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README.md
In survutils: Utility Functions for Survival Analysis
survutils
This package uses functional programming
principles to
iteratively run Cox regression and plot its results. The results are
reported in tidy data
frames. Additional utility
functions are available for working with other aspects of survival
analysis such as survival curves, C-statistics, etc. It has the
following features (grouped by major topics):
Cox Regression
get_cox_res: Run univariate or multivariate cox regression.iter_get_cox_res: Wrapper over get_cox_res to faciliate ease of
multiple get_cox_res runs. Internally, this makes use of
purrr:map to iterate over a list of features.plot_cox_res: Generates a forest plot of the univariate or
multivariate cox regression results from get_cox_res.
Kaplan Meier Estimates/Curves
get_surv_prob: Calculates the survival probability at specified
times from a survival curve.get_nrisk_tbl: Provides a number at risk table as typically seen
in
publications.get_logrank_res: Runs a log-rank test.
Other
get_c_stat: Calculate C-statistics.
How to Install
To get the released version from CRAN:
install.packages("survutils")
You can also get survutils through conda:
conda install -c fongchun r-survutils
To install the latest developmental version from github:
devtools::install_github("tinyheero/survutils")
Cox Regression
survutils provides a nice wrapper function get_cox_res that allows
you to quickly run an univariate or multivariate cox regression on a set
of data. The input data is a data.frame for instance (taking the colon
dataset from the survival R package as the example):
library("survival")
library("knitr")
library("survutils")
library("dplyr")
head(colon) %>%
select(age, obstruct, time, status, rx) %>%
kable()
| age| obstruct| time| status| rx |
|----:|---------:|-----:|-------:|:--------|
| 43| 0| 1521| 1| Lev+5FU |
| 43| 0| 968| 1| Lev+5FU |
| 63| 0| 3087| 0| Lev+5FU |
| 63| 0| 3087| 0| Lev+5FU |
| 71| 0| 963| 1| Obs |
| 71| 0| 542| 1| Obs |
The relevant columns are:
age and obstruct: These are the features we want to regress on.time: Time to event.status: Event status (1 for event; 0 for non-event).rx: Different treatment groups.
Then to run get_cox_res:
endpoint <- "time"
endpoint.code <- "status"
features <- c("age", "obstruct")
cox.res.df <- get_cox_res(colon, endpoint, endpoint.code, features)
#> Detected multiple features. Running multivariate cox regression
kable(cox.res.df)
| term | estimate| std.error| statistic| p.value| conf.low| conf.high| test_type |
|:---------|----------:|----------:|-----------:|----------:|----------:|----------:|:-----------|
| age | 0.9983432| 0.0028040| -0.5913434| 0.5542904| 0.9928717| 1.003845| multicox |
| obstruct | 1.2677379| 0.0808045| 2.9359039| 0.0033258| 1.0820531| 1.485287| multicox |
This runs a multivariate cox regression on the entire set of data. We
can plot the results using plot_cox_res:
plot_cox_res(cox.res.df)
#> Adding significance line
This gives us a forest plot with the hazard ratio and confidence
evidence for each feature. If we are interested in running cox
regression within each treatment group, we can make use of the group
parameter.
group <- "rx"
cox.res.df <- get_cox_res(colon, endpoint, endpoint.code, features, group)
#> Detected multiple features. Running multivariate cox regression
kable(cox.res.df)
| group | term | estimate| std.error| statistic| p.value| conf.low| conf.high| test_type |
|:--------|:---------|----------:|----------:|-----------:|----------:|----------:|----------:|:-----------|
| Obs | age | 1.0026174| 0.0046032| 0.5678581| 0.5701313| 0.9936124| 1.0117040| multicox |
| Obs | obstruct | 1.2123725| 0.1319705| 1.4592591| 0.1444938| 0.9360576| 1.5702528| multicox |
| Lev | age | 1.0042268| 0.0048754| 0.8651343| 0.3869651| 0.9946764| 1.0138689| multicox |
| Lev | obstruct | 1.4151910| 0.1293943| 2.6837694| 0.0072797| 1.0981822| 1.8237097| multicox |
| Lev+5FU | age | 0.9869403| 0.0051866| -2.5345800| 0.0112582| 0.9769584| 0.9970242| multicox |
| Lev+5FU | obstruct | 1.0844978| 0.1677669| 0.4835103| 0.6287334| 0.7805940| 1.5067186| multicox |
Notice how the output data.frame now has cox regression results for each
treatment group (i.e. Obs, Lev, Lev+5FU). We can use the plot_cox_res
function again and pass in a facet.formula to plot these results very
easily:
plot_cox_res(cox.res.df,
facet.formula = ". ~ group")
#> Adding significance line
This will facet the groups (per column) so that we can visualize the cox
regression results for each treatment group. The formula is the format
for ggplot2::facet_grid with the full documentation listed
here. In short, the
left hand side of the formula indicates what you want to facet by row.
