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... Show moreScience 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. Show less
Benschop, C.C.G.; Gaag, K.J. van der; Vreede, J. de; Backx, A.J.; Leeuw, R.H. de; Zuniga, S.; ... ; Sijen, T. 2021
The interpretation of short tandem repeat (STR) profiles can be challenging when, for example, alleles are masked due to allele sharing among contributors and/or when they are subject to drop-out,... Show moreThe interpretation of short tandem repeat (STR) profiles can be challenging when, for example, alleles are masked due to allele sharing among contributors and/or when they are subject to drop-out, for instance from sample degradation. Mixture interpretation can be improved by increasing the number of STRs and/or loci with a higher discriminatory power. Both capillary electrophoresis (CE, 6-dye) and massively parallel sequencing (MPS) provide a platform for analysing relatively large numbers of autosomal STRs. In addition, MPS enables distinguishing between sequence variants, resulting in enlarged discriminatory power. Also, MPS allows for small amplicon sizes for all loci as spacing is not an issue, which is beneficial with degraded DNA. Altogether, MPS has the potential to increase the weights of evidence for true contributors to (complex) DNA profiles. In this study, likelihood ratio (LR) calculations were performed using STR profiles obtained with two different MPS systems and analysed using different settings: 1) MPS PowerSeqTM Auto System profiles analysed using FDSTools equipped with optimized settings such as noise correction, 2) ForenSeqTM DNA Signature Prep Kit profiles analysed using the default settings in the Universal Analysis Software (UAS), and 3) ForenSeqTM DNA Signature Prep Kit profiles analysed using FDSTools empirically adapted to cope with one-directional reads and provisional, basic settings. The LR calculations used genotyping data for two- to four-person mixtures varying for mixture proportion, level of drop-out and allele sharing and were generated with the continuous model EuroForMix. The LR results for the over 2000 sets of propositions were affected by the variation for the number of markers and analysis settings used in the three approaches. Nevertheless, trends for true and non-contributors, effects of replicates, assigned number of contributors, and model validation results were comparable for the three MPS approaches and alike the trends known for CE data. Based on this analogy, we regard the probabilistic interpretation of MPS STR data fit for forensic DNA casework. In addition, guidelines were derived on when to apply LR calculations to MPS autosomal STR data and report the corresponding results. Show less
Discussion: Strictly lobar MBs strongly predict CAA in non-ICH individuals when found in a hospital context. However, their diagnostic accuracy in the general population appears limited. (C) 2015... Show moreDiscussion: Strictly lobar MBs strongly predict CAA in non-ICH individuals when found in a hospital context. However, their diagnostic accuracy in the general population appears limited. (C) 2015 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved. Show less
