如何循环运行pmsampsize函数批量计算样本量与结局事件数？

运行以下R代码可得到单个参数下的样本量计算结果：

library(pmsampsize)

pmsampsize(type = "s", csrsquared = 0.5, parameters = 10, rate = 0.065,
timepoint = 2, meanfup = 2.07)

输出结果：

NB: Assuming 0.05 acceptable difference in apparent & adjusted R-squared 
NB: Assuming 0.05 margin of error in estimation of overall risk at time point = 2  
NB: Events per Predictor Parameter (EPP) assumes overall event rate = 0.065  
 
             Samp_size Shrinkage Parameter CS_Rsq Max_Rsq Nag_Rsq   EPP
Criteria 1        5143     0.900        30  0.051   0.555   0.092 23.07
Criteria 2        1039     0.648        30  0.051   0.555   0.092  4.66
Criteria 3 *      5143     0.900        30  0.051   0.555   0.092 23.07
Final SS          5143     0.900        30  0.051   0.555   0.092 23.07
 
 Minimum sample size required for new model development based on user inputs = 5143, 
 corresponding to 10646 person-time** of follow-up, with 692 outcome events 
 assuming an overall event rate = 0.065 and therefore an EPP = 23.07  

需要通过循环函数，针对csrsquared（取值序列R=seq(from=0, to=0.5, by=0.1)）和parameters（取值序列PA=seq(from=1, to=10, by=1)）的所有组合，批量运行pmsampsize函数，计算对应的最小样本量和结局事件数，并整理成如下格式的表格：

示例结构：

R        PA    minimum sample size    outcome events
0        1        
0        2
0        3
0        4

编写了如下代码但无法正常运行，请求帮助修改：

library(tidyverse)  
library (pmsampsize) 
foo = function(R,PA){  
    T=pmsampsize(type = "s", csrsquared = R, parameters = PA, 
 rate = 0.065,timepoint = 2, meanfup = 2.07)
    T$events
    T$sample_size }

df = tibble(  
    R=seq(from = 0, to = 0.5,by=0.1)  
    PA=seq(from = 0, to = 10,by=1) )

原代码存在3个核心问题：自定义函数仅返回单个值、参数组合未生成全量、数据框创建语法错误。修正后的代码如下：

library(tidyverse)
library(pmsampsize)

# 定义函数，同时返回样本量和结局事件数
calc_sample_events <- function(R, PA) {
  result <- pmsampsize(type = "s", csrsquared = R, parameters = PA,
                       rate = 0.065, timepoint = 2, meanfup = 2.07)
  tibble(
    minimum_sample_size = result$sample_size,
    outcome_events = result$events
  )
}

# 生成R和PA的所有参数组合
df <- expand_grid(
  R = seq(from = 0, to = 0.5, by = 0.1),
  PA = seq(from = 1, to = 10, by = 1)
)

# 批量计算并合并结果
final_df <- df %>%
  rowwise() %>%
  mutate(calc_sample_events(R, PA)) %>%
  ungroup()

# 查看最终表格
print(final_df)

关键修正点： #

• 用expand_grid生成R和PA的全量组合，解决原代码未覆盖所有参数配对的问题；
• 自定义函数返回包含两个目标指标的tibble，确保能同时获取样本量和事件数；
• 通过rowwise() + mutate()实现逐行调用计算函数，自动将结果合并到原数据框；
• 修正原数据框创建时的语法错误，同时将PA的起始值调整为1，匹配需求中的参数序列。

运行代码后，final_df即为所需的完整结果表格。

内容的提问来源于stack exchange，提问作者elisa