The right hand side of the formula indicates what you want to facet by
column. By specifically . ~ group, we are indicating we do not want to
facet by row (this is indicated by the .) and we want to facet the
group variable by column.
We could have facetted by row too very easily:
plot_cox_res(cox.res.df,
facet.formula = "group ~ .")
#> Adding significance line
There are also other options (see ?plot_cox_res for full options) such
as the ability to add colors:
cox.res.df %>%
mutate(sig_flag = p.value < 0.05) %>%
plot_cox_res(facet.formula = ". ~ group", color.col = "sig_flag")
Running Cox Regression Multiple Times
One useful function is the iter_get_cox_res which allows you to easily
run the get_cox_res function multiple times without needing to setup a
for loop yourself. This is useful in situations where you might need to
perform multiple pairwise multivariate Cox regression analysis to test
the independence of a novel prognostic biomarker to existing biomarkers.
The input to the iter_get_cox_res function is the same as
get_cox_res with the only exception being the features parameter which
takes a list of vectors. Each element in the list indicates the features
you want to perform Cox regression on:
features <- list(c("age", "obstruct"),
c("age"))
iter_get_cox_res.df <-
iter_get_cox_res(colon, endpoint, endpoint.code, features)
#> Detected multiple features. Running multivariate cox regression
#> Detected only one feature. Running univariate cox regression
The output is a data frame with a iter_num column indicating a
separate Cox regression result from get_cox_res:
kable(iter_get_cox_res.df, caption = "Iterative Cox Regression Results")
| iter_num | term | estimate| std.error| statistic| p.value| conf.low| conf.high| test_type |
|:----------|:---------|----------:|----------:|-----------:|----------:|----------:|----------:|:-----------|
| 1 | age | 0.9983432| 0.0028040| -0.5913434| 0.5542904| 0.9928717| 1.003845| multicox |
| 1 | obstruct | 1.2677379| 0.0808045| 2.9359039| 0.0033258| 1.0820531| 1.485287| multicox |
| 2 | age | 0.9975589| 0.0027949| -0.8744983| 0.3818469| 0.9921094| 1.003038| unicox |
One could plot then the multiple Cox regression with facet by row as
follows:
plot_cox_res(iter_get_cox_res.df,
facet.formula = "iter_num ~ .", facet.scales = "free_y")
#> Adding significance line
By default, all features will appear in each facet. The facet.scales
parameter drops features on the y-axes that are not part of the specific
Cox regression.
