diff --git a/_pkgdown.yml b/_pkgdown.yml
index 67e26bd7..2b8e2d5c 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -142,6 +142,7 @@ articles:
       - GentleIntroductionToGSD
       - gsDesignPackageOverview
       - SpendingFunctionOverview
+      - ConditionalPowerPlot
       - nNormal
       - hGraph
       - GraphicalMultiplicity
diff --git a/vignettes/ConditionalPowerPlot.Rmd b/vignettes/ConditionalPowerPlot.Rmd
new file mode 100644
index 00000000..ec4a3fdc
--- /dev/null
+++ b/vignettes/ConditionalPowerPlot.Rmd
@@ -0,0 +1,166 @@
+---
+title: "Conditional Power and Conditional Error"
+output: rmarkdown::html_vignette
+bibliography: gsDesign.bib
+vignette: >
+  %\VignetteIndexEntry{Conditional Power and Conditional Error}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include=FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  dev = "ragg_png",
+  dpi = 96,
+  fig.retina = 1,
+  fig.width = 7.2916667,
+  fig.asp = 0.618,
+  fig.align = "center",
+  out.width = "80%"
+)
+
+options(width = 58)
+```
+
+## Introduction
+
+```{r, echo=FALSE, message=FALSE, warning=FALSE}
+library(gsDesign)
+library(tidyr)
+library(knitr)
+library(tibble)
+```
+
+We provide a simple plot of conditional power at the time of interim analysis.
+While group sequential boundaries should be designed to be the primary decision boundaries, 
+conditional power evaluations can be useful supportive information. 
+
+## Design
+
+We consider the default design from `gsSurv()`, only altering the targeted hazard ratio to `hr = 0.7`, trial duration of `T = 36` and minimum follow-up of `minfup = 24`. This implies expected enrollment duration of 12 months at a constant rate.
+We note that the expected interim analysis timing leaves enough time for a data monitoring committee (DMC) to review and make recommendations to the trial sponsor between analyses.
+We use the `toInteger()` function to round sample size and event counts to appropriate integers resulting in a slightly larger power (90.05% versus the targeted 90%) and slightly altered interim timing (117/353 = 0.3314, 235/353 = 0.6657) compared to plan (0.3333, 0.6667).
+
+```{r}
+design <- gsSurv(hr = 0.7, lambdaC = log(2) / 12, minfup = 24, T = 36) %>% toInteger()
+design %>% gsBoundSummary()
+```
+
+We also provide a textual summary. 
+
+```{r, results = 'asis'}
+cat(design %>% summary())
+```
+
+## Update design at time of interim analysis
+
+Assume when the first interim is performed, there are 125 instead of the planned 117 endpoints included in the analysis.
+We update the bounds as follows:
+
+```{r}
+update <- gsDesign(
+  k = design$k,
+  test.type = design$test.type,
+  alpha = design$alpha,
+  beta = design$beta,
+  sfu = design$upper$sf,
+  sfupar = design$upper$param,
+  sfl = design$lower$sf,
+  sflpar = design$lower$param,
+  n.I = c(117, design$n.I[2:3]),
+  maxn.IPlan = design$n.I[design$k],
+  delta = design$delta,
+  delta1 = design$delta1,
+  delta0 = design$delta0
+)
+gsBoundSummary(
+  update,
+  deltaname = "HR",
+  logdelta = TRUE,
+  Nname = "Events",
+  digits = 4,
+  ddigits = 2,
+  tdigits = 1,
+  exclude = c(
+    "B-value", "CP", "CP H1", "PP",
+    paste0("P(Cross) if HR=", round(c(design$hr0, design$hr), digits = 2))
+  )
+)
+```
+
+## Testing and conditional power
+
+We assume an interim p-value of 0.04, one-sided.
+This does not come close to the first efficacy or futility bound above.
+However, it is a trend in the right direction.
