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Survival Analysis Key variable = time until some event • time from treatment to death • time for a fracture to heal • time from surgery to relapse Censored observations • subjects removed from data set at some stage without suffering an event [lost to follow-up or died from unrelated event] • study period ends with some subjects not suffering an event Example Patient Time at entry (months) Time at death/ censoring Dead or censored Survival time 1 0.0 11.8 D 11.8 2 0.0 12.5 C 12.5 * 3 0.4 18.0 C 17.6* 4 1.2 6.6 D 5.4 5 3.0 18.0 C 15.0* Survival analysis uses information about subjects who suffer an event and subjects who do not suffer an event Life Table • Shows pattern of survival for a group of subjects • Assesses number of subjects at risk at each time point and estimates the probability of survival at each point Motion sickness data N=21 subjects placed in a cabin and subjected to vertical motion Endpoint = time to vomit Motion sickness data • 14 survived 2 hours without vomiting • 5 subjects vomited at 30, 50, 51, 82 and 92 minutes respectively • 2 subjects requested an early stop to the experiment at 50 and 66 minutes respectively Life table Subject 1 2 3 Survival time (min) 30 50 50 * Survival proportion 0.952 0.905 4 5 6 7 8 – 21 51 66* 82 92 120* 0.855 0.801 0.748 Calculation of survival probabilities pk = pk-1 x (rk – fk)/ rk where p = probability of surviving to time k r = number of subjects still at risk f = number of events (eg. death) at time k Calculation of survival probabilities Time 30 mins : (21 – 1)/21 = 0.952 Time 50 mins : 0.952 x (20 – 1)/20 = 0.905 Time 51 mins : 0.905 x (18 – 1)/18 = 0.854 Kaplan-Meier survival curve • Graph of the proportion of subjects surviving against time • Drawn as a step function (the proportion surviving remains unchanged between events) Survival Curve 1.0 Survival probability .8 .6 .4 .2 0.0 0 30 60 TIME (mins) 90 120 Kaplan-Meier survival curve • times of censored observations indicated by ticks • numbers at risk shown at regular time intervals Summary statistics 1. Median survival time 2. Proportion surviving at a specific time point Survival Curve 1.0 Survival probability .8 .6 .4 .2 0.0 0 30 60 TIME (mins) 90 120 Comparison of survival in two groups Log rank test Nonparametric – similar to chi-square test SPSS Commands • Analyse – Survival – Kaplan-Meier • Time = length of time up to event or last follow-up • Status = variable indicating whether event has occurred • Options – plots - survival SPSS Commands (more than one group) • Factor = categorical variable showing grouping • Compare factor – choose log rank test Example RCT of 23 cancer patients 11 received chemotherapy Main outcome = time to relapse Proportion relapse-free Chemotherapy example 1.0 .8 .6 .4 Chemotherapy Yes .2 Yes-censored No 0.0 No-censored 0 20 40 60 80 100 120 Time (weeks) 140 160 180 Chemotherapy example No chemotherapy Median relapse-free time = 23 weeks Proportion surviving to 28 weeks = 0.39 Chemotherapy Median relapse-free time = 31 weeks Proportion surviving to 28 weeks = 0.61 The Cox model Proportional hazards regression analysis Generalisation of simple survival analysis to allow for multiple independent variables which can be binary, categorical and continuous The Cox Model Dependent variable = hazard Hazard = probability of dying at a point in time, conditional on surviving up to that point in time = “instantaneous failure rate” The Cox Model Log [hi(t)] = log[h0(t)] + ß1x1 + ß2x2 + …….. ßkxk where [h0(t)] = baseline hazard and x1 ,x2 , …xk are covariates associated with subject i The Cox Model hi(t) = h0(t) exp [ß1x1 + ß2x2 + …….. ßkxk] where [h0(t)] = baseline hazard and x1 ,x2 , …xk are covariates associated with subject i The Cox Model Interpretation of binary predictor variable defining groups A and B: Exponential of regression coefficient, b, = hazard ratio (or relative risk) = ratio of event rate in group A and event rate in group B = relative risk of the event (death) in group A compared to group B The Cox Model Interpretation of continuous predictor variable: Exponential of regression coefficient, b, refers to the increase in hazard (or relative risk) for a unit increase in the variable The Cox Model Model fitting: • Similar to that for linear or logistic regression analysis • Can use stepwise procedures such as ‘Forward Wald’ to obtain the ‘best’ subset of predictors The Cox model Proportional hazards regression analysis Assumption: Effects of the different variables on event occurrence are constant over time [ie. the hazard ratio remains constant over time] SPSS Commands • Analyse – Survival – Cox regression • Time = length of time up to event or last follow-up • Status = variable indicating whether event has occurred • Covariates = predictors (continuous and categorical) • Options – plots and 95% CI for exp(b) The Cox model Check of assumption of proportional hazards (for categorical covariate): • Survival curves • Hazard functions • Complementary log-log curves For each, the curves for each group should not cross and should be approximately parallel