You can even combine this with the group parameter:
iter_get_cox_res.group.df <-
iter_get_cox_res(colon, endpoint, endpoint.code, features,
group = "rx")
#> Detected multiple features. Running multivariate cox regression
#> Detected only one feature. Running univariate cox regression
kable(iter_get_cox_res.group.df, caption = "Iterative Cox Regression Results with Groups")
| iter_num | group | term | estimate| std.error| statistic| p.value| conf.low| conf.high| test_type |
|:----------|:--------|:---------|----------:|----------:|-----------:|----------:|----------:|----------:|:-----------|
| 1 | Obs | age | 1.0026174| 0.0046032| 0.5678581| 0.5701313| 0.9936124| 1.0117040| multicox |
| 1 | Obs | obstruct | 1.2123725| 0.1319705| 1.4592591| 0.1444938| 0.9360576| 1.5702528| multicox |
| 1 | Lev | age | 1.0042268| 0.0048754| 0.8651343| 0.3869651| 0.9946764| 1.0138689| multicox |
| 1 | Lev | obstruct | 1.4151910| 0.1293943| 2.6837694| 0.0072797| 1.0981822| 1.8237097| multicox |
| 1 | Lev+5FU | age | 0.9869403| 0.0051866| -2.5345800| 0.0112582| 0.9769584| 0.9970242| multicox |
| 1 | Lev+5FU | obstruct | 1.0844978| 0.1677669| 0.4835103| 0.6287334| 0.7805940| 1.5067186| multicox |
| 2 | Obs | age | 1.0019378| 0.0045949| 0.4213225| 0.6735196| 0.9929551| 1.0110018| unicox |
| 2 | Lev | age | 1.0032152| 0.0048522| 0.6615663| 0.5082492| 0.9937198| 1.0128013| unicox |
| 2 | Lev+5FU | age | 0.9866753| 0.0051580| -2.6006591| 0.0093045| 0.9767506| 0.9967007| unicox |
plot_cox_res(iter_get_cox_res.group.df,
facet.formula = "iter_num ~ group", facet.scales = "free_y")
#> Adding significance line
Kaplan Meier Estimates/Curves
If you have generated a Kaplan-meier, there are several functions you
can use to retrieve important statistics. For example, the
get_surv_prob function is used for retrieving survival probability at
certain times. Here is an example of how to generate survival
probabilities for just the “Obs” arm at times 100, 200, and 300:
library("dplyr")
library("survival")
library("survutils")
times <- c(100, 200, 300)
colon %>%
filter(rx == "Obs") %>%
survfit(Surv(time, status) ~ 1, data = .) %>%
get_surv_prob(times)
#> [1] 0.9730159 0.9174603 0.8476190
Here is a small trick you can employ to get the survival probability
that for both arms simultaneously:
library("purrr")
library("reshape2")
surv.prob.res <-
colon %>%
split(.$rx) %>%
map(~ survfit(Surv(time, status) ~ 1, data = .)) %>%
map(get_surv_prob, times)
surv.prob.res.df <- as_data_frame(surv.prob.res)
colnames(surv.prob.res.df) <- names(surv.prob.res)
surv.prob.res.df <-
surv.prob.res.df %>%
mutate(surv_prob_time = times)
surv.prob.res.df %>%
melt(id.vars = "surv_prob_time", value.name = "surv_prob",
variable.name = "group") %>%
kable()
| surv_prob_time| group | surv_prob|
|-----------------:|:--------|-----------:|
| 100| Obs | 0.9730159|
| 200| Obs | 0.9174603|
| 300| Obs | 0.8476190|
| 100| Lev | 0.9708942|
| 200| Lev | 0.9110224|
| 300| Lev | 0.8543053|
| 100| Lev+5FU | 0.9785720|
| 200| Lev+5FU | 0.9439177|
| 300| Lev+5FU | 0.9059629|
You can also retrieve a number at risks table using the get_nrisk_tbl
function. Here we will use it to get the number at risk at time 100,
200, and 300:
survfit(Surv(time, status) ~ rx, data = colon) %>%
get_nrisk_tbl(timeby = 100) %>%
filter(time %in% c(100, 200, 300)) %>%
kable()
| strata | time| n.risk|
|:-----------|-----:|-------:|
| rx=Obs | 100| 613|
| rx=Obs | 200| 578|
| rx=Obs | 300| 534|
| rx=Lev | 100| 601|
| rx=Lev | 200| 563|
| rx=Lev | 300| 528|
| rx=Lev+5FU | 100| 593|
| rx=Lev+5FU | 200| 572|
