Presentation on theme: "Surviving Survival Analysis"— Presentation transcript:
1Surviving Survival Analysis Today’s Talk:Surviving Survival AnalysisBy Kelley MizukamiBy Dr. Olga Korosteleva
2OUTLINE What is Survival Analysis? Censored Data Kaplan-Meier EstimatorLog-Rank TestCox Regression Model
3WHAT IS SURVIVAL ANALYSIS? Branch of statistics that focuses on time-to-event data and their analysis.Survival data deal with time until occurrence of any well-defined event.The outcome variable examined is the survival time (the time until the occurrence of the event).Special because it can incorporate information about censored data into analysis.
4OBJECTIVES OF SURVIVAL ANALYSIS? Estimate probability that an individual surpasses some time-to-event for a group of individuals.Ex) probability of surviving longer than two months until second heart attach for a group of MI patients.Compare time-to-event between two or more groups.Ex) Treatment vs placebo patients for a randomized controlled trial.Assess the relationship of covariates to time-to-event.Ex) Does weight, BP, sugar, height influence the survival time for a group of patients?
5SITUATIONS WHEN WE CAN USE SURVIVAL ANALYSIS We can use survival analysis when you wish to analyze survival times or “time-to-event” times“Time-to-Event” include:Time to deathTime until response to a treatmentTime until relapse of a diseaseTime until cancellation of serviceTime until resumption of smoking by someone who had quitTime until certain percentage of weight loss
6MORE EXAMPLESSuppose you wish to analyze the time it takes for a student to complete a series of classes.Response /Status Variable: Time it takes to complete, statusPredictor Variables: Age, Gender, Race, GPASuppose you wish to analyze the time between admittance to the hospital until death for a lung cancer patient.Response/Status Variables : Length-of-Follow up, statusPredictor Variables: Age, Gender, Race, White Blood Counts, Tumor Type, Treatment Type, Cancerous Mass Size
7MORE EXAMPLESSuppose you are interested in comparing the time until you lose 10% body weight on one of two exercise programs.Response/Status Variables: Time it Takes, StatusPredictor Variables: Age, Gender, Starting Weight, BP, BMI, Exercise ProgramSuppose you are interested in the time it takes before one sees results for a certain treatment.Predictor Variables: Age, Gender, Type of Treatment, Weight, Height, exercise (Y/N), healthy eating (Y/N)
8MORE EXAMPLESSuppose you wish to compare the time it takes before you cancel your cable TV service when you use two different cable providers.Response/Status Variables: Time it Takes, StatusPredictor Variables: Age, Gender, Race, Cable Provider, Average Income, Average number of complaints per month
9DATA Survival data can be one of two types: Complete Data Censored DataComplete data – the value of each sample unit is observed or known.Censored data – the time to the event of interest may not be observed or the exact time is not known.We distinguish complete data from censored data by adding a “+” to any values that are censored. (i.e. 4+)
10CENSORED DATA Censored data can occur when: The event of interest is death, but the patient is still alive at the time of analysis.The individual was lost to follow-up without having the event of interest.The event of interest is death by cancer but the patient died of an unrelated cause, such as a car accident.The patient is dropped from the study without having experienced the event of interest due to a protocol violation.Even if an observation is censored we will still include it in our analysis.
12FUNCTION DESCRIBING SURVIVAL TIMES 𝑇 is a random variable that represents survival time.The distribution of survival time can be described by the survival function.
13SURVIVAL FUNCTIONLet T denote the survival time, a random variable with the survival function:𝑠 𝑡 =𝑃(𝑇≥𝑡)Probability that a subject selected at random survives longer than time t.Properties𝑠 𝑡=0 =1𝑠(𝑡) is bounded by 0 and 1, it is a probability𝑠(𝑡) is a non-increasing function
14SURVIVAL FUNCTIONIf there is no censoring, then a good estimator of 𝑠(𝑡), at time 𝑡, is:𝑠 𝑡 = number of patients surviving longer than time 𝑡 total number of patients on trialBut usually there is censoring. Therefore we can estimate 𝑠(𝑡) using the Kaplan-Meier estimator.
