Random Effects Structure Research Articles

Muscarinic receptor agonists in animal models of psychosis: protocol for a systematic review and meta-analysis

Background Muscarinic receptor agonism is a promising mechanism of action for treating psychosis, not present in most D2R-blocking antipsychotics. Xanomeline, an M1/M4-preferring agonist, has shown efficacy in late-stage clinical trials, with more compounds being investigated. Therefore, we aim to synthesize evidence on the preclinical efficacy of muscarinic receptor agonists in animal models of psychosis to provide unique insights and evidence-based information to guide drug development. Methods We plan a systematic review and meta-analysis of in vivo animal studies comparing muscarinic receptor agonists or positive allosteric modulators with control conditions and existing D2R-blocking antipsychotics in animals subjected to any method that induces behavioural changes of relevance for psychosis. We will identify eligible studies by searching multiple electronic databases. At least two independent reviewers will conduct the study selection and data extraction using prespecified forms and assess the risk of bias with the SYRCLE’s tool. Our primary outcomes include locomotor activity and prepulse inhibition measured with standardized mean differences. We will examine other behavioural readouts of relevance for psychosis as secondary outcomes, such as social interaction and cognitive function. We will synthesize the data using multi-level meta-analysis with a predefined random-effects structure, considering the non-independence of the data. In meta-regressions we will explore potential sources of heterogeneity from a predefined list of characteristics of the animal population, model, and intervention. We will assess the confidence in the evidence considering a self-developed instrument thatconsiders the internal and external validity of the evidence. Protocol registration PROSPERO-ID: CRD42024520914

Family resemblance in color-patch size is not affected by stress experience in a cichlid fish.

Animal body coloration is often linked to social dominance and mating success. This is because it can carry information on an animal's body condition and competitive ability by reflecting the genetic quality of individuals or by responding to their current or past living conditions. The present study investigates genetic and environmental effects on a conspicuous color pattern of the cichlid fish Tropheus sp. black "Ikola," in which the size of a carotenoid-based yellow area on the body co-varies with social dominance. To examine environmental plasticity of the color pattern, we tested for effects of early-life stress, induced by reduced feeding of juveniles prior to color pattern formation, as well as effects of a stress treatment administered to fully colored adult fish. None of the stress treatments affected the color pattern as quantified by the width of the yellow bar. However, offspring bar width was correlated to parental values in mid-parent-mid-offspring regression analyses, and animal models estimated significant additive genetic effects on bar width, indicating heritability of the trait. Depending on the random effects structure of the animal models (i.e., whether including or excluding maternal and brood effects), narrow-sense heritability estimates for bar width ranged between 0.2 and 0.8, with the strongest statistical support for the highest estimate. In each of the alternative models, a large proportion of the total variance in bar width was explained by the included random effects, suggesting that bar width is strongly determined by genetic factors or shared maternal and brood environments, with limited scope for environmental influences later in life.

Predicting individual thermal preferences in an office: Assessing the performance of mixed-effects models

Multiple studies suggested that existing thermal comfort models inadequately predict occupants' individual thermal preferences. Personalised comfort models offer an alternative to conventional comfort models aiming to forecast individual's thermal preference. Implementation of these personalised models in occupant-centric control of heating, ventilation, and air-conditioning (HVAC) systems can enhance their performance.A promising technique for personalised comfort modelling is mixed-effects (ME) modelling. A ME model accounts for fixed effects, representing the trends in the general sample, and for random effects, representing variations of specific clusters in the data. In contrast to fixed-effects (FE) models, ME models can capture individual differences. However, its effectiveness in predicting occupants' thermal preferences based on field measurement data, as well as the influence of variations in ME models on prediction accuracy, remains to be thoroughly investigated.This study aims to assess the prediction accuracy of ME models in contrast to FE models using field measurement data, including thermal preference votes from 30 unique occupants. The prediction performance was evaluated across three testing scenarios, each representing a different application of the models. Furthermore, two random effect structures were tested for the ME model: an intercept-only model and an intercept and slope model. The results show that ME models, in comparison to FE models, achieve an improved prediction performance of 8.0 % on average and up to 28.4 % for individual occupants. Moreover, the addition of a random slope to the ME resulted in deteriorated predictions. Finally, occupants’ individual variations were determined with an uncertainty of 6 % after 20 observations.

Maintaining the validity of inference from linear mixed models in stepped-wedge cluster randomized trials under misspecified random-effects structures.

Linear mixed models are commonly used in analyzing stepped-wedge cluster randomized trials. A key consideration for analyzing a stepped-wedge cluster randomized trial is accounting for the potentially complex correlation structure, which can be achieved by specifying random-effects. The simplest random effects structure is random intercept but more complex structures such as random cluster-by-period, discrete-time decay, and more recently, the random intervention structure, have been proposed. Specifying appropriate random effects in practice can be challenging: assuming more complex correlation structures may be reasonable but they are vulnerable to computational challenges. To circumvent these challenges, robust variance estimators may be applied to linear mixed models to provide consistent estimators of standard errors of fixed effect parameters in the presence of random-effects misspecification. However, there has been no empirical investigation of robust variance estimators for stepped-wedge cluster randomized trials. In this article, we review six robust variance estimators (both standard and small-sample bias-corrected robust variance estimators) that are available for linear mixed models in R, and then describe a comprehensive simulation study to examine the performance of these robust variance estimators for stepped-wedge cluster randomized trials with a continuous outcome under different data generators. For each data generator, we investigate whether the use of a robust variance estimator with either the random intercept model or the random cluster-by-period model is sufficient to provide valid statistical inference for fixed effect parameters, when these working models are subject to random-effect misspecification. Our results indicate that the random intercept and random cluster-by-period models with robust variance estimators performed adequately. The CR3 robust variance estimator (approximate jackknife) estimator, coupled with the number of clusters minus two degrees of freedom correction, consistently gave the best coverage results, but could be slightly conservative when the number of clusters was below 16. We summarize the implications of our results for the linear mixed model analysis of stepped-wedge cluster randomized trials and offer some practical recommendations on the choice of the analytic model.

