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reinstate ratet01 (#223)
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closes #221

---------

Co-authored-by: Emily de la Rua <[email protected]>
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@ayogasekaram @edelarua
ayogasekaram and edelarua authored Feb 27, 2024
1 parent 8f19657 commit aea2699
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58 changes: 57 additions & 1 deletion book/tables/efficacy/ratet01.qmd
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Expand Up @@ -5,6 +5,8 @@ subtitle: Event Rate Summary for Recurrent Events

------------------------------------------------------------------------

{{< include ../../test-utils/envir_hook.qmd >}}

This example will focus on data from a single visit "WEEK 1 DAY 8" and a single endpoint "FKSI-FWB".
Only evaluable populations will be analyzed.

Expand All @@ -29,9 +31,63 @@ anl <- df_explicit_na(anl)
## Customized Table

```{r variant1, test = list(result_v1 = "result")}
# Under development
lyt <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("ARM", ref_group = "B: Placebo", split_fun = ref_group_position("first")) %>%
analyze_vars(
"AVAL_f",
var_labels = "Number of exacerbations per patient",
.stats = c("count_fraction"),
.formats = c("count_fraction" = "xx (xx.xx%)"),
.label = c("Number of exacerbations per patient")
) %>%
summarize_glm_count(
vars = "AVAL",
variables = list(arm = "ARM", offset = "lgTMATRSK", covariates = NULL),
conf_level = 0.95,
distribution = "poisson",
rate_mean_method = "emmeans",
var_labels = "Unadjusted exacerbation rate (per year)",
table_names = "unadj",
.stats = c("rate"),
.labels = c(rate = "Rate")
) %>%
summarize_glm_count(
vars = "AVAL",
variables = list(arm = "ARM", offset = "lgTMATRSK", covariates = c("REGION1")),
conf_level = 0.95,
distribution = "quasipoisson",
rate_mean_method = "ppmeans",
var_labels = "Adjusted (QP) exacerbation rate (per year)",
table_names = "adj-qp",
.stats = c("rate", "rate_ci", "rate_ratio", "rate_ratio_ci", "pval"),
.labels = c(
rate = "Rate", rate_ci = "Rate CI", rate_ratio = "Rate Ratio",
rate_ratio_ci = "Rate Ratio CI", pval = "p value"
)
) %>%
summarize_glm_count(
vars = "AVAL",
variables = list(arm = "ARM", offset = "lgTMATRSK", covariates = c("REGION1")),
conf_level = 0.95,
distribution = "negbin",
rate_mean_method = "emmeans",
var_labels = "Adjusted (NB) exacerbation rate (per year)",
table_names = "adj-nb",
.stats = c("rate", "rate_ci", "rate_ratio", "rate_ratio_ci", "pval"),
.labels = c(
rate = "Rate", rate_ci = "Rate CI", rate_ratio = "Rate Ratio",
rate_ratio_ci = "Rate Ratio CI", pval = "p value"
)
)
result <- build_table(
lyt = lyt,
df = anl
)
result
```

{{< include ../../test-utils/save_results.qmd >}}

## `teal` App

```{r teal, opts.label=c('skip_if_testing', 'app')}
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4 changes: 2 additions & 2 deletions package/DESCRIPTION
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Expand Up @@ -36,10 +36,10 @@ Suggests:
stringr,
svglite (>= 2.1.2),
teal (>= 0.14.0),
teal.modules.clinical (>= 0.8.16),
teal.modules.clinical (>= 0.9.0),
teal.modules.general (>= 0.2.16),
teal.transform (>= 0.5.0),
tern (>= 0.9.3),
tern (>= 0.9.3.9019),
tern.mmrm (>= 0.3.0),
tern.rbmi,
testthat (>= 3.0.0),
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34 changes: 34 additions & 0 deletions package/tests/testthat/_snaps/ratet01/markdown-snaps.md
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@@ -0,0 +1,34 @@
# result_v1

Code
print(data_snap[[i]])
Output
B: Placebo A: Drug X C: Combination
(N=134) (N=134) (N=132)
———————————————————————————————————————————————————————————————————————————————————————————————————
Number of exacerbations per patient
0 10 (7.46%) 6 (4.48%) 11 (8.33%)
1 25 (18.66%) 21 (15.67%) 14 (10.61%)
2 38 (28.36%) 41 (30.60%) 33 (25.00%)
3 22 (16.42%) 26 (19.40%) 21 (15.91%)
4 13 (9.70%) 18 (13.43%) 30 (22.73%)
5 11 (8.21%) 10 (7.46%) 12 (9.09%)
6 10 (7.46%) 7 (5.22%) 7 (5.30%)
7 3 (2.24%) 4 (2.99%) 3 (2.27%)
8 1 (0.75%) 1 (0.75%) 1 (0.76%)
9 1 (0.75%) 0 (0.00%) 0 (0.00%)
Unadjusted exacerbation rate (per year)
Rate 7.2364 8.2148 9.8131
Adjusted (QP) exacerbation rate (per year)
Rate 2.4550 2.8514 3.4304
Rate CI (1.6194, 3.7219) (1.8974, 4.2850) (2.2946, 5.1284)
Rate Ratio 1.1615 1.3973
Rate Ratio CI (0.6462, 2.0877) (0.7789, 2.5067)
p value 0.6169 0.2619
Adjusted (NB) exacerbation rate (per year)
Rate 2.7374 2.8150 2.9789
Rate CI (2.3932, 3.1311) (2.4681, 3.2106) (2.6152, 3.3932)
Rate Ratio 1.0283 1.0882
Rate Ratio CI (0.8856, 1.1941) (0.9381, 1.2624)
p value 0.7140 0.2643

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