16KAPLAN-MEIER (KM) ESTIMATOR Helps us find 𝑠(𝑘) when there are censored data.To find this KM estimator break up survival probability into a sequence of conditions.The probability of surviving 𝑘 (𝑘 ≥2) or more years from the beginning of the study is a product of observed survival rates.𝑠 𝑘 = 𝑝 1 𝑝 2 𝑝 3 ⋯ 𝑝 𝑘
17KAPLAN-MEIER ESTIMATOR 𝑠 𝑡 = 𝑗| 𝑡 (𝑗) ≤𝑡 𝑝 𝑗 = 𝑗| 𝑡 (𝑗) ≤𝑡 𝑛 𝑗 − 𝑑 𝑗 𝑛 𝑗𝑝 𝑗 : estimated by the proportion of people living through 𝑡 (𝑗) out of those who have survived beyond 𝑡 (𝑗−1)𝑛 𝑗 : number at risk at 𝑡 (𝑗)𝑑 𝑗 : number who died at 𝑡 (𝑗)𝑛 𝑗 - 𝑑 𝑗 = number who survived beyond 𝑡 (𝑗)
18HOW TO CALCULATE THE KM ESTIMATOR EVENT TIMES (n=12):RECALL: 𝑠 𝑡 = 𝑗| 𝑡 (𝑗) ≤𝑡 𝑝 𝑗 = 𝑗| 𝑡 (𝑗) ≤𝑡 𝑛 𝑗 − 𝑑 𝑗 𝑛 𝑗𝑠 2 = 𝑠 0 𝑝 2 = =0.92Skip censoring points since they don’t change until we get to the next time point.𝑠 5 = 𝑠 2 𝑝 5 = =0.825𝑠 6 = 𝑠 5 𝑝 6 = =0.73𝑠 10 = 𝑠 6 𝑝 10 = =0.63𝑠 16 = 𝑠 10 𝑝 16 = −2 5 = =0.37𝑠 27 = 𝑠 16 𝑝 27 = =0.25𝑠 30 = 𝑠 27 𝑝 30 = =0.13𝑠 32 = 𝑠 30 𝑝 32 = =0
20EXAMPLE DATA The MYEL Data Set: Myelomatosis Patients The MYEL data set contains survival times for 25 patients diagnosed with myelomatosis (Peto et al., 1977). The patients were randomly assigned to two drug treatments. The variables are as follows:DUR is the time in days from the point of randomization to either death or censoringSTATUS has a value of 1 if dead and a value of 0 if alive.This tells is that the censored value will be 0 if the patient is alive and 1 or uncensored if they are deadTREAT specifies a value of 1 or 2 that corresponds to the two treatments.RENAL has a value of 1 if renal functioning was normal
21WHAT DO THE DATA LOOK LIKE? Snapshot of the datadurstatustreatrenal811802632852522240220631957670
22KM EXAMPLE USING SPSS Analyze > Survival > Kaplan Meier Time: DurStatus: status(1)Here define 1 since it the value indicating event has occurred (i.e. death)Options: Check off survival plot
29EXAMPLE USING SPSS Factor: Treat Analyze > Survival > Kaplan MeierTime: durStatus: status(1)Here define 1 since it the value indicating event has occurred (i.e. death).Factor: TreatOptions: Check off survival plotClick on “Compare Factor” and choose “Log-Rank”
34SURVIVAL MODELSModels that relate the time that passes before some event occurs to one or more covariates that may be associated with that amount of time.
35COX REGRESSION MODELThis model produces a survival function that predicts the probability that an event has occurred at a given time t, for given predictor variables (covariates).
36COX REGRESSION MODEL 𝜆 𝑡, 𝑥 𝑖 = 𝜆 0 𝑡 𝑒 𝛽 ′ 𝑥 𝑖 𝑡 is the time 𝜆 𝑡, 𝑥 𝑖 = 𝜆 0 𝑡 𝑒 𝛽 ′ 𝑥 𝑖𝑡 is the time𝑥 𝑖 are the covariates for the 𝑖 th individual𝜆 0 𝑡 is the baseline hazard function. This is the function when all the covariates equal to zero.
37HAZARD FUNCTION The hazard function: 𝜆 𝑡 = lim Δ𝑡 →0 𝑃 𝑡<𝑇<𝑡+Δ𝑡 𝑇≥𝑡) ∆ 𝑡This is the risk of failure immediately after time 𝑡, given they have survived past time t.
38INTERPRETATION OF THE BETAS First we need to find the ratio when there is a one unit increase in the covariate, provided the other covariates stay fixed.𝜆(𝑡, 𝑥 1 +1) 𝜆(𝑡, 𝑥 1 ) = 𝜆 0 𝑡 𝑒 𝛽 1 ( 𝑥 1 +1) 𝜆 0 𝑡 𝑒 𝛽 1 ( 𝑥 1 ) = 𝑒 𝛽 1We interpret 𝛽 1 as the increase in log hazard per unit of 𝑥.
39EXAMPLE USING SPSS Analyze > Survival > Cox Regression SPSS fits the model with minus beta coefficients: 𝜆 𝑡, 𝑥 𝑖 = 𝜆 0 𝑡 𝑒 −𝛽 ′ 𝑥 𝑖 It has to be taken into account when interpreting the coefficientsTime: DurStatus: status(1)Censoring value: 1Covariates: treat, renalCategorical: treat, renal
41OUTPUT Interpretation: The hazard for patients receiving treatment 2 is 28.8% of that for treatment 1 patients.Patients with normal renal function have 1.6% hazard as compared to those whose renal function is abnormal.