arXiv subjects

Heidi Seibold

Publications and source records attributed to Heidi Seibold.

12 recordsLinked to original sources

arXiv2025

Ten simple rules for PIs to integrate Research Software Engineering into their research group

Research Software Engineering (RSEng) is a key success factor in producing high-quality research software, which in turn enables and improves research outcomes. However, as a principal investigator or leader of a research group you may not know what RSEng is, where to get started with it, or how to use it to maximize its benefit for your research. RSEng also often comes with technical complexity, and therefore reduced accessibility to some researchers. The ten simple rules presented in this paper aim to improve the accessibility of RSEng, and provide practical and actionable advice to PIs and leaders for integrating RSEng into their research group. By following these rules, readers can improve the quality, reproducibility, and trustworthiness of their research software, ultimately leading to better, more reproducible and more trustworthy research outcomes.

cs.SE↗

arXiv2023

Foundational Competencies and Responsibilities of a Research Software Engineer: Current State and Suggestions for Future Directions

The term Research Software Engineer, or RSE, emerged a little over 10 years ago as a way to represent individuals working in the research community but focusing on software development. The term has been widely adopted and there are a number of high-level definitions of what an RSE is. However, the roles of RSEs vary depending on the institutional context they work in. At one end of the spectrum, RSE roles may look similar to a traditional research role. At the other extreme, they resemble that of a software engineer in industry. Most RSE roles inhabit the space between these two extremes. Therefore, providing a straightforward, comprehensive definition of what an RSE does and what experience, skills and competencies are required to become one is challenging. In this community paper we define the broad notion of what an RSE is, explore the different types of work they undertake, and define a list of fundamental competencies as well as values that define the general profile of an RSE. On this basis, we elaborate on the progression of these skills along different dimensions, looking at specific types of RSE roles, proposing recommendations for organisations, and giving examples of future specialisations. An appendix details how existing curricula fit into this framework.

cs.SE↗

arXiv2023

Improving Software Engineering in Biostatistics: Challenges and Opportunities

Programming is ubiquitous in applied biostatistics; adopting software engineering skills will help biostatisticians do a better job. To explain this, we start by highlighting key challenges for software development and application in biostatistics. Silos between different statistician roles, projects, departments, and organizations lead to the development of duplicate and suboptimal code. Building on top of open-source software requires critical appraisal and risk-based assessment of the used modules. Code that is written needs to be readable to ensure reliable software. The software needs to be easily understandable for the user, as well as developed within testing frameworks to ensure that long term maintenance of the software is feasible. Finally, the reproducibility of research results is hindered by manual analysis workflows and uncontrolled code development. We next describe how the awareness of the importance and application of good software engineering practices and strategies can help address these challenges. The foundation is a better education in basic software engineering skills in schools, universities, and during the work life. Dedicated software engineering teams within academic institutions and companies can be a key factor for the establishment of good software engineering practices and catalyze improvements across research projects. Providing attractive career paths is important for the retainment of talents. Readily available tools can improve the reproducibility of statistical analyses and their use can be exercised in community events. [...]

stat.CO↗

arXiv2022

What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?

Estimation of heterogeneous treatment effects (HTE) is of prime importance in many disciplines, ranging from personalized medicine to economics among many others. Random forests have been shown to be a flexible and powerful approach to HTE estimation in both randomized trials and observational studies. In particular "causal forests", introduced by Athey, Tibshirani and Wager (2019), along with the R implementation in package grf were rapidly adopted. A related approach, called "model-based forests", that is geared towards randomized trials and simultaneously captures effects of both prognostic and predictive variables, was introduced by Seibold, Zeileis and Hothorn (2018) along with a modular implementation in the R package model4you. Here, we present a unifying view that goes beyond the theoretical motivations and investigates which computational elements make causal forests so successful and how these can be blended with the strengths of model-based forests. To do so, we show that both methods can be understood in terms of the same parameters and model assumptions for an additive model under L2 loss. This theoretical insight allows us to implement several flavors of "model-based causal forests" and dissect their different elements in silico. The original causal forests and model-based forests are compared with the new blended versions in a benchmark study exploring both randomized trials and observational settings. In the randomized setting, both approaches performed akin. If confounding was present in the data generating process, we found local centering of the treatment indicator with the corresponding propensities to be the main driver for good performance. Local centering of the outcome was less important, and might be replaced or enhanced by simultaneous split selection with respect to both prognostic and predictive effects.

stat.ME↗

arXiv2020

An Environment for Sustainable Research Software in Germany and Beyond: Current State, Open Challenges, and Call for Action

Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin.

cs.GL↗

arXiv2019

Open Science in Software Engineering

Open science describes the movement of making any research artefact available to the public and includes, but is not limited to, open access, open data, and open source. While open science is becoming generally accepted as a norm in other scientific disciplines, in software engineering, we are still struggling in adapting open science to the particularities of our discipline, rendering progress in our scientific community cumbersome. In this chapter, we reflect upon the essentials in open science for software engineering including what open science is, why we should engage in it, and how we should do it. We particularly draw from our experiences made as conference chairs implementing open science initiatives and as researchers actively engaging in open science to critically discuss challenges and pitfalls, and to address more advanced topics such as how and under which conditions to share preprints, what infrastructure and licence model to cover, or how do it within the limitations of different reviewing models, such as double-blind reviewing. Our hope is to help establishing a common ground and to contribute to make open science a norm also in software engineering.

