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Load library and the data

# Load library
library(hcruR)

# Load sample data
data(hcru_sample_data)
data <- hcru_sample_data
head(hcru_sample_data)
#> # A tibble: 6 × 11
#>   patient_id cohort  encounter_id care_setting visit_date admission_date
#>   <chr>      <chr>   <chr>        <chr>        <date>     <date>        
#> 1 P_1001     control E_645327     IP           2023-08-23 2023-08-23    
#> 2 P_1001     control E_591096     ED           2023-08-27 NA            
#> 3 P_1001     control E_864419     OP           2023-09-25 NA            
#> 4 P_1001     control E_379584     IP           2023-10-03 2023-10-03    
#> 5 P_1001     control E_617025     ED           2024-02-07 NA            
#> 6 P_1001     control E_379047     ED           2024-03-01 NA            
#> # ℹ 5 more variables: discharge_date <date>, encounter_date <date>,
#> #   index_date <date>, period <chr>, cost_usd <dbl>

Generate HCRU summary using dplyr (this can be used for create HCRU plots)

# Step 1: Estimate HCRU
hcru_summary <- estimate_hcru(data,
  cohort_col = "cohort",
  patient_id_col = "patient_id",
  admit_col = "admission_date",
  discharge_col = "discharge_date",
  index_col = "index_date",
  visit_col = "visit_date",
  encounter_id_col = "encounter_id",
  setting_col = "care_setting",
  cost_col = "cost_usd",
  readmission_col = "readmission",
  time_window_col = "period",
  los_col = "length_of_stay",
  custom_var_list = NULL,
  pre_days = 180,
  post_days = 365,
  readmission_days_rule = 30,
  group_var_main = "cohort",
  group_var_by = "care_setting",
  test = NULL,
  timeline = "Pre",
  gt_output = FALSE
)
#> Warning: The `update` argument of `modify_header()` is deprecated as of gtsummary 2.0.0.
#> ℹ Use `modify_header(...)` input instead. Dynamic dots allow for syntax like
#>   `modify_header(!!!list(...))`.
#> ℹ The deprecated feature was likely used in the gtsummary package.
#>   Please report the issue at <https://github.com/ddsjoberg/gtsummary/issues>.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.

hcru_summary
#> $`Summary by settings using dplyr`
#> # A tibble: 757 × 15
#>    patient_id cohort  care_setting time_window  Days  Month   Year Visits   Cost
#>    <chr>      <chr>   <chr>        <chr>       <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
#>  1 P_1001     control ED           Post          319 10.5   0.873       2  4677.
#>  2 P_1001     control ED           Pre            18  0.592 0.0492      1  1871.
#>  3 P_1001     control IP           Post           21  0.690 0.0575      1 23591.
#>  4 P_1001     control IP           Pre            22  0.723 0.0602      1 23142.
#>  5 P_1001     control OP           Post           13  0.427 0.0356      1   699.
#>  6 P_1002     treatm… ED           Post          271  8.91  0.741       2  3088.
#>  7 P_1002     treatm… ED           Pre           153  5.03  0.419       1  1869.
#>  8 P_1002     treatm… IP           Post          270  8.88  0.739       3 42218.
#>  9 P_1002     treatm… OP           Pre           195  6.41  0.534       2  1910.
#> 10 P_1003     treatm… ED           Post           59  1.94  0.161       1  4895.
#> # ℹ 747 more rows
#> # ℹ 6 more variables: LOS <dbl>, Readmission <dbl>, Visit_PPPM <dbl>,
#> #   Visit_PPPY <dbl>, Cost_PPPM <dbl>, Cost_PPPY <dbl>

Generate HCRU summary using gtsummary (a publication ready output)

# Step 1: Estimate HCRU
hcru_summary_gt <- estimate_hcru(data,
  cohort_col = "cohort",
  patient_id_col = "patient_id",
  admit_col = "admission_date",
  discharge_col = "discharge_date",
  index_col = "index_date",
  visit_col = "visit_date",
  encounter_id_col = "encounter_id",
  setting_col = "care_setting",
  cost_col = "cost_usd",
  readmission_col = "readmission",
  time_window_col = "period",
  los_col = "length_of_stay",
  custom_var_list = NULL,
  pre_days = 180,
  post_days = 365,
  readmission_days_rule = 30,
  group_var_main = "cohort",
  group_var_by = "care_setting",
  test = NULL,
  timeline = "Pre",
  gt_output = TRUE
)

hcru_summary_gt$`Summary by settings using gtsummary`
Summary Table
Characteristic
ED
IP
OP
control, N = 501 treatment, N = 511 p-value2 control, N = 531 treatment, N = 651 p-value3 control, N = 501 treatment, N = 561 p-value4
Visits

0.2

0.7

0.4
    1 43 (86%) 39 (76%)
41 (77%) 50 (77%)
41 (82%) 42 (75%)
    2 7 (14%) 12 (24%)
11 (21%) 15 (23%)
7 (14%) 13 (23%)
Cost

>0.9

0.7

0.7
    Sum 150,620 153,831
1,066,097 1,333,135
62,427 67,378
    Mean (SD) 3,012 (1,866) 3,016 (1,768)
20,115 (11,119) 20,510 (9,822)
1,249 (794) 1,203 (834)
    Median (Q1, Q3) 2,769 (1,689, 4,020) 2,651 (1,630, 4,341)
18,748 (12,805, 27,399) 19,962 (12,593, 25,844)
1,048 (551, 1,704) 1,140 (462, 1,773)
    Q1 1,689 1,630
12,805 12,593
551 462
    Q3 4,020 4,341
27,399 25,844
1,704 1,773
    Min - Max 221 - 9,208 381 - 8,467
3,171 - 50,838 4,203 - 50,380
111 - 3,299 129 - 3,432
Visit_PPPM

