Fix outdated argument names and use canonical style for text elements

README.Rmd

@@ -65,12 +65,12 @@ This is a basic example which shows you how to solve a common problem.
 We assume there is a 4 month delay in treatment effect. Specifically, we
 assume a hazard ratio of 1 for 4 months and 0.6 thereafter. For this
 example we assume an exponential failure rate and low exponential
-dropout rate. The `enrollRates` specification indicates an expected
+dropout rate. The `enroll_rate` specification indicates an expected
 enrollment duration of 12 months with exponential inter-arrival times.
 
 ```{r, message=FALSE, warning=FALSE}
-library(gsDesign)
 library(gsDesign2)
+library(gsDesign)
 library(dplyr)
 library(gt)
 
@@ -106,10 +106,9 @@ fail_rate %>%
 ### Step 2: derive a fixed design with no interim analyses
 
 Computing a fixed sample size design with 2.5% one-sided Type I error
-and 90% power. We specify a trial duration of 36 months with
-`analysisTimes`. Enrollment duration is the sum of
-`enrollRates$duration`. We used the `fixed_design()` routine since there
-is a single analysis:
+and 90% power. We specify a trial duration of 36 months with `analysis_time`.
+Enrollment duration is the sum of `enroll_rate$duration`.
+We used `fixed_design()` since there is a single analysis:
 
 ```{r}
 fd <- fixed_design(
@@ -131,13 +130,16 @@ fd$enroll_rate %>%
   as_raw_html(inline_css = FALSE)
 ```
 
-The failure and dropout rates remain unchanged from what was input. The
-summary is obtained below. The columns are Design (sample size
-derivation method), N (sample size; generally you will round up to an
-even number), Events (generally you will round up), Bound (Z value for
-efficacy; this is the inverse normal from 1 - alpha), alpha (1-sided
-alpha level for testing), Power (power corresponding to enrollment,
-failure rate and trial targeted events).
+The failure and dropout rates remain unchanged from what was input.
+The summary is obtained below. The columns are:
+
+- `Design`: sample size derivation method.
+- `N`: sample size; generally you will round up to an even number.
+- `Event`: generally you will round up.
+- `Bound`: Z value for efficacy; this is the inverse normal from 1 - alpha.
+- `alpha`: 1-sided alpha level for testing.
+- `Power`: power corresponding to enrollment, failure rate, and
+  trial targeted events.
 
 ```{r}
 fd %>%
@@ -149,16 +151,16 @@ fd %>%
 ### Step 3: group sequential design
 
 We provide a simple example for a group sequential design that
-demonstrates a couple of features not available in the *gsDesign*
-package. The first is specifying analysis times by calendar time rather
+demonstrates a couple of features not available in the gsDesign package.
+The first is specifying analysis times by calendar time rather
 than information fraction. The second is not having an efficacy and
 futility bound at each analysis. This is in addition to having methods
 for non-proportional hazards as demonstrated in the fixed design above
 and again here.
 
-We use an O’Brien-Fleming spending function to derive our efficacy
+We use an O'Brien-Fleming spending function to derive our efficacy
 bounds at 24 and 36 months. For futility, we simply require a nominally
-significant trend in the wrong direction (p&lt;0.1) after 8 months, a
+significant trend in the wrong direction ($p < 0.1$) after 8 months, a
 trend in favor of experimental treatment after 14 months ($Z > 0$) and
 no bound later ($Z = -\infty$). Thus, we have two efficacy analyses and
 two separate, earlier futility analysis. Power is set to 80% due to the
@@ -187,11 +189,11 @@ gsd <- gs_design_ahr(
 ```
 
 Now we summarize the derived design. The summary table is further
-described in vignette *Summarize group sequential designs in nice gt
-tables*. Note that the design trend in favor of experimental treatment
-is very minor at 8 months due to the delayed effect assumption used (see
-AHR at analysis 1 in table). The design trend at 16 months is somewhat
-more favorable when we are looking for HR &lt; 1 (favoring experimental
+described in the vignette *Summarize group sequential designs in gt tables*.
+Note that the design trend in favor of experimental treatment
+is very minor at 8 months due to the delayed effect assumption used
+(see AHR at analysis 1 in table). The design trend at 16 months is somewhat
+more favorable when we are looking for HR < 1 (favoring experimental
 treatment) for a proof of concept. Actual bounds and timing selected for
 a trial are situation dependent, but we hope the suggestions here are
 provocative for what might be considered.

README.md

@@ -44,12 +44,12 @@ This is a basic example which shows you how to solve a common problem.
 We assume there is a 4 month delay in treatment effect. Specifically, we
 assume a hazard ratio of 1 for 4 months and 0.6 thereafter. For this
 example we assume an exponential failure rate and low exponential
-dropout rate. The `enrollRates` specification indicates an expected
+dropout rate. The `enroll_rate` specification indicates an expected
 enrollment duration of 12 months with exponential inter-arrival times.
 
