gs_design_ahr() derives info of all infinity values

[nanxstats]

POC:

library(gsDesign2)

gs_design_ahr(
  enroll_rate = define_enroll_rate(
    duration = c(2, 2, 10),
    rate = c(3, 6, 9),
  ),
  fail_rate = define_fail_rate(
    duration = 40,
    fail_rate = 0.0495,
    dropout_rate = 0.001,
    hr = 1
  ),
  alpha = 0.0125,
  beta = 0.2,
  info_frac = c(0.4, 0.8, 1),
  analysis_time = 36,
  ratio = 1,
  binding = FALSE,
  upper = gs_spending_bound,
  upar = list(
    sf = gsDesign::sfLDOF,
    sfupar = 1,
    total_spend = 0.0125,
    param = NULL,
    timing = NULL
  ),
  lower = gs_b,
  lpar = c(0, -Inf, -Inf),
  r = 18,
  tol = 1e-6
)

This yields:

Error in if (min(info - fastlag(info, first = 0)) <= 0) { : 
  missing value where TRUE/FALSE needed

The call stack is:

6.
check_info(info) at gs_power_npe.R#253
5.
gs_power_npe(theta = theta, theta0 = theta0, theta1 = theta1,
info = info * min_x, info0 = info0 * min_x, info1 = info *
min_x, info_scale = info_scale, upper = upper, upar = upar,
test_upper = test_upper, lower = lower, lpar = lpar, test_lower = test_lower, ... at gs_design_npe.R#375
4.
gs_design_npe(theta = y$theta, theta0 = 0, theta1 = theta1, info = y$info,
info0 = y$info0, info1 = info1, info_scale = info_scale,
alpha = alpha, beta = beta, binding = binding, upper = upper,
upar = upar, test_upper = test_upper, lower = lower, lpar = lpar, ... at gs_design_ahr.R#323
3.
withCallingHandlers(expr, message = function(c) if (inherits(c,
classes)) tryInvokeRestart("muffleMessage"))
2.
suppressMessages(allout <- gs_design_npe(theta = y$theta, theta0 = 0,
theta1 = theta1, info = y$info, info0 = y$info0, info1 = info1,
info_scale = info_scale, alpha = alpha, beta = beta, binding = binding,
upper = upper, upar = upar, test_upper = test_upper, lower = lower, ... at gs_design_ahr.R#323
1.
gs_design_ahr(enroll_rate = define_enroll_rate(duration = c(2,
2, 10), rate = c(3, 6, 9), ), fail_rate = define_fail_rate(duration = 40,
fail_rate = 0.0495, dropout_rate = 0.001, hr = 1), alpha = 0.0125,
beta = 0.2, info_frac = c(0.4, 0.8, 1), analysis_time = 36, ...

When I insert print(info) before the if call in check_info(), it looks like info has become purely Inf:

[1]  7.983019 15.966057 19.957571
[1]  7.983019 15.966057 19.957571
[1]  7.983019 15.966057 19.957571
[1] Inf Inf Inf
Error in if (min(info - fastlag(info, first = 0)) <= 0) { : 
  missing value where TRUE/FALSE needed

Naturally,

info <- c(Inf, Inf, Inf)
min(info - fastlag(info, first = 0)) <= 0

returns NA.

[nanxstats]

It appears using NULL for info_frac makes it work. However, the output does not work with to_integer():

library(gsDesign2)

design <- gs_design_ahr(
  enroll_rate = define_enroll_rate(
    duration = c(2, 2, 10),
    rate = c(3, 6, 9),
  ),
  fail_rate = define_fail_rate(
    duration = 40,
    fail_rate = 0.0495,
    dropout_rate = 0.001,
    hr = 1
  ),
  alpha = 0.0125,
  beta = 0.2,
  info_frac = NULL,
  analysis_time = 36,
  ratio = 1,
  binding = FALSE,
  upper = gs_spending_bound,
  upar = list(
    sf = gsDesign::sfLDOF,
    sfupar = 1,
    total_spend = 0.0125,
    param = NULL,
    timing = NULL
  ),
  lower = gs_b,
  lpar = c(0, -Inf, -Inf),
  r = 18,
  tol = 1e-6
)

design |> to_integer()

Error in if (cond1 || cond2 || cond3) { : 
  missing value where TRUE/FALSE needed
In addition: Warning messages:
1: In c(floor(event[1:(n_analysis - 1)]), ceiling(event[n_analysis])) %>%  :
  NAs introduced by coercion to integer range
2: In (ceiling(x$analysis$n[n_analysis]/multiply_factor) * multiply_factor) %>%  :
  NAs introduced by coercion to integer range

[jdblischak]

I checked some previous commits to see if this is a recent bug. I can only go back so far since the example uses define_enroll_rate() and define_fail_rate() (added about 6 months ago).

