Inpatient blood glucose data for 1200 patients

Description

A simulated dataset containing the variability of inpatient point-of-care blood glucose (BG) measurements from 1200 non-critical care adult patients in medical ward. BG variability is measured as the standard deviation of the BG readings within a day. Data was simulated based on real data.

Usage

bg_variability

Format

A data frame with 1200 rows and 7 variables:

subject_id

Subject ID of each patient.

case_id

Case ID, with 1 and 2 referring to the first and second follow-up respectively.

y

BG variability of the first and second follow-up.

t

Binary indicator for the second follow-up.

sd0

Baseline BG variability.

age

Patients' age.

female

Binary indicator for being female.

Inpernal function: generate commonly used summary statistics for estimates.

Description

Inpernal function: generate commonly used summary statistics for estimates.

Usage

compile_est(
  var,
  est,
  se = NULL,
  z_score = NULL,
  pval = NULL,
  value_null = 0,
  ci_lower = NULL,
  ci_upper = NULL,
  prefix = NULL,
  postfix = NULL
)

Arguments

Details

Vectorised, as long as the length of the input match.

Apply the three-step workflow for the analysis of two repeated outcomes from each subject

Description

Apply the three-step workflow for the analysis of two repeated outcomes from each subject

Usage

cprobit(
  formula,
  dat,
  index,
  transform = NULL,
  lambda = NA,
  resid_pval_threshold = 0.05
)

## S3 method for class 'cprobit'
summary(object, plot = FALSE, ...)

## S3 method for class 'cprobit'
print(x, ...)

Arguments

Details

Specify the formula for the repeated measurements instead of the change in the outcome, but without any time-invariant component that would have been eliminated after taking the difference. Interaction between two variables can be specified in the formula using * or :, but users need to create their own variable for interaction involving three or more variables.

If transform = NULL, the workflow will determine the need for a Box-Cox transforamtion on the outcome (i.e., Step 3) based on the residual diagnostics in Step 2. A Box-Cox transforamtion will be used if the p-value of the Lilliefors test is smaller than resid_pval_threshold (default is 0.05). If transform = TRUE, analyses will always be performed on both the observed and Box-Cox transformed outcomes. If transform = FALSE, analysis will only be performed on the observed outcomes.

Value

Returns a list.

References

• GEP Box, DR Cox. An Analysis of Transformations. Journal of the Royal Statistical Society. Series B (Methodological). 1964;26:211–52.

• DM Hawkins, S Weisberg. Combining the box-cox power and generalised log transformations to accommodate nonpositive responses in linear and mixed-effects linear models. South African Stat J. 2017;51:317–28.

• HW Lilliefors. On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown. J Am Stat Assoc. 1967;62:399.

• Y Ning, NC Støer, PJ Ho, SL Kao, KY Ngiam, EYH Khoo, SC Lee, ES Tai, M Hartman, M Reilly, CS Tan. Robust estimation of the effect of an exposure on the change in a continuous outcome. BMC Medical Research Methodology (in press).

Examples

# Apply the three-step workflow to assess the association between the
# baseline glucose variability and the change in the glucose variability in
# the subsequent two days.
# Although age and gender are available, they do not need to be explicitly
# adjusted for in the cprobit model.
data(bg_variability)
head(bg_variability)
model <- cprobit(formula = y ~ t + t:sd0, dat = bg_variability,
                 index = c("subject_id", "case_id"))
summary(model, plot = TRUE)

Inpernal function: step 1 of the proposed workflow

Description

Implements the Step 1 of the proposed workflow, where a cprobit model is applied to analyse whether there is an increase in the outcome within each subject.

Usage

cprobit_step1(y_name, x_names, dat_diff, var_names = NULL)

Arguments

Value

Returns a data.frame summarising the Step 1 estimates (coef) and the covariance matrix for the Step 1 estimates (vcov).

Inpernal function: estimate the SD of error terms in the difference model

Description

Inpernal function: estimate the SD of error terms in the difference model

Usage

estimate_sd_error(beta_c, y1, y2, lambda = NA, design_mat_diff)

Arguments

Value

Returns the estimate for sigma_delta if lambda = NULL, or sigma_delta_lambda on the transformed scale.

Inpernal function: compute geometric mean of a positive variable

Description

Inpernal function: compute geometric mean of a positive variable

Usage

geom_mean(x)

Arguments

Inpernal function: compute difference in the (transformed) outcome

Description

Inpernal function: compute difference in the (transformed) outcome

Usage

get_v(y1, y2, lambda = NA, scaled = TRUE)

Arguments

Value

Returns the difference in the observed outcomes if lambda = NA, or the difference in the scaled transformed outcomes with transformation parameter lambda.

Inpernal function: construct design matrix without the intercept term.

Description

Inpernal function: construct design matrix without the intercept term.

Usage

make_design_mat(lp, dat, remove_intercept = TRUE)

Arguments

Value

Returns a list containing the constructed design matrix and the original variable names. In the column names of the design matrix returned , any : in variable names are replaced with . to avoid computational issues when using the design matrix to fit model.

Inpernal function: profile log-likelihood of lambda

Description

Inpernal function: profile log-likelihood of lambda

Usage

profile_llh(lambda, beta_c, y1, y2, design_mat_diff)

Arguments

Value

Returns the profile log likelihood (not the negative value).

Inpernal function: update Step 1 estimates to obtain linear exposure effect on (transformed) outcome

Description

Inpernal function: update Step 1 estimates to obtain linear exposure effect on (transformed) outcome

Usage

update_estimate(
  y1_name,
  y2_name,
  var_names = NULL,
  dat_diff,
  res_step1,
  transform = FALSE
)

Arguments

Value

Returns a list: a data.frame summarising the estimated linear exposure effect, the estimated standard deviation of the error terms from the difference model, the covariance matrix of the estimated exposure effects, a data.frame summarising the estimated transforamtion parameter, and the residuals.