 ``` r
-library(gsDesign)
 library(gsDesign2)
+library(gsDesign)
 library(dplyr)
 library(gt)
 
@@ -114,9 +114,9 @@ fail_rate %>%
 
 Computing a fixed sample size design with 2.5% one-sided Type I error
 and 90% power. We specify a trial duration of 36 months with
-`analysisTimes`. Enrollment duration is the sum of
-`enrollRates$duration`. We used the `fixed_design()` routine since there
-is a single analysis:
+`analysis_time`. Enrollment duration is the sum of
+`enroll_rate$duration`. We used `fixed_design()` since there is a single
+analysis:
 
 ``` r
 fd <- fixed_design(
@@ -158,12 +158,16 @@ fd$enroll_rate %>%
 </div>
 
 The failure and dropout rates remain unchanged from what was input. The
-summary is obtained below. The columns are Design (sample size
-derivation method), N (sample size; generally you will round up to an
-even number), Events (generally you will round up), Bound (Z value for
-efficacy; this is the inverse normal from 1 - alpha), alpha (1-sided
-alpha level for testing), Power (power corresponding to enrollment,
-failure rate and trial targeted events).
+summary is obtained below. The columns are:
+
+- `Design`: sample size derivation method.
+- `N`: sample size; generally you will round up to an even number.
+- `Event`: generally you will round up.
+- `Bound`: Z value for efficacy; this is the inverse normal from 1 -
+  alpha.
+- `alpha`: 1-sided alpha level for testing.
+- `Power`: power corresponding to enrollment, failure rate, and trial
+  targeted events.
 
 ``` r
 fd %>%
@@ -213,16 +217,16 @@ fd %>%
 ### Step 3: group sequential design
 
 We provide a simple example for a group sequential design that
-demonstrates a couple of features not available in the *gsDesign*
-package. The first is specifying analysis times by calendar time rather
-than information fraction. The second is not having an efficacy and
-futility bound at each analysis. This is in addition to having methods
-for non-proportional hazards as demonstrated in the fixed design above
-and again here.
+demonstrates a couple of features not available in the gsDesign package.
+The first is specifying analysis times by calendar time rather than
+information fraction. The second is not having an efficacy and futility
+bound at each analysis. This is in addition to having methods for
+non-proportional hazards as demonstrated in the fixed design above and
+again here.
 
 We use an O’Brien-Fleming spending function to derive our efficacy
 bounds at 24 and 36 months. For futility, we simply require a nominally
-significant trend in the wrong direction (p\<0.1) after 8 months, a
+significant trend in the wrong direction ($p < 0.1$) after 8 months, a
 trend in favor of experimental treatment after 14 months ($Z > 0$) and
 no bound later ($Z = -\infty$). Thus, we have two efficacy analyses and
 two separate, earlier futility analysis. Power is set to 80% due to the
@@ -251,7 +255,7 @@ gsd <- gs_design_ahr(
 ```
 
 Now we summarize the derived design. The summary table is further
-described in vignette *Summarize group sequential designs in nice gt
+described in the vignette *Summarize group sequential designs in gt
 tables*. Note that the design trend in favor of experimental treatment
 is very minor at 8 months due to the delayed effect assumption used (see
 AHR at analysis 1 in table). The design trend at 16 months is somewhat