Title: Cross-Validated Predictions from GEE
Version: 0.3-0
Date: 2019-07-20
Maintainer: Dimitris Rizopoulos <d.rizopoulos@erasmusmc.nl>
BugReports: https://github.com/drizopoulos/cvGEE/issues
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>.
Suggests: geepack, lattice, knitr, rmarkdown, pkgdown
Encoding: UTF-8
LazyLoad: yes
LazyData: yes
License: GPL (≥ 3)
URL: https://drizopoulos.github.io/cvGEE/, https://github.com/drizopoulos/cvGEE
VignetteBuilder: knitr
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-07-20 18:16:39 UTC; drizo
Author: Dimitris Rizopoulos ORCID iD [aut, cre]
Repository: CRAN
Date/Publication: 2019-07-23 14:52:05 UTC

Proper Scoring Rules for Generalized Estimating Equations

Description

Calculates the logarithmic, quadratic/Brier and spherical scoring rules based on generalized estimation equations.

Details

Package: cvGEE
Type: Package
Version: 0.3-0
Date: 2019-07-20
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.

Author(s)

Dimitris Rizopoulos

Maintainer: Dimitris Rizopoulos <d.rizopoulos@erasmusmc.nl>

References

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

Didanosine versus Zalcitabine in HIV Patients

Description

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.

Format

A data frame with 1408 observations on the following 9 variables.

patient

patients identifier; in total there are 467 patients.

Time

the time to death or censoring.

death

a numeric vector with 0 denoting censoring and 1 death.

CD4

the CD4 cells count.

obstime

the time points at which the CD4 cells count was recorded.

drug

a factor with levels ddC denoting zalcitabine and ddI denoting didanosine.

gender

a factor with levels female and male.

prevOI

a factor with levels AIDS denoting previous opportunistic infection (AIDS diagnosis) at study entry, and noAIDS denoting no previous infection.

AZT

a factor with levels intolerance and failure denoting AZT intolerance and AZT failure, respectively.

Note

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.

References

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.

Proper Scoring Rules for Generalized Estimating Equations

Description

Calculates the logarithmic, quadratic/Brier and spherical scoring rules based on generalized estimation equations.

Usage

cv_gee(object, rule = c("all", "quadratic", "logarithmic", "spherical"), 
  max_count = 500, K = 5L, M = 10L, seed = 1L, return_data = FALSE)

Arguments

object

an object inheriting from class "geeglm" of the geepack.

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 K folds will be performed. The reported scoring rules values are the average over the M replicates.

seed

numeric scalre providing the seed used in the procedure.

return_data

logical; if TRUE the values of the scoring rules are ruturned as extra columns of the data behind object.

Value

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.

Author(s)

Dimitris Rizopoulos d.rizopoulos@erasmusmc.nl

References

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

Examples

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")

Mayo Clinic Primary Biliary Cirrhosis Data

Description

Followup of 312 randomised patients with primary biliary cirrhosis, a rare autoimmune liver disease, at Mayo Clinic.

Format

A data frame with 1945 observations on the following 20 variables.

id

patients identifier; in total there are 312 patients.

years

number of years between registration and the earlier of death, transplantion, or study analysis time.

status

a factor with levels alive, transplanted and dead.

drug

a factor with levels placebo and D-penicil.

age

at registration in years.

sex

a factor with levels male and female.

year

number of years between enrollment and this visit date, remaining values on the line of data refer to this visit.

ascites

a factor with levels No and Yes.

hepatomegaly

a factor with levels No and Yes.

spiders

a factor with levels No and Yes.

edema

a 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).

serBilir

serum bilirubin in mg/dl.

serChol

serum cholesterol in mg/dl.

albumin

albumin in gm/dl.

alkaline

alkaline phosphatase in U/liter.

SGOT

SGOT in U/ml.

platelets

platelets per cubic ml / 1000.

prothrombin

prothrombin time in seconds.

histologic

histologic stage of disease.

status2

a numeric vector with the value 1 denoting if the patient was dead, and 0 if the patient was alive or transplanted.

Note

The data frame pbc2.id contains the first measurement for each patient. This data frame is used to fit the survival model.

References

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.