How do you handle censoring in R survival analysis?

Code an event indicator as 1 when the event occurred and 0 when the case was censored, then pass it to Surv(time, event). Survival methods use the censored cases up to their last observed time rather than dropping them, so coding censoring as the event or removing those rows biases the results.

How do you get a hazard ratio in R?

Fit a Cox model with coxph(Surv(time, event) ~ predictors, data = df) and read summary(); the exp(coef) column gives the hazard ratio for each predictor with its confidence interval. A hazard ratio above 1 indicates higher risk of the event and below 1 indicates lower risk.

How do you check the proportional hazards assumption in R?

Run cox.zph() on the fitted Cox model, which tests the scaled Schoenfeld residuals. A significant p-value flags a violation, and plot(cox.zph(model)) should show roughly flat residuals over time. If the assumption fails, add a time interaction, stratify on the predictor, or use a model that does not require proportional hazards.

How to run a survival analysis in R

Q: How do you run a survival analysis in R?

Load the survival package, build the outcome with Surv(time, event) where event is 1 for the event and 0 for censored, estimate the Kaplan-Meier curve with survfit(), compare groups with survdiff() for the log-rank test, and model predictors with coxph(). Plot the curves with ggsurvplot() from survminer.

To run a survival analysis in R, you use the survival package: build the outcome with Surv(time, event), estimate the Kaplan-Meier curve with survfit(), compare groups with the log-rank test, and model predictors with a Cox proportional hazards regression in coxph(). The analysis that holds up depends on coding censoring correctly and checking the proportional hazards assumption. This guide covers the full workflow with code.

What survival analysis handles that regression cannot

Survival analysis models the time until an event, such as relapse, death, or equipment failure, while correctly handling censoring, the participants who leave the study or reach the end without the event. A plain linear regression in R cannot represent censored cases, and a logistic regression throws away the timing by reducing the outcome to a yes or no. Survival methods keep both.

Coding time and the censoring indicator

You need a time column and an event indicator coded 1 for the event and 0 for censored. The Surv() object pairs them:

library(survival)
df$event <- ifelse(df$status == "relapsed", 1, 0)
surv_obj <- Surv(time = df$months, event = df$event)

Coding censoring as if it were the event, or dropping censored cases entirely, biases every result that follows. This is the single most common error in applied survival work.

The Kaplan-Meier estimate

Estimate and plot the survival function overall, then split by a grouping variable to compare curves:

fit <- survfit(surv_obj ~ group, data = df)
summary(fit)          # survival probabilities over time

library(survminer)
ggsurvplot(fit, data = df, pval = TRUE, risk.table = TRUE)

The survminer package draws a publication-ready curve with the number at risk beneath it and the log-rank p-value on the plot, which is exactly what a results chapter needs.

Comparing groups with the log-rank test

The log-rank test asks whether two or more survival curves differ across the whole follow-up:

survdiff(surv_obj ~ group, data = df)

It is the survival counterpart of comparing group means, but it weighs the entire curve rather than a single time point, so it detects differences that a snapshot comparison would miss.

The Cox proportional hazards model

To estimate the effect of predictors on the hazard, fit a Cox model. Its coefficients exponentiate into hazard ratios, much as logistic coefficients become odds ratios:

cox <- coxph(Surv(months, event) ~ group + age + stage, data = df)
summary(cox)          # exp(coef) column is the hazard ratio

A hazard ratio above 1 means a higher instantaneous risk of the event; below 1 means a lower risk. The conceptual detail behind the Cox model is covered in the interpretation of regression coefficients more broadly.

Checking the proportional hazards assumption

The Cox model assumes the hazard ratio is constant over time. Test it with the scaled Schoenfeld residuals:

ph <- cox.zph(cox)
ph                     # a significant p flags a violation
plot(ph)               # residuals should be flat over time

When the assumption fails for a predictor, the usual remedies are to add a time interaction for that term, stratify on it, or fit a model that does not require proportional hazards. Report the check; reviewers look for it.

Reporting survival analysis

Report the Kaplan-Meier median survival per group with the log-rank p, then each Cox predictor as a hazard ratio with its 95% confidence interval and p-value. A clean sentence reads: the treatment group had longer survival than control (log-rank p = .008), and in the Cox model treatment was associated with a lower hazard, HR = 0.62, 95% CI [0.44, 0.88], p = .007. The formatting rules sit in presenting statistics in APA style, and the narrative belongs in your results section of a research paper.