Package 'tern.gee'

tern.gee Package

Description

Create tables and graphs for GEE model fits.

Author(s)

Maintainer: Joe Zhu [email protected]

Authors:

• Daniel Sabanés Bové [email protected]

• Emily de la Rua [email protected]

Other contributors:

• F. Hoffmann-La Roche AG [copyright holder, funder]

See Also

Useful links:

• https://insightsengineering.github.io/tern.gee/

• https://github.com/insightsengineering/tern.gee/

• Report bugs at https://github.com/insightsengineering/tern.gee/issues

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Example dataset for tern.gee package.

Description

Measurements of FEV1 (forced expired volume in one second) is a measure of how quickly the lungs can be emptied. Low levels of FEV1 may indicate chronic obstructive pulmonary disease (COPD).

Usage

fev_data

Format

A tibble with 800 rows and 7 variables:

• USUBJID: unique subject identifier.

• AVISIT: visit number.

• ARMCD: treatment, TRT or PBO.

• RACE: 3-category race.

• SEX: sex.

• FEV1_BL: FEV1 at baseline (%).

• FEV1: FEV1 at study visits.

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Fit a GEE Model

Description

Fit a GEE Model

Usage

fit_gee(
  vars = vars_gee(),
  data,
  regression = c("logistic"),
  cor_struct = c("unstructured", "toeplitz", "compound symmetry", "auto-regressive")
)

Arguments

vars	(list) see vars_gee().
data	(data.frame) input data.
regression	(string) choice of regression model.
cor_struct	(string) assumed correlation structure.

Details

The correlation structure can be:

• unstructured: No constraints are placed on the correlations.

• toeplitz: Assumes a banded correlation structure, i.e. the correlation between two time points depends on the distance between the time indices.

• ⁠compound symmetry⁠: Constant correlation between all time points.

• auto-regressive: Auto-regressive order 1 correlation matrix.

Value

Object of class tern_gee as well as specific to the kind of regression which was used.

Examples

df <- fev_data
df$AVAL <- as.integer(fev_data$FEV1 > 30)

fit_gee(vars = vars_gee(arm = "ARMCD"), data = df)

fit_gee(vars = vars_gee(arm = "ARMCD"), data = df, cor_struct = "compound symmetry")

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Methods for GEE Models

Description

Additional methods which can simplify working with the GEE result object.

Usage

## S3 method for class 'tern_gee'
VarCorr(x, sigma = 1, ...)

## S3 method for class 'tern_gee'
QIC(object, ...)

Arguments

x	(tern_gee) result of fit_gee().
sigma	an optional numeric value used as a multiplier for the standard deviations. The default is x$sigma or 1 depending on class(x).
...	further optional arguments passed to other methods (none for the methods documented here).
object	(tern_gee) result of fit_gee().

Value

VarCorr() returns the estimated covariance matrix, and QIC() returns the QIC value.

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Extract Least Square Means from a GEE Model

Description

Extract Least Square Means from a GEE Model

Usage

lsmeans(
  object,
  conf_level = 0.95,
  weights = "proportional",
  specs = object$vars$arm,
  ...
)

## S3 method for class 'tern_gee_logistic'
lsmeans(
  object,
  conf_level = 0.95,
  weights = "proportional",
  specs = object$vars$arm,
  ...
)

Arguments

object	(tern_gee) result of fit_gee().
conf_level	(proportion) confidence level
weights	(string) type of weights to be used for the least square means, see emmeans::emmeans() for details.
specs	(string or formula) specifications passed to emmeans::emmeans()
...	additional arguments for methods

Value

A data.frame with least-square means and contrasts. Additional classes allow to dispatch downstream methods correctly, too.

Examples

df <- fev_data
df$AVAL <- rbinom(n = nrow(df), size = 1, prob = 0.5)
fit <- fit_gee(vars = vars_gee(arm = "ARMCD"), data = df)

lsmeans(fit)

lsmeans(fit, conf_level = 0.90, weights = "equal")

---

Tabulation of a GEE Model

Description

Functions to produce tables from a fitted GEE produced with fit_gee().

Usage

## S3 method for class 'tern_gee'
as.rtable(x, type = c("coef", "cov"), ...)

s_lsmeans_logistic(df, .in_ref_col)

a_lsmeans_logistic(df, .in_ref_col)

summarize_gee_logistic(
  lyt,
  ...,
  table_names = "lsmeans_logistic_summary",
  .stats = NULL,
  .formats = NULL,
  .indent_mods = NULL,
  .labels = NULL
)

Arguments

x	(data.frame) the object which should be converted to an rtable.
type	(character) type of table to extract from tern_gee object.
...	additional arguments for methods.
df	(data.frame) data set resulting from lsmeans().
.in_ref_col	(logical) TRUE when working with the reference level, FALSE otherwise.
lyt	(layout) input layout where analyses will be added to.
table_names	(character) this can be customized in case that the same vars are analyzed multiple times, to avoid warnings from rtables.
.stats	(character) statistics to select for the table.
.formats	(named character or list) formats for the statistics.
.indent_mods	(named integer) indent modifiers for the labels.
.labels	(named character) labels for the statistics (without indent).

Value

The functions have different purposes:

• as.rtable() returns either the coefficient table or the covariance matrix as an rtables object.

• s_lsmeans_logistic() returns several least square mean statistics from the GEE.

• a_lsmeans_logistic() is the formatted analysis function and returns the formatted statistics.

• summarize_gee_logistic() is the analyze function and returns the modified rtables layout.

Functions

• as.rtable(tern_gee): Extracts the coefficient table or covariance matrix estimate from a tern_gee object.

• s_lsmeans_logistic(): Statistics function which extracts estimates from a lsmeans() data frame based on a logistic GEE model.

• a_lsmeans_logistic(): Formatted Analysis function which can be further customized by calling rtables::make_afun() on it. It is used as afun in rtables::analyze().

• summarize_gee_logistic(): Analyze function for tabulating least-squares means estimates from logistic GEE least square mean results.

Examples

library(dplyr)

df <- fev_data %>%
  mutate(AVAL = as.integer(fev_data$FEV1 > 30))
df_counts <- df %>%
  select(USUBJID, ARMCD) %>%
  unique()

lsmeans_df <- lsmeans(fit_gee(vars = vars_gee(arm = "ARMCD"), data = df))

s_lsmeans_logistic(lsmeans_df[1, ], .in_ref_col = TRUE)

s_lsmeans_logistic(lsmeans_df[2, ], .in_ref_col = FALSE)
basic_table() %>%
  split_cols_by("ARMCD") %>%
  add_colcounts() %>%
  summarize_gee_logistic(
    .in_ref_col = FALSE
  ) %>%
  build_table(lsmeans_df, alt_counts_df = df_counts)

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Set Variables to Use in GEE Model

Description

Set Variables to Use in GEE Model

Usage

vars_gee(
  response = "AVAL",
  covariates = c(),
  id = "USUBJID",
  arm = "ARM",
  visit = "AVISIT"
)

Arguments

response	(character) name of response variable.
covariates	(character) vector of names of variables to use as covariates.
id	(character) name of variable to use to identify unique IDs.
arm	(character) name of arm variable.
visit	(character) name of visit variable.

Value

A list of variables that can be used as the vars argument in fit_gee().

Examples

vars_gee()

vars_gee(
  response = "CHG",
  covariates = c("SEX", "RACE"),
  id = "SUBJID",
  arm = "ARMCD",
  visit = "AVISITN"
)

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