DEC
02
2020

# applied survival analysis using r exercises

Some are very strong predictors (sex, ECOG score). Now, let’s try creating a categorical variable on lung\$age with cut pounts at 0, 62 (the mean), and +Infinity (no upper limit). This happens when you track the sample/subject through the end of the study and the event never occurs. If we just focus on breast cancer, look at how big the data is! Cox PH regression can assess the effect of both categorical and continuous variables, and can model the effect of multiple variables at once. There are lots of ways to modify the plot produced by base R’s plot() function. The survival package is one of the few “core” packages that comes bundled with your basic R installation, so you probably didn’t need to install.packages() it. So, for a categorical variable like sex, going from male (baseline) to female results in approximately ~40% reduction in hazard. Survival Analysis is a sub discipline of statistics. Now, check out the help for ?summary.survfit. But it could also be the time until a hardware failure in a mechanical system, time until recovery, time someone remains unemployed after losing a job, time until a ripe tomato is eaten by a grazing deer, time until someone falls asleep in a workshop, etc. The log-rank test is asking if survival curves differ significantly between two groups. 96,97 In the example, mothers were asked if they would give the presented samples that had been stored for different times to their children. Using survfit(Surv(..., ...,)~..., data=colondeath), create a survival curve separately for males versus females. Create the survival object if you don’t have it yet, and instead of using summary(), use plot() instead. We’re not going to go into any more detail here, because there’s another package called survminer that provides a function called ggsurvplot() that makes it much easier to produce publication-ready survival plots, and if you’re familiar with ggplot2 syntax it’s pretty easy to modify. Cox regression is asking which of many categorical or continuous variables significantly affect survival.↩, Surv() can also take start and stop times, to account for left censoring. Fit a parametric survival regression model. Interestingly, the Karnofsky performance score as rated by the physician was marginally significant, while the same score as rated by the patient was not. These are location-scale models for an arbitrary transform of the time variable; the most common cases use a log transformation, leading to accelerated failure time models. This plot is substantially more informative by default, just because it automatically color codes the different groups, adds axis labels, and creates and automatic legend. Textbook Examples Applied Survival Analysis: Regression Modeling of Time to Event Data, Second Edition by David W. Hosmer, Jr., Stanley Lemeshow and Susanne May This is one of the books available for loan from Academic Technology Services (see Statistics Books for Loan for other such books and details about borrowing). Whether or not there was detectable cancer in >=4 lymph nodes, showing the p-value and confidence bands. You can play fast and loose with how you specify the arguments to Surv. Academia.edu is a platform for academics to share research papers. (New in survminer 0.2.4: the survminer package can now determine the optimal cutpoint for one or multiple continuous variables at once, using the surv_cutpoint() and surv_categorize() functions. A background in basic linear regression and categorical data analysis, as well as a basic knowledge of calculus and the R system, will help the reader to fully appreciate the information presented. 12(3):601-7, 1994.↩, Where “dead” really refers to the occurance of the event (any event), not necessarily death.↩, Predictive Analytics & Forecasting Influenza, Using the survminer package, plot a Kaplan-Meier curve for this analysis with confidence intervals and showing the p-value. In fact, it isn’t even the only R/Bioconductor package. coxph() implements the regression analysis, and models specified the same way as in regular linear models, but using the coxph() function. For example, you might want to simultaneously examine the effect of race and socioeconomic status, so as to adjust for factors like income, access to care, etc., before concluding that ethnicity influences some outcome. Is it significant? This series of exercises reviews some of the ... epidemiologic scenario taken from Tomas Aragon’s book "Applied Epdemiology Using R". In this kind of analysis you implicitly assume that the rates are constant over the period of the study, or as defined by the different groups you defined. And we can use that sequence vector with a summary call on sfit to get life tables at those intervals separately for both males (1) and females (2). Here we’ll create a simple survival curve that doesn’t consider any different groupings, so we’ll specify just an intercept (e.g., ~1) in the formula that survfit expects. The data is now housed at the Genomic Data Commons Portal. Prerequisites: Familiarity with R is required (including working with data frames, installing/using packages, importing data, and saving results); familiarity with dplyr and ggplot2 packages is highly recommended. We could continue adding a labels= option here to label the groupings we create, for instance, as “young” and “old”. Survival analysis does this by comparing the hazard at different times over the observation period. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. You must complete the setup here prior to class. Let’s pull out data for PAX8, GATA-3, and the estrogen receptor genes from breast, ovarian, and endometrial cancer, and plot the expression of each with a box plot. How are sex and status coded? Query individual genes, find coexpressed genes. SURVIVAL ANALYSIS A great many studies in statistics deal with deaths or with failures of components: the numbers of deaths, the timing of death, and the risks of death to which different classes of individuals are exposed. The curve is horizontal over periods where no event occurs, then drops vertically corresponding to a change in the survival function at each time an event occurs. We’re going to use the survivalTCGA() function from the RTCGA package to pull out survival information from the clinical data. Finally, we could assign the result of this to a new object in the lung dataset. It does this by looking at vital status (dead or alive) and creating a times variable that’s either the days to death or the days followed up before being censored. D.B. Read reviews from world’s largest community for readers. Similar to how survivalTCGA() was a nice helper function to pull out survival information from multiple different clinical datasets, expressionsTCGA() can pull out specific gene expression measurements across different cancer types. We’ll also be using the dplyr package, so let’s load that too. Offered by Imperial College London. Let’s look at breast cancer, ovarian cancer, and glioblastoma multiforme. If you go back and head(lung) the data, you can see how these are related. Hibbert, in Comprehensive Chemometrics, 2009. But you can reorder this if you want with factor(). Let’s go back to the lung cancer data and run a Cox regression on sex. But at p=.39, the difference in survival between those younger than 62 and older than 62 are not significant. Let’s go back to the colon cancer dataset. