I don’t use STATA or SPSS so I can’t say wht those programs do.

1. Yes, you need to test proportional hazards

2. Survival analysis is designed to deal with censored data; logistic regression is not.

3. I think you should determine random vs. fixed based on whether the effects are random or fixed, not on statistical measures like teh ones you mention.

Peter

]]>Discrete Time Survival Analysis: 1- Is it necessary for time-dependent covariates to test any violation of the proportional hazards assumptions? 2- What is the difference between panel logistic regression and discrete time survival analysis except including a baseline hazard? Really, If I include time dummy variables (12 dummy variables) in the model, it would take a long time to implement or it reports not concave. 3- Is it necessary to do Hausman, Breusch-Pagan, Chow tests in order to determine fixed effects or random effects should be used? 4- In Stata, why is the result of implementing random effects parametric survival model different from that of implementing random effects panel logistic? I know that the former reports hazard rate but the latter reports odds ratio. However, the significance of predictors also are different. a- statistics-longitudinal/panel data-binary outcomes-logistic regression b- statistics-longitudinal/panel data-parametric survival regression. Is it necessary for both to include baseline hazard function (ln(time))? According to the following link, it seems that logit command is used in conjunction with temporal dummy variables, which is referred to as non-parametric because we used temporal dummy variables. If xtlogit is used, there is no need to use temporal dummy variables, so in this way discrete time survival analysis will be the same as unbalanced panel logistic. Besides, heterogeneity is just applicable to random effects. Also, it is suggested that cloglog is preferred over logit. http://www.stata.com/statalist/archive/2014-03/msg00027.html Besides, I read here (http://stats.stackexchange.com/questions/73355/duration-analysis-of-unemployment) that Chamberlain\’s estimator (which should be used for discrete time survival analysis) is not currently implemented in R. Please find the attached, which is some part of my bankruptcy data. In fact, the real number of my covariates could be large, which some kind of variable selection approach should be adopted. I also find this related post (http://www.ibm.com/support/knowledgecenter/en/SSLVMB_22.0.0/com.ibm.spss.statistics.cs/spss/tutorials/genlin_ulcer_howto.htm#genlin_ulcer_howto) in SPSS. I am so sorry for asking a lot but really I am too confused. I couldn\’t find any material which could provides answers for my questions. Thanks in advance. Best regards, ]]>

1- Is it necessary for time-dependent covariates to test any violation of the proportional hazards assumptions?

2- What is the difference between panel logistic regression and discrete time survival analysis except including a baseline hazard?

Really, If I include time dummy variables (12 dummy variables) in the model, it would take a long time to implement or it reports not concave.

3- Is it necessary to do Hausman, Breusch-Pagan, Chow tests in order to determine fixed effects or random effects should be used?

4- In Stata, why is the result of implementing random effects parametric survival model different from that of implementing random effects panel logistic? I know that the former reports hazard rate but the latter reports odds ratio. However, the significance of predictors also are different.

a- statistics-longitudinal/panel data-binary outcomes-logistic regression

b- statistics-longitudinal/panel data-parametric survival regression.

Is it necessary for both to include baseline hazard function (ln(time))?

According to the following link, it seems that logit command is used in conjunction with temporal dummy variables, which is referred to as non-parametric because we used temporal dummy variables. If xtlogit is used, there is no need to use temporal dummy variables, so in this way discrete time survival analysis will be the same as unbalanced panel logistic.

Besides, heterogeneity is just applicable to random effects. Also, it is suggested that cloglog is preferred over logit.

http://www.stata.com/statalist/archive/2014-03/msg00027.html

Besides, I read here (http://stats.stackexchange.com/questions/73355/duration-analysis-of-unemployment) that Chamberlain’s estimator (which should be used for discrete time survival analysis) is not currently implemented in R.

Please find the attached, which is some part of my bankruptcy data. In fact, the real number of my covariates could be large, which some kind of variable selection approach should be adopted. I also find this related post (http://www.ibm.com/support/knowledgecenter/en/SSLVMB_22.0.0/com.ibm.spss.statistics.cs/spss/tutorials/genlin_ulcer_howto.htm#genlin_ulcer_howto) in SPSS.

I am so sorry for asking a lot but really I am too confused. I couldn’t find any material which could provides answers for my questions.

Thanks in advance.

Best regards,