Hello everyone,
I am asking you my problem:
I have a database that contains 1500 machines that are located in 8 different companies. The goal is to know the cause of death of the machines that have lived the longest (I have about 10 explanatory variables). To do this, I focus on the machines that have lived more than 1500 days. Among them, there are 900 that died at more than 1500days and 1 that is still alive and has more than 1500days.
I wanted to use Cox in my problem, which would allow me to have, for each variable in the model, the risk of death.
Except that here I only have one machine still alive at 1500days and 900 are dead. So I wouldn't have enough censored data in my model. Is this a problem?
Is there any other method that could be used and that would be more appropriate here to answer my problem?
I would like to stress something:
Machines that died before their 1500 days are machines that died due to an external factor. I only deal with machines that have lived well (more than 1500 days) to find out why they died, what variables had an impact on their death, so that in the future I can build machines that live even longer.
If someone can help me to find a correct method to use in this case.
Thanks.
I am asking you my problem:
I have a database that contains 1500 machines that are located in 8 different companies. The goal is to know the cause of death of the machines that have lived the longest (I have about 10 explanatory variables). To do this, I focus on the machines that have lived more than 1500 days. Among them, there are 900 that died at more than 1500days and 1 that is still alive and has more than 1500days.
I wanted to use Cox in my problem, which would allow me to have, for each variable in the model, the risk of death.
Except that here I only have one machine still alive at 1500days and 900 are dead. So I wouldn't have enough censored data in my model. Is this a problem?
Is there any other method that could be used and that would be more appropriate here to answer my problem?
I would like to stress something:
Machines that died before their 1500 days are machines that died due to an external factor. I only deal with machines that have lived well (more than 1500 days) to find out why they died, what variables had an impact on their death, so that in the future I can build machines that live even longer.
If someone can help me to find a correct method to use in this case.
Thanks.