---
title: "Single outcome event of interest"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Single outcome event of interest}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE, warning = FALSE, message = FALSE,
  out.width = "100%",
  comment = "#>"
)
```

## Set up

Let us first load the packages required.

```{r}
library(CDMConnector)
library(CohortSurvival)
library(dplyr)
library(ggplot2)
```

We will create a cdm reference containing our example MGUS2 survival dataset. In practice you would use the CDMConnector package to connect to your data mapped to the OMOP CDM.

```{r}
cdm <- CohortSurvival::mockMGUS2cdm()
```

The mgus2 dataset contains survival data of 1341 sequential patients with monoclonal gammopathy of undetermined significance (MGUS), converted to the OMOP CDM from the one in the package `survival`. For more information see `?survival::mgus2` .

In this vignette we will first estimate survival following a diagnosis of MGUS, with death being our primary outcome of interest. In a study we would typically need to first define study cohorts ourselves, before starting the analysis, but in the case of our example data we already have these cohorts available: `mgus_diagnosis` for our target cohort and `death_cohort` for our outcome cohort.

If you have a target cohort and a death table in your cdm but not a death cohort per se, you can use the function [deathCohort]{https://ohdsi.github.io/CohortConstructor/articles/a01_building_base_cohorts.html?q=death#death-cohort} from CohortConstructor to create it. The most efficient way to do so is to restrict the creation to your target cohort, by calling `deathCohort(cdm, name = "death_cohort", subsetCohort = "name_of_your_target_cohort_table")`.

In our target cohort we also have a number of additional features (age or sex of the patients, for instance) recorded, which we will use for stratification.

Let us first take a quick look at the data we will be working with:

```{r}
cdm$mgus_diagnosis %>% 
  glimpse()

cdm$death_cohort %>% 
  glimpse()
```

## Overall survival

To estimate survival we only need to call the `estimateSingleEventSurvival()` function. This will give us all necessary results to assess the survival of the whole target cohort. Note that the output of this function will be in a `summarised_result` format. For more information on this type of object, check [the documentation](https://cran.r-project.org/package=omopgenerics/vignettes/summarised_result.html) of the package `omopgenerics`.

This will allow us to use functions from other packages that work with `summarised_result` objects, for instance, for plotting or visualising our output.

Alternatively, we can convert the output to a more intuitive format if we prefer to use other tools.

Let us first produce these overall results. Note that the minimal amount of inputs necessary for this function are the `cdm` object and the names of the target and outcome cohorts, which must be cohort tables in the cdm provided.

```{r}
MGUS_death <- estimateSingleEventSurvival(
  cdm,
  targetCohortTable = "mgus_diagnosis",
  outcomeCohortTable = "death_cohort"
)
MGUS_death %>% 
  glimpse()
class(MGUS_death)
```

The main output is a `summarised_result` object with the usual columns. All the information retrieved by the estimation process (estimates, events, summary and attrition) is stored in the main output table, `MGUS_death`, and its `settings` attribute provides information on the different result types available. We can check this with the `settings()` function from `omopgenerics`.

```{r}
settings(MGUS_death)
```

An advantage of working with a `summarised_result` object is that we can join these results with results from other packages like `IncidencePrevalence` or `CohortCharacteristics` and view them all together in a Shiny App.

The main visualisation functions in the package also work on this `summarised_result` object. For instance, if we wish to plot the survival estimates we can run the following line of code:

```{r}
plotSurvival(MGUS_death)
```

We can also decide to add a basic risk table underneath the plot, by setting the argument `riskTable = TRUE`.

```{r,fig.width=9,fig.height=9}
plotSurvival(MGUS_death, riskTable = TRUE)
```

This plotting function has some other parameters which can be tuned. For instance, we can plot the cumulative failure instead of the survival probability by asking for `cumulativeFailure = TRUE`. You can check all the plotting options in `?plotSurvival`.

