208 research outputs found
De zoekende onderzoeker
No full textOratie uitgesproken door prof.dr. J.J. Goeman bij de aanvaarding van het ambt van hoogleraar in de Analyse van Hoog-dimensionele Medische Data aan de Universiteit Leiden op vrijdag 17 november 2017Oratie uitgesproken door prof.dr. J.J. Goeman bij de aanvaarding van het ambt van hoogleraar in de Analyse van Hoog-dimensionele Medische Data aan de Universiteit Leiden op vrijdag 17 november 2017Analysis and Stochastic
Statistical modelling for integrative analysis of multi-omics data
No full textGoeman, J.J. [Promotor]Wiel, M.A. van de [Promotor]Menezes, R.X. de [Copromotor
On trees and forests. Meta-analysis and between-study heterogeneity in practice
No full textContains fulltext :
150178.pdf (Publisher’s version ) (Open Access)The main objective of the thesis is to give insight into the application of meta-analysis methodology and to reflect on the role of between-study heterogeneity in the realistic setting where most meta-analyses are based on just a few studies and where some of these studies are small or very small.
Presenting and evaluating the variation in the strength of the effect across studies is a key feature of meta-analyses. Acceptance of this variation is crucial and fundamental to the use of a random-effects approach. Heterogeneity plays an important role, both in the statistical estimation of the summary effect and in the clinical interpretation of the results of a meta-analysis.Radboud Universiteit Nijmegen, 12 januari 2016Promotor : Goeman, J.J
Multiple Testing for Exploratory Research
No full textDevelopment and application of statistical models for medical scientific researc
On Selecting and Conditioning in Multiple Testing and Selective Inference
No full textWe investigate a class of methods for selective inference that condition on a
selection event. Such methods follow a two-stage process. First, a data-driven
(sub)collection of hypotheses is chosen from some large universe of hypotheses.
Subsequently, inference takes place within this data-driven collection,
conditioned on the information that was used for the selection. Examples of
such methods include basic data splitting, as well as modern data carving
methods and post-selection inference methods for lasso coefficients based on
the polyhedral lemma. In this paper, we adopt a holistic view on such methods,
considering the selection, conditioning, and final error control steps together
as a single method. From this perspective, we demonstrate that multiple testing
methods defined directly on the full universe of hypotheses are always at least
as powerful as selective inference methods based on selection and conditioning.
This result holds true even when the universe is potentially infinite and only
implicitly defined, such as in the case of data splitting. We provide a
comprehensive theoretical framework, along with insights, and delve into
several case studies to illustrate instances where a shift to a non-selective
or unconditional perspective can yield a power gain
The Inheritance Procedure: Multiple Testing of Tree-structured Hypotheses
No full textDevelopment and application of statistical models for medical scientific researc
Comparing three groups
No full textFor multiple comparisons in analysis of variance, the practitioners' handbooks generally advocate standard methods such as Bonferroni, or an F-test followed by Tukey's honest significant difference method. These methods are known to be suboptimal compared to closed testing procedures, but improved methods can be complex in the general multigroup set-up. In this note, we argue that the case of three-groups is special: with three groups, closed testing procedures are powerful and easy to use. We describe four different closed testing procedures specifically for the three-group set-up. The choice of method should be determined by assessing which of the comparisons are considered primary and which are secondary, as dictated by subject-matter considerations. We describe how all four methods can be used with any standard software.Development and application of statistical models for medical scientific researc
Comments on: hierarchical inference for genome-wide association studies by Jelle J. Goeman and Stefan Bohringer
No full textDevelopment and application of statistical models for medical scientific researc
Permutation-Based True Discovery Guarantee by Sum Tests
No full textSum-based global tests are highly popular in multiple hypothesis testing. In
this paper we propose a general closed testing procedure for sum tests, which
provides lower confidence bounds for the proportion of true discoveries (TDP),
simultaneously over all subsets of hypotheses. These simultaneous inferences
come for free, i.e., without any adjustment of the alpha-level, whenever a
global test is used. Our method allows for an exploratory approach, as
simultaneity ensures control of the TDP even when the subset of interest is
selected post hoc. It adapts to the unknown joint distribution of the data
through permutation testing. Any sum test may be employed, depending on the
desired power properties. We present an iterative shortcut for the closed
testing procedure, based on the branch and bound algorithm, which converges to
the full closed testing results, often after few iterations; even if it is
stopped early, it controls the TDP. We compare the properties of different
choices for the sum test through simulations, then we illustrate the
feasibility of the method for high dimensional data on brain imaging and
genomics data.Comment: Main: 27 pages, 3 figures. Appendices: 19 pages, 7 figure
Efficient approximate leave-one-out cross-validation for ridge and lasso
No full textIn this thesis an approximation method is discussed that provides similar results to leave-one-out cross-validation but is less time-consuming. By means of this approximation method, estimating the optimal values of ridge and lasso parameters will take less time and carrying out (an approximated version of) double LOOCV will become practically feasible. The method can be used in generalized linear models as well as in Cox' proportional hazards model. In order to show its usefulness, the method is tested on several microarray data sets.BioinformaticsMedia and Knowledge EngineeringElectrical Engineering, Mathematics and Computer Scienc
- …
