A note on applying the BCH method under linear equality and inequality constraints

Researchers often wish to relate estimated scores on latent variables to exogenous covariates not previously used in analyses. The BCH method corrects for asymptotic bias in estimates due to these scores’ uncertainty and has been shown to be relatively robust. When applying the BCH approach however, two problems arise. First, negative cell proportions can be obtained. Second, the approach cannot deal with situations where marginals need to be fixed to specific values, such as edit restrictions. The BCH approach can handle these problems when placed in a framework of quadratic loss functions and linear equality and inequality constraints. This research note gives the explicit form for equality constraints and demonstrates how solutions for inequality constraints may be obtained using numerical methods

Estimating classification error under edit restrictions in combined survey-register data using Multiple Imputation Latent Class modelling (MILC)

Both registers and surveys can contain classication errors. These errors can be estimated by making use of information that is obtained when making use of a combined dataset. We propose a new method based on latent class modelling that estimates the number of classification errors in the multiple sources, and simultaneously takes impossible combinations with other variables into account. Furthermore, we use the latent class model to multiply impute a new variable, which enhances the quality of statistics based on the combined dataset. The performance of this method is investigated by a simulation study, which shows that whether the method can be applied depends on the entropy of the LC model and the type of analysis a researcher is planning to do. Furthermore, the method is applied to a combined dataset from Statistics Netherlands

A framework for privacy preserving digital trace data collection through data donation

A potentially powerful method of social-scientific data collection and investigation has been created by an unexpected institution: the law. Article 15 of the EU’s 2018 General Data Protection Regulation (GDPR) mandates that individuals have electronic access to a copy of their personal data, and all major digital platforms now comply with this law by providing users with “data download packages” (DDPs). Through voluntary donation of DDPs, all data collected by public and private entities during the course of citizens’ digital life can be obtained and analyzed to answer social-scientific questions – with consent. Thus, consented DDPs open the way for vast new research opportunities. However, while this entirely new method of data collection will undoubtedly gain popularity in the coming years, it also comes with its own questions of representativeness and measurement quality, which are often evaluated systematically by means of an error framework. Therefore, in this paper we provide a blueprint for digital trace data collection using DDPs, and devise a “total error framework” for such projects. Our error framework for digital trace data collection through data donation is intended to facilitate high quality social-scientific investigations using DDPs while critically reflecting its unique methodological challenges and sources of error. In addition, we provide a quality control checklist to guide researchers in leveraging the vast opportunities afforded by this new mode of investigation

Fair inference on error-prone outcomes

Fair inference in supervised learning is an important and active area of research, yielding a range of useful methods to assess and account for fairness criteria when predicting ground truth targets. As shown in recent work, however, when target labels are error-prone, potential prediction unfairness can arise from measurement error. In this paper, we show that, when an error-prone proxy target is used, existing methods to assess and calibrate fairness criteria do not extend to the true target variable of interest. To remedy this problem, we suggest a framework resulting from the combination of two existing literatures: fair ML methods, such as those found in the counterfactual fairness literature on the one hand, and, on the other, measurement models found in the statistical literature. We discuss these approaches and their connection resulting in our framework. In a healthcare decision problem, we find that using a latent variable model to account for measurement error removes the unfairness detected previously.Comment: Online supplementary code is available at https://dx.doi.org/10.5281/zenodo.370815

Achieving Fair Inference Using Error-Prone Outcomes

Recently, an increasing amount of research has focused on methods to assess and account for fairness criteria when predicting ground truth targets in supervised learning. However, recent literature has shown that prediction unfairness can potentially arise due to measurement error when target labels are error prone. In this study we demonstrate that existing methods to assess and calibrate fairness criteria do not extend to the true target variable of interest, when an error-prone proxy target is used. As a solution to this problem, we suggest a framework that combines two existing fields of research: fair ML methods, such as those found in the counterfactual fairness literature and measurement models found in the statistical literature. Firstly, we discuss these approaches and how they can be combined to form our framework. We also show that, in a healthcare decision problem, a latent variable model to account for measurement error removes the unfairness detected previously