| rx=Lev+5FU | 300| 549|
R Session
devtools::session_info()
#> Session info -------------------------------------------------------------
#> setting value
#> version R version 3.4.2 (2017-09-28)
#> system x86_64, darwin13.4.0
#> ui unknown
#> language (EN)
#> collate en_CA.UTF-8
#> tz Europe/London
#> date 2018-02-10
#> Packages -----------------------------------------------------------------
#> package * version date source
#> assertthat 0.2.0 2017-04-11 cran (@0.2.0)
#> backports 1.1.1 2017-09-25 CRAN (R 3.4.2)
#> base * 3.4.2 2017-10-12 local
#> bindr 0.1 2016-11-13 cran (@0.1)
#> bindrcpp * 0.2 2017-06-17 cran (@0.2)
#> broom 0.4.3 2017-11-20 cran (@0.4.3)
#> colorspace 1.3-2 2016-12-14 cran (@1.3-2)
#> compiler 3.4.2 2017-10-12 local
#> datasets * 3.4.2 2017-10-12 local
#> devtools 1.13.3 2017-08-02 CRAN (R 3.4.2)
#> digest 0.6.12 2017-01-27 CRAN (R 3.4.2)
#> dplyr * 0.7.4 2017-09-28 cran (@0.7.4)
#> evaluate 0.10.1 2017-06-24 CRAN (R 3.4.2)
#> foreign 0.8-69 2017-06-21 CRAN (R 3.4.2)
#> ggplot2 2.2.1 2016-12-30 cran (@2.2.1)
#> glue 1.2.0 2017-10-29 cran (@1.2.0)
#> graphics * 3.4.2 2017-10-12 local
#> grDevices * 3.4.2 2017-10-12 local
#> grid 3.4.2 2017-10-12 local
#> gtable 0.2.0 2016-02-26 cran (@0.2.0)
#> highr 0.6 2016-05-09 CRAN (R 3.4.2)
#> htmltools 0.3.6 2017-04-28 CRAN (R 3.4.2)
#> knitr * 1.17 2017-08-10 CRAN (R 3.4.2)
#> labeling 0.3 2014-08-23 cran (@0.3)
#> lattice 0.20-35 2017-03-25 CRAN (R 3.4.2)
#> lazyeval 0.2.1 2017-10-29 cran (@0.2.1)
#> magrittr 1.5 2014-11-22 CRAN (R 3.4.2)
#> Matrix 1.2-11 2017-08-16 CRAN (R 3.4.2)
#> memoise 1.1.0 2017-04-21 CRAN (R 3.4.2)
#> methods 3.4.2 2017-10-12 local
#> mnormt 1.5-5 2016-10-15 cran (@1.5-5)
#> munsell 0.4.3 2016-02-13 cran (@0.4.3)
#> nlme 3.1-131 2017-02-06 CRAN (R 3.4.2)
#> parallel 3.4.2 2017-10-12 local
#> pillar 1.1.0 2018-01-14 cran (@1.1.0)
#> pkgconfig 2.0.1 2017-03-21 cran (@2.0.1)
#> plyr 1.8.4 2016-06-08 cran (@1.8.4)
#> psych 1.7.8 2017-09-09 cran (@1.7.8)
#> purrr * 0.2.4 2017-10-18 cran (@0.2.4)
#> R6 2.2.2 2017-06-17 CRAN (R 3.4.2)
#> Rcpp 0.12.13 2017-09-28 CRAN (R 3.4.2)
#> reshape2 * 1.4.3 2017-12-11 cran (@1.4.3)
#> rlang 0.1.6 2017-12-21 cran (@0.1.6)
#> rmarkdown 1.6 2017-06-15 CRAN (R 3.4.2)
#> rprojroot 1.2 2017-01-16 CRAN (R 3.4.2)
#> scales 0.5.0 2017-08-24 cran (@0.5.0)
#> splines 3.4.2 2017-10-12 local
#> stats * 3.4.2 2017-10-12 local
#> stringi 1.1.5 2017-04-07 CRAN (R 3.4.2)
#> stringr 1.2.0 2017-02-18 CRAN (R 3.4.2)
#> survival * 2.41-3 2017-04-04 CRAN (R 3.4.2)
#> survutils * 1.0.1 2018-02-10 local (tinyheero/survutils@NA)
#> tibble 1.4.2 2018-01-22 cran (@1.4.2)
#> tidyr 0.8.0 2018-01-29 cran (@0.8.0)
#> tools 3.4.2 2017-10-12 local
#> utils * 3.4.2 2017-10-12 local
#> withr 2.0.0 2017-07-28 CRAN (R 3.4.2)
#> yaml 2.1.14 2016-11-12 CRAN (R 3.4.2)
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survutils
This package uses functional programming principles to iteratively run Cox regression and plot its results. The results are reported in tidy data frames. Additional utility functions are available for working with other aspects of survival analysis such as survival curves, C-statistics, etc. It has the following features (grouped by major topics):
Cox Regression
get_cox_res: Run univariate or multivariate cox regression.iter_get_cox_res: Wrapper overget_cox_resto faciliate ease of multipleget_cox_resruns. Internally, this makes use ofpurrr:mapto iterate over a list of features.plot_cox_res: Generates a forest plot of the univariate or multivariate cox regression results fromget_cox_res.
