README.md

In ck2136/SurvivalCurveExtraction: Exctract Survival Distribution Parameters from Digitized Curves

SurvivalCurveExtraction

This repository contains the codes necessary to produce an interactive shiny dash board. The original Guyot et al’s code is embedded in the dashboard. The original purpose of the document was to serve as the analysis toolkit for generating survival distribution parameters and fit statistics for health technology assessment (HTA) or cost-effectiveness analysis of pharmaceutical interventions.

Purpose

1. Allowing user to input datasets (as .csv files) and provide summary statistics of simulated Individual Patient Data (IPD) generated from survival plots of other studies.

2. Users can specify which distributions to include (e.g. Weibull, Gompertz, and even Splines).

3. Graphical display of the Kaplan-Meier plot and fitted parametric and semi-parametric distributions can also be extracted through the dashboard.

4. Parameters of the parametric distributions and fit statistics can be obtained..

Installation

You can install the released version of SurvivalCurveExtraction from CRAN with:

install.packages("SurvivalCurveExtraction")

This package won’t likely be released so…

# devtools::install_github("ck2136/SurvivalCurveExtraction")

Directions

There ar 3 main steps for extracting survival distribution parameters:

1. Digitize the survival curve
2. Create .csv files
3. Input the .csv files to generate best fitting curve to generated survival data

1. Digitizing the Survival Curve

1. Before embarking on the use of the dashboard, the user is required to have the necessary data files extracted from previous studies as described in Guyot et al. The first dataset should look like below:

df1 <- read.csv(paste0(here(),"/data-raw/digitized_data.csv"), header = FALSE)
df1 %>%
  head %>%
  kable 

| V1 | V2 | | -: | --------: | | 0 | 0.9954050 | | 1 | 0.9988023 | | 2 | 0.9946494 | | 3 | 0.9729813 | | 4 | 0.9683260 | | 5 | 0.9635587 |

The first 6 rows of the .csv file is shown to illustrate that as (t) goes from 0 to (T), the value of (S(t)) should be monotone decreasing from (1) to (0).

This file can be extracted from the website WebPlotDigitizer. In fact let’s walk through one example of a study using maintenance Olaparib in patients with newly diagnosed advanced Ovarian cancer. The survival curve that we’re going to try to extract is the Progression Free Survival (PFS) curve for patients that were using Olaparib.

Once you download/extract the file from the website, you now want to click the Launch button and load the image. You’ll see a screen like below:

Once the file is loaded and you select the Plot Type as 2D (X-Y) Plot click on the Align Axes button.

The next step is to select the points X1,X2, Y1, and Y2 and then input the numeric values as a reference point.

Proceed! TIP: Once you select each point, you can move the points around (in order to make better calibrations) by using the keyboard arrow keys.

Once you are done with selecting each of the points mentioned above, click on the Complete! button. You’ll see a box appear where now you enter in the values for each of the points X1,X2, Y1, and Y2. For this exercise we want (X1 = Y1 = 0) and (X2 = 57) and (Y2 = 1).

Now that we’ve set the reference points for our data points, now we need to select the data points that is to be recorded. On the right had side of the screen there are 4 buttons next to Mask. Usually for these exercises I use a pen and draw over the line that I’ll be extracting as such:

Once you’ve drawn over the curve make sure the Foreground Color is the appropriate color of the data points.

Now click on Run and you’ll see that red dots are generated! We’re almost done! At this point we can play around with the Algorithm to determine if we want to add more points or reduce the number of points. Once you feel ready, we can go to the left hand side of the website and click on View Data. Since we aren’t too concerned about going down to (>5) digits, I usually format the code to have (5) fixed digits. (Click on the Format button after selecting Fixed Digits). Then ou can download the .csv file. I’ve named it moore_b_ola.csv.

We’ve successfully digitized the curve data hooray!!!

Create .csv Files

Data check for monotone decreasing

This step is very important as this is the part where we are going to check the data for any inconsistencies. This data will later be used to generate survival distribution parameters for simulations that will impact the results! Typically the results will be identifying whether or not a certain treatment is more cost-effective compared to another. If we do a poor job in this step, the results of this study will NOT be appropriate and will lead to BIASED and INCORRECT conclusions. Let’s do a good job in terms of data checking such that our efforts here will yield appropriate conclusions.