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. North Central Cancer Treatment Group. Major improvements of the second edition are the inclusion of the R language as one of the application tools, a new section on bootstrap estimation methods, a revised explanation and treatment of tree classifiers as well as extra examples and exercises. This is the hazard ratio – the multiplicative effect of that variable on the hazard rate (for each unit increase in that variable). First, let’s turn the colon data into a tibble, then filter the data to only include the survival data, not the recurrence data. We’ll cover more of these below. Click “Chemotherapy for Stage B/C colon cancer”, or be specific with ?survival::colon. Kaplan-Meier curves are good for visualizing differences in survival between two categorical groups,4 but they don’t work well for assessing the effect of quantitative variables like age, gene expression, leukocyte count, etc. This is the common shorthand you’ll often see for right-censored data. Fit another Cox regression model accounting for age, sex, and the number of nodes with detectable cancer. Simple query interface across all cancers for any mRNA, miRNA, or lncRNA gene (try SERPINA1), Precomputed Cox PH regression for every gene, for every cancer. Also, the x … Kaplan-Meier curves are good for visualizing differences in survival between two categorical groups, and the log-rank test you get when you ask for pval=TRUE is useful for asking if there are differences in survival between different groups. That 0.00111 p-value is really close to the p=0.00131 p-value we saw on the Kaplan-Meier plot. Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. Other readers will always be interested in your opinion of the books you've read. It’s more interesting to run summary on what it creates. 4.12.8.3 Survival Analysis. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. If you don’t have dplyr you can use the base subset() function instead. The R package(s) needed for this chapter is the survival package. The help tells you that when there are two unnamed arguments, they will match time and event in that order. Generally, survival analysis lets you model the time until an event occurs,1 or compare the time-to-event between different groups, or how time-to-event correlates with quantitative variables. You give it a list of clinical datasets to pull from, and a character vector of variables to extract. Try creating a survival object called s, then display it. But, what if we chose a different cut point, say, 70 years old, which is roughly the cutoff for the upper quartile of the age distribution (see ?quantile). Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. You can directly calculate the log-rank test p-value using survdiff(). Course materials for learning how to perform applied cost-effectiveness analysis with R - hesim-dev/rcea. The Kaplan-Meier curve illustrates the survival function. Create survival curves for each different subtype. Take a look at the size of the BRCA.mRNA dataset, show a few rows and columns. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. Many of the data sets discussed in the text are available in the accompanying R package “asaur” (for “Applied Survival Analysis Using R”), while others are in other packages. It looks like this, where $$T$$ is the time of death, and $$Pr(T>t)$$ is the probability that the time of death is greater than some time $$t$$. It shows the number at risk (number still remaining), and the cumulative survival at that instant. $$S$$ is a probability, so $$0 \leq S(t) \leq 1$$, since survival times are always positive ($$T \geq 0$$). The sample is censored in that you only know that the individual survived up to the loss to followup, but you don’t know anything about survival after that.2. How is this different from the lung data? That’s because the KM plot is showing the log-rank test p-value. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. See ?colon for more information about this dataset. The form of the Cox PH model is: $log(h(t)) = log(h_0(t)) + \beta_1 x_1 + \beta_2 x_2 + ... + \beta_p x_p$. This tells us that compared to the baseline brca group, GBM patients have a ~18x increase in hazards, and ovarian cancer patients have ~5x worse survival. Next, let’s load the RTCGA.clinical package and get a little help about what’s available there. Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. The extent of differentiation (well, moderate, poor), showing the p-value. How does survival differ by each type? See the help for ?expressionsTCGA. Take a look at the built in colon dataset. Use the same command to examine how many samples you have for each kidney sample type, separately by sex. This will show a life table. The response variable you create with Surv() goes on the left hand side of the formula, specified with a ~. Refer to this blog post for more information.). The best way to start getting comfortable with a new language is to use it. These tables show a row for each time point where either the event occured or a sample was censored. It’s a step function illustrating the cumulative survival probability over time. Just try creating a K-M plot for the nodes variable, which has values that range from 0-33. Handouts: Download and print out these handouts and bring them to class: In the class on essential statistics we covered basic categorical data analysis – comparing proportions (risks, rates, etc) between different groups using a chi-square or fisher exact test, or logistic regression. This could also happen due to the sample/subject dropping out of the study for reasons other than death, or some other loss to followup. You could also flip the sign on the coef column, and take exp(0.531), which you can interpret as being male resulting in a 1.7-fold increase in hazard, or that males die ad approximately 1.7x the rate per unit time as females (females die at 0.588x the rate per unit time as males). The alternative lets you specify interval data, where you give it the start and end times (time and time2). Look at the range of followup times in the lung dataset with range(). eBook File: Applied-survival-analysis-using-r.PDF Book by Dirk F. Moore, Applied Survival Analysis Using R Books available in PDF, EPUB, Mobi Format. They’re answering a similar question in a different way: the regression model is asking, “what is the effect of age on survival?”, while the log-rank test and the KM plot is asking, “are there differences in survival between those less than 70 and those greater than 70 years old?”. Survival analysis also goes by reliability theory in engineering, duration analysis in economics, and event history analysis in sociology.↩, This describes the most common type of censoring – right censoring. Of exercises reviews some of the study and the p-value Parametric survival model notice... Dplyr you can write a book review and share your experiences a time component censored! To start getting comfortable with a Kaplan-Meier plot you created a colondeath in! 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