Kaplan Meier Estimates/Curves
get_surv_prob: Calculates the survival probability at specified times from a survival curve.get_nrisk_tbl: Provides a number at risk table as typically seen in publications.get_logrank_res: Runs a log-rank test.
Other
get_c_stat: Calculate C-statistics.
How to Install
To get the released version from CRAN:
install.packages("survutils")
You can also get survutils through conda:
conda install -c fongchun r-survutils
To install the latest developmental version from github:
devtools::install_github("tinyheero/survutils")
Cox Regression
survutils provides a nice wrapper function get_cox_res that allows
you to quickly run an univariate or multivariate cox regression on a set
of data. The input data is a data.frame for instance (taking the colon
dataset from the survival R package as the example):
library("survival")
library("knitr")
library("survutils")
library("dplyr")
head(colon) %>%
select(age, obstruct, time, status, rx) %>%
kable()
| age| obstruct| time| status| rx | |----:|---------:|-----:|-------:|:--------| | 43| 0| 1521| 1| Lev+5FU | | 43| 0| 968| 1| Lev+5FU | | 63| 0| 3087| 0| Lev+5FU | | 63| 0| 3087| 0| Lev+5FU | | 71| 0| 963| 1| Obs | | 71| 0| 542| 1| Obs |
The relevant columns are:
ageandobstruct: These are the features we want to regress on.time: Time to event.status: Event status (1 for event; 0 for non-event).rx: Different treatment groups.
Then to run get_cox_res:
endpoint <- "time"
endpoint.code <- "status"
features <- c("age", "obstruct")
cox.res.df <- get_cox_res(colon, endpoint, endpoint.code, features)
#> Detected multiple features. Running multivariate cox regression
kable(cox.res.df)
| term | estimate| std.error| statistic| p.value| conf.low| conf.high| test_type | |:---------|----------:|----------:|-----------:|----------:|----------:|----------:|:-----------| | age | 0.9983432| 0.0028040| -0.5913434| 0.5542904| 0.9928717| 1.003845| multicox | | obstruct | 1.2677379| 0.0808045| 2.9359039| 0.0033258| 1.0820531| 1.485287| multicox |
This runs a multivariate cox regression on the entire set of data. We
can plot the results using plot_cox_res:
plot_cox_res(cox.res.df)
#> Adding significance line
This gives us a forest plot with the hazard ratio and confidence
evidence for each feature. If we are interested in running cox
regression within each treatment group, we can make use of the group
parameter.
group <- "rx"
cox.res.df <- get_cox_res(colon, endpoint, endpoint.code, features, group)
#> Detected multiple features. Running multivariate cox regression
kable(cox.res.df)
| group | term | estimate| std.error| statistic| p.value| conf.low| conf.high| test_type | |:--------|:---------|----------:|----------:|-----------:|----------:|----------:|----------:|:-----------| | Obs | age | 1.0026174| 0.0046032| 0.5678581| 0.5701313| 0.9936124| 1.0117040| multicox | | Obs | obstruct | 1.2123725| 0.1319705| 1.4592591| 0.1444938| 0.9360576| 1.5702528| multicox | | Lev | age | 1.0042268| 0.0048754| 0.8651343| 0.3869651| 0.9946764| 1.0138689| multicox | | Lev | obstruct | 1.4151910| 0.1293943| 2.6837694| 0.0072797| 1.0981822| 1.8237097| multicox | | Lev+5FU | age | 0.9869403| 0.0051866| -2.5345800| 0.0112582| 0.9769584| 0.9970242| multicox | | Lev+5FU | obstruct | 1.0844978| 0.1677669| 0.4835103| 0.6287334| 0.7805940| 1.5067186| multicox |
Notice how the output data.frame now has cox regression results for each
treatment group (i.e. Obs, Lev, Lev+5FU). We can use the plot_cox_res
function again and pass in a facet.formula to plot these results very
easily:
plot_cox_res(cox.res.df,
facet.formula = ". ~ group")
#> Adding significance line
This will facet the groups (per column) so that we can visualize the cox
regression results for each treatment group. The formula is the format
for ggplot2::facet_grid with the full documentation listed
here. In short, the
left hand side of the formula indicates what you want to facet by row.
