I have a question regarding continuous-time survival analysis in MPlus. I am looking at survival in a sample of elderly over several years, based on gender. I have 3 variables...
FEMALE is coded 0=male, 1=female. DIED indicates whether the person died (0=living at end of study, 1=not living). LASTTIME is the years post-baseline at the time of death (DIED=1) or the final assessment (DIED=0).
The MPLUS input is...
USEVARIABLES ARE FEMALE DIED LASTTIME; SURVIVAL = LASTTIME (ALL); TIMECENSORED = DIED (1 = NOT 0 = RIGHT); ANALYSIS: TYPE=MISSING; BASEHAZARD = OFF; MODEL: LASTTIME ON FEMALE;
I obtain an estimate for LASTTIME ON FEMALE of -0.787
I know that women were less likely to die.
Given that DIED is not coded as in the Mplus users guide (where 0=not right censored, 1=right censored), I want to make certain that I interpret the output correctly.
Is it the log of the odds-ratio for survival for females relative to males? Is it the log of the odds of death for females to males? Is it a predicting years post-baseline at death/final assessment? Or something else?
In essence, (1) should I recode my indicator for time censoring and (2) how should I interpret the estimate for the FEMALE effect.
Thanks for your swift reply. I have a following question about the interpretation of the results. Let's consider my own data in which the continuous covariate is externalizing problems at age 3 and in which the critical event is parental divorce between age 3 and age 12. When I regress divorce (age of child in months at divorce) on ext, then I get an estimate of 0.021 (p=0.002). So, the odds of the probability of divorce is 1,021 times higher when children have a one unit increase in ext. Does this mean that children with 10 units increase have a 1.021^10=1.23=23% more chance that there parents will divorce? Or does it mean that ext is related to EARLIER divorce? Please correct me if my interpretation is wrong. Is the estimate similar to the hazard ratio? (sorry if this is a stupid question, but I am new to survival analysis). Thanks alot in advance.
The log odds increase for a 10 unit increase in x (externalizing) is 10*0.021. If you are new to survival analysis, I recommend warmly the pedagogical book by Singer & Willet, Applied Longitudinal Data Analysis. They discuss things not only in terms of formulas but also in carefully worded words, including how to interpret results.