Graphics
Kaplan-Meier Plot (KMG01)
1. Kaplan-Meier Plot (without comparative statistics)
- The
kmg01template produces the standard Kaplan-Meier Plot. - Users are expected to select a particular parameter for analysis.
- Users are expected to select the treatment groups to compare, otherwise, all treatment groups available in the input datasets will be plotted.
- The comparative statistics are not included by default.
- The estimation of median survival time per treatment group by default.
- More arguments in the
g_kmandcontrol_coxphfunctions can be passed through, please use the Help to find out more information.
proc_data <- log_filter(syn_data, PARAMCD == "OS", "adtte")
run(kmg01, proc_data, dataset = "adtte")
2. Kaplan-Meier Plot (with comparative statistics)
To enable the comparative statistics (hazard ratio and p-value), the argument annot_coxph needs to be set to TRUE. The compare group is determined by the levels in the factorized variable of treatment group and the first level is used as reference group in the statistics.
proc_data <- log_filter(syn_data, PARAMCD == "OS", "adtte")
run(
kmg01,
proc_data,
dataset = "adtte",
annot_coxph = TRUE,
control_annot_coxph = tern::control_coxph_annot(x = 0.33, y = 0.42)
)
3. Kaplan-Meier Plot (without censoring marks)
To suppress the censoring marks, set the argument cencor_show to FALSE.
proc_data <- log_filter(syn_data, PARAMCD == "OS", "adtte")
run(kmg01, proc_data, dataset = "adtte", censor_show = FALSE)
4. Kaplan-Meier Plot (without estimation of median survival time)
proc_data <- log_filter(syn_data, PARAMCD == "OS", "adtte")
run(kmg01, proc_data, dataset = "adtte", annot_surv_med = FALSE)
5. Kaplan-Meier Plot (with statistical annotation of either median or min of survival time)
To add the statistics annotation, use the function annot_stats. Options are min or median.
proc_data <- log_filter(syn_data, PARAMCD == "OS", "adtte")
run(kmg01, proc_data, dataset = "adtte", annot_stats = "median")
run(kmg01, proc_data, dataset = "adtte", annot_stats = c("min", "median"))
6. Kaplan-Meier Plot (without the table of patients at risk)
proc_data <- log_filter(syn_data, PARAMCD == "OS", "adtte")
run(kmg01, proc_data, dataset = "adtte", annot_at_risk = FALSE)
Mean Plot (MNG01)
1. Plot of Mean and Confidence Interval (with Table Section)
- The
mng01template produces the standard mean plot. - Note that the template
mng01is quite general. The users are expected to specify the analysis dataset and the visit variable in therunfunction, and select the parameters prior to therunfunction. - The table of summary statistics is included by default.
- The variable Analysis Value
AVALis used for plotting by default. - If the input dataset contains results of the same analyses in multiple units,(e.g. SI/CV units in
ADLB), please make sure that the parameters in appropriate units are selected in advance.
proc_data <- log_filter(syn_data, PARAMCD == "DIABP", "advs")
run(mng01, proc_data, dataset = "advs", x_var = c("AVISIT", "AVISITN"))
#> $`Diastolic Blood Pressure`
2. Plot of Mean and Confidence Interval of Change from Baseline of Vital Signs
proc_data <- log_filter(syn_data, PARAMCD == "DIABP", "advs")
run(mng01, proc_data, dataset = "advs", x_var = c("AVISIT", "AVISITN"), y_var = "CHG")
#> `geom_line()`: Each group consists of only one observation.
#> ℹ Do you need to adjust the group aesthetic?
#> $`Diastolic Blood Pressure`
3. Plot of Mean (+/-SD) (Changing the Statistics)
To change the statistics, use the argument interval_fun. Options are mean_ci, mean_sei, mean_sdi, median_ci, quantiles,range.
proc_data <- log_filter(syn_data, PARAMCD == "DIABP", "advs")
run(mng01, proc_data, dataset = "advs", x_var = c("AVISIT", "AVISITN"), interval_fun = "mean_sdi")
#> $`Diastolic Blood Pressure`
4. Plot of Mean and Confidence Interval (Modify Alpha Level)
To change the alpha level of the confidence interval, use the argument control = control_analyze_vars(conf_level = <0.xx>). Note that this is only in effect when interval_fun is set to mean_ci.
proc_data <- log_filter(syn_data, PARAMCD == "DIABP", "advs")
run(
mng01, proc_data,
dataset = "advs", x_var = c("AVISIT", "AVISITN"),
interval_fun = "mean_ci", control = tern::control_analyze_vars(conf_level = 0.80)
)
#> $`Diastolic Blood Pressure`
5. Plot of Mean and Confidence Interval （With Number of Patients Only)
proc_data <- log_filter(syn_data, PARAMCD == "DIABP", "advs")
run(mng01, proc_data, dataset = "advs", x_var = c("AVISIT", "AVISITN"), table = "n")
#> $`Diastolic Blood Pressure`
6. Plot of Mean and Confidence Interval (without Table Section)
proc_data <- log_filter(syn_data, PARAMCD == "DIABP", "advs")
run(mng01, proc_data, dataset = "advs", x_var = c("AVISIT", "AVISITN"), table = NULL)
#> $`Diastolic Blood Pressure`
A new argument has been added to control the theme (e.g. setting the angle of the axis); see an example below:
ggtheme <- ggplot2::theme(
panel.grid = ggplot2::element_line(colour = "black", linetype = 3),
panel.background = ggplot2::element_rect(fill = "white"),
legend.position = "top",
axis.text.x = ggplot2::element_text(angle = 22, hjust = 1, vjust = 1)
)
run(mng01, syn_data, dataset = "adlb", ggtheme = ggtheme)
#> $`Alanine Aminotransferase Measurement`
#>
#> $`C-Reactive Protein Measurement`
#>
#> $`Immunoglobulin A Measurement`