```{r}
plotSurvival(MGUS_death, ribbon = FALSE)
plotSurvival(MGUS_death, cumulativeFailure = TRUE)
```

Because the output of the function `plotSurvival` is a ggplot, the user can also change other aspects of the plot manually by adding ggplot lines of code to the plot itself. For instance, we can switch the plot axes:

```{r}
plotSurvival(MGUS_death) + theme_bw() + ggtitle("Plot survival") + coord_flip()
```

If we want the time in the x axis in another unit (years or months), you can use the parameter `timeScale` from the function:

```{r}
plotSurvival(MGUS_death, timeScale = "years")
plotSurvival(MGUS_death, timeScale = "months")
```

We could clearly do the same for any other time scaling. For months, we would typically divide the estimates by 30.4375.

Apart from the survival estimates retrieved, our main output also contains information on the survival events, summary and attrition. There are different functions in the package which we can use to present that information in a tidy way.

The function `tableSurvival()` provides a survival summary. This includes the number of records (people in the target cohort), the number of events (people who experience the outcome event in the target cohort), and the median survival and restricted mean survival if available. This is provided for all combinations of target and outcome cohorts and stratifications available in the result.

```{r}
tableSurvival(MGUS_death)
```

Note that if less than 50% of the people in the target cohort experience the event, the median survival will not be reported (i.e. the output will be `NA(NA,NA)`).

Note, additionally, that restricted mean survival is reported for the whole study period (as long as follow up is available) by default, unless asked otherwise. Therefore, `restrictedMeanFollowUp = NULL` will provide this result for follow up as long as available. This parameter can be changed in the main estimation function, if the user wants to. If this parameter is set to a number greater than the follow up available, the summary table will display `NA` for this value. For instance, if we ask for a restricted mean follow up of 2 years in a target cohort that we can only follow for 300 days, `tableSurvival` will report a Restricted mean survival of `NA`.

This summary table function can also report survival at requested times. For instance, we might want to know the survival estimates for the cohort at 30, 90 and 180 days. We would do that as such:

```{r}
tableSurvival(MGUS_death, times = c(30,90,180))
```

If we ask for a survival estimate which is not available, the function will not return it. For instance, in this case, follow up is not available after 500 days, so:

```{r}
tableSurvival(MGUS_death, times = c(30,90,180, 500))
```

Other options of this table include giving the estimates in years instead of days, or changing its style (we can either manually ask for the aesthetic we want ot use one of the predefined ones). Check `?tableSurvival` for additional options included in the function parameters.

```{r}
tableSurvival(MGUS_death, times = c(1,2), timeScale = "years")

tableSurvival(MGUS_death, times = c(1,2), style = "darwin")
```

One of them is a special input called `.options`, which allows the user to ask for a variety of additional formatting choices. To see the list of options, we can call `optionsTableSurvival` :

```{r}
optionsTableSurvival()
```

So, for instance, if we want to add a title to the table and also change the decimal mark to "," and the big mark to ".", we would call:

```{r}
tableSurvival(MGUS_death, .options = list(title = "Survival summary",
                                          decimalMark = ",",
                                          bigMark = "."))
```

Another function that provides tabular results is `riskTable`. We may use it if we want a tidy presentation of the number of people at risk, number of people experiencing an event, and number of people censored, at all point of time available. Note that the timepoints will be the ones provided by the estimation function, which is by default `eventGap = 30`, so every 30 days. We can change that parameter at that stage if we want aggregated events at a smaller or bigger interval. Other options for this table are similar to the ones of `tableSurvival`.

```{r}
riskTable(MGUS_death)
```

## Survival format

If we are only working on a survival study, and we want to manually inspect the result tables, we might want to put them in an easier format to work with.

The function `asSurvivalResult()` allows us to convert the result to a survival format, with the main table containing the estimates and the information on events, summary and attrition being stored as attributes.

```{r}
# Transforming the output to a survival result format
MGUS_death_survresult <- MGUS_death %>% 
  asSurvivalResult() 
MGUS_death_survresult %>%
  glimpse()
# Events, attrition and summary are now attributes of the result object
attr(MGUS_death_survresult,"events") %>%
  glimpse()
attr(MGUS_death_survresult,"summary") %>%
  glimpse()
attr(MGUS_death_survresult,"attrition") %>%
  glimpse()
```

We can now directly check all estimates for times 10 to 15, for instance, like this:

```{r}
MGUS_death_survresult %>%
  filter(time %in% c(10:15))
```

The plotting and tabulating functions will work with both input types (`summarised_result` and `survival_result`). However, when specifying facetting and colouring options for the plots, we will need to use column names from the survival format. To know what variables are available to call, one can use the function `availableSurvivalGrouping()`. 

## Changing estimation parameters

So far we have seen how to estimate survival for the whole cohort with the default settings, and how to easily inspect and present the results.

The main function, `estimateSingleEventSurvival()`, has many input parameters that we can change to provide results more targeted to our needs. You can check all options, as usual, with `?estimateSingleEventSurvival`. We will comment on some of these options in this section.

By default, `outcomeWashout` is set to Inf, which means that we only include people who have not had the outcome before index date (as defined by their entry to the target cohort). We can change that and ask, for instance, for people to only be excluded from the estimation for a 30 day washout period instead. In this case, we would write `estimateSingleEventSurvival(cdm, "mgus_diagnosis", "death_cohort", outcomeWashout = 30)`.

Other options include requiring a minimum number of survival days (the default is set to `minimumSurvivalDays = 1`) or cutting the follow-up at a certain amount of time (the default is set to `followUpDays = Inf`, i.e. as much follow up as available).

We might want to retrieve event information at a smaller interval than the default, which is every 30 days. Note that if we use the usual exporting functions from `omopgenerics`, small numbers will be suppressed. This can be changed, but it is likely that you cannot report small counts anyway. Bigger intervals allow for aggregated counts which tend to be bigger.

Note that if you need to specify the event gap you want in the plotting function. If you change it in the estimation output, you will have to change it in the plot accordingly (with either the same `eventGap` number or a multiple, like we do in the following plot).

```{r,fig.width=13,fig.height=13}
MGUS_death_gap7 <- estimateSingleEventSurvival(cdm, "mgus_diagnosis", "death_cohort", eventGap = 7)
plotSurvival(MGUS_death_gap7, riskTable = TRUE, riskInterval = 14)
```

For all of these tweaks we need to run a separate analysis of survival, i.e. each run allows for only one distinct input parameter, except for the target and outcome cohorts. However, we can bind the results together and both plotting and table functions will automatically display all results with the required variables. Let us see that by estimating survival with a specific date censoring.

```{r}
MGUS_death_fu <- estimateSingleEventSurvival(
  cdm,
  targetCohortTable = "mgus_diagnosis",
  outcomeCohortTable = "death_cohort",
  followUpDays = 100
)

MGUS_death_all <- omopgenerics::bind(
  MGUS_death,
  MGUS_death_fu
)

MGUS_death_all %>%
  asSurvivalResult() %>%
  glimpse()
```
Note that the survival result has an additional column `follow_up_days`, as we would expect. Let us now see that both the `tableSurvival` and `plotSurvival` functions display all required information.

```{r}
plotSurvival(MGUS_death_all, facet = "follow_up_days")

tableSurvival(MGUS_death_all)
```

## With stratification

One of the most important parameters that we might be interested in using is the `strata` one. To estimate survival for particular strata of interest we need these features to have been added to the target cohort table, as columns. Therefore, for instance, if we want to stratify our estimation by sex, we need a column named `sex` with the necessary information in the target cohort of interest.

In our mgus example, we already have a number of cohort characteristics added to our diagnosis cohort. If we want to stratify by age group, by sex, and by both at the same time, we would do the following:

```{r}
MGUS_death_strata <- estimateSingleEventSurvival(cdm,
  targetCohortTable = "mgus_diagnosis",
  outcomeCohortTable = "death_cohort",
  strata = list(c("age_group"),
                c("sex"),
                c("age_group", "sex"))
) 
```

All the previous potential changes in other parameters, as well as the different visualisation options, still apply in this case. Therefore, we can get the summary table using `tableSurvival`, with now a row for each strata:

```{r}
tableSurvival(MGUS_death_strata)
```

To plot the results property, we will have to specify faceting and colouring options. To know what variables are available for plotting, we can use the function `availableSurvivalGrouping()` as mentioned before.

```{r}
availableSurvivalGrouping(MGUS_death_strata)
```

Now we know the column names in the survival format, which have non-unique values (as expected), are `age_group` and `sex`.
One choice is to separate the plots into sex and display different age groups in each one of them:

```{r, fig.height=6, fig.width=8}
plotSurvival(MGUS_death_strata,
             facet = "sex",
             colour = "age_group")
```

We can add the respective risk tables under each plot as well.