+
+```{r}
+# Nominal 1-sided p-value
+p <- 0.04
+```
+
+This translates to a first order approximation of the Cox regression estimate with the @Schoenfeld1981 approximation:
+
+```{r}
+zn2hr(-qnorm(p), n = update$n.I[1])
+```
+Typically, conditional power is reported based on 3 different assumptions about the future treatment effect:
+
+1) The observed HR; here we base this on the above approximation.
+2) No treatment effect; this translates to conditional error.
+3) The originally targeted treatment effect in the design.
+
+These are displayed below, translated to the hazard ratio scale:
+
+```{r}
+cp <- gsCP(x = update, i = 1, zi = -qnorm(p))
+# 3 treatment effects as outlined above
+# design$ratio is the experimental:control randomization ratio
+exp(-cp$theta * sqrt((1 + design$ratio)^2 / design$ratio))
+```
+
+Now we display the probability of crossing an efficacy boundary before a futility boundary conditional on the above observed p-value at the first interim analysis from the above call to `gsCP()`.
+The columns of the resulting matrix correspond to the above treatment effects. 
+The rows correspond to the second interim and final analyses.
+Adding the numbers in the first column, we get a conditional power of `r round(sum(cp$upper$prob[,1]),3)` for the treatment effect observed at the first interim.
+The third column yields a conditional power of `r round(sum(cp$upper$prob[,3]),3)` for the originally targeted hazard ratio of `r design$hr`.
+Finally, the second column yields a conditional error under the assumption of a future hazard ratio of 1 (no underlying treatment effect) of 
+`r round(sum(cp$upper$prob[,2]), 3)`.
+This could be used with the conditional error method of @muller2004general to adapt the design for endpoints other than the time-to-event example used here (e.g., a binary outcome).
+
+```{r}
+cp$upper$prob
+```
+
+We demonstrate a conditional power plot that may be of some use.
+We will assume a wide range of potential underlying hazard ratios for future events.
+
+```{r}
+hr <- seq(.6, 1.1, .01)
+```
+
+We compute conditional probabilities based on the observed interim 1 p-value over this range:
+
+```{r}
+# Translate hazard ratio to standardized effect size
+theta <- -log(hr) * sqrt(design$ratio / (1 + design$ratio)^2)
+cp <- gsCP(x = update, i = 1, zi = -qnorm(p), theta = theta)
+```
+
+Finally, we plot conditional power as a function of future HR.
+Note the use of the power plot option for the **gsDesign** plot function.
+The `offset = 1` argument changes the legend to be labeled with "Future Analysis" 2 and 3.
+The solid black line shows the conditional probability of crossing any future bound prior to crossing a lower bound.
+In general, black lines on this plot will show the cumulative conditional probability of crossing an efficacy bound prior to crossing a lower bound by the time of any given future analysis by different underlying treatment effect assumptions.
+The red lines show 1 minus the cumulative conditional probability of crossing a lower bound prior to crossing an efficacy bound by any given future analysis by different underlying treatment effect (HR) assumptions.
+
+```{r}
+plot(cp, xval = hr, xlab = "Future HR", ylab = "Conditional Power/Error", 
+     main="Conditional probability of crossing future bound", offset = 1)
+```
+
+
+
+## References
diff --git a/vignettes/gsDesign.bib b/vignettes/gsDesign.bib
index 7dcca386..8b3352bf 100644
--- a/vignettes/gsDesign.bib
+++ b/vignettes/gsDesign.bib
@@ -187,6 +187,17 @@ @article{MaurerBretz2013
   pages   = {311--320}
 }
 
+@article{muller2004general,
+  title={A general statistical principle for changing a design any time during the course of a trial},
+  author={M{\"u}ller, Hans-Helge and Sch{\"a}fer, Helmut},
+  journal={Statistics in medicine},
+  volume={23},
+  number={16},
+  pages={2497--2508},
+  year={2004},
+  publisher={Wiley Online Library}
+}
+
 @book{PLWBook,
   author    = {Michael A. Proschan and  K. K. Gordon Lan and Janet Turk Wittes},
   title     = {Statistical Monitoring of Clinical Trials: A Unified Approach},