arXiv subjects

Kari Auranen

Publications and source records attributed to Kari Auranen.

10 recordsLinked to original sources

arXiv2026

DiPPER: A Bayesian approach to differential prevalence analysis with applications in microbiome studies

Recent evidence suggests that analyzing the presence/absence of taxonomic features can offer a compelling alternative to differential abundance analysis in microbiome studies. However, standard approaches to differential prevalence analysis face challenges with boundary cases and multiple testing. To address these limitations, we developed DiPPER (Differential Prevalence via Probabilistic Estimation in R), a method based on Bayesian hierarchical modeling. We benchmarked our method against existing differential prevalence methods, along with two differential abundance tools, using publicly available data from 57 human gut microbiome studies. We observed considerable variation in performance across the evaluated methods. Importantly, DiPPER demonstrated high sensitivity to detect potentially differentially prevalent features while maintaining a well-calibrated family-wise error rate under the global null hypothesis. Most notably, it outperformed the alternatives in the replication of findings across independent studies. Furthermore, DiPPER provides differential prevalence estimates and uncertainty intervals that are inherently adjusted for multiple testing.

stat.ME↗

arXiv2026

Causal treatment effect decompositions with time-to-event outcomes under competing events

Inference about treatment effects for time-to-event outcomes is often obscured by the presence of competing events. A particularly complex situation arises when the treatment influences the occurrence of the competing event. A comprehensive assessment should then account for different mechanisms by which the treatment and the competing event together produce the apparent treatment effect. Here, we propose a decomposition of the treatment's effect on the event of interest (target), characterising how it arises due to four distinct mechanisms involving both the target and competing events. Based on a causal model, the decomposition relies on cross-world estimands reflecting counterfactual scenarios in which the treatment affects the two events as if set to conflicting levels. We specify exchangeability and consistency assumptions under which the decomposition can be estimated from observed data. We discuss how the new decomposition reveals the role of the competing event and serves as a basis for defining causally interpretable estimands in the presence of competing events. Finally, we demonstrate the use of the four-way decomposition with datasets from two randomised trials.

stat.ME↗

arXiv2026

Evaluation metrics for temporal preservation in synthetic longitudinal patient data

This study introduces a set of metrics for evaluating temporal preservation in synthetic longitudinal patient data, defined as artificially generated data that mimic real patients' repeated measurements over time. The proposed metrics assess how synthetic data reproduces key temporal characteristics, categorized into marginal, covariance, individual-level and measurement structures. We show that strong marginal-level resemblance may conceal distortions in covariance and disruptions in individual-level trajectories. Temporal preservation is influenced by factors such as original data quality, measurement frequency, and preprocessing strategies, including binning, variable encoding and precision. Variables with sparse or highly irregular measurement times provide limited information for learning temporal dependencies, resulting in reduced resemblance between the synthetic and original data. No single metric adequately captures temporal preservation; instead, a multidimensional evaluation across all characteristics provides a more comprehensive assessment of synthetic data quality. Overall, the proposed metrics clarify how and why temporal structures are preserved or degraded, enabling more reliable evaluation and improvement of generative models and supporting the creation of temporally realistic synthetic longitudinal patient data.

cs.LG↗

arXiv2025

Mediation analysis in longitudinal intervention studies with an ordinal treatment-dependent confounder

In interventional health studies, causal mediation analysis can be employed to investigate mechanisms through which the intervention affects the targeted health outcome. Identifying direct and indirect (i.e. mediated) effects from empirical data become complicated, however, when the mediator-outcome association is confounded by a variable itself affected by the treatment. Here, we investigate identification of mediational effects under such post-treatment confounding in a setting with a longitudinal mediator, time-to-event outcome and a trichotomous ordinal treatment-dependent confounder. If the intervention always affects the treatment-dependent confounder only in one direction (monotonicity), we show that the mediational effects are identified up to a stratum-specific sensitivity parameter and derive their empirical non-parametric expressions. The feasibility of the monotonicity assumption can be assessed using empirical data, based on restrictions on the marginal distributions of counterfactuals of the treatment-dependent confounder. We avoid pitfalls related to post-treatment conditioning by treating the mediator as a functional entity and defining the time-to-event outcome as a restricted disease-free time. In an empirical analysis, we use data from the Finnish Diabetes Prevention Study to assess the extent to which the effect of a lifestyle intervention on avoiding type 2 diabetes is mediated through weight reduction in a high-risk population, with other health-related changes acting as treatment-dependent confounders.

stat.AP↗

arXiv2025

Elementary methods provide more replicable results in microbial differential abundance analysis

Differential abundance analysis is a key component of microbiome studies. Although dozens of methods exist there is currently no consensus on the preferred methods. While the correctness of results in differential abundance analysis is an ambiguous concept and cannot be fully evaluated without setting the ground truth and employing simulated data, we argue that a well-performing method should be effective in producing highly reproducible results. We compared the performance of 14 differential abundance analysis methods by employing datasets from 53 taxonomic profiling studies based on 16S rRNA gene or shotgun metagenomic sequencing. For each method, we examined how the results replicated between random partitions of each dataset and between datasets from separate studies. While certain methods showed good consistency, some widely used methods were observed to produce a substantial number of conflicting findings. Overall, when considering consistency together with sensitivity, the best performance was attained by analyzing relative abundances with a non-parametric method (Wilcoxon test or ordinal regression model) or linear regression/t-test. Moreover, a comparable performance was obtained by analyzing presence/absence of taxa with logistic regression.

