Rescale a prop_scr object
rescale(x, n = NULL, scale_factor = NULL)a prop_scr obj
Desired sample size that the external data should effectively contribute to
the analysis of the internal trial data. This will be used to scale the
external weights if scale_factor is not specified
Value to multiple all weights by. This will be used to scale the
external weights if n is not specified
a prop_scr object with rescaled weights
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)
# weights in a propensity score object can be rescaled to achieve a desired effective sample size (i.e., sum of weights)
rescale(ps_obj, n = 75)
#>
#> ── Model ───────────────────────────────────────────────────────────────────────
#> • cov1 + cov2 + cov3 + cov4
#>
#> ── Propensity Scores and Weights ───────────────────────────────────────────────
#> • Effective sample size of the external arm: 75
#> # A tibble: 150 × 4
#> subjid Internal `Propensity Score` `Inverse Probability Weight`
#> <int> <lgl> <dbl> <dbl>
#> 1 1 FALSE 0.333 0.465
#> 2 2 FALSE 0.288 0.377
#> 3 3 FALSE 0.539 1.09
#> 4 4 FALSE 0.546 1.12
#> 5 5 FALSE 0.344 0.487
#> 6 6 FALSE 0.393 0.601
#> 7 7 FALSE 0.390 0.594
#> 8 8 FALSE 0.340 0.479
#> 9 9 FALSE 0.227 0.273
#> 10 10 FALSE 0.280 0.362
#> # ℹ 140 more rows
#>
#> ── Absolute Standardized Mean Difference ───────────────────────────────────────
#> # A tibble: 4 × 3
#> covariate diff_unadj diff_adj
#> <chr> <dbl> <dbl>
#> 1 cov1 0.339 0.0461
#> 2 cov2 0.0450 0.0204
#> 3 cov3 0.160 0.000791
#> 4 cov4 0.308 0.00857
# Or by a predetermined factor
rescale(ps_obj, scale_factor = 1.5)
#>
#> ── Model ───────────────────────────────────────────────────────────────────────
#> • cov1 + cov2 + cov3 + cov4
#>
#> ── Propensity Scores and Weights ───────────────────────────────────────────────
#> • Effective sample size of the external arm: 121
#> # A tibble: 150 × 4
#> subjid Internal `Propensity Score` `Inverse Probability Weight`
#> <int> <lgl> <dbl> <dbl>
#> 1 1 FALSE 0.333 0.751
#> 2 2 FALSE 0.288 0.608
#> 3 3 FALSE 0.539 1.75
#> 4 4 FALSE 0.546 1.81
#> 5 5 FALSE 0.344 0.785
#> 6 6 FALSE 0.393 0.970
#> 7 7 FALSE 0.390 0.958
#> 8 8 FALSE 0.340 0.773
#> 9 9 FALSE 0.227 0.441
#> 10 10 FALSE 0.280 0.584
#> # ℹ 140 more rows
#>
#> ── Absolute Standardized Mean Difference ───────────────────────────────────────
#> # A tibble: 4 × 3
#> covariate diff_unadj diff_adj
#> <chr> <dbl> <dbl>
#> 1 cov1 0.339 0.0461
#> 2 cov2 0.0450 0.0204
#> 3 cov3 0.160 0.000791
#> 4 cov4 0.308 0.00857