Running the example above with either commits 03d6a52 or 107265a, I get the same error from check_info():

Error in if (min(info - lag(info, default = 0)) <= 0) { : 
  missing value where TRUE/FALSE needed

> traceback()
6: check_info(info) at gs_power_npe.R#253
5: gs_power_npe(theta = theta, theta0 = theta0, theta1 = theta1, 
       info = info * min_x, info0 = info0 * min_x, info1 = info * 
           min_x, info_scale = info_scale, upper = upper, upar = upar, 
       test_upper = test_upper, lower = lower, lpar = lpar, test_lower = test_lower, 
       binding = binding, r = r, tol = tol) at gs_design_npe.R#375
4: gs_design_npe(theta = y$theta, theta0 = 0, theta1 = theta1, info = y$info, 
       info0 = y$info0, info1 = info1, info_scale = info_scale, 
       alpha = alpha, beta = beta, binding = binding, upper = upper, 
       upar = upar, test_upper = test_upper, lower = lower, lpar = lpar, 
       test_lower = test_lower, r = r, tol = tol) at gs_design_ahr.R#322
3: withCallingHandlers(expr, message = function(c) if (inherits(c, 
       classes)) tryInvokeRestart("muffleMessage"))
2: suppressMessages(allout <- gs_design_npe(theta = y$theta, theta0 = 0, 
       theta1 = theta1, info = y$info, info0 = y$info0, info1 = info1, 
       info_scale = info_scale, alpha = alpha, beta = beta, binding = binding, 
       upper = upper, upar = upar, test_upper = test_upper, lower = lower, 
       lpar = lpar, test_lower = test_lower, r = r, tol = tol)) at gs_design_ahr.R#322
1: gs_design_ahr(enroll_rate = define_enroll_rate(duration = c(2, 
       2, 10), rate = c(3, 6, 9), ), fail_rate = define_fail_rate(duration = 40, 
       fail_rate = 0.0495, dropout_rate = 0.001, hr = 1), alpha = 0.0125, 
       beta = 0.2, info_frac = c(0.4, 0.8, 1), analysis_time = 36, 
       ratio = 1, binding = FALSE, upper = gs_spending_bound, upar = list(sf = gsDesign::sfLDOF, 
           sfupar = 1, total_spend = 0.0125, param = NULL, timing = NULL), 
       lower = gs_b, lpar = c(0, -Inf, -Inf), r = 18, tol = 1e-06)

[LittleBeannie]

gs_design_ahr(
  enroll_rate = define_enroll_rate(
    duration = c(2, 2, 10),
    rate = c(3, 6, 9),
  ),
  fail_rate = define_fail_rate(
    duration = 40,
    fail_rate = 0.0495,
    dropout_rate = 0.001,
    hr = 1
  ),
  alpha = 0.0125,
  beta = 0.2,
  info_frac = c(0.4, 0.8, 1),
  analysis_time = 36,
  ratio = 1,
  binding = FALSE,
  upper = gs_spending_bound,
  upar = list(
    sf = gsDesign::sfLDOF,
    sfupar = 1,
    total_spend = 0.0125,
    param = NULL,
    timing = NULL
  ),
  lower = gs_b,
  lpar = c(0, -Inf, -Inf),
  r = 18,
  tol = 1e-6
)

Hello @nanxstats , thank you for raising this important question. I would like to understand the reason behind setting hr = 1. In practical scenarios, such settings are uncommon. If we were to change it to a positive number smaller than 1, it would yield the desired results. Please inform me if this response addresses your question, allowing us to appropriately close this matter.

[nanxstats]

@LittleBeannie Sure, from Keaven:

hr must not be equal to 1 throughout the study as this is the null hypothesis.
We could check this at input to avoid confusing output.
For gs_power_ahr() this should NOT be an issue as it would just be approximating the Type I error.