stat.AP↗

arXiv2024

Methods for generating and evaluating synthetic longitudinal patient data: a systematic review

The rapid growth in data availability has facilitated research and development, yet not all industries have benefited equally due to legal and privacy constraints. The healthcare sector faces significant challenges in utilizing patient data because of concerns about data security and confidentiality. To address this, various privacy-preserving methods, including synthetic data generation, have been proposed. Synthetic data replicate existing data as closely as possible, acting as a proxy for sensitive information. While patient data are often longitudinal, this aspect remains underrepresented in existing reviews of synthetic data generation in healthcare. This paper maps and describes methods for generating and evaluating synthetic longitudinal patient data in real-life settings through a systematic literature review, conducted following the PRISMA guidelines and incorporating data from five databases up to May 2024. Thirty-nine methods were identified, with four addressing all challenges of longitudinal data generation, though none included privacy-preserving mechanisms. Resemblance was evaluated in most studies, utility in the majority, and privacy in just over half. Only a small fraction of studies assessed all three aspects. Our findings highlight the need for further research in this area.

stat.ME↗

arXiv2024

Hypothesis-driven mediation analysis for compositional data: an application to gut microbiome

Biological sequencing data consist of read counts, e.g. of specified taxa and often exhibit sparsity (zero-count inflation) and overdispersion (extra-Poisson variability). As most sequencing techniques provide an arbitrary total count, taxon-specific counts should ideally be treated as proportions under the compositional data-analytic framework. There is increasing interest in the role of the gut microbiome composition in mediating the effects of different exposures on health outcomes. Most previous approaches to compositional mediation have addressed the problem of identifying potentially mediating taxa among a large number of candidates. We here consider causal inference in compositional mediation when a priori knowledge is available about the hierarchy for a restricted number of taxa, building on a single hypothesis structured in terms of contrasts between appropriate sub-compositions. Based on the theory on multiple contemporaneous mediators and the assumed causal graph, we define non-parametric estimands for overall and coordinate-wise mediation effects, and show how these indirect effects can be estimated from empirical data based on simple parametric linear models. The mediators have straightforward and coherent interpretations, related to specific causal questions about the interrelationships between the sub-compositions. We perform a simulation study focusing on the impact of sparsity and overdispersion on estimation of mediation. While unbiased, the precision of the estimators depends, for any given magnitude of indirect effect, on sparsity and the relative magnitudes of exposure-to-mediator and mediator-to-outcome effects in a complex manner. We demonstrate the approach on empirical data, finding an inverse association of fibre intake on insulin level, mainly attributable to direct rather than indirect effects.

stat.AP↗

arXiv2024

On the distribution of isometric log-ratio transformations under extra-multinomial count data

Compositional data arise when count observations are normalised into proportions adding up to unity. To allow use of standard statistical methods, compositional proportions can be mapped from the simplex into the Euclidean space through the isometric log-ratio (ilr) transformation. When the counts follow a multinomial distribution with fixed class-specific probabilities, the distribution of the ensuing ilr coordinates has been shown to be asymptotically multivariate normal. We here derive an asymptotic normal approximation to the distribution of the ilr coordinates when the counts show overdispersion under the Dirichlet-multinomial mixture model. Using a simulation study, we then investigate the practical applicability of the approximation against the empirical distribution of the ilr coordinates under varying levels of extra-multinomial variation and the total count. The approximation works well, except with a small total count or high amount of overdispersion. These empirical results remain even under population-level heterogeneity in the total count. Our work is motivated by microbiome data, which often exhibit considerable extra-multinomial variation and are increasingly treated as compositional through scaling taxon-specific counts into proportions. We conclude that if the analysis of empirical data relies on normality of the ilr coordinates, it may be advisable to choose a taxonomic level where counts are less sparse so that the distribution of taxon-specific class probabilities remains unimodal.

stat.ME↗

arXiv2020

Estimation of marriage incidence rates by combining two cross-sectional retrospective designs: Event history analysis of two dependent processes

The aim of this work is to develop methods for studying the determinants of marriage incidence using marriage histories collected under two different types of retrospective cross-sectional study designs. These designs are: sampling of ever married women before the cross-section, a prevalent cohort, and sampling of women irrespective of marital status, a general cross-sectional cohort. While retrospective histories from a prevalent cohort do not identify incidence rates without parametric modelling assumptions, the rates can be identified when combined with data from a general cohort. Moreover, education, a strong endogenous covariate, and marriage processes are correlated. Hence, they need to be modelled jointly in order to estimate the marriage incidence. For this purpose, we specify a multi-state model and propose a likelihood-based estimation method. We outline the assumptions under which a likelihood expression involving only marriage incidence parameters can be derived. This is of particular interest when either retrospective education histories are not available or related parameters are not of interest. Our simulation results confirm the gain in efficiency by combining data from the two designs, while demonstrating how the parameter estimates are affected by violations of the assumptions used in deriving the simplified likelihood expressions. Two Indian National Family Health Surveys are used as motivation for the methodological development and to demonstrate the application of the methods.

stat.ME↗

arXiv2017

Optimal design of observational studies: overview and synthesis

We review typical design problems encountered in the planning of observational studies and propose a unifying framework that allows us to use the same concepts and notation for different problems. In the framework, the design is defined as a probability measure in the space of observational processes that determine whether the value of a variable is observed for a specific unit at the given time. The optimal design is then defined, according to Bayesian decision theory, to be the one that maximizes the expected utility related to the design. We present examples on the use of the framework and discuss methods for deriving optimal or approximately optimal designs.

math.ST↗