R：重复实验1000次，每次重复保存变量

Question

我进行了模拟，其中创建了 2 组，每组 100 名患者。数据框包含患者、干预类型（R 用于参考，T 用于测试）和恢复状态（1 或 0）。然后我需要做一个实验：为概率差异制定置信区间。 我需要重复实验 1000 次（每次实验都会创建一个新的患者样本组）并在每次重复中保存结果（df_prop2）。最后我应该得到一个包含 1000 行的数据框（df_prop2）（7 个变量的 1000 个观测值）。我不明白为什么使用我的代码我得到的数据框有 1 行和 1007 列。请帮忙

这是我的代码：

library(dplyr)
library(tidyr)

set.seed(123)
p_R <- 0.1 #true probability for reference
p_T <- 0.2 #true probabiluty for Test
sample_size <- 100 #I have 100 patients in 2 groups

n_rep <- 1000 #number of repeats

df_all_repeats <- data.frame(
  n_exp = rep(1:n_rep, each = 2*sample_size),
  arm = rep(c('R', 'T'), each = sample_size),
  patient_ID = rep(1:sample_size, n_rep))

df_all_repeats <- df_all_repeats %>%
  group_by(n_exp) %>%
  mutate(recovery_status = c(sample(c(1,0), sample_size, replace = TRUE, prob = c(p_R, 1 - p_R)),
                      sample(c(1,0), sample_size, replace = TRUE, prob = c(p_T, 1 - p_T))))

#Experiment
df_prop2 <- df_all_repeats %>% 
  group_by(arm) %>% 
  group_by(n_exp) %>% 
  dplyr::summarise(x = sum(recovery_status),
                   n = n()) %>% 
  ungroup() %>% 
  dplyr::summarise(X = list(x), N = list(n)) %>% 
  rowwise() %>% 
  mutate(tst = list(broom::tidy(prop.test(X, N)))) %>% 
  unnest(tst) 
  
print(df_prop2)

Answer 1

这是一个也简化了数据生成的解决方案：

library(dplyr)
library(tidyr)
library(broom)

p_R <- 0.1 # true probability for reference
p_T <- 0.2 # true probabiluty for Test
sample_size <- 100 # number of patients
n_rep <- 1000 # number of repeats

set.seed(123)
df_all_repeats <- expand.grid(
  patient_id = 1:sample_size,
  arm = c("R", "T"),
  n_exp = 1:n_rep,
  stringsAsFactors = FALSE) %>% 
  as_tibble() %>% ## data is sorted n_exp, arm, patient_id
  mutate(recovery_status = c(sample(1:0, sample_size, replace = TRUE,
                                    prob = c(p_R, 1 - p_R)),
                             sample(1:0, sample_size, replace = TRUE, 
                                    prob = c(p_T, 1 - p_T))),
         .by = n_exp)

df_all_repeats %>%
  group_by(n_exp, arm) %>%
  summarise(x = sum(recovery_status), 
            n = n()) %>%
  summarise(broom::tidy(prop.test(x, n)))

# # A tibble: 1,000 × 10
#    n_exp estimate1 estimate2 statistic  p.value parameter conf.low conf.high method                                                           alternative
#    <int>     <dbl>     <dbl>     <dbl>    <dbl>     <dbl>    <dbl>     <dbl> <chr>                                                            <chr>      
#  1     1      0.07      0.19    5.35   0.0207           1   -0.222   -0.0183 2-sample test for equality of proportions with continuity corre… two.sided  
#  2     2      0.1       0.19    2.58   0.108            1   -0.197    0.0168 2-sample test for equality of proportions with continuity corre… two.sided  
#  3     3      0.09      0.27    9.79   0.00175          1   -0.294   -0.0665 2-sample test for equality of proportions with continuity corre… two.sided  
#  4     4      0.08      0.21    5.81   0.0160           1   -0.236   -0.0241 2-sample test for equality of proportions with continuity corre… two.sided  
#  5     5      0.07      0.21    7.02   0.00807          1   -0.244   -0.0358 2-sample test for equality of proportions with continuity corre… two.sided  
#  6     6      0.03      0.18   10.4    0.00124          1   -0.242   -0.0576 2-sample test for equality of proportions with continuity corre… two.sided  
#  7     7      0.08      0.16    2.32   0.128            1   -0.179    0.0194 2-sample test for equality of proportions with continuity corre… two.sided  
#  8     8      0.12      0.15    0.171  0.679            1   -0.135    0.0746 2-sample test for equality of proportions with continuity corre… two.sided  
#  9     9      0.08      0.28   12.2    0.000471         1   -0.313   -0.0872 2-sample test for equality of proportions with continuity corre… two.sided  
# 10    10      0.11      0.13    0.0473 0.828            1   -0.120    0.0800 2-sample test for equality of proportions with continuity corre… two.sided  
# # ℹ 990 more rows
# # ℹ Use `print(n = ...)` to see more rows