| Title: | Cross-Validated Predictions from GEE |
|---|---|
| Description: | Calculates predictions from generalized estimating equations and internally cross-validates them using the logarithmic, quadratic and spherical proper scoring rules; Kung-Yee Liang and Scott L. Zeger (1986) <doi:10.1093/biomet/73.1.13>. |
| Authors: | Dimitris Rizopoulos [aut, cre]
|
| Maintainer: | Dimitris Rizopoulos <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 0.3-5 |
| Built: | 2024-11-08 04:13:14 UTC |
| Source: | https://github.com/drizopoulos/cvgee |
A randomized clinical trial in which both longitudinal and survival data were collected to compare the efficacy and safety of two antiretroviral drugs in treating patients who had failed or were intolerant of zidovudine (AZT) therapy.
A data frame with 1408 observations on the following 9 variables.
patientpatients identifier; in total there are 467 patients.
Timethe time to death or censoring.
deatha numeric vector with 0 denoting censoring and 1 death.
CD4the CD4 cells count.
obstimethe time points at which the CD4 cells count was recorded.
druga factor with levels ddC denoting zalcitabine and ddI denoting didanosine.
gendera factor with levels female and male.
prevOIa factor with levels AIDS denoting previous opportunistic infection (AIDS
diagnosis) at study entry, and noAIDS denoting no previous infection.
AZTa factor with levels intolerance and failure denoting AZT intolerance and
AZT failure, respectively.
The data frame aids.id contains the first CD4 cell count measurement for each patient. This data frame is used to
fit the survival model.
Goldman, A., Carlin, B., Crane, L., Launer, C., Korvick, J., Deyton, L. and Abrams, D. (1996) Response of CD4+ and clinical consequences to treatment using ddI or ddC in patients with advanced HIV infection. Journal of Acquired Immune Deficiency Syndromes and Human Retrovirology 11, 161–169.
Guo, X. and Carlin, B. (2004) Separate and joint modeling of longitudinal and event time data using standard computer packages. The American Statistician 58, 16–24.
Calculates the logarithmic, quadratic/Brier and spherical scoring rules based on generalized estimation equations.
cv_gee(object, rule = c("all", "quadratic", "logarithmic", "spherical"), max_count = 500, K = 5L, M = 10L, seed = 1L, return_data = FALSE)cv_gee(object, rule = c("all", "quadratic", "logarithmic", "spherical"), max_count = 500, K = 5L, M = 10L, seed = 1L, return_data = FALSE)
object |
an object inheriting from class |
rule |
character string indicating the type of scoring rule to be used. |
max_count |
numeric scalar or vector denoting the maximum count up to which to calculate probabilities; this is relevant for count response data. |
K |
numeric scalar indicating the number of folds used in the cross-validation procedure. |
M |
numeric scalar denoting how many times the split of the data in |
seed |
numeric scalre providing the seed used in the procedure. |
return_data |
logical; if |
A list or a data.frame with elements or (extra) columns the values of the logarithmic, quadratic and spherical scoring rules calculated based on the GEE object.
Dimitris Rizopoulos [email protected]
Carvalho, A. (2016). An overview of applications of proper scoring rules. Decision Analysis 13, 223-242. doi:10.1287/deca.2016.0337
Liang, K.Y. and Zeger, S.L. (1986). Longitudinal data analysis using generalized linear models. Biometrika 73, 13-22. doi:10.1093/biomet/73.1.13
library("geepack") library("lattice") pbc2$serBilirD <- as.numeric(pbc2$serBilir > 1.2) fm1 <- geeglm(serBilirD ~ year, family = binomial(), data = pbc2, id = id, corstr = "exchangeable") fm2 <- geeglm(serBilirD ~ year * drug, family = binomial(), data = pbc2, id = id, corstr = "exchangeable") plot_data <- cv_gee(fm1, return_data = TRUE, M = 5) plot_data$model_year <- plot_data$.score plot_data$model_year_drug <- unlist(cv_gee(fm2, M = 5)) xyplot(model_year + model_year_drug ~ year | .rule, data = plot_data, type = "smooth", auto.key = TRUE, layout = c(3, 1), scales = list(y = list(relation = "free")), xlab = "Follow-up time (years)", ylab = "Scoring Rules")library("geepack") library("lattice") pbc2$serBilirD <- as.numeric(pbc2$serBilir > 1.2) fm1 <- geeglm(serBilirD ~ year, family = binomial(), data = pbc2, id = id, corstr = "exchangeable") fm2 <- geeglm(serBilirD ~ year * drug, family = binomial(), data = pbc2, id = id, corstr = "exchangeable") plot_data <- cv_gee(fm1, return_data = TRUE, M = 5) plot_data$model_year <- plot_data$.score plot_data$model_year_drug <- unlist(cv_gee(fm2, M = 5)) xyplot(model_year + model_year_drug ~ year | .rule, data = plot_data, type = "smooth", auto.key = TRUE, layout = c(3, 1), scales = list(y = list(relation = "free")), xlab = "Follow-up time (years)", ylab = "Scoring Rules")
Calculates the logarithmic, quadratic/Brier and spherical scoring rules based on generalized estimation equations.
| Package: | cvGEE |
| Type: | Package |
| Version: | 0.3-5 |
| Date: | 2019-07-29 |
| License: | GPL (>=3) |
The package provides the estimated values of the scoring rules for each observation of the original dataset. These values can be summarized/averaged or used in figures to evaluate how the GEE performs in different ranges of the data.
Dimitris Rizopoulos
Maintainer: Dimitris Rizopoulos <[email protected]>
Carvalho, A. (2016). An overview of applications of proper scoring rules. Decision Analysis 13, 223-242. doi:10.1287/deca.2016.0337
Liang, K.Y. and Zeger, S.L. (1986). Longitudinal data analysis using generalized linear models. Biometrika 73, 13-22. doi:10.1093/biomet/73.1.13
Followup of 312 randomised patients with primary biliary cirrhosis, a rare autoimmune liver disease, at Mayo Clinic.
A data frame with 1945 observations on the following 20 variables.
idpatients identifier; in total there are 312 patients.
yearsnumber of years between registration and the earlier of death, transplantion, or study analysis time.
statusa factor with levels alive, transplanted and dead.
druga factor with levels placebo and D-penicil.
ageat registration in years.
sexa factor with levels male and female.
yearnumber of years between enrollment and this visit date, remaining values on the line of data refer to this visit.
ascitesa factor with levels No and Yes.
hepatomegalya factor with levels No and Yes.
spidersa factor with levels No and Yes.
edemaa factor with levels No edema (i.e., no edema and no diuretic therapy for edema),
edema no diuretics (i.e., edema present without diuretics, or edema resolved by diuretics), and
edema despite diuretics (i.e., edema despite diuretic therapy).
serBilirserum bilirubin in mg/dl.
serCholserum cholesterol in mg/dl.
albuminalbumin in gm/dl.
alkalinealkaline phosphatase in U/liter.
SGOTSGOT in U/ml.
plateletsplatelets per cubic ml / 1000.
prothrombinprothrombin time in seconds.
histologichistologic stage of disease.
status2a numeric vector with the value 1 denoting if the patient was dead, and 0 if the patient was alive or transplanted.
The data frame pbc2.id contains the first measurement for each patient. This data frame is used to
fit the survival model.
Fleming, T. and Harrington, D. (1991) Counting Processes and Survival Analysis. Wiley, New York.
Therneau, T. and Grambsch, P. (2000) Modeling Survival Data: Extending the Cox Model. Springer-Verlag, New York.