Censored vs Event in Proc lifetest and Proc phreg using SAS

 

/*MOCK DATA*/

/*First step : Derive censored or LOA event for each subject Based on the definitions below from SAP, for example use  week 96 as a cut-off */

/*The time to LOA is defined as the time from randomization to the time of first visit that the LOA criteria are met. 

Patients without LOA per the definition will be censored at the last observation time point. */

/*A value of 0 indicates a censored observation, while a value of 1 indicates an event.*/

data survival_data;

  input id time status treatment $ treatment_n age genotype $;

  datalines;

1  5  1 Placebo 0 40 GenA

2  7  0 Placebo 0 55 GenB

3  6  1 Placebo 0 60 GenA

4  9  1 Placebo 0 50 GenC

5  4  0 Placebo 0 45 GenB

6  8  1 Placebo 0 52 GenA

7  3  1 Active  1 43 GenA

8  5  0 Active  1 46 GenB

9  6  1 Active  1 49 GenC

10 9  1 Active  1 58 GenB

11 7  0 Active  1 53 GenC

12 8  1 Active  1 47 GenA

13 4  1 Placebo 0 44 GenB

14 10 0 Active  1 56 GenC

15 9  1 Placebo 0 59 GenA

16 6  1 Active  1 41 GenB

;

run;



/*Second Step: check the K-M plot,Performs log-rank test comparing survival distributions across levels of treatment, whether the two survival lines by different treatment group are crossed or parallel. If almost parallel, you can conduct the cox model below*/

proc lifetest data=survival_data plots=survival(atrisk=0 to 20 by 2); 

  time time*status(0);

  strata treatment;

run;

/*Third Step*/

/*Supplemental Analyses

Cox proportional hazards regression analysis will be conducted with treatment group, genotype and age group as covariates. Hazard ratio and associated 95% confidence intervals will be estimated. 

*/


ods select none; 

ods output ParameterEstimates=param_est

           HazardRatios=hr_est;

proc phreg data=survival_data;

  class treatment (ref='Placebo') genotype / param=ref; /* specify reference level */

  model time*status(0) = treatment age genotype/*genotype only for comined column*/;

  hazardratio treatment / cl=both;  /* show HR and 95% CI for treatment */

run;

ods select all; 



/*get p-value from param_est, row=parameter=treatment, column=pr>chisq*/

/*get hazard ratio,Wald-based CI from hr-est*/




In SAS survival procedures like PROC PHREG,

time*status(censor_code) tells SAS which values in your event indicator mean “no event yet” (censored).

How it works

  • status variable = indicator of whether the event happened

    • Could be coded 0/1, or with other numeric or character values.

  • The value you put inside parentheses = the censor code(s).

  • SAS will treat all values in parentheses as censored.

  • All other values = event occurred.

Examples

1. Event coded as 1, censored coded as 0 (most common)

sas
model time*status(0) = ...;

  • status = 0 → censored (no event at last follow-up)

  • status = 1 → event occurred

2. Event coded as 0, censored coded as 1 (less common)

sas
model time*status(1) = ...;

  • status = 1 → censored

  • status = 0 → event occurred

3. Multiple censor codes

If your data uses different numbers for different censoring reasons:

sas
model time*status(0,2) = ...;

  • status = 0 or status = 2 → censored

  • All other values → event

Rule of thumb

  • Look at how your event variable is coded.

  • Put the value(s) for censored observations in parentheses.

  • Do not put the event value(s) in parentheses.


Comments

Post a Comment

Popular posts from this blog

Analysis of Repeated Measures Data using SAS

Image
 1. Basic Concepts of Repeated Measures: In this basic setup of a completely randomized design with repeated measures, there are two factors, treatments and time. Treatment is called the between-subjects factor because levels of treatment can change only between subjects; all measurements on the same subject will represent the same treatment. Time is called a within-subjects factor because different measurements on the same subject are taken at different times.  In repeated measures experiments, interest centers on (1) how treatment means differ, (2) how treatment means change over time, and (3) how differences between treatment means change over time. 2. Four-step procedure for mixed model analysis: Step 1: Model the mean structure, usually by specification of the fixed effects. Step 2: Specify the covariance structure, between subjects as well as within subjects. Step 3: Fit the mean model accounting for the covariance structure. Step 4: Make statistical inference bas...
Read more

Medical information for Melanoma, Merkel cell carcinoma and tumor mutation burden

  Background: Melanoma Also called: malignant melanoma The most serious type of skin cancer. Melanoma occurs when the pigment-producing cells that give color to the skin become cancerous. Symptoms might include a new, unusual growth or a change in an existing mole. Melanomas can occur anywhere on the body. Treatment may involve surgery, radiation, medications, or in some cases chemotherapy. Merkel cell carcinoma  (MCC) it is a very rare ,  highly aggressive, neuroendocrine carcinoma of the skin ;  it has a worse survival probability compared with more common cutaneous malignancies, including metastatic melanoma, and has been termed the “most lethal” skin cancer  as   p atients with metastatic MCC have a historical 5-year overall survival (OS) rate of 18% What Is Tumor Mutational Burden? TMB is defined as the frequency of certain mutations within a tumor’s genes.  To be counted towards TMB, mutations have to alter the protein that is made from a gene. ...
Read more

Four essential statistical functions for simulation in SAS

  PDF function : This function is the probability density function. It returns the probability density at a given point for a variety of distributions. (For discrete distribution, the PDF function evaluates the probability mass function.) CDF function : This function is the cumulative distribution function. The CDF returns the probability that an observation from the specified distribution is less than or equal to a particular value. For continuous distributions, this is the area under the PDF up to a certain point. QUANTILE function : This function is closely related to the CDF function, but solves an inverse problem. Given a probability, P, it returns the smallest value,  q , for which CDF( q ) is greater than or equal to P. RAND function : This function generates a random sample from a distribution. In SAS/IML software, use the  RANDGEN subroutine , which fills up an entire matrix at once.
Read more