The right hand side of the formula indicates what you want to facet by
column. By specifically . ~ group, we are indicating we do not want to
facet by row (this is indicated by the .) and we want to facet the
group variable by column.
We could have facetted by row too very easily:
plot_cox_res(cox.res.df,
facet.formula = "group ~ .")
#> Adding significance line
There are also other options (see ?plot_cox_res for full options) such
as the ability to add colors:
cox.res.df %>%
mutate(sig_flag = p.value < 0.05) %>%
plot_cox_res(facet.formula = ". ~ group", color.col = "sig_flag")
Running Cox Regression Multiple Times
One useful function is the iter_get_cox_res which allows you to easily
run the get_cox_res function multiple times without needing to setup a
for loop yourself. This is useful in situations where you might need to
perform multiple pairwise multivariate Cox regression analysis to test
the independence of a novel prognostic biomarker to existing biomarkers.
The input to the iter_get_cox_res function is the same as
get_cox_res with the only exception being the features parameter which
takes a list of vectors. Each element in the list indicates the features
you want to perform Cox regression on:
features <- list(c("age", "obstruct"),
c("age"))
iter_get_cox_res.df <-
iter_get_cox_res(colon, endpoint, endpoint.code, features)
#> Detected multiple features. Running multivariate cox regression
#> Detected only one feature. Running univariate cox regression
The output is a data frame with a iter_num column indicating a
separate Cox regression result from get_cox_res:
kable(iter_get_cox_res.df, caption = "Iterative Cox Regression Results")
| iter_num | term | estimate| std.error| statistic| p.value| conf.low| conf.high| test_type | |:----------|:---------|----------:|----------:|-----------:|----------:|----------:|----------:|:-----------| | 1 | age | 0.9983432| 0.0028040| -0.5913434| 0.5542904| 0.9928717| 1.003845| multicox | | 1 | obstruct | 1.2677379| 0.0808045| 2.9359039| 0.0033258| 1.0820531| 1.485287| multicox | | 2 | age | 0.9975589| 0.0027949| -0.8744983| 0.3818469| 0.9921094| 1.003038| unicox |
One could plot then the multiple Cox regression with facet by row as follows:
plot_cox_res(iter_get_cox_res.df,
facet.formula = "iter_num ~ .", facet.scales = "free_y")
#> Adding significance line
By default, all features will appear in each facet. The facet.scales
parameter drops features on the y-axes that are not part of the specific
Cox regression.
