Survival-Analysis | Survival Analysis VS Machine Learning | Machine Learning library
kandi X-RAY | Survival-Analysis Summary
kandi X-RAY | Survival-Analysis Summary
The wpbc dataset (available at learningdatabases/breast-cancerwisconsin/wpbc.data). It is available at databases/breastcancer-wisconsin/wpbc.names. We want to predict the probability of relapse (“recurrent”) at 24 months. To do this, you will compare the methods of survival analysis (Cox models, survival random forests,. . . ) with 1the classification methods. Performance measurements (including AUC) will be made on a test sub-sample consisting of 20 to 30% of the data (be careful to stratify well!).
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Trending Discussions on Survival-Analysis
QUESTION
I am new to survival analysis and I have been reading many research paper where the authors report adjusted (age and gender) and unadjusted hazard ratios along with confidence intervals. I am currently using CoxPHFitter from lifelines python package but I am unable to extract hazard ratios. I have followed many links e.g. https://databricks.com/notebooks/survival_analysis/survival_analysis_03_modeling_hazards.html and https://towardsdatascience.com/survival-analysis-part-a-70213df21c2e but none of them give any details on how to extract hazard ratio along with confidence intervals for adjusted or unadjusted cox regression. Using the "baseline_hazard" does give hazard ratio for the intervals but no confidence interval (I am not sure whether this is the right variable to look at) The "confidence_intervals" provides the confidence intervals of the covariates but I am looking for hazard ratio of the fitted model. Can anyone please help me with this? I am new to this analysis
...ANSWER
Answered 2022-Mar-15 at 12:28The hazard ratios (labelled exp(coef)
) and confidence intervals are available in the cph.summary
, and in a prettier format with cph.print_summary()
.
QUESTION
I am working with R. Using a tutorial, I was able to create a statistical model and produce visual plots for some of the outputs:
...ANSWER
Answered 2021-May-20 at 01:23As per the ggplot2 documentation, you need to provide a data.frame()
or object that can be converted (coerced) to a data.frame()
. In this case, if you want to reproduce the plot above in ggplot2, you will need to manually set up the data frame yourself.
Below is an example of how you could set up the data to display the plot in ggplot2.
Data FrameFirst we create a data.frame()
with the variables that we want to plot. The easiest way to do this is to just group them all in as separate columns. Note that I have used the as.numeric()
function to first coerce the predicted values to a vector, because they were previously a data.table
row, and if you don't convert them they are maintained as rows.
QUESTION
I am using the R programming language. I am trying to follow this tutorial over here: https://rviews.rstudio.com/2017/09/25/survival-analysis-with-r/ (bottom of the page).
I have slightly modified the code for this tutorial and have plotted the "staircases" (i.e. "survival functions", in the below picture "red", "blue", "green") corresponding to 3 of the observations in the data:
...ANSWER
Answered 2020-Dec-25 at 23:39The issue is that when you draw a plot in base
graphics draw directly on a device. The line of your code grob= plot(r_fit$unique.death.times, pred[1,], type = "l", col = "red")
creates a NULL
object (unlike ggplot
which would return a plot object).
You can make the plot directly in ggplot
(there are a few ways of doing this but I've done a simple example bolow) and convert it with ggplotly
:
QUESTION
I am trying to follow a tutorial in R (https://rviews.rstudio.com/2017/09/25/survival-analysis-with-r/).The computer I am using for work does not have a USB port or internet connection - it only has R with a few libraries installed. My work computer has "survival, ranger, ggplot2 and dplyr". However, it does not have "ggfortify". I am trying to figure out how to plot the graphs from the tutorial without 'ggfortify'. Here is the code I am using below:
...ANSWER
Answered 2020-Nov-27 at 21:20You are going to have to do some detective work!
I have time for part #2 today. So: It turns out, that the information about the strata is contained in the element km_trt_fit$strata
. It looks like this:
QUESTION
I am following a R tutorial over here https://rviews.rstudio.com/2017/09/25/survival-analysis-with-r/
The computer I use for work does not have internet access nor a USB port - it only has R with some preinstalled libraries. The tutorial requires "survival", "ggplot2", "ranger", "dplyr" and "ggfortify". The computer I use for work has all of these libraries EXCEPT ggfortfiy. Apparently, a function called "autoplot" is required from the ggfortify library to make some of the plots in this tutorial.