Spatial contrast sensitivity in adolescents with autism spectrum disorders

Adolescents with autism spectrum disorders (ASD) and typically developing (TD) controls underwent a rigorous psychophysical assessment that measured contrast sensitivity to seven spatial frequencies (0.5-20 cycles/degree). A contrast sensitivity function (CSF) was then fitted for each participant, from which four measures were obtained: visual acuity, peak spatial frequency, peak contrast sensitivity, and contrast sensitivity at a low spatial frequency. There were no group differences on any of the four CSF measures, indicating no differential spatial frequency processing in ASD. Although it has been suggested that detail-oriented visual perception in individuals with ASD may be a result of differential sensitivities to low versus high spatial frequencies, the current study finds no evidence to support this hypothesis

Port: A software tool for digital data donation

Recently, a new workflow has been introduced that allows academic researchers to partner with individuals interested in donating their digital trace data for academic research purposes (Boeschoten, Ausloos, et al., 2022). In this workflow, the digital traces of participants are processed locally on their own devices in such a way that only the subset of participants’ digital trace data that is of legitimate interest to a research project are shared with the researcher, which can only occur after the participant has provided their informed consent.This data donation workflow consists of the following steps: First, the participant requests a digital copy of their personal data at the platform of interest, such as Google, Meta, Twitter and other digital platforms, i.e., their Data Download Package (DDP). Platforms, as data controllers, are required as per the European Union’s General Data Protection Regulation (GDPR) to share a digital copy with each participant requesting such a copy. Second, they download the DDP onto their personal device. Third, by means of local processing, only thedata points of interest to the researcher are extracted from that DDP. Fourth, the participant inspects the extracted data points after which the participant can consent to donate. Only after providing this consent, the donated data is sent to a storage location and can be accessed by the researcher, which would mean that the storage location can be accessed for further analysis.In this paper, we introduce Port. Port is a software tool that allows researchers to configure the local processing step of the data donation workflow, allowing the researcher to collect exactly the digital traces needed to answer their research question. When using Port, a researcher can decide:• Which digital platforms are investigated;• Which digital traces are collected;• How the extracted digital traces are visually presented to the participant;• What is communicated to the participant

Using a genetic, observational study as a strategy to estimate the potential cost-effectiveness of pharmacological CCR5 blockade in dialysis patients

Background and objective Randomized clinical trials are expensive and time consuming. Therefore, strategies are needed to prioritise tracks for drug development. Genetic association studies may provide such a strategy by considering the differences between genotypes as a proxy for a natural, lifelong, randomized at conception, clinical trial. Previously an association with better survival was found in dialysis patients with systemic inflammation carrying a deletion variant of the CC-chemokine receptor 5 (CCR5). We hypothesized that in an analogous manner, pharmacological CCR5 blockade could protect against inflammation-driven mortality and estimated if such a treatment would be cost-effective. Methods A genetic screen and treat strategy was modelled using a decision-analytic Markov model, in which patients were screened for the CCR5 deletion 32 polymorphism and those with the wild type and systemic inflammation were treated with pharmacological CCR5 blockers. Kidney transplantation and mortality rates were calculated using patient level data. Extensive sensitivity analyses were performed. Results The cost-effectiveness of the genetic screen and treat strategy was (sic)18 557 per life year gained and (sic)21 896 per quality-adjusted life years gained. Concordance between the genetic association and pharmacological effectiveness was a main driver of cost-effectiveness. Sensitivity analyses showed that even a modest effectiveness of pharmacological CCR5 blockade would result in a treatment strategy that is good value for money. Conclusion Pharmacological blockade of the CCR5 receptor in inflamed dialysis patients can be incorporated in a potentially cost-effective screen and treat programme. These findings provide formal rationale for clinical studies. This study illustrates the potential of genetic association studies for drug development, as a source of Mendelian randomized evidence from an observational setting. Pharmacogenetics and Genomics 21: 417-425 (C) 2011 Wolters Kluwer Health | Lippincott Williams & Wilkins