You can even combine this with the group parameter:
iter_get_cox_res.group.df <-
iter_get_cox_res(colon, endpoint, endpoint.code, features,
group = "rx")
#> Detected multiple features. Running multivariate cox regression
#> Detected only one feature. Running univariate cox regression
kable(iter_get_cox_res.group.df, caption = "Iterative Cox Regression Results with Groups")
| iter_num | group | term | estimate| std.error| statistic| p.value| conf.low| conf.high| test_type | |:----------|:--------|:---------|----------:|----------:|-----------:|----------:|----------:|----------:|:-----------| | 1 | Obs | age | 1.0026174| 0.0046032| 0.5678581| 0.5701313| 0.9936124| 1.0117040| multicox | | 1 | Obs | obstruct | 1.2123725| 0.1319705| 1.4592591| 0.1444938| 0.9360576| 1.5702528| multicox | | 1 | Lev | age | 1.0042268| 0.0048754| 0.8651343| 0.3869651| 0.9946764| 1.0138689| multicox | | 1 | Lev | obstruct | 1.4151910| 0.1293943| 2.6837694| 0.0072797| 1.0981822| 1.8237097| multicox | | 1 | Lev+5FU | age | 0.9869403| 0.0051866| -2.5345800| 0.0112582| 0.9769584| 0.9970242| multicox | | 1 | Lev+5FU | obstruct | 1.0844978| 0.1677669| 0.4835103| 0.6287334| 0.7805940| 1.5067186| multicox | | 2 | Obs | age | 1.0019378| 0.0045949| 0.4213225| 0.6735196| 0.9929551| 1.0110018| unicox | | 2 | Lev | age | 1.0032152| 0.0048522| 0.6615663| 0.5082492| 0.9937198| 1.0128013| unicox | | 2 | Lev+5FU | age | 0.9866753| 0.0051580| -2.6006591| 0.0093045| 0.9767506| 0.9967007| unicox |
plot_cox_res(iter_get_cox_res.group.df,
facet.formula = "iter_num ~ group", facet.scales = "free_y")
#> Adding significance line
Kaplan Meier Estimates/Curves
If you have generated a Kaplan-meier, there are several functions you
can use to retrieve important statistics. For example, the
get_surv_prob function is used for retrieving survival probability at
certain times. Here is an example of how to generate survival
probabilities for just the “Obs” arm at times 100, 200, and 300:
library("dplyr")
library("survival")
library("survutils")
times <- c(100, 200, 300)
colon %>%
filter(rx == "Obs") %>%
survfit(Surv(time, status) ~ 1, data = .) %>%
get_surv_prob(times)
#> [1] 0.9730159 0.9174603 0.8476190
Here is a small trick you can employ to get the survival probability that for both arms simultaneously:
library("purrr")
library("reshape2")
surv.prob.res <-
colon %>%
split(.$rx) %>%
map(~ survfit(Surv(time, status) ~ 1, data = .)) %>%
map(get_surv_prob, times)
surv.prob.res.df <- as_data_frame(surv.prob.res)
colnames(surv.prob.res.df) <- names(surv.prob.res)
surv.prob.res.df <-
surv.prob.res.df %>%
mutate(surv_prob_time = times)
surv.prob.res.df %>%
melt(id.vars = "surv_prob_time", value.name = "surv_prob",
variable.name = "group") %>%
kable()
| surv_prob_time| group | surv_prob| |-----------------:|:--------|-----------:| | 100| Obs | 0.9730159| | 200| Obs | 0.9174603| | 300| Obs | 0.8476190| | 100| Lev | 0.9708942| | 200| Lev | 0.9110224| | 300| Lev | 0.8543053| | 100| Lev+5FU | 0.9785720| | 200| Lev+5FU | 0.9439177| | 300| Lev+5FU | 0.9059629|
You can also retrieve a number at risks table using the get_nrisk_tbl
function. Here we will use it to get the number at risk at time 100,
200, and 300:
