monitOS

monitOS helps clinical teams define positivity thresholds for monitoring overall survival (OS) in pivotal trials for indolent cancers.

It turns a small set of trial assumptions into a table of operating characteristics you can use at interim and final analyses:

• the unacceptable detrimental effect on OS you want to rule out (hr_null)
• the plausible beneficial effect on OS you still consider realistic (hr_alt)
• the deaths expected at each analysis (events)

The framework is described in the monitOS paper.

Install and launch

Install from CRAN:

install.packages("monitOS")

Load the package:

library(monitOS)

Use the app

The fastest way to explore the method is through the interactive app.

• 🌐 Try it online (Shinylive) — runs entirely in your browser via WebAssembly, no installation required.
• 💻 Run locally with R:

monitOS::run_app()

What the inputs mean

monitOS is driven by a few clinically meaningful choices.

Concept	Argument	What it means
Unacceptable OS detriment	hr_null	The smallest OS HR that would already be too harmful to accept
Plausible OS benefit	hr_alt	The OS HR that still represents a realistic beneficial effect
Planned deaths	events	Cumulative deaths expected at each analysis
Final-analysis false positive rate	falsepos	One-sided error rate tolerated at the final analysis
Primary-analysis power	power_int	Required power at the primary analysis when the true OS HR equals hr_alt
Randomization ratio	rand_ratio	Experimental-to-control allocation ratio
Incremental-benefit scenario	hr_marg_benefit	Optional weaker-benefit scenario used for sensitivity analysis

Key convention: OS HR < 1 indicates benefit.

Quick start

This minimal example creates positivity thresholds for three planned analyses:

library(monitOS)

plan <- bounds(
  events = c(60, 110, 178),
  power_int = 0.90,
  falsepos = 0.025,
  hr_null = 1.30,
  hr_alt = 0.80
)

plan$summary

What to look for:

• OS HR threshold for positivity tells you the largest observed OS HR that still provides enough reassurance at each analysis.
• One-sided false positive error rate shows the tolerated error at each stage.
• Level of 2-sided CI needed to rule out delta null tells you the confidence level needed to exclude the unacceptable detriment.

Example 1: Standard 1:1 design

library(monitOS)

fl_trial <- bounds(
  events = c(50, 80, 110, 140, 175),
  power_int = 0.90,
  falsepos = 0.025,
  hr_null = 1.30,
  hr_alt = 0.80,
  rand_ratio = 1
)

fl_trial$summary

Interpretation: as evidence accumulates, the positivity threshold typically becomes more stringent. Early analyses may tolerate an observed OS HR slightly above 1, while the final analysis usually requires a threshold below 1.

Example 2: Unequal randomization with sensitivity analysis

library(monitOS)

cll_trial <- bounds(
  events = c(60, 95, 130, 165, 200),
  power_int = 0.90,
  falsepos = 0.025,
  hr_null = 1.25,
  hr_alt = 0.80,
  rand_ratio = 2,
  hr_marg_benefit = 0.95
)

cll_trial$summary

This adds an incremental-benefit column showing how often the monitoring thresholds would still be met if the true OS effect were more modest than the main alternative.

Need help?

• Package vignette: https://opensource.nibr.com/monitOS/articles/monitOS.html
• Interactive app: https://opensource.nibr.com/monitOS/shinylive-app/
• Function reference:

?bounds
?run_app

• Paper: https://www.tandfonline.com/doi/full/10.1080/19466315.2024.2365648
• Issues and questions: https://github.com/Novartis/monitOS/issues