Re: [R] psm/survreg coefficient values ?
In survreg() the predictor is log(characteristic life) for Weibull (= exponential when scale=1) - ie the 63.2%ile. For the others the predictor is log(median). This causes problems when comparing predictions and a better way IMHO is to correct the Weibull prediction by a factor (log(2))^(1/scale). This is only a simple multiple unless the shape parameter is also being modelled, when a completely different solution may arise. Such heterogeneity modelling cannot of course be done within survreg(). On Monday 18 June 2007 22:56:54 Frank E Harrell Jr wrote: sj wrote: I am using psm to model some parametric survival data, the data is for length of stay in an emergency department. There are several ways a patient's stay in the emergency department can end (discharge, admit, etc..) so I am looking at modeling the effects of several covariates on the various outcomes. Initially I am trying to fit a survival model for each type of outcome using the psm function in the design package, i.e., all patients who's visits come to an end due to any event other than the event of interest are considered to be censored. Being new to the psm and survreg packages (and to parametric survival modeling) I am not entirely sure how to interpret the coefficient values that psm returns. I have included the following code to illustrate code similar to what I am using on my data. I suppose that the coefficients are somehow rescaled , but I am not sure how to return them to the original scale and make sense out of the coefficients, e.g., estimate the the effect of higher acuity on time to event in minutes. Any explanation or direction on how to interpret the coefficient values would be greatly appreciated. this is from the documentation for survreg.object. coefficientsthe coefficients of the linear.predictors, which multiply the columns of the model matrix. It does not include the estimate of error (sigma). The names of the coefficients are the names of the single-degree-of-freedom effects (the columns of the model matrix). If the model is over-determined there will be missing values in the coefficients corresponding to non-estimable coefficients. code: LOS - sort(rweibull(1000,1.4,108)) AGE - sort(rnorm(1000,41,12)) ACUITY - sort(rep(1:5,200)) EVENT - sample(x=c(0,1),replace=TRUE,1000) psm(Surv(LOS,EVENT)~AGE+as.factor(ACUITY),dist='weibull') output: psm(formula = Surv(LOS, CENS) ~ AGE + as.factor(ACUITY), dist = weibull) Obs Events Model L.R. d.f. P R2 10005132387.62 5 0 0.91 Value Std. Error z p (Intercept) 1.10550.04425 24.98 8.92e-138 AGE 0.07720.00152 50.93 0.00e+00 ACUITY=2 0.09440.01357 6.96 3.39e-12 ACUITY=3 0.17520.02111 8.30 1.03e-16 ACUITY=4 0.13910.02722 5.11 3.18e-07 ACUITY=5-0.05440.03789 -1.43 1.51e-01 Log(scale)-2.72870.03780 -72.18 0.00e+00 Scale= 0.0653 best, Spencer I have a case study using psm (survreg wrapper) in my book. Briefly, coefficients are on the log median survival time scale. Frank -- Best wishes John John Logsdon Try to make things as simple Quantex Research Ltd, Manchester UK as possible but not simpler [EMAIL PROTECTED] [EMAIL PROTECTED] +44(0)161 445 4951/G:+44(0)7717758675 www.quantex-research.com __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] psm/survreg coefficient values ?
On Tue, 19 Jun 2007, John Logsdon wrote: In survreg() the predictor is log(characteristic life) for Weibull (= exponential when scale=1) - ie the 63.2%ile. For the others the predictor is log(median). This causes problems when comparing predictions and a better way IMHO is to correct the Weibull prediction by a factor (log(2))^(1/scale). This is only a simple multiple unless the shape parameter is also being modelled, when a completely different solution may arise. Such heterogeneity modelling cannot of course be done within survreg(). Except, of course, for a discrete predictor of heterogeneity, using strata(). -thomas On Monday 18 June 2007 22:56:54 Frank E Harrell Jr wrote: sj wrote: I am using psm to model some parametric survival data, the data is for length of stay in an emergency department. There are several ways a patient's stay in the emergency department can end (discharge, admit, etc..) so I am looking at modeling the effects of several covariates on the various outcomes. Initially I am trying to fit a survival model for each type of outcome using the psm function in the design package, i.e., all patients who's visits come to an end due to any event other than the event of interest are considered to be censored. Being new to the psm and survreg packages (and to parametric survival modeling) I am not entirely sure how to interpret the coefficient values that psm returns. I have included the following code to illustrate code similar to what I am using on my data. I suppose that the coefficients are somehow rescaled , but I am not sure how to return them to the original scale and make sense out of the coefficients, e.g., estimate the the effect of higher acuity on time to event in minutes. Any explanation or direction on how to interpret the coefficient values would be greatly appreciated. this is from the documentation for survreg.object. coefficientsthe coefficients of the linear.predictors, which multiply the columns of the model matrix. It does not include the estimate of error (sigma). The names of the coefficients are the names of the single-degree-of-freedom effects (the columns of the model matrix). If the model is over-determined there will be missing values in the coefficients corresponding to non-estimable coefficients. code: LOS - sort(rweibull(1000,1.4,108)) AGE - sort(rnorm(1000,41,12)) ACUITY - sort(rep(1:5,200)) EVENT - sample(x=c(0,1),replace=TRUE,1000) psm(Surv(LOS,EVENT)~AGE+as.factor(ACUITY),dist='weibull') output: psm(formula = Surv(LOS, CENS) ~ AGE + as.factor(ACUITY), dist = weibull) Obs Events Model L.R. d.f. P R2 10005132387.62 5 0 0.91 Value Std. Error z p (Intercept) 1.10550.04425 24.98 8.92e-138 AGE 0.07720.00152 50.93 0.00e+00 ACUITY=2 0.09440.01357 6.96 3.39e-12 ACUITY=3 0.17520.02111 8.30 1.03e-16 ACUITY=4 0.13910.02722 5.11 3.18e-07 ACUITY=5-0.05440.03789 -1.43 1.51e-01 Log(scale)-2.72870.03780 -72.18 0.00e+00 Scale= 0.0653 best, Spencer I have a case study using psm (survreg wrapper) in my book. Briefly, coefficients are on the log median survival time scale. Frank -- Best wishes John John Logsdon Try to make things as simple Quantex Research Ltd, Manchester UK as possible but not simpler [EMAIL PROTECTED] [EMAIL PROTECTED] +44(0)161 445 4951/G:+44(0)7717758675 www.quantex-research.com __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Thomas Lumley Assoc. Professor, Biostatistics [EMAIL PROTECTED] University of Washington, Seattle __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] psm/survreg coefficient values ?
