I have just started to read about survival analysis,
I have read in this interesting article about survival analysis, and why use it rather than the famous multiple linear regression to estimate the time of the event occurance. Mainly because linear regression cannot handle right censoring (i.e. if the event has not yet occured to some of objects of study after the study duration).
My question is, hypothetically, what if I have the data of heart disease patients, say 1M, 0.5M patients have had a heart attack, and 0.5M patients haven't had a heart attack, in a 10 year period. What if I use only the 0.5M patients that had a heart attack to build a regression model to estimate the time period it will take for a patient to have a heart attack?. Or, to make it more thorough, use both data to predict whether a patient will have a heart attack or not, and if so, estimate when the event will happen. This way, the problem with the right censoring issue will be eliminated.
Would there be any faulty/wrong thought process to this methodology? If there is, could you also show me how survival analysis (or perhaps another alternative method) tackles it?
I am trying to get a deeper understanding of the subject. Thank you, in advance.