search for: comorbidities

...iver disease. Having developed my comorbidity score as the linear predictor from a Cox regression model I want to compare the discriminative ability of my comorbidity measure with the "old" comorbidity measure, Charlson's Comorbidity Index. I have nearly 10,000 deaths and 36 candidate comorbidities. I wish to compare the discrimination of the two comorbidity measures, i.e. I have two non-nested Cox models. I get the following output with > rcorrp.cens(myscore.lp, charlson.lp, Surv(time, dead), method=1): x1 = My comorbidity score, x2 = Charlson [,1] Dxy...

...iver disease. Having developed my comorbidity score as the linear predictor from a Cox regression model I want to compare the discriminative ability of my comorbidity measure with the "old" comorbidity measure, Charlson's Comorbidity Index. I have nearly 10,000 deaths and 36 candidate comorbidities. I wish to compare the discrimination of the two comorbidity measures, i.e. I have two non-nested Cox models. I get the following output with > rcorrp.cens(myscore.lp, charlson.lp, Surv(time, dead), method=1): x1 = My comorbidity score, x2 = Charlson [,1] Dxy...

...###################### if (flag[1, 6] == 1) flag[1, 5] = 0 if (flag[1, 18] == 1) flag[1, 19] = 0 if (flag[1, 11] == 1) flag[1, 10] = 0 ######################################################################## # redefining comorbidities by eliminating the DRG directly related # to comorbidity, thus limiting the screens to principal # diagnoses not directly related to comorbidity in question ######################################################################## if (flag[1, 1] == 1 && drg[1...

I downloaded data from http://www.hcp.med.harvard.edu/ncs/ Which provides data in DTA (STATA), XPT (SAS), and POR (SPSS) formats all of which I have tried to read with the foreign package but I am not able to load any of them. I have 2 gb of RAM, but R crashes when the memory gets just over 1 GB. I am using Windows version 2.1.1. The size of the DTA file is 48 MB; the xpt file is 188

...1, type = "residual). --> fastbw may drop med_a and keep the med_a:med_b interaction instead. fit2 <-lrm(outcome~age + gender + comorb_a + comorb_b + comorb_c + other predictors, x = TRUE, y = TRUE, data = example) fastbw(fit2, type = "residual). --> fastbw may drop some of the comorbidities, but I would like to consider them as one group. Any help is much appreciated! Maaike ------------------------------------------------------------------------------ De informatie opgenomen in dit bericht kan vertrouwelijk zijn en is uitsluitend bestemd voor de geadresseerde. Indien u dit beri...

Hi all, I'm using the medical risk package to determine the risk stratification Index based on the ICD9 codes. Although, I have been successful in using it, I'm unable to interpret the output. here is the sample code # Calculate RSI for each patient ("id") in dataframe cases <- data.frame(id=c(1,1,1,2,2,2,2,2), +

Hi All, I have been able to modify the x-axis to start at zero by adding xlow and xhigh parameters; that was pretty simple. I have been unable to find the location of the code that would turn off the information weighting of the box size (I have smaller randomized trials getting less weight than a much larger non-randomized trial). The function is forestplot() from rmeta. Thanks for any