Dot and Forest plots

Create Dot and Forest plots and associated data

create_dot_forest_plot(
  data,
  drug,
  benefit,
  risk,
  filters,
  category,
  type_graph,
  type_risk,
  select_nnx,
  x_scale_fixed_free,
  ci_method,
  space_btwn_out_yn = "Y"
)

Arguments

data

(data.frame) dataset

drug

(character) selected drug

benefit

(character) selected benefit

risk

(character) selected risk

filters

(character) selected filter

category

(character) selected category

type_graph

(character) selected way to display binary outcomes

type_risk

(character) selected way to display risk outcomes (crude proportions, exposure-adjusted rates (per 100 PYs))

select_nnx

(character) show NNT/NNH

x_scale_fixed_free

(character) free or fixed x-axis scale

ci_method

(character) selected method to display

space_btwn_out_yn

(character) control spacing between outcomes confidence intervals (Supplied in effects table, Calculated within the program)

Examples

dot_plot_src <- subset(effects_table, !is.na(Prop1))
bdin <- subset(dot_plot_src, Factor == "Benefit")
rdin <- subset(dot_plot_src, Factor == "Risk")

create_dot_forest_plot(
  data = dot_plot_src,
  drug = unique(dot_plot_src$Trt1),
  benefit = unique(bdin$Outcome),
  risk = unique(rdin$Outcome),
  filters = "None",
  category = "All",
  type_graph = "Absolute risk",
  type_risk = "Crude proportions",
  select_nnx = "Y",
  x_scale_fixed_free = "Fixed",
  ci_method = "Calculated",
  space_btwn_out_yn = "N"
)
#> [2024-07-17 15:32:12] > Prepare Dot plot data for binary outcomes
#> [2024-07-17 15:32:12] > trigger analysis based on type
#> [2024-07-17 15:32:12] >
#> absolute risk CI for binary outcomes is calculated and saved
#> [2024-07-17 15:32:12] > Prepare Forest plot data for absolute risk
#> [2024-07-17 15:32:12] > Prepare Dot plot data for binary outcomes
#> [2024-07-17 15:32:12] > trigger analysis based on type
#> [2024-07-17 15:32:12] >
#> absolute risk CI for binary outcomes is calculated and saved
#> [2024-07-17 15:32:12] > Prepare Forest plot data for absolute risk
#> [2024-07-17 15:32:12] > Dataout object from the create_order_label_der function is created
#> [2024-07-17 15:32:12] > Prepare data for Dot and Forest plots
#> [2024-07-17 15:32:12] > Create Dot plot
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.
#> [2024-07-17 15:32:12] > Create Forest plot
#> [2024-07-17 15:32:12] > Create Dot plot
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.
#> [2024-07-17 15:32:12] > Create Forest plot
#> [2024-07-17 15:32:12] > Create Dot and Forest plots and associated data
#> $myplot_lft0
#> NULL
#> 
#> $myplot_rgt0
#> NULL
#> 
#> $myplot_lft1

#> 
#> $myplot_rgt1

#> 
#> $myplot_lft2

#> 
#> $myplot_rgt2

#> 
#> $myplotdata1
#> # A tibble: 32 × 12
#>     rate treatment type   factor  outcome        group neword allobs mylab  fobs
#>    <dbl> <fct>     <chr>  <chr>   <chr>          <int>  <dbl>  <dbl> <chr> <dbl>
#>  1  0.46 Drug A    Binary Benefit Primary Effic…     1      5     16 "   …     4
#>  2  0.05 Placebo   Binary Benefit Primary Effic…     1      5     16 "   …     4
#>  3  0.2  Drug B    Binary Benefit Primary Effic…     2      4     16 "   …     4
#>  4  0.06 Placebo   Binary Benefit Primary Effic…     2      4     16 "   …     4
#>  5  0.46 Drug C    Binary Benefit Primary Effic…     3      3     16 "   …     4
#>  6  0.04 Placebo   Binary Benefit Primary Effic…     3      3     16 "   …     4
#>  7  0.14 Drug D    Binary Benefit Primary Effic…     4      2     16 "   …     4
#>  8  0.03 Placebo   Binary Benefit Primary Effic…     4      2     16 "   …     4
#>  9  0.19 Drug A    Binary Risk    Reoccurring AE     1     -2     16 "   …    12
#> 10  0.03 Placebo   Binary Risk    Reoccurring AE     1     -2     16 "   …    12
#> # ℹ 22 more rows
#> # ℹ 2 more variables: adjust_number <int>, mins_y <dbl>
#> 
#> $myplotdata2
#> # A tibble: 16 × 16
#>    treatment type   factor outcome group neword allobs mylab  fobs adjust_number
#>    <fct>     <chr>  <chr>  <chr>   <int>  <dbl>  <dbl> <chr> <dbl>         <int>
#>  1 Drug A    Binary Benef… Primar…     1      5     16 "   …     4             1
#>  2 Drug B    Binary Benef… Primar…     2      4     16 "   …     4             1
#>  3 Drug C    Binary Benef… Primar…     3      3     16 "   …     4             1
#>  4 Drug D    Binary Benef… Primar…     4      2     16 "   …     4             1
#>  5 Drug A    Binary Risk   Reoccu…     1     -2     16 "   …    12             2
#>  6 Drug B    Binary Risk   Reoccu…     4     -3     16 "   …    12             2
#>  7 Drug C    Binary Risk   Reoccu…     7     -4     16 "   …    12             2
#>  8 Drug D    Binary Risk   Reoccu…    10     -5     16 "   …    12             2
#>  9 Drug A    Binary Risk   Rare S…     2     -6     16 "   …    12             3
#> 10 Drug B    Binary Risk   Rare S…     5     -7     16 "   …    12             3
#> 11 Drug C    Binary Risk   Rare S…     8     -8     16 "   …    12             3
#> 12 Drug D    Binary Risk   Rare S…    11     -9     16 "   …    12             3
#> 13 Drug A    Binary Risk   Liver       3    -10     16 "   …    12             4
#> 14 Drug B    Binary Risk   Liver       6    -11     16 "   …    12             4
#> 15 Drug C    Binary Risk   Liver       9    -12     16 "   …    12             4
#> 16 Drug D    Binary Risk   Liver      12    -13     16 "   …    12             4
#> # ℹ 6 more variables: mins_y <dbl>, diff <dbl>, se <dbl>, lower <dbl>,
#> #   upper <dbl>, Trt2 <chr>
#>