# Introduction. Survival Analysis. Censoring. Plan of Talk

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1 Survival Analysis Mark Lunt Arthritis Research UK Centre for Excellence in Epidemiology University of Manchester 01/12/2015 Survival Analysis is concerned with the length of time before an event occurs. Initially, developed for events that can only occur once (e.g. death) Using time to event is more efficient that just whether or not the event has occured. It may be inconvenient to wait until the event occurs in all subjects. Need to include subjects whose time to event is not known (censored). Plan of Talk Exact time that event occured (or will occur) is unknown. Most commonly right-censored: we know the event has not occured yet. Maybe because the subject is lost to follow-up, or study is over. Makes no difference provided loss to follow-up is unrelated to outcome.

2 Examples: Chronological Time Examples: Followup Time Time(months) Patient Accrual Observation Time(months) Followup Time Other types of censoring : Survival Curves Left : Event had already occured before the study started. Subject cannot be included in study. May lead to bias. Interval : We know event occured between two fixed times, but not exactly when. E.g. Radiological damage: only picked up when film is taken. : S(t) probability of surviving to time t. If there are r k subjects at risk during the k th time-period, of whom f k fail, probability of surviving this time-period for those who reach it is r k f k r k Probability of surviving the end of the k th time-period is the probability of surviving to the end of the (k 1) th time-period, times the probability of surviving the k th time-period. i.e S(k) = S(k 1) r k f k r k

3 Motion Sickness Study Motion Sickness Study Life-Table 21 subjects put in a cabin on a hydraulic piston, Bounced up and down for 2 hours, or until they vomited, whichever occured first. Time to vomiting is our survival time. Two subjects insisted on ending the experiment early, although they had not vomited (censored). Is censoring independent of expected event time? 14 subjects completed the 2 hours without vomiting. 5 subjects failed ID Time Censored r k f k S(t) 1 30 No /21 = No /20 S(30) = Yes /19 S(50) = No /18 S(50) = Yes /17 S(51) = No /16 S(66) = No /15 S(82) = Yes /14 S(92) = Yes /14 S(92) = Kaplan Meier Survival Curves Stata commands for Survival Analysis Plot of S(t) against (t). Always start at (0, 1). Can only decrease. Drawn as a step function, with a downwards step at each failure time. stset: sets data as survival Takes one variable: followup time Option failure = 1 if event occurred, 0 if censored sts list: produces life table sts graph: produces Kaplan Meier plot

4 Stata Output Kaplan Meier Curve: example sts list if group == 1 failure _d: _t: fail time Beg. Net Survivor Std. Time Total Fail Lost Function Error [95% Conf. Int.] Kaplan Meier survival estimate Comparing Survivor Functions Comparing Survivor Functions Null Hypothesis Survival in both groups is the same Alternative Hypothesis 1 Groups are different 2 One group is consistently better 3 One group is better at fixed time t 4 Groups are the same until time t, one group is better after 5 One group is worse up to time t, better afterwards. No test is equally powerful against all alternatives. Can use Logrank test Most powerful against consistent difference Modified Wilcoxon Test Most powerful against early differences Regression Should decide which one to use beforehand.

5 Motion Sickness Revisited Curves Less than 1/3 of subjects experienced an endpoint in first study. Further 28 subjects recruited Freqency and amplitude of vibration both doubled Intention was to induce vomiting sooner Were they successful? Kaplan Meier survival estimates, by group group = 1 group = 2 Comparison of Survivor Functions What to avoid sts test group gives logrank test for differences between groups sts test group, wilcoxon gives Wilcoxon test Test χ 2 p Logrank Wilcoxon Compare mean survival in each group. makes this meaningless Overinterpret the tail of a survival curve. There are generally few subjects in tails Compare proportion surviving in each group at a fixed time. Depends on arbitrary choice of time Lacks power compared to survival analysis Fine for description, not for hypothesis testing

6 The Hazard Function Cannot often simply compare groups, must adjust for other prognostic factors. Predicting survival function S is tricky. Easier to predict the hazard function. Hazard function h(t) is the risk of dying at time t, given that you ve survived until then. Can be calculated from the survival function. Survival function can be calculated from the hazard function. Hazard function easier to model Hazard for all cause mortality for time since birth Options for Modelling Hazard Function Comparing Hazard Functions Exponential Distribution Log Logistic Distribution Parametric Model Semi-parametric models (unrestricted baseline hazard) Smoothed baseline hazard time Untreated Treated Unknown Baseline Hazard time Untreated Treated Non constant Hazard Ratio time time Untreated Treated Untreated Treated

