summarize_continuous_covariates.RdSummarizes the selected continuous covariates: number of values, mean, median, quantiles and range.
summarize_continuous_covariates(run, covariates = NULL, quantiles = c(0.05, 0.25, 0.75, 0.95), baseline_only = TRUE)
| run |
|
|---|---|
| covariates | character vector of continous covariates names. Default is
|
| quantiles | numeric vector of quantiles. Default are 5th, 25th, 75th and 95th percentiles. |
| baseline_only | logical. Consider only the baseline (= first) values of the subjects. Default is
|
A data frame.
EXAMPLERUN %>% summarize_continuous_covariates()#> # A tibble: 8 x 12 #> covariate n n_distinct mean median sd `5.0%` `25.0%` `75.0%` `95.0%` #> <fct> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Age (y) 527 58 52.5 56 13.0 24 46 62 70 #> 2 BMI (kg/… 527 438 28.0 27.4 4.60 21.5 24.7 30.7 36.7 #> 3 Baseline… 527 320 140. 134. 33.0 101 117. 157. 196. #> 4 Creatini… 527 517 109. 104. 30.4 67.6 87.3 128. 159. #> 5 Baseline… 527 291 2.55 2.39 1.07 1.22 1.81 3.08 4.63 #> 6 Glomerul… 527 480 94.1 90.8 21.1 66.6 79.6 105. 133. #> 7 Baseline… 527 437 7.66 6.99 3.06 3.73 5.61 9.14 14.0 #> 8 Weight (… 527 345 80.6 79.2 16.4 57.3 69.0 89.4 110. #> # … with 2 more variables: min <dbl>, max <dbl>#> # A tibble: 8 x 27 #> covariate n n_distinct mean median sd `5.0%` `10.0%` `15.0%` `20.0%` #> <fct> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Age (y) 527 58 52.5 56 13.0 24 33 38 42 #> 2 BMI (kg/… 527 438 28.0 27.4 4.60 21.5 22.4 23.4 24.2 #> 3 Baseline… 527 320 140. 134. 33.0 101 106. 110. 114. #> 4 Creatini… 527 517 109. 104. 30.4 67.6 74.7 80.2 83.9 #> 5 Baseline… 527 291 2.55 2.39 1.07 1.22 1.46 1.6 1.71 #> 6 Glomerul… 527 480 94.1 90.8 21.1 66.6 70.2 75.3 77.6 #> 7 Baseline… 527 437 7.66 6.99 3.06 3.73 4.44 4.93 5.27 #> 8 Weight (… 527 345 80.6 79.2 16.4 57.3 60.5 63.9 66.8 #> # … with 17 more variables: `25.0%` <dbl>, `30.0%` <dbl>, `35.0%` <dbl>, #> # `40.0%` <dbl>, `45.0%` <dbl>, `50.0%` <dbl>, `55.0%` <dbl>, `60.0%` <dbl>, #> # `65.0%` <dbl>, `70.0%` <dbl>, `75.0%` <dbl>, `80.0%` <dbl>, `85.0%` <dbl>, #> # `90.0%` <dbl>, `95.0%` <dbl>, min <dbl>, max <dbl>EXAMPLERUN %>% summarize_continuous_covariates(quantiles = NULL)#> # A tibble: 8 x 8 #> covariate n n_distinct mean median sd min max #> <fct> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Age (y) 527 58 52.5 56 13.0 18 75 #> 2 BMI (kg/m²) 527 438 28.0 27.4 4.60 18.8 44.6 #> 3 Baseline LDLC (mg/dL) 527 320 140. 134. 33.0 88.5 356 #> 4 Creatinine clearance (mL/mi… 527 517 109. 104. 30.4 38.1 253. #> 5 Baseline free PCSK9 (nM) 527 291 2.55 2.39 1.07 0 7.46 #> 6 Glomerular filtration rate … 527 480 94.1 90.8 21.1 42.6 187. #> 7 Baseline total PCSK9 (nM) 527 437 7.66 6.99 3.06 2.36 19.6 #> 8 Weight (kg) 527 345 80.6 79.2 16.4 45.8 154.#> # A tibble: 72 x 13 #> # Groups: covariate [8] #> covariate Study n n_distinct mean median sd `5.0%` `25.0%` `75.0%` #> <fct> <ord> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Age (y) 0 30 23 38.1 35.5 15.5 18.5 24.5 50 #> 2 Age (y) 1 24 16 33.8 35 12.3 19 23 41.8 #> 3 Age (y) 2 55 29 48.9 52 13.2 22.1 41.5 59 #> 4 Age (y) 3 24 17 35.7 36 9.71 21.3 29 42.8 #> 5 Age (y) 4 72 31 50.9 54 10.8 28.6 44 59 #> 6 Age (y) 5 149 38 57.4 58 10.1 39 50 65 #> 7 Age (y) 6 60 30 58.2 59.5 8.93 44.0 50.8 65 #> 8 Age (y) 7 61 31 53.8 55 9.77 36 50 60 #> 9 Age (y) 8 52 20 60.8 61 4.63 53.6 58 64 #> 10 BMI (kg/… 0 30 30 26.4 26.6 2.60 22.3 24.4 28.5 #> # … with 62 more rows, and 3 more variables: `95.0%` <dbl>, min <dbl>, #> # max <dbl>#> # A tibble: 72 x 9 #> # Groups: covariate [8] #> covariate Study n n_distinct mean median sd min max #> <fct> <ord> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Age (y) 0 30 23 38.1 35.5 15.5 18 65 #> 2 Age (y) 1 24 16 33.8 35 12.3 18 53 #> 3 Age (y) 2 55 29 48.9 52 13.2 18 65 #> 4 Age (y) 3 24 17 35.7 36 9.71 20 54 #> 5 Age (y) 4 72 31 50.9 54 10.8 21 65 #> 6 Age (y) 5 149 38 57.4 58 10.1 24 75 #> 7 Age (y) 6 60 30 58.2 59.5 8.93 37 73 #> 8 Age (y) 7 61 31 53.8 55 9.77 25 74 #> 9 Age (y) 8 52 20 60.8 61 4.63 49 72 #> 10 BMI (kg/m²) 0 30 30 26.4 26.6 2.60 21.7 30.5 #> # … with 62 more rows