cs.SE↗

arXiv2019

Survival Forests under Test: Impact of the Proportional Hazards Assumption on Prognostic and Predictive Forests for ALS Survival

We investigate the effect of the proportional hazards assumption on prognostic and predictive models of the survival time of patients suffering from amyotrophic lateral sclerosis (ALS). We theoretically compare the underlying model formulations of several variants of survival forests and implementations thereof, including random forests for survival, conditional inference forests, Ranger, and survival forests with $L_1$ splitting, with two novel variants, namely distributional and transformation survival forests. Theoretical considerations explain the low power of log-rank-based splitting in detecting patterns in non-proportional hazards situations in survival trees and corresponding forests. This limitation can potentially be overcome by the alternative split procedures suggested herein. We empirically investigated this effect using simulation experiments and a re-analysis of the PRO-ACT database of ALS survival, giving special emphasis to both prognostic and predictive models.

stat.ME↗

arXiv2018

Subgroup identification in dose-finding trials via model-based recursive partitioning

An important task in early phase drug development is to identify patients, which respond better or worse to an experimental treatment. While a variety of different subgroup identification methods have been developed for the situation of trials that study an experimental treatment and control, much less work has been done in the situation when patients are randomized to different dose groups. In this article we propose new strategies to perform subgroup analyses in dose-finding trials and discuss the challenges, which arise in this new setting. We consider model-based recursive partitioning, which has recently been applied to subgroup identification in two arm trials, as a promising method to tackle these challenges and assess its viability using a real trial example and simulations. Our results show that model-based recursive partitioning can be used to identify subgroups of patients with different dose-response curves and improves estimation of treatment effects and minimum effective doses, when heterogeneity among patients is present.

stat.ME↗

arXiv2017

OpenML: An R Package to Connect to the Machine Learning Platform OpenML

OpenML is an online machine learning platform where researchers can easily share data, machine learning tasks and experiments as well as organize them online to work and collaborate more efficiently. In this paper, we present an R package to interface with the OpenML platform and illustrate its usage in combination with the machine learning R package mlr. We show how the OpenML package allows R users to easily search, download and upload data sets and machine learning tasks. Furthermore, we also show how to upload results of experiments, share them with others and download results from other users. Beyond ensuring reproducibility of results, the OpenML platform automates much of the drudge work, speeds up research, facilitates collaboration and increases the users' visibility online.

stat.ML↗

arXiv2016

Generalised Linear Model Trees with Global Additive Effects

Model-based trees are used to find subgroups in data which differ with respect to model parameters. In some applications it is natural to keep some parameters fixed globally for all observations while asking if and how other parameters vary across subgroups. Existing implementations of model-based trees can only deal with the scenario where all parameters depend on the subgroups. We propose partially additive linear model trees (PALM trees) as an extension of (generalised) linear model trees (LM and GLM trees, respectively), in which the model parameters are specified a priori to be estimated either globally from all observations or locally from the observations within the subgroups determined by the tree. Simulations show that the method has high power for detecting subgroups in the presence of global effects and reliably recovers the true parameters. Furthermore, treatment-subgroup differences are detected in an empirical application of the method to data from a mathematics exam: the PALM tree is able to detect a small subgroup of students that had a disadvantage in an exam with two versions while adjusting for overall ability effects.

stat.CO↗

arXiv2016

Model-based Recursive Partitioning for Subgroup Analyses

The identification of patient subgroups with differential treatment effects is the first step towards individualised treatments. A current draft guideline by the EMA discusses potentials and problems in subgroup analyses and formulated challenges to the development of appropriate statistical procedures for the data-driven identification of patient subgroups. We introduce model-based recursive partitioning as a procedure for the automated detection of patient subgroups that are identifiable by predictive factors. The method starts with a model for the overall treatment effect as defined for the primary analysis in the study protocol and uses measures for detecting parameter instabilities in this treatment effect. The procedure produces a segmented model with differential treatment parameters corresponding to each patient subgroup. The subgroups are linked to predictive factors by means of a decision tree. The method is applied to the search for subgroups of patients suffering from amyotrophic lateral sclerosis that differ with respect to their Riluzole treatment effect, the only currently approved drug for this disease.

stat.ME↗

arXiv2016

Individual Treatment Effect Prediction for ALS Patients

A treatment for a complicated disease may be helpful for some but not all patients, which makes predicting the treatment effect for new patients important yet challenging. Here we develop a method for predicting the treatment effect based on patient char- acteristics and use it for predicting the effect of the only drug (Riluzole) approved for treating Amyotrophic Lateral Sclerosis (ALS). Our proposed method of model-based ran- dom forests detects similarities in the treatment effect among patients and on this basis computes personalised models for new patients. The entire procedure focuses on a base model, which usually contains the treatment indicator as a single covariate and takes the survival time or a health or treatment success measurement as primary outcome. This base model is used both to grow the model-based trees within the forest, in which the patient characteristics that interact with the treatment are split variables, and to com- pute the personalised models, in which the similarity measurements enter as weights. We applied the personalised models using data from several clinical trials for ALS from the PRO-ACT database. Our results indicate that some ALS patients benefit more from the drug Riluzole than others. Our method allows shifting from stratified medicine to person- alised medicine and can also be used in assessing the treatment effect for other diseases studied in a clinical trial.

stat.ME↗