I really kinda like this module a lot as much as I’m disappointed with the final grade. If I were to draw a similarity to a foundational module, I would say this module is most like ST2132. ST2131 only came in in one of the last few chapters and in the form of moment-generating functions and iterated expectations so it was a very small part. But the thinking was most similar to ST2132. It involved MLE and all. There was a lot on MLE but it wasn’t just MLE alone. When it got to the semiparametric Cox model, it was more of computation of rank likelihood. Basically, I guess you could say that this module focuses a lot on how you would model survival data. Maybe model is too strong a word for technical dudes. I guess you can say it touches mostly on regression, or along that line. The thinking is very similar. I mean when we do regression, we think of whether the assumptions of OLS, to take the simplest case, are satisfied and what the solutions are if they aren’t. A similar thing is done in this module. For example, in applying Cox’s model, we assume proportional hazards. But what do we do if the assumption is violated? Also, you sort of see the realism in this module where we separate out different groups of individuals into stratas and assign a unique characteristic to them before carrying out analysis in R. And talking about R, this module uses a lot of R such that you begin questioning the presence of certain topics with regards to examination purposes. Then again, that’s what makes the module more realistic. You take the data to a software, look at the coefficients and begin thinking bout’ its implications for your model.
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