survfit(Surv(time, status) ~ rx, data = colon) %>%
get_nrisk_tbl(timeby = 100) %>%
filter(time %in% c(100, 200, 300)) %>%
kable()
| strata | time| n.risk| |:-----------|-----:|-------:| | rx=Obs | 100| 613| | rx=Obs | 200| 578| | rx=Obs | 300| 534| | rx=Lev | 100| 601| | rx=Lev | 200| 563| | rx=Lev | 300| 528| | rx=Lev+5FU | 100| 593| | rx=Lev+5FU | 200| 572| | rx=Lev+5FU | 300| 549|
R Session
devtools::session_info()
#> Session info -------------------------------------------------------------
#> setting value
#> version R version 3.4.2 (2017-09-28)
#> system x86_64, darwin13.4.0
#> ui unknown
#> language (EN)
#> collate en_CA.UTF-8
#> tz Europe/London
#> date 2018-02-10
#> Packages -----------------------------------------------------------------
#> package * version date source
#> assertthat 0.2.0 2017-04-11 cran (@0.2.0)
#> backports 1.1.1 2017-09-25 CRAN (R 3.4.2)
#> base * 3.4.2 2017-10-12 local
#> bindr 0.1 2016-11-13 cran (@0.1)
#> bindrcpp * 0.2 2017-06-17 cran (@0.2)
#> broom 0.4.3 2017-11-20 cran (@0.4.3)
#> colorspace 1.3-2 2016-12-14 cran (@1.3-2)
#> compiler 3.4.2 2017-10-12 local
#> datasets * 3.4.2 2017-10-12 local
#> devtools 1.13.3 2017-08-02 CRAN (R 3.4.2)
#> digest 0.6.12 2017-01-27 CRAN (R 3.4.2)
#> dplyr * 0.7.4 2017-09-28 cran (@0.7.4)
#> evaluate 0.10.1 2017-06-24 CRAN (R 3.4.2)
#> foreign 0.8-69 2017-06-21 CRAN (R 3.4.2)
#> ggplot2 2.2.1 2016-12-30 cran (@2.2.1)
#> glue 1.2.0 2017-10-29 cran (@1.2.0)
#> graphics * 3.4.2 2017-10-12 local
#> grDevices * 3.4.2 2017-10-12 local
#> grid 3.4.2 2017-10-12 local
#> gtable 0.2.0 2016-02-26 cran (@0.2.0)
#> highr 0.6 2016-05-09 CRAN (R 3.4.2)
#> htmltools 0.3.6 2017-04-28 CRAN (R 3.4.2)
#> knitr * 1.17 2017-08-10 CRAN (R 3.4.2)
#> labeling 0.3 2014-08-23 cran (@0.3)
#> lattice 0.20-35 2017-03-25 CRAN (R 3.4.2)
#> lazyeval 0.2.1 2017-10-29 cran (@0.2.1)
#> magrittr 1.5 2014-11-22 CRAN (R 3.4.2)
#> Matrix 1.2-11 2017-08-16 CRAN (R 3.4.2)
#> memoise 1.1.0 2017-04-21 CRAN (R 3.4.2)
#> methods 3.4.2 2017-10-12 local
#> mnormt 1.5-5 2016-10-15 cran (@1.5-5)
#> munsell 0.4.3 2016-02-13 cran (@0.4.3)
#> nlme 3.1-131 2017-02-06 CRAN (R 3.4.2)
#> parallel 3.4.2 2017-10-12 local
#> pillar 1.1.0 2018-01-14 cran (@1.1.0)
#> pkgconfig 2.0.1 2017-03-21 cran (@2.0.1)
#> plyr 1.8.4 2016-06-08 cran (@1.8.4)
#> psych 1.7.8 2017-09-09 cran (@1.7.8)
#> purrr * 0.2.4 2017-10-18 cran (@0.2.4)
#> R6 2.2.2 2017-06-17 CRAN (R 3.4.2)
#> Rcpp 0.12.13 2017-09-28 CRAN (R 3.4.2)
#> reshape2 * 1.4.3 2017-12-11 cran (@1.4.3)
#> rlang 0.1.6 2017-12-21 cran (@0.1.6)
#> rmarkdown 1.6 2017-06-15 CRAN (R 3.4.2)
#> rprojroot 1.2 2017-01-16 CRAN (R 3.4.2)
#> scales 0.5.0 2017-08-24 cran (@0.5.0)
#> splines 3.4.2 2017-10-12 local
#> stats * 3.4.2 2017-10-12 local
#> stringi 1.1.5 2017-04-07 CRAN (R 3.4.2)
#> stringr 1.2.0 2017-02-18 CRAN (R 3.4.2)
#> survival * 2.41-3 2017-04-04 CRAN (R 3.4.2)
#> survutils * 1.0.1 2018-02-10 local (tinyheero/survutils@NA)
#> tibble 1.4.2 2018-01-22 cran (@1.4.2)
#> tidyr 0.8.0 2018-01-29 cran (@0.8.0)
#> tools 3.4.2 2017-10-12 local
#> utils * 3.4.2 2017-10-12 local
#> withr 2.0.0 2017-07-28 CRAN (R 3.4.2)
#> yaml 2.1.14 2016-11-12 CRAN (R 3.4.2)
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