**SAS day 28: Proc Lifetest 1**

What are the foundations for FDA or NDA to approve a new medication besides its safety concerns?

Many of us would think about the efficacy of the drug. If we dig in the question a bit further, how do we evaluate the efficacy then?

**Survival Analysis** is one of the coolest/ most critical methods in Clinical Trials, it is the golden test for medication efficacy: *how long will patients survive*.

**Proc Lifetest** is the most widely used model to **evaluate the result of survival analysis**; it computes and displays the product-limit estimate of the survivor function.

today we will go over the basic Syntax to generate a KM Plot and read the tables.

**Key Words:**

**Patient at Risk**：Number of patients still survive at a specified time point

**Fail**: Number of patients died

**Censor/ Event (**cnsr**)**:

*Censor=1 (Event=0)*: the patients still survive up to this time point

*Event=1(Censor=0)* : the patient died

**note: the censoring rule could be extremely perplexing, I **just used the simplest example** to show the basic concept*

**Survival time(aval)**: Most of clinical trial studies use *month* as a unit, year or days are also legit.

**Kaplan Meier: **used to estimate the survival function from Time to Event Data

**LogRank Test: ** test the null hypothesis that there is no difference between the populations in the probability of an event (here a death) at any time point.

*the most popular statistical test in LifeTest

### Data Preparation : ADSL, ADTTE

data adsl; set adam.adsl; i=_n_; keep subjid i; run; data adtte; do i =1 to 100; aval= rand("Uniform",0, 60); cnsr= rand("BERNOULLI", 0.88); if mod(i,2) =0 then paramcd="PFS"; if mod(i,2)^=0 then paramcd="DOR"; if mod(i,2) =0 then trtan="Placebo"; if mod(i,2)^=0 then trtan="Exlir"; output; end; run; data lifetest; merge adsl(in=a) adtte(in=b); if a; by i; run;

*note: I used Rand function created a dummy dataset.

**Sample Data **

**Proc Lifetest:**

ods trace on; ods output productlimitestimates=surv(keep=nhl timelist Survival left); proc lifetest data=lifetest plots=survival(atrisk=0 to 60 by 3) method=km timelist=0 to 60 by 3 ; strata trtan/test=(logrank); time aval*cnsr(1); run;

**Sample Output**

**Validation Dataset:**

As we can see the **“Left” column is patient at risk** and** “**Trtan**” is the strata** on the graph

and** Timelist is the survival time(aval)** point (3 months, 6months), **Survival is the probability of overall patient survival** until the specified time point.

**Summary:**

As the Kaplan-Meier graph demonstrated, the two treatment group does not show a significant difference, which makes sense, because I used a uniform random number generator. I hope all the drugs can demonstrate such an amazing survival effect! Proc Lifetest is an import application for survival analysis, next time, we will go over the *Adjust option, 95% CI*, and how to calculate* LogRank Test value*.

**Happy Studying!** 💃

**Reference:**

https://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC403858/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3932959/

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