I am using psm to model some parametric survival data, the data is for length of stay in an emergency department. There are several ways a patient's stay in the emergency department can end (discharge, admit, etc..) so I am looking at modeling the effects of several covariates on the various outcomes. Initially I am trying to fit a survival model for each type of outcome using the psm function in the design package, i.e., all patients who's visits come to an end due to any event other than the event of interest are considered to be censored. Being new to the psm and survreg packages (and to parametric survival modeling) I am not entirely sure how to interpret the coefficient values that psm returns. I have included the following code to illustrate code similar to what I am using on my data. I suppose that the coefficients are somehow rescaled , but I am not sure how to return them to the original scale and make sense out of the coefficients, e.g., estimate the the effect of higher acuity on time to event in minutes. Any explanation or direction on how to interpret the coefficient values would be greatly appreciated. this is from the documentation for survreg.object. coefficientsthe coefficients of the linear.predictors, which multiply the columns of the model matrix. It does not include the estimate of error (sigma). The names of the coefficients are the names of the single-degree-of-freedom effects (the columns of the model matrix). If the model is over-determined there will be missing values in the coefficients corresponding to non-estimable coefficients. code: LOS - sort(rweibull(1000,1.4,108)) AGE - sort(rnorm(1000,41,12)) ACUITY - sort(rep(1:5,200)) EVENT - sample(x=c(0,1),replace=TRUE,1000) psm(Surv(LOS,EVENT)~AGE+as.factor(ACUITY),dist='weibull') output: psm(formula = Surv(LOS, CENS) ~ AGE + as.factor(ACUITY), dist = weibull) Obs Events Model L.R. d.f. P R2 10005132387.62 5 0 0.91 Value Std. Error z p (Intercept) 1.10550.04425 24.98 8.92e-138 AGE 0.07720.00152 50.93 0.00e+00 ACUITY=2 0.09440.01357 6.96 3.39e-12 ACUITY=3 0.17520.02111 8.30 1.03e-16 ACUITY=4 0.13910.02722 5.11 3.18e-07 ACUITY=5-0.05440.03789 -1.43 1.51e-01 Log(scale)-2.72870.03780 -72.18 0.00e+00 Scale= 0.0653 best, Spencer [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] psm/survreg coefficient values ?
sj wrote: I am using psm to model some parametric survival data, the data is for length of stay in an emergency department. There are several ways a patient's stay in the emergency department can end (discharge, admit, etc..) so I am looking at modeling the effects of several covariates on the various outcomes. Initially I am trying to fit a survival model for each type of outcome using the psm function in the design package, i.e., all patients who's visits come to an end due to any event other than the event of interest are considered to be censored. Being new to the psm and survreg packages (and to parametric survival modeling) I am not entirely sure how to interpret the coefficient values that psm returns. I have included the following code to illustrate code similar to what I am using on my data. I suppose that the coefficients are somehow rescaled , but I am not sure how to return them to the original scale and make sense out of the coefficients, e.g., estimate the the effect of higher acuity on time to event in minutes. Any explanation or direction on how to interpret the coefficient values would be greatly appreciated. this is from the documentation for survreg.object. coefficientsthe coefficients of the linear.predictors, which multiply the columns of the model matrix. It does not include the estimate of error (sigma). The names of the coefficients are the names of the single-degree-of-freedom effects (the columns of the model matrix). If the model is over-determined there will be missing values in the coefficients corresponding to non-estimable coefficients. code: LOS - sort(rweibull(1000,1.4,108)) AGE - sort(rnorm(1000,41,12)) ACUITY - sort(rep(1:5,200)) EVENT - sample(x=c(0,1),replace=TRUE,1000) psm(Surv(LOS,EVENT)~AGE+as.factor(ACUITY),dist='weibull') output: psm(formula = Surv(LOS, CENS) ~ AGE + as.factor(ACUITY), dist = weibull) Obs Events Model L.R. d.f. P R2 10005132387.62 5 0 0.91 Value Std. Error z p (Intercept) 1.10550.04425 24.98 8.92e-138 AGE 0.07720.00152 50.93 0.00e+00 ACUITY=2 0.09440.01357 6.96 3.39e-12 ACUITY=3 0.17520.02111 8.30 1.03e-16 ACUITY=4 0.13910.02722 5.11 3.18e-07 ACUITY=5-0.05440.03789 -1.43 1.51e-01 Log(scale)-2.72870.03780 -72.18 0.00e+00 Scale= 0.0653 best, Spencer I have a case study using psm (survreg wrapper) in my book. Briefly, coefficients are on the log median survival time scale. Frank -- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
