This post is part of our Q&A series. A question from two graduate students in our Fall 2017 offering of “Survival Analysis and Causality” at Berkeley: Question: Hi Mark, Below are [two] questions [we thought might interest you]. Looking forward to your thoughts on these! Best, S and I Most competing risk analyses assume that the competing risks are independent of one another. What would be your advice on handling the same style of survival data when the occurrence of one of the competing events is informative of the occurrence of the other?
This post is part of our Q&A series. A question from two graduate students in our Fall 2017 offering of “Survival Analysis and Causality” at Berkeley: Question: Hi Mark, [We] were wondering what the implications were for selecting leave one observation out versus leave one cluster out when performing cross-validation on a longitudinal data structure. We understand that computational constraints may render leave one observation out cross validation to be undesirable, however are we implicitly biasing our model selection by our choice in cross-validation technique?
Welcome! This is the research blog of Mark van der Laan. Over the last few years, communication in science has evolved; indeed, many exciting and inspiring research-related ideas are now first communicated informally, with blog posts and the like, before formal publication in academic journals. Blog posts provide an excellent medium through which interesting ideas can be communicated quickly and concisely. We plan to use this blog to share ideas, tips, and examples from our research – and to establish an open dialogue with researchers around the world.