The main point of this step is to identify if our data points are non-increasing (ideally decreasing as per a usual survival curve). One can visually check the data by plotting and also numerically check the data. Below is a plot of the (S(t)) by (t).

plot(df1$V1, df1$V2, xlab = "t", ylab = "S(t)")

It’s clear that the second data point has a greater (S(t)) value compared to the first data point. This clearly needs to be adjusted. As all survival curves usually have the first point as (S(t) = 1), we should replace the value to be 1. The code monotone_check() takes the original .csv file and converts it to a data file with (S(t)) as being monotone decreasing. Below it is in action:

df1 %>%
  head
#>   V1        V2
#> 1  0 0.9954050
#> 2  1 0.9988023
#> 3  2 0.9946493
#> 4  3 0.9729813
#> 5  4 0.9683260
#> 6  5 0.9635587

df1edited <- monotone_check(df1)
par(mfrow=c(1,2))
plot(df1$V1, df1$V2, xlab = "t", ylab = "S(t)", main = "Original")
plot(df1edited$Time, df1edited$`S(t)`, xlab = "t", ylab = "S(t)", main = "Data Checked")

For convenience, the monotone_check() function has been implemented in the shinyapp so if the user doesn’t want to deal with it manually, the application will deal with it foryou. (The user should still do a data check to make sure that the (S(t)) are monotone decreasing).

Once the data has been checked for monotone decreasing, we can go ahead and use the cleaned df1edited file as our first file indicated by Guyot et al. The next .csv file needs to look like below:

df2 <- read.csv(paste0(here(),"/data-raw/digitized_nrisk.csv"), header = TRUE)
df2 %>%
  kable 

| Rownumber | Time | Lower.index | Upper.index | Number.at.risk | | --------: | ---: | ----------: | ----------: | -------------: | | 1 | 0 | 1 | 3 | 260 | | 2 | 3 | 4 | 6 | 238 | | 3 | 6 | 7 | 9 | 226 | | 4 | 9 | 10 | 12 | 216 | | 5 | 12 | 13 | 15 | 206 | | 6 | 15 | 16 | 18 | 196 | | 7 | 18 | 19 | 21 | 190 | | 8 | 21 | 22 | 24 | 178 | | 9 | 24 | 25 | 27 | 170 | | 10 | 27 | 28 | 30 | 150 | | 11 | 30 | 31 | 33 | 137 | | 12 | 33 | 34 | 36 | 132 | | 13 | 36 | 37 | 39 | 108 | | 14 | 39 | 40 | 42 | 82 | | 15 | 42 | 43 | 45 | 42 | | 16 | 45 | 46 | 48 | 34 | | 17 | 48 | 49 | 51 | 3 | | 18 | 51 | 52 | 54 | 2 | | 19 | 54 | 55 | 57 | 0 |

• The columns need be as such:

  • Column 1: Row Number.

  • Column 2: Time in Months.

  • Column 3: Lower interval.

  • Column 4: Upper interval.

  • Column 5: Number at risk.

The values in each of the columns should reflect that of the values that are from the number at risk information from the original survival curve. At (t = 0), the number at risk for the Olaparib group is (260). So our interval for number at risk during this period will be ([0, 3)). The Lower index value (Column 3) should correspond to the index value in df$edited$Index which is 1. The Upper index value (Column 4) should be just before 3, in our case therefore should be (t = 2.5) which corresponds to (\text{index} = 6). Therefore, following along this logic, the user needs to create a .csv file that looks like df2.

Once the file has been created, we are ready to insert these .csv files into the dashboard!

Input the .csv files to generate best fitting curve to generated survival data

There are several ways that one could start the dashboard application:

1. Use shinyapps.io
2. Clone/Fork the github repo and run the application using Rstudio

Since it’s easier to use the web-application. I’m going to go with the server. Click to access shinyapps.io

In the Introduction tab there is a guideline that everyone should be able to follow.

The introduction is short and should be read everytime to reinforce the requirements for the extraction of the survival parameter estimates, the assumptions, and the workflow.

In the Data Input and Summary tab, we can enter our two .csv files created. The /data-raw/ folder should have the files digitized_data.csv and digitized_nrisk.csv for tutorial purposes. Select the appropriate columns that represent (S(t)) and (t). The numbes of events are (102) in the original study but you may play around with that value to see how the parameters change.

Note: You will also notice that the Results tabe will show up once you enter in both .csv files.

The Results tab will show

1. Parameter estimates
2. Fit Statistics
3. Plots

With appropriate (monotone decreasing data that is sensible) data, the desired outcomes should be achieved.

Contact

If there are any questions please do not hesitate to contact me

Code of Conduct

Please note that the ‘SurvivalCurveExtraction’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

ck2136/SurvivalCurveExtraction documentation built on March 20, 2021, 5:18 p.m.