Package 'marcox'

Diabetes Study Data

Description

A dataset containing clinical information from a diabetes study.

Usage

data(diabetes)

Format

A data frame with 166 rows and 6 variables:

risk

Numeric: Risk score of the patient.

cens

Binary (0/1): Censoring indicator (1 = event occurred, 0 = censored).

time

Numeric: Time to event or censoring (in months).

id

Integer: Patient ID.

trt

Binary (0/1): Treatment indicator (1 = treated, 0 = control).

age

Binary (0/1): Age group indicator (1 = older, 0 = younger).

Source

Hypothetical clinical study data.

Examples

data(diabetes)
  summary(diabetes)

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Generate Simulated Datasets for Cox Proportional Hazards Model

Description

This function generates multiple datasets for survival analysis based on a Cox proportional hazards model. The baseline hazard function follows either a Weibull or an exponential distribution, depending on the values of lambda. The function ensures that the maximum observed time in both the control and treatment groups is checked for censoring. If the maximum time is not censored, it is forced to be censored to maintain the desired censoring rate.

Usage

gendat(
  type = "bin",
  dimension = 10,
  K = 30,
  n = 2,
  lambda = c(1, 2),
  b1 = c(log(2), -0.1),
  theta = 8,
  censrate = 0.3
)

Arguments

type	Character. If type = 'bin', the covariates are generated as binary variables; if type = 'cont' continuous covariates are generated.
dimension	Integer. The number of datasets to be generated.
K	Integer. The number of clusters (groups) within each dataset.
n	Integer. The number of samples within each cluster.
lambda	Numeric vector. A two-element vector specifying the parameters for the baseline distribution: If lambda = c(a, b), where a > 1, the baseline follows a Weibull distribution. If lambda = c(1, b), the baseline follows an exponential distribution.
b1	Vector. The regression coefficient for the covariates, affecting the hazard function. We suggest that the maximum of b1 should be lower than 2.
theta	Numeric. A parameter controlling the dependency structure between survival times within clusters. Higher values indicate stronger within-cluster correlation.
censrate	Numeric. The target censoring rate for the dataset.

Value

A list containing:

• data - A list of data frames, each containing a generated dataset.

• censoringrates - A numeric vector representing the censoring rate for each dataset.

• mean(censoringrates) - The mean censoring rate across all datasets.

Examples

# Generate binary covariate datasets with 1 datasets, 10 clusters, and 6 samples per cluster
print(gendat(type = 'bin', dimension = 1, K = 6, n = 10, lambda = c(1, 2),
      b1 = c(log(2),-log(2)), theta = 8, censrate = 0.5))

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Kidney Disease Study Data

Description

A dataset containing survival analysis information related to kidney disease patients.

Usage

data(kidney_data)

Format

A data frame with 76 rows and 5 variables:

time

Numeric: Time to event or censoring (in days).

cens

Binary (0/1): Censoring indicator (1 = event occurred, 0 = censored).

age

Numeric: Age of the patient in years.

sex

Binary (0/1): Sex of the patient (1 = male, 0 = female).

type

Categorical (0,1,2,3): Kidney disease type classification.

Source

Hypothetical survival study data.

Examples

data(kidney_data)
  summary(kidney_data)

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Analysis for Cox Proportional Hazards Models

Description

This function performs marcox analysis for Cox proportional hazards models, incorporating clustered data and handling time-dependent covariates. It estimates coefficients, standard errors, and p-values based on the specified formula and dataset.

Usage

marcox(
  formula,
  data,
  sep = NULL,
  method = "exchangeable",
  col_id = "id",
  div = NULL,
  k_value = 1,
  plot_x = NULL,
  x_axis = "Time",
  y_axis = "Survival Rates",
  size = 0.5,
  diagnose = FALSE
)

Arguments

formula	A model formula that uses the Surv() function to define the survival outcome. It should include both continuous and categorical covariates, where categorical variables must be specified using the factormar() function.
data	The file path or the dataset(matrix) to be analyzed. If a file path is provided, the file will be loaded into a matrix. The file should be in a tabular format (e.g., .csv, .txt).
sep	Character. The sep parameter specifies the character that separates the fields in each line of the file. For instance, for a comma-separated file, set sep = ",", and for a tab-separated file, set sep = "\t".
method	The method employed to solve the correlation coefficient: Exchangeable correlation structure: method = 'exchangeable'<> Autoregressive(AR-1): method = 'ar1' k-dependent: method = 'kdependent' Toeplitz: method = 'toeplitz' Independent: method = 'independent' Unstructured: method = 'unstructured'
col_id	Character. The name of column that identifies the clusters.
div	Integer. The number of observation points per sample. If provided, the data will be divided accordingly. If the data has complex observational situations, please preprocess the data before using this function.
k_value	The k value only for k-dependent structure. The default value is 1.
diagnose	Diagnose option.

Details

The marcox() function is specifically designed for survival data analysis using Cox proportional hazards models. It handles both clustered and time-dependent covariates effectively. The survival outcome must be defined using the Surv() function in the model formula, and covariates can be included directly or by converting categorical variables with the factormar() function.

Value

A list containing the following components:

• coef - The estimated regression coefficients.

• exp(coef) - The exponentiated coefficients (hazard ratios).

• se(coef) - The standard errors of the estimated coefficients.

• z - The z-statistics for testing the significance of the coefficients.

• p - The p-values associated with the coefficients.

• (hidden).correlation - Correlation coefficients of the data.

Examples

formula <- Surv(time, cens) ~ sex + factormar('type', d_v=c(1,2,3))
  marcox(formula, data = kidney_data, div = 2, method = 'exchangeable', plot = TRUE, plot_x = 'sex')

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