 2.9.2 and Windows XP. I am trying to determine if there is a statistically significant difference between two c-statistics (or equivalently D statistics). In Stata

Hi, In the boot package, the original statistic is simply the statistic function evaluated on the original data (called t0). However, in Harrell et al 1996 "Multivariable prognostic models..." Stats Med vol 15, pp. 361--387, it is different (p. 372): The statistic function evaluated on the original data is called "D_app" (apparent statistic), whereas "D_orig"

>If you are looking at radioactive decay maybe but how often do >you actually see exponential KM curves in real life? Exponential curves are rare. But proportional hazards does not imply exponential. > A trial design could in fact try to get all the control sample to "event" at the same time if enough was known about prognostic factors and natural trajectory You are a

Hi, I would like to use a random Forest model to get an idea about which variables from a dataset may have some prognostic significance in a smallish study. The default for the number of trees seems to be 500. I tried changing the default to ntree=2000 or ntree=200 and the results appear identical. Have changed mtry from mtry=5 to mtry=6 successfully. Have seen same problem on both a Windows

void inverse_mdct_slow(float *buffer, int n) { =A0=A0 int i,j; =A0=A0 int n2 =3D n >> 1; =A0=A0 float *x =3D (float *) malloc(sizeof(*x) * n2); =A0=A0 memcpy(x, buffer, sizeof(*x) * n2); =A0=A0 for (i=3D0; i < n; ++i) { =A0=A0=A0=A0=A0 float acc =3D 0; =A0=A0=A0=A0=A0 for (j=3D0; j < n2; ++j) =A0=A0=A0=A0=A0=A0=A0=A0 // formula from paper: =A0=A0=A0=A0=A0=A0=A0=A0 //acc +=3D n/4.0f *

Hi R experts, I am trying to do a prognostic model validation study, using cancer survival data. There are 2 data sets - 1500 cases used to develop a nomogram, and another of 800 cases used as an independent validation cohort. I have validated the nomogram in the original data (easy with the Design tools), and then want to show that it also has good results with the independent data using 60