0.6

0.059

>0.9
    Sum 32.28 48.96
69.12 52.23
48.34 56.04
    Mean (SD) 0.65 (1.03) 0.96 (2.32)
1.30 (2.10) 0.80 (1.40)
0.97 (2.18) 1.00 (2.25)
    Median (Q1, Q3) 0.28 (0.23, 0.52) 0.31 (0.25, 0.45)
0.54 (0.31, 0.80) 0.37 (0.25, 0.66)
0.39 (0.25, 0.68) 0.40 (0.26, 0.68)
    Q1 0.23 0.25
0.31 0.25
0.25 0.26
    Q3 0.52 0.45
0.80 0.66
0.68 0.68
    Min - Max 0.18 - 6.08 0.17 - 15.21
0.17 - 10.14 0.17 - 10.14
0.19 - 15.21 0.17 - 15.21
Visit_PPPY

0.6

0.059

>0.9
    Sum 388 588
831 628
581 673
    Mean (SD) 8 (12) 12 (28)
16 (25) 10 (17)
12 (26) 12 (27)
    Median (Q1, Q3) 3 (3, 6) 4 (3, 5)
7 (4, 10) 4 (3, 8)
5 (3, 8) 5 (3, 8)
    Q1 3 3
4 3
3 3
    Q3 6 5
10 8
8 8
    Min - Max 2 - 73 2 - 183
2 - 122 2 - 122
2 - 183 2 - 183
Cost_PPPM

0.8

0.5

0.4
    Sum 91,236 135,168
1,352,632 898,000
40,212 47,274
    Mean (SD) 1,825 (3,856) 2,650 (6,501)
25,521 (49,388) 13,815 (31,643)
804 (1,296) 844 (1,590)
    Median (Q1, Q3) 743 (498, 1,316) 760 (493, 1,316)
7,821 (3,584, 14,898) 6,447 (4,567, 12,358)
375 (222, 912) 334 (203, 703)
    Q1 498 493
3,584 4,567
222 203
    Q3 1,316 1,316
14,898 12,358
912 703
    Min - Max 93 - 22,330 141 - 33,841
1,121 - 234,312 1,113 - 246,638
68 - 8,373 32 - 9,937
Cost_PPPY

0.8

0.5

0.4
    Sum 1,096,318 1,624,220
16,253,639 10,790,638
483,198 568,061
    Mean (SD) 21,926 (46,330) 31,847 (78,122)
306,672 (593,467) 166,010 (380,231)
9,664 (15,577) 10,144 (19,103)
    Median (Q1, Q3) 8,928 (5,984, 15,813) 9,137 (5,919, 15,814)
93,975 (43,062, 179,024) 77,465 (54,876, 148,500)
4,507 (2,670, 10,955) 4,010 (2,436, 8,449)
    Q1 5,984 5,919
43,062 54,876
2,670 2,436
    Q3 15,813 15,814
179,024 148,500
10,955 8,449
    Min - Max 1,123 - 268,322 1,699 - 406,644
13,475 - 2,815,570 13,370 - 2,963,676
813 - 100,608 387 - 119,403
    3


1 (1.9%) 0 (0%)
2 (4.0%) 1 (1.8%)
LOS




0.8


    Sum


390.0 519.0



    Mean (SD)


7.4 (3.7) 8.0 (4.9)



    Median (Q1, Q3)


7.0 (5.0, 10.0) 7.0 (4.0, 10.0)



    Q1


5.0 4.0



    Q3


10.0 10.0



    Min - Max


2.0 - 18.0 2.0 - 22.0



Readmission


9 (17%) 6 (9.2%) 0.2


1 n (%)
2 Pearson’s Chi-squared test; Wilcoxon rank sum test
3 Fisher’s exact test; Wilcoxon rank sum test; Pearson’s Chi-squared test
4 Fisher’s exact test; Wilcoxon rank sum test

Generate the HCRU plot: average visits by cohort and time-line

# Calculate the average visits
sum_df1 <- hcru_summary$`Summary by settings using dplyr` |>
  dplyr::group_by(
    .data[["time_window"]],
    .data[["cohort"]],
    .data[["care_setting"]]
  ) |>
  dplyr::summarise(
    AVG_VISIT = mean(.data[["Visits"]], na.rm = TRUE), .groups = "drop"
  )

# Load the plot_hcru function
p1 <- plot_hcru(
  summary_df = sum_df1,
  x_var = "time_window",
  y_var = "AVG_VISIT",
  cohort_col = "cohort",
  facet_var = "care_setting",
  facet_var_n = 3,
  title = "Average visits by domain and cohort",
  x_label = "Healthcare Setting (Domain)",
  y_label = "Average visit",
  fill_label = "Cohort"
)

p1

Generate HCRU plot for average cost by cohort and timeline

# Calculate the total cost
df2 <- hcru_summary$`Summary by settings using dplyr` |>
  dplyr::group_by(
    .data[["time_window"]],
    .data[["cohort"]],
    .data[["care_setting"]]
  ) |>
  dplyr::summarise(
    AVG_COST = sum(.data[["Cost"]], na.rm = TRUE), .groups = "drop"
  )

p2 <- plot_hcru(
  summary_df = df2,
  x_var = "time_window",
  y_var = "AVG_COST",
  cohort_col = "cohort",
  facet_var = "care_setting",
  facet_var_n = 3,
  title = "Average cost by domain and cohort",
  x_label = "Healthcare Setting (Domain)",
  y_label = "Average cost",
  fill_label = "Cohort"
)

p2

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.