Trim a prop_scr object
trim(x, low = NULL, high = NULL, quantile = FALSE)A prop_scr object
Low cut-off such that all participants with propensity scores less
than this value (or quantile if quantile = TRUE) are removed. If left
NULL no lower bound will be used
High cut-off such that all participants with propensity scores
greater than this value (or quantile if quantile = TRUE) are removed. If
left NULL no upper bound will be used
True/False value to determine if the cut-off values are based directly on the propensity scores (false) or their quantiles (true). By default this is false.
a prop_scr object with a trimmed propensity score distribution
This function uses R's default method of quantile calculation (type 7)
library(dplyr)
ps_obj <- calc_prop_scr(internal_df = filter(int_binary_df, trt == 0),
external_df = ex_binary_df,
id_col = subjid,
model = ~ cov1 + cov2 + cov3 + cov4)
trim(ps_obj, low = 0.3, high = 0.7)
#>
#> ── Model ───────────────────────────────────────────────────────────────────────
#> • cov1 + cov2 + cov3 + cov4
#>
#> ── Propensity Scores and Weights ───────────────────────────────────────────────
#> • Effective sample size of the external arm: 60
#> # A tibble: 84 × 4
#> subjid Internal `Propensity Score` `Inverse Probability Weight`
#> <int> <lgl> <dbl> <dbl>
#> 1 1 FALSE 0.333 0.500
#> 2 3 FALSE 0.539 1.17
#> 3 4 FALSE 0.546 1.20
#> 4 5 FALSE 0.344 0.524
#> 5 6 FALSE 0.393 0.646
#> 6 7 FALSE 0.390 0.639
#> 7 8 FALSE 0.340 0.515
#> 8 12 FALSE 0.356 0.553
#> 9 15 FALSE 0.344 0.524
#> 10 17 FALSE 0.440 0.785
#> # ℹ 74 more rows
#>
#> ── Absolute Standardized Mean Difference ───────────────────────────────────────
#> # A tibble: 4 × 3
#> covariate diff_unadj diff_adj
#> <chr> <dbl> <dbl>
#> 1 cov1 0.265 0.434
#> 2 cov2 0.0367 0.0163
#> 3 cov3 0.0123 0.0950
#> 4 cov4 0.272 0.372