Saturday, September 05, 2015

Understanding the endpoints in oncology: overall survival, progression free survival, hazard ratio, censored value

For clinical trials in oncology area, various terms related to the study endpoints are confusing to non-statisticians. The commonly used terms such as overall survival, progression free survival, censor, and hazard ratio are not straightforward to non-statisticians.

FDA guidance “Guidance for Industry Clinical Trail Endpoints for the Approval of Cancer Drugs and Biologics” delineated various endpoint measures and provided guidance on which endpoint should be used in which situation.

Overall Survival versus Survival Rate: The term overall survival can be easily confused with the survival rate or can be easily thought as the survival rate. While these two terms are related, they measure different things. Overall Survival is a measure of time to event and Survival rate measures the percentage of subjects who survived (at the end of the study, after 3 years, 5 years,…). 


Perhaps, it is clearer to understand the differences if we put these terms side-by-side for a comparison.

Table 1: Comparison of Overall Survival and Survival Rate


Overall Survival (OS)
Survival Rate
Measuring how long a patient can survive

Measuring how many patients survive during a given time (3 year, 5 years)
The time from the randomization or the start of the study treatment to the death
the percentage of subjects who are alive at the end of the study
Hard endpoint since both are based on the death event
Commonly used in clinical trials
Commonly used in epidemiology
Recommended study endpoint
Not usually used as the study endpoint
Analyzed using survival analysis or time to event analysis methods (Kaplan-Meier estimate, log-rank test, proportional hazard model,…)
Analyzed as proportion/rate/ratio (Chi-square test, CMH test,..) or dichotomous variable using logistic regression.


  
Table 2: Comparison of Overall Survival and Progression Free Survival

Overall Survival (OS)
Progression Free Survival (PFS)
Measuring how long a patient can survive

Measuring how long a patient can live without disease progression

The time from the randomization or the start of the study treatment to the death (all causes)
the time from the randomization or the start of the study treatment to the disease progression including death
Commonly used in clinical trials
Hard endpoint
Surrogate endpoint
Analyzed using survival analysis or time to event analysis methods (Kaplan-Meier estimate, log-rank test, proportional hazard model,…)
Study with OS as the primary efficacy endpoint requires relatively larger sample size and longer follow-up duration
Study with PFS as the primary efficacy endpoint requires relatively smaller sample size and shorter follow-up duration

PFS may or may not predict the OS.

How to determine the disease progression may get tricky. Disease progression often relies on the imaging (for example, using imaging to determine if there is any change in tumor size)

Disease free survival (DFS), event free survival (EFS), time to progression (TTP) et al have the similar features to the Progression Free Survival (PFS). 
  • Disease free survival: time to disease reoccurring. Measuring the length of time after treatment during which no disease is found. 
  • Event free survival: Time from randomization* to disease progression, death, or discontinuation of treatment for any reason (eg, toxicity, patient preference, or initiation, of a new treatment without documented progression). may be useful in evaluation of highly toxic therapies
  • Time to progression: Time from randomization* until objective tumor progression; does not include deaths

Table 3: Comparison of Hazard Ratio and Risk Ratio

Hazard Ratio (HR)
Risk Ratio (RR)
For time to event variables for example OS and PFS
For binary variables (such as live/death, success/not success)
The Hazard ratio is the ratio of the probability of an event (death or progression) in the experimental arm to the probability in the comparator arm.

A hazard is the rate at which events happen
The risk ratio (or relative risk) is the ratio of the risk of an event in the two groups.

The hazard ratio is a related measure that weights the risk change according to when events occur over time
The relative risk is a measure of the relative change in the risk of a preventable event.
Calculated using (Cox) proportional hazard model (for example using SAS PROC PHREG)
Calculated by dividing two proportions (for example proportion of subjects who survived in active group / proportion of subjects who survived in placebo group). In SAS, it can be obtained using PROC FREQ or PROC Logistic
Assumption that the data follows the proportional hazard
No such assumption is needed
A hazard ratio of 2 means the event will occur twice as often at each time point (at any given instantaneous time point) given a one-unit increase in the predictor.
A risk ratio of 2 means that the event is 2 time more probable given a one-unit increase in the predictor

Risk ratio and relative risk are two terms that can be used interchangeably. Risk ratio and odds ratio are similar and have the same features, but with different formula for calculation. For clinical trials, both Risk Ratio and Odds Ratio are used. For epidemiology studies especially the case-control studies, Odds Ratio is usually used. See table below for comparison in calculating the Risk Ratio and Odds Ratio.

Table 4: Calculation of risk ratio (RR), odds ratio (OR) and risk difference (RD) from a 2×2 table
The results of a clinical trial can be displayed as a 2×2 table:

Event
(‘Success’)
No event
(‘Fail’)
Total
Experimental intervention
SE
FE
NE
Control intervention
SC
FC
NC

where SE, SC, FE and FC are the numbers of participants with each outcome (‘S’ or ‘F’) in each group (‘E’ or ‘C’). The following summary statistics can be calculated:




The term ‘censored value’ is used to describe an incomplete measure. For example, for a biomarker or laboratory measures, if the value is too low or too high that exceeding the quantification limit (for example exceeding the lower limit of quantification – LLQ), we would indicate the value is less than LLQ, but the exact value is unknown.

The “censored value” is especially common in cancer clinical trials. For patients who did not experience the event, the time to event will be censored. Suppose patients are followed in a study for 20 weeks. A patient who does not experience the event of interest for the duration of the study is said to be censored (exactly right censored). The interpretation is that even though the subject does not experience the event and we cannot calculate the time to event, we set up the time to event as a censored value for time to event analyses.  


For cancer clinical trials, it is essential to understand whether or not the study endpoint is to count the events or to measure the time to events. Measuring the time to events is more commonly accepted endpoint in cancer clinical trials. 

References: 



1 comment:

Malika Garg said...

Hi I have been following your blog for some months now. Just wanted to say, thank you for making this area so readable and understandable.