Setting up to demonstrate jointVIP

See the Get started with jointVIP vignette to get started on how to use jointVIP package. Using the same data sets, this vignette’s main purpose is to demonstrate other options that are available.

library(jointVIP)

# gentle reminder of how to create a new jointVIP object
new_jointVIP = create_jointVIP(treatment = treatment,
                               outcome = outcome,
                               covariates = covariates,
                               pilot_df = pilot_df,
                               analysis_df = analysis_df)

# gentle reminder of how to create a new post_jointVIP object
post_optmatch_jointVIP = create_post_jointVIP(new_jointVIP,
                                             post_analysis_df = optmatch_df)

Demonstration for additional options in summary() and print()

# # simplest usage
# summary(new_jointVIP)

summary(new_jointVIP, 
        smd = 'pooled', 
        use_abs = FALSE, 
        bias_tol = 0.005)
#> Max bias is 0.093
#> Min bias is -0.003
#> 4 variables are above the desired 0.005 absolute bias tolerance
#> 8 variables can be plotted

print(new_jointVIP, 
      smd = 'pooled', 
      use_abs = FALSE, 
      bias_tol = 0.005)
#>           bias
#> log_re75 0.093
#> log_re74 0.038
#> marr     0.007
#> nodegree 0.005

# not run
# get_measures(new_jointVIP, smd = 'cross-sample')

The summary() and print() functions have the same additional parameters and uses rounded numbers to the third decimal place.

• The smd parameter allows only pooled or cross-sample options. The cross-sample is based on the analysis sample numerator and pilot sample denominator (equivalent to standardized version of the one-sample omitted variable bias). The cross-sample version is the default. The pooled version of the standardized mean difference (standard) can be specified. The pooled option is the standard option used in balance tables and Love plots that uses both treated and control variances from the analysis data set to construct the SMD. Note this pooled option applies the same formula to both binary and continuous variables.

• The use_abs parameter takes in either TRUE or FALSE, stating if set TRUE (default), the absolute measures are used. Otherwise, signed measured are used if set to be FALSE.

• The bias_tol parameter is to set the absolute bias tolerance that one wishes to examine at a glance. The default is 0.01.

Under the hood, get_measures() function is used to calculate. If the researcher wishes to save the measures calculated, perhaps get_measures() would be used; example is shown above. Only signed measures are presented as outputs for that function.

Demonstration for additional options in plot()

# # simplest usage
# plot(new_jointVIP)

plot(new_jointVIP, 
     smd = 'pooled', 
     use_abs = FALSE, 
     plot_title = 'Signed version of the jointVIP with pooled SMD')

plot(new_jointVIP, 
     bias_curve_cutoffs = c(0.005, 0.05, 0.10),
     text_size = 5, 
     label_cut_std_md = 0.1,
     max.overlaps = 15,
     plot_title = 'Increased text size and bias curve specifications',
     expanded_y_curvelab = 0.002
     #label_cut_outcome_cor = 0.2,
     #label_cut_bias = 0.1
     )

plot(new_jointVIP, 
     bias_curves = FALSE,
     add_var_labs = FALSE,
     plot_title = 'No bias curves or variable labels'
     )

There are many parameters for the plot() option. The smd and use_abs options functions the same as above. The other main parameter input is plot_title, which allows users to specify the title of the plot. Additional parameters not listed as a main parameter is explained and example usage is shown above.

• bias_curve_cutoffs: draws bias curves by the specifications. This is only used when smd is specified as cross-sample.
• text_size: text size of the variable labels can be increased.
• max.overlaps: maximum overlap of the variable labels.
• label_cut_std_md: standardized mean difference label cutoff, an example would be, if you wish to label all variables with standardized mean difference above 0.1.
• label_cut_outcome_cor: outcome correlation label cutoff, an example would be, if you wish to label all variables with outcome correlation above 0.2.
• label_cut_bias: bias label cutoff, an example would be, if you wish to label all variables with bias difference above 0.1.
• bias_curves: TRUE (default) draws the omitted variable bias curves, FALSE suppresses the bias curves. If bias_curve_cutoffs also specified, bias_curves takes priority. This is only used when smd is specified as cross-sample.
• add_var_labs: TRUE (default) adds variable labels. This suppresses all label_cut inputs if specified FALSE.
• expanded_y_curvelab: if one wishes to expand the y-axis, the bias curve labels don’t automatically get updated. So, this allows the fine-tuning of the bias-curve labels. Typically this is used under the hood for bootstrap version of the plot. However, user can specify this if they wish. This is only used when smd is specified as cross-sample.

Post-analysis parameters examples

The same variables are specified in the Get started with jointVIP vignette; here we choose a matching example to demonstrate the additional parameters.

# get_post_measures(post_optmatch_jointVIP, smd = 'cross-sample')

summary(post_optmatch_jointVIP,
        use_abs = FALSE,
        bias_tol = 0.01,
        post_bias_tol = 0.001)
#> Max bias is 0.113
#> Min bias is -0.003
#> 2 variables are above the desired 0.01 absolute bias tolerance
#> 8 variables can be plotted
#> 
#> Max absolute post-bias is 0.005
#> Post-measure has 6 variable(s) above the desired 0.001 absolute bias tolerance

print(post_optmatch_jointVIP,
      bias_tol = 0.001)
#>           bias post_bias
#> log_re75 0.113     0.005
#> log_re74 0.045     0.003
#> marr     0.005     0.003
#> nodegree 0.005     0.004
#> black    0.003     0.002
#> age      0.002     0.003
#> educ     0.001     0.001
#> hisp     0.001     0.001

plot(post_optmatch_jointVIP, 
     plot_title = "Post-match jointVIP using rcbalance matching",
     smd = 'cross-sample',
     use_abs = FALSE,
     add_post_labs = TRUE,
     post_label_cut_bias = 0.001)