Classical Survival Power Analysis (Intermediate)

Legacy power analysis and sample size calculation for survival studies using Lachin-Foulkes and Schoenfeld methods from gsDesign package.

Usage

classicalSurvivalPower(
  data,
  calculation_type = "sample_size",
  method = "lachin_foulkes",
  hazard_control = 0.083,
  hazard_treatment = 0.042,
  hazard_ratio = 0.6,
  study_duration = 24,
  accrual_duration = 12,
  dropout_rate = 0,
  allocation_ratio = 1,
  alpha = 0.025,
  beta = 0.1,
  power = 0.9,
  sided = "one_sided",
  entry_type = "unif",
  gamma = 0,
  sample_size_input = 100,
  events_input = 50,
  show_summary = TRUE,
  show_formulas = FALSE,
  show_interpretation = TRUE,
  show_power_plot = FALSE,
  show_timeline_plot = FALSE,
  power_plot_range = "auto",
  export_results = FALSE,
  export_power_curve = FALSE
)

Arguments

data

The data as a data frame (optional for power calculations).

calculation_type

Type of power calculation to perform.

method

Method for power/sample size calculation.

hazard_control

Event hazard rate for control group (events per person-time unit).

hazard_treatment

Event hazard rate for treatment group (events per person-time unit).

hazard_ratio

Hazard ratio (treatment/control) for Schoenfeld method.

study_duration

Maximum study duration (months or years).

accrual_duration

Patient accrual (recruitment) duration.

dropout_rate

Equal dropout hazard rate for both groups.

allocation_ratio

Randomization ratio (treatment:control).

alpha

Type I error rate (significance level).

beta

Type II error rate (1 - power).

power

Statistical power (1 - beta).

sided

One-sided or two-sided statistical test.

entry_type

Pattern of patient entry into the study.

gamma

Rate parameter for exponential entry (0 if uniform entry).

sample_size_input

Total sample size when calculating power.

events_input

Number of events when calculating power with Schoenfeld method.

show_summary

Whether to display comprehensive study design summary.

show_formulas

Whether to display mathematical formulas used.

show_interpretation

Whether to include clinical interpretation of results.

show_power_plot

Whether to display power curve visualization.

show_timeline_plot

Whether to display study timeline visualization for Lachin-Foulkes method.

power_plot_range

Sample size range for power plots (format 'min,max' or 'auto').

export_results

Whether to export detailed results for external analysis.

export_power_curve

Whether to export power curve data points.

Value

A results object containing:

results$instructions					a html
results$power_results					a html
results$formulas					a html
results$interpretation					a html
results$power_plot					an image
results$timeline_plot					an image
results$exported_results					an output
results$exported_power_curve					an output
results$export_summary					a html

Details

This is the original survival power analysis function. For enhanced functionality including competing risks, RMST analysis, and additional methods, also consider 'Comprehensive Survival Power Analysis'.

Examples

# \donttest{
# Example usage:
library(gsDesign)
# Sample size for survival study
nSurvival(lambda1 = 0.2, lambda2 = 0.1, Ts = 24, Tr = 12)
# Number of events required
nEvents(hr = 0.6, alpha = 0.025, beta = 0.1)
# }