7 Parametric Regression Cox (Proportional Hazards) Regression Assumes that the shape of the hazard function is known. Estimates parameters that define the hazard function. Need to test that the hazard function is the correct shape. Was only option at one time. Now that semi-parametric regression is available, not used unless there are strong a priori grounds to assume a particular distribution. More powerful than semi-parametric if distribution is known Assumes shape of hazard function is unknown Given covariates x, assumes that the hazard at time t, h(t, x) = h 0 (t) Ψ(x) where Ψ = exp(β 1 x 1 + β 2 x ). Semi-parametric: h 0 is non-parametric, Ψ is parametric. t affects h 0, not Ψ x affects Ψ, not h 0 : Interpretation Suppose x 1 increases from x 0 to x 0 + 1, h(t, x 0 ) = h 0 (t) e (β 1x 0 ) h(t, x 0 + 1) = h 0 (t) e (β 1(x 0 +1)) h(t,x 0+1) h(t,x 0 ) = e β 1 = h 0 (t) e (β 1x 0 ) e β 1 = h(t, x 0 ) e β 1 Results may be presented as β or e β β > 0 e β > 1 risk increased β < 0 e β < 1 risk decreased Should include a confidence interval. i.e. the Hazard Ratio is e β 1

8 : Testing Assumptions in Stata We assume hazard ratio is constant over time: should test. Possible tests: Plot observed and predicted survival curves: should be similar. Plot log( log (S(t))) against log(t) for each group: should give parallel lines. Formal statistical test: Overall Each variable May need to fit interaction between time period and predictor: assume constant hazard ratio on short intervals, not over entire period. stcox varlist performs regression using varlist as predictors Option nohr gives coefficients in place of hazard ratios Testing Proportional Hazards : Example stcoxkm produced plots of observed and predicted survival curves stphplot produces log( log (S(t))) against log(t) (log-log plot) estat phtest gives overall test of proportional hazards estat phtest, detail gives test of proportional hazards for each variable.. stcox i.group Cox regression -- Breslow method for ties No. of subjects = 49 Number of obs = 49 No. of failures = 19 Time at risk = 4457 LR chi2(1) = 3.32 Log likelihood = Prob > chi2 = _t Haz. Ratio Std. Err. z P> z [95% Conf. Interval] group

9 Testing Assumptions: Kaplan-Meier Plot Testing Assumptions: log-log plot Survival Probability Observed: group = 1 Observed: group = 2 Predicted: group = 1 Predicted: group = 2 ln[ ln(survival Probability)] ln() group = 1 group = 2 Testing Assumptions: Formal Test Allowing for Non-Proportional Hazards. estat phtest Test of proportional hazards assumption chi2 df Prob>chi global test Effect of covariate varies with time Need to produce different estimates of effects at different times Use stsplit to split one record per person into several Fit covariate of interest in each time period separately

10 Non-Proportional Hazards Example Non-Proportional Hazards Example Survival Probability stcox i.treatment2 _t Haz. Ratio Std. Err. z P> z [95% Conf. Interval] treatment estat phtest Test of proportional hazards assumption Time: Time chi2 df Prob>chi global test Observed: treatment2 = Standard Predicted: treatment2 = Standard Observed: treatment2 = Drug B Predicted: treatment2 = Drug B Non-Proportional Hazards Example: Fitting time-varying effect Non-Proportional Hazards Example stsplit period, at(10) gen t1 = treatment2*(period == 0) gen t2 = treatment2*(period == 10). stcox t1 t2 _t Haz. Ratio Std. Err. z P> z [95% Conf. Interval] t t estat phtest Test of proportional hazards assumption Time: Time chi2 df Prob>chi global test Survival Probability Observed: t1 = 0 Observed: t1 = 1 Predicted: t1 = 0 Predicted: t1 = 1 Survival Probability Observed: t2 = 0 Observed: t2 = 1 Predicted: t2 = 0 Predicted: t2 = 1

11 Time varying covariates Normally, survival predicted by baseline covariates Covariates may change over time Can have several records for each subject, with different covariates Each record ends with a censoring event, unless the event of interest occurred at that time Need to have unique identifier for each individual so that stata knows which observations belong together Option tvc() is for variables that increase linearl with time

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