ALL-IN meta-analysis

Science is typically a patchwork of research contributions without much coordination. Especially in clinical trials, the follow-up studies that we do fail to be the most promising. They are also not always designed for the extra evidence that is needed. If they are, standard statistics makes it impossible to take such strategy into account. This dissertation is about accumulating scientific knowledge, about the statistical problems with existing methods (accumulation bias) and about new statistical methods to do better. We can summarize scientific results efficiently by going ALL-IN. Science is a major gamble: there is little certainty when we embark on a new study. But gambling can be done strategically and clinical trials can use earlier results to decide whether a new study is necessary and optimally designed. ALL-IN meta-analysis can help scientists to prioritize research, interpret findings in the context of existing results and gamble strategically with their next study. Hence reducing so-called Research Waste. But there is more to it. ALL-IN meta-analysis can be a bottom-up approach. Statistical results become much easier to communicate. Instead of progressing one publication at a time, with everyone focusing on their own paper, clinical science can be more of a continuous collaborative effort.

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