```{r,fig.width=17,fig.height=18}
plotSurvival(MGUS_death_strata,
             colour = c("age_group", "sex"),
             riskTable = TRUE)
```

Because of the density of information in the tables, sometimes the visualisation is not ideal. You might need to export the plot as an image directly into your computer and specify a size big enough to display the tables properly.

When facetting plots, the user can post-process the default graphic to customise facets. Next we show the default facets obtained for "age_group" and "sex" levels.
```{r,fig.width=17,fig.height=18}
plotSurvival(MGUS_death_strata, facet = c("age_group", "sex"))
```

Next, we create customised facets by using functions from the ggplot2 package. We set "age_group" to columns and "sex" to rows:
```{r,fig.width=17,fig.height=18}
plotSurvival(MGUS_death_strata) +
  facet_grid(rows = vars(sex), cols = vars(age_group))
  
```

## Multiple target or outcome cohorts

Sometimes we might want to estimate survival of multiple targets and/or outcomes. For the sake of this example, let us use the cohort of people who are diagnosed with a progression to multiple myeloma, which we have saved in our mgus2 dataset under the name `progression`, as an additional target cohort. We can then use again the `death_cohort` as the survival outcome.

The `estimateSingleSurvival` function does not allow different tables to be passed for the target and outcome cohorts. If we have the additional target in another table, like in our case, we will need to estimate survival in a separate call and group the results a posteriori using the function `bind` from `omopgenerics`:

```{r}
MM_death <- estimateSingleEventSurvival(cdm, "progression", "death_cohort")
MGUS_MM_death <- bind(MGUS_death, MM_death)
```

The main output now has the information for both target cohorts, as we can see in the settings table. We can check the summary table and plot the estimates coloured by target cohort like this:

```{r}
settings(MGUS_MM_death)
tableSurvival(MGUS_MM_death)
plotSurvival(MGUS_MM_death, colour = "target_cohort")
```

An alternative to running the estimation function twice is to have (or create) a table with both target or outcome cohorts together, each one with a different `cohort_definition_id`. Then, `estimateSingleEventSurvival` will automatically run the estimation process for all combinations of target and outcome cohorts available, and produce independent results.

Imagine, in our case, that we want to estimate time to event for people with a mgus diagnosis for both outcomes, progression to multiple myeloma and death. We need to unite both outcome tables into one, which we can do manually or using the function `bind` from `omopgenerics`:

```{r}
cdm <- bind(cdm$progression, cdm$death_cohort, name = "outcome_cohorts")
```

Now we can use the new `outcome_cohorts` table as our outcome table and we will get results for both analyses. Likewise, we can check the summary and plot for both outcomes:

```{r}
MGUS_death_prog <- estimateSingleEventSurvival(cdm, "mgus_diagnosis", "outcome_cohorts")
tableSurvival(MGUS_death_prog)
plotSurvival(MGUS_death_prog, colour = "outcome")
```

## Checking the assumption of proportionality between outcomes
If you want to test the proportional hazards assumption between outcomes or across strata, you can use the `logLog` parameter in the survival plot to visually assess the proportionality. With the aforementioned new `outcome_cohorts` table of the two outcomes we can check this as such, for `death` and `progression`:

```{r}
plotSurvival(MGUS_death_prog, colour = "outcome", logLog = TRUE)
```

## Export and import a survival output

The survival output with all its attributes can be exported and imported with the usual tools from `omopgenerics`, which are also available as exportable functions in this package. Therefore, to export the main output, with the usual parameters, we need to do the following:

```{r}
x <- tempdir()
exportSummarisedResult(MGUS_death, path = x, fileName = "result.csv")
```

```{r}
MGUS_death_imported <- importSummarisedResult(path = file.path(x, "result.csv"))
```

You can read more about exporting and importing `summarised_result` objects [here](https://cran.r-project.org/package=omopgenerics/vignettes/summarised_result.html).

Note that results are not suppressed at any point in `CohortSurvival` by default. However, when exporting results with `exportSummarisedResult`, any counts below 5 will be turned into `NA`. That can be changed with the parameter `minCellCount`. If left as is, the suppression will be done for small counts, as we can see by plotting the result after exporting and importing. Note the difference between the following risk table and the one we plotted at the beginning of this vignette:

```{r,fig.width=9,fig.height=9}
plotSurvival(MGUS_death_imported, riskTable = TRUE)
```

## Disconnect from the cdm database connection

We finish by disconnecting from the cdm.

```{r}
cdmDisconnect(cdm)
```
