SURVIVAL ANALYSIS.ppt
- 2. What is Survival Analysis? • Survival Analysis is referred to statistical methods for analyzing survival data • Survival data could be derived from laboratory studies of animals or from clinical and epidemiologic studies • Survival data could relate to outcomes for studying acute or chronic diseases
- 3. What is Survival Time? • Survival time refers to a variable which measures the time from a particular starting time (e.g., time initiated the treatment) to a particular endpoint of interest (e.g., attaining certain functional abilities) • It is important to note that for some subjects in the study a complete survival time may not be available due to censoring
- 4. Censored Data Some patients may still be alive or in remission at the end of the study period The exact survival times of these subjects are unknown These are called censored observation or censored times and can also occur when individuals are lost to follow-up after a period of study
- 5. Censoring is present when we have some information about a subject’s event time, but we don’t know the exact event time. There are generally three reasons why censoring might occur: • A subject does not experience the event before the study ends • A person is lost to follow-up during the study period • A person withdraws from the study Censored Data
- 6. • Fixed type I censoring occurs when a study is designed to end after C years of follow-up. In this case, everyone who does not have an event observed during the course of the study is censored at C years. • In random type I censoring, the study is designed to end after C years, but censored subjects do not all have the same censoring time. • In type II censoring, a study ends when there is a prespecified number of events. Types of right-censoring
- 7. Random Right Censoring • Suppose 4 patients with acute leukemia enter a clinical study for three years • Remission times of the four patients are recorded as 10, 15+, 35 and 40 months • 15+ indicate that for one patient the remission time is greater than 15 months but the actual value is unknown
- 8. Important Areas of Application • Clinical Trials (e.g., Recovery Time after heart surgery) • Longitudinal or Cohort Studies (e.g., Time to observing the event of interest) • Life Insurance (e.g., Time to file a claim) • Quality Control & Reliability in Manufacturing (e.g., The amount of force needed to damage a part such that it is not useable)
- 9. Survival Function or Curve Let T denote the survival time S(t) = P(surviving longer than time t ) = P(T > t) The function S(t) is also known as the cumulative survival function. 0 S( t ) 1 Ŝ(t)=number of patients surviving longer than t total number of patients in the study
- 12. E.g: Four patients’ survival time are 10, 20, 35 and 40 months. Estimate the survival function. 0 0.2 0.4 0.6 0.8 1 0 10 20 30 40 50 Month % Surviving
- 13. Example: Four patients’ survival data are 10, 15+, 35 and 40 months. Estimate the survival function 0 0.2 0.4 0.6 0.8 1 0 10 20 30 40 50 Month % Surviving
- 14. In 1958, Product-Limit (P-L) method was introduced by Kaplan and Meier (K-M) • As you move from left to right in estimation of the survival curve first assign equal weights to each observation. • Redistribute equally the pre-assigned weight to the censored observations to all observations to the right of each censored observation • Median survival is a point of time when S(t) is 0.5 • Mean is equal to the area under the survival curve
- 15. A few critical features of P-L or K-M Estimator • The PL method assumes that censoring is independent of the survival times • K-M estimates are limited to the time interval in which the observations fall • If the largest observation is uncensored, the PL estimate at that time equals zero
- 16. Comparison Of Two Survival Curves • Let S1(t) and S2(t) be the survival functions of the two groups. • The null hypothesis is H0: S1(t) =S2(t), for all t > 0 • The alternative hypothesis is: H1: S1(t) S2(t), for some t > 0
- 19. The Logrank Test • SPSS, SAS, S-Plus and many other statistical software packages have the capability of analyzing survival data • Logrank Test can be used to compare two survival curves • A p-value of less than 0.05 based on the Logrank test indicate a difference between the two survival curves
- 20. EXAMPLE • Survival time of 30 patients with Acute Myeloid Leukemia (AML) • Two possible prognostic factors Age = 1 if Age of the patient 50 Age = 0 if Age of the patient < 50 Cellularity = 1 if cellularity of marrow clot section is 100% Cellularity = 0 otherwise
- 21. Format of the DATA Survival Times and Data of Two Possible Prognostic Factors of 30 AML Patients * Censored = 1 if Lost to follow-up Censored = 0 if Data is Complete
- 22. Comparing the survival curves by Age Groups using Logrank Test
- 23. Comparing the survival curves by Cellularity using Logrank Test
- 24. Hazard Function • The hazard function h(t) of survival time T gives the conditional failure rate • The hazard function is also known as the instantaneous failure rate, force of mortality, and age-specific failure rate • The hazard function gives the risk of failure per unit time during the aging process
- 25. Multivariate Analysis: (CPHM) Cox's Proportional Hazards Model • CPHM is a technique for investigating the relationship between survival time and independent variables • A PHM possesses the property that different individuals have hazard functions that are proportional to one another
- 26. Comparing the survival curves by Age Groups after Adjusting Cellularity using CPHM
- 27. Comparing the survival curves by Cellularity Groups after Adjusting Age using CPHM