When I try to run the code from the tutorial:
...ANSWER
Answered 2020-Nov-27 at 09:28Yes, this is possible, because the autoplot
function uses ggplot2
under the hood:
QUESTION
I am a newbie in using and making sense of ML methods and currently doing survival analysis using gbm
package in R.
I have difficulty understanding some of the output of the survival prediction model. I have checked this tutorial and this post but still, find trouble in making sense of the outputted survival prediction model.
Here is my code for analysis based on example data:
...ANSWER
Answered 2020-Oct-09 at 05:00Amer. Thx for your reading of my tutorial!
As you mentioned that "The output returned from the predict
function represents the f(x)
component of the hazard function ( h(t|x)=lambda(t)*exp(f(x))
)", maybe we need to understand the hazard function, i.e. h(t|x).
Before this, please sure that you have the basic knowledge of survival analysis. if not, it's recommended to read the great post. I think the post would help you solve the questions.
Back to your questions:
- Exactly, we can get the hazard ratios of log scale by invoking the
predict
function. Therefore, the hazard ratio can be calculated byexp()
. - Sure! Relying on the values of hazard ratio, we can divide the population into low-risk and high-risk groups. Alternatively, you can use the median of hazard ratios as the cutoff value. I think the cutoff value should be derived from the training set, and then test in the test set. If your model is effective, KM plots for low and high-risk groups would have a significant difference (measured by log-rank test statistically).
- Calibration curve plots are often used to evaluated the performance of model that outputs probabilities or likelihoods ranged from [0.0, 1.0]. We can calculate the survival function, and then specify a time point of interest, e.g. 5-Year. At last, we compare the survival probabilities with the actual survival state at the specified time, which is just the same as we do evaluating a binary classification model. More details of obtaining survival function can refer to my tutorial, and the principles can be found in that post aforementioned.
QUESTION
Unfortunately I am not a programmer and I am encountering the error:
...ANSWER
Answered 2020-Apr-16 at 18:26You should run the program in Jupyter, or at least IPython. The %
is IPython-specific syntax, and pylab
is an IPython magic command for using matplotlib. Also note the file extension: .ipynb is a Jupyter notebook (formerly called IPython notebook), while a Python file would have a .py extension.
QUESTION
I just recently started to learn shiny.
Overview of this question: (1) How it works in R incl. script, (2) How it should work in Shiny, (3) Data
Question: where to write the shiny input$ID
in this function to make it calculate the nomogram
/nom
-score based on the UI
-inputs?
(1) How it works in R incl. script
I am performing survival-analysis, and I have produced a rather simple nomogram using the rms-package. The nomogram can be found below and is stored in nom
.
The nomogram currently looks like this:
The nomogram is based on some input covariates from my datasheet p
( which too is attached below [just as a sample]).
ANSWER
Answered 2020-Mar-31 at 12:48Here is something to start from.
You will need to provide output
for your score and score group somewhere. As a quick example, I used verbatimTextOutput
. However, you can provide text output, a table, or plot here.
In your server
you can have a reactive
expression to calculate the score based on the input
values provided. As each input
changes, your scores should change. A second function would calculate group based on score. You can use cut
instead of multiple if/then.
QUESTION
I am doing survival-analysis with the presence of competing risks
. I use the prodlim-package
, which I find quite useful. However, I do not like the build-in graphics, and would like to apply ggplot
instead.
Question: how can I extract the prodlim summary()
-output and load it into a data frame accessible to ggplot2?
Perhaps a function can be written to do this? I have previously received help on StackOverflow in terms of loading a summary()
-output into a dataframe
, but with a different package than prodlim
.
ANSWER
Answered 2020-Mar-30 at 19:08You can access the resulting list up to the level where the table for the plot is found and save it as a data.frame. You can check the structure of the list by using str
.
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