62 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of HelsinkiFI5 papers
- Aalto UniversityFI4 papers
- Helsinki Institute for Information TechnologyFI4 papers
- Institute for Molecular Medicine FinlandFI3 papers
- University of JyväskyläFI3 papers
- HiLIFE – Elämäntieteiden Instituutti2 papers
- Finland UniversityFI1 paper
- Imperial College LondonGB1 paper
- MRC Centre for Environment and Health1 paper
- University of Eastern FinlandFI1 paper
- University of ManchesterGB1 paper
- University of OuluFI1 paper
12 papers
Mediation analysis in longitudinal intervention studies with an ordinal treatment-dependent confounder
Mikko Valtanen, Tommi Härkänen, Matti Uusitupa +3
In interventional health studies, causal mediation analysis can be employed to investigate mechanisms through which the intervention affects the targeted health outcome. Identifyin…
Informative Bayesian Neural Network Priors for Weak Signals
Tianyu Cui, Aki Havulinna, Pekka Marttinen +1
Encoding domain knowledge into the prior over the high-dimensional weight space of a neural network is challenging but essential in applications with limited data and weak signals.…
Adjusting for selective non-participation with re-contact data in the FINRISK 2012 survey
Juho Kopra, Tommi Härkänen, Hanna Tolonen +4
Aims: A common objective of epidemiological surveys is to provide population-level estimates of health indicators. Survey results tend to be biased under selective non-participatio…
Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation
Iiris Sundin, Tomi Peltola, Muntasir Mamun Majumder +6
Predicting the efficacy of a drug for a given individual, using high-dimensional genomic measurements, is at the core of precision medicine. However, identifying features on which…
Bayesian subcohort selection for longitudinal covariate measurements in follow-up studies
Jaakko Reinikainen, Juha Karvanen
We consider planning longitudinal covariate measurements in follow-up studies where covariates are time-varying. We assume that the entire cohort cannot be selected for longitudina…
Dynamic Modelling of Health and its application to the large scale analysis of Body Mass Index, using data from consecutive set of surveys
Vladislav Moltchanov
The methods used so far for the analysis of time changes in population health suffer from the lack of causality in their design. This results in problems with their implementation…
Correcting for non-ignorable missingness in smoking trends
Juho Kopra, Tommi Härkänen, Hanna Tolonen +1
Data missing not at random (MNAR) is a major challenge in survey sampling. We propose an approach based on registry data to deal with non-ignorable missingness in health examinatio…
Multiple Output Regression with Latent Noise
Jussi Gillberg, Pekka Marttinen, Matti Pirinen +7
In high-dimensional data, structured noise caused by observed and unobserved factors affecting multiple target variables simultaneously, imposes a serious challenge for modeling, b…
Dynamic Modeling in Health Research as a framework for developing statistical applications free of misuse of statistics
Vladislav Moltchanov
We introduce a novel framework for developing statistical applications in health research, based on dynamic modeling of the investigated processes. We formulate the principles of d…
Genome-wide association studies with high-dimensional phenotypes
Pekka Marttinen, Jussi Gillberg, Aki Havulinna +2
High-dimensional phenotypes hold promise for richer findings in association studies, but testing of several phenotype traits aggravates the grand challenge of association studies,…
Characterizing the generalized lambda distribution by L-moments
Juha Karvanen, Arto Nuutinen
The generalized lambda distribution (GLD) is a flexible four parameter distribution with many practical applications. L-moments of the GLD can be expressed in closed form and are g…
Efficient initial designs for binary response data
Juha Karvanen
In this paper we introduce a binary search algorithm that efficiently finds initial maximum likelihood estimates for sequential experiments where a binary response is modeled by a…