powersurvival: Power Analysis for Survival Studies

In sbalci/ClinicoPathJamoviModule: Analysis for Clinicopathological Research

Power Analysis for Survival Studies

Description

Calculates power, sample size, or minimum detectable hazard ratio for survival studies. This helps researchers design adequately powered studies to detect meaningful differences in survival between groups.

Usage

powersurvival(
  data,
  calc_type = "sample_size",
  study_design = "simple",
  hazard_ratio = 0.7,
  power = 0.8,
  alpha = 0.05,
  sample_size = 200,
  allocation_ratio = 1,
  prob_event = 0.5,
  accrual_time = 1,
  follow_up_time = 3,
  median_survival = 5,
  loss_followup = 0.05
)

Arguments

data	The data as a data frame. Note that power analysis typically doesn't require actual data - it's used for study planning.
calc_type	Select what to calculate. 'Power' calculates statistical power given sample size and hazard ratio. 'Sample Size' determines required sample size for desired power and hazard ratio. 'Hazard Ratio' calculates the minimum detectable effect size given sample size and power.
study_design	Select the study design complexity. 'Simple' assumes a fixed follow-up period for all subjects. 'Complex' allows for accrual period and variable follow-up times.
hazard_ratio	The hazard ratio to detect. Values < 1 indicate protective effects (treatment better than control); values > 1 indicate harmful effects (control better than treatment).
power	The probability of detecting an effect if one exists (1 minus the Type II error rate). Conventional values are 0.8 or 0.9.
alpha	The Type I error rate (probability of falsely rejecting the null hypothesis). Conventional value is 0.05.
sample_size	The total number of subjects across all groups. For sample size calculation, this is a starting value for the search algorithm.
allocation_ratio	The ratio of control group size to treatment group size. 1 indicates equal allocation. Values > 1 mean more subjects in the control group; values < 1 mean more in the treatment group.
prob_event	The overall probability of observing the event (e.g., death) during the study period. This affects the number of events observed, which is crucial for power.
accrual_time	The period over which participants are recruited, in years. Only used for complex designs.
follow_up_time	The additional follow-up period after accrual ends, in years. Only used for complex designs.
median_survival	The median survival time in the control group, in years. Used to estimate the baseline hazard rate. Only used for complex designs.
loss_followup	The annual rate of loss to follow-up (attrition). Only used for complex designs.

Value

A results object containing:

results$message					a html
results$power_result					a html
results$sample_size_result					a html
results$hazard_ratio_result					a html
results$power_plot					an image

Examples

# Example power calculation for a survival study
# powersurvival(
#   calc_type = "sample_size",
#   hazard_ratio = 0.7,
#   power = 0.8,
#   alpha = 0.05,
#   prob_event = 0.5,
#   allocation_ratio = 1
# )

sbalci/ClinicoPathJamoviModule documentation built on June 13, 2025, 9:34 a.m.