Subgroup detection in linear growth curve models with generalized linear mixed model (GLMM) trees.

Growth curve models are popular tools for studying the development of a response variable within subjects over time. Heterogeneity between subjects is common in such models, and researchers are typically interested in explaining or predicting this heterogeneity. We show how generalized linear mixed-effects model (GLMM) trees can be used to identify subgroups with different trajectories in linear growth curve models. Originally developed for clustered cross-sectional data, GLMM trees are extended here to longitudinal data. The resulting extended GLMM trees are directly applicable to growth curve models as an important special case. In simulated and real-world data, we assess performance of the extensions and compare against other partitioning methods for growth curve models. Extended GLMM trees perform more accurately than the original algorithm and LongCART, and similarly accurate compared to structural equation model (SEM) trees. In addition, GLMM trees allow for modeling both discrete and continuous time series, are less sensitive to (mis-)specification of the random-effects structure and are much faster to compute.

Multilevel Latent Differential Structural Equation Model with Short Time Series and Time-Varying Covariates: A Comparison of Frequentist and Bayesian Estimators

Continuous-time modeling using differential equations is a promising technique to model change processes with longitudinal data. Among ways to fit this model, the Latent Differential Structural Equation Modeling (LDSEM) approach defines latent derivative variables within a structural equation modeling (SEM) framework, thereby allowing researchers to leverage advantages of the SEM framework for model building, estimation, inference, and comparison purposes. Still, a few issues remain unresolved, including performance of multilevel variations of the LDSEM under short time lengths (e.g., 14 time points), particularly when coupled multivariate processes and time-varying covariates are involved. Additionally, the possibility of using Bayesian estimation to facilitate the estimation of multilevel LDSEM (M-LDSEM) models with complex and higher-dimensional random effect structures has not been investigated. We present a series of Monte Carlo simulations to evaluate three possible approaches to fitting M-LDSEM, including: frequentist single-level and two-level robust estimators and Bayesian two-level estimator. Our findings suggested that the Bayesian approach outperformed other frequentist approaches. The effects of time-varying covariates are well recovered, and coupling parameters are the least biased especially using higher-order derivative information with the Bayesian estimator. Finally, an empirical example is provided to show the applicability of the approach.

Accounting for informative observation process in transition models of binary longitudinal outcome: Application to medical record data.

When extracting medical record data to form a retrospective cohort, investigators typically focus on a pre-specified study window, and select subjects who had hospital visits during that study window. However, such data extraction may suffer from an informative observation process, since sicker patients may have hospital visits more frequently. For example, Consecutive Pregnancy Study is a retrospective cohort study of women with multiple pregnancies in 23 Utah hospitals from 2003 to 2010, where the interest is to understand the risk factors of recurrent pregnancy outcomes, such as preterm birth. The observation process is informative in the sense that, women with adverse pregnancy outcomes may be less likely/willing/able to endure subsequent pregnancies. We proposed a three-part joint model with shared random effects structure to address this analytic complication. Particularly, a first-order transition model is used to model the longitudinal binary outcome; a gamma regression model is assumed for the inter-pregnancy intervals; a continuation ratio model specifies the probability of continuing with more births in the future. We note that the latter two parts give rise to a parametric cure-rate survival model. The performance of the proposed method was examined in extensive simulation studies, with both correctly and mis-specified models. The analyses of Consecutive Pregnancy Study data further demonstrate the inadequacies of fitting the transition model alone ignoring the informative observation process.

Data aggregation can lead to biased inferences in Bayesian linear mixed models and Bayesian analysis of variance.

Bayesian linear mixed-effects models (LMMs) and Bayesian analysis of variance (ANOVA) are increasingly being used in the cognitive sciences to perform null hypothesis tests, where a null hypothesis that an effect is zero is compared with an alternative hypothesis that the effect exists and is different from zero. While software tools for Bayes factor null hypothesis tests are easily accessible, how to specify the data and the model correctly is often not clear. In Bayesian approaches, many authors use data aggregation at the by-subject level and estimate Bayes factors on aggregated data. Here, we use simulation-based calibration for model inference applied to several example experimental designs to demonstrate that, as with frequentist analysis, such null hypothesis tests on aggregated data can be problematic in Bayesian analysis. Specifically, when random slope variances differ (i.e., violated sphericity assumption), Bayes factors are too conservative for contrasts where the variance is small and they are too liberal for contrasts where the variance is large. Running Bayesian ANOVA on aggregated data can-if the sphericity assumption is violated-likewise lead to biased Bayes factor results. Moreover, Bayes factors for by-subject aggregated data are biased (too liberal) when random item slope variance is present but ignored in the analysis. These problems can be circumvented or reduced by running Bayesian LMMs on nonaggregated data such as on individual trials, and by explicitly modeling the full random effects structure. Reproducible code is available from https://osf.io/mjf47/. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Reliability and Feasibility of Linear Mixed Models in Fully Crossed Experimental Designs

The use of linear mixed models (LMMs) is increasing in psychology and neuroscience research In this article, we focus on the implementation of LMMs in fully crossed experimental designs. A key aspect of LMMs is choosing a random-effects structure according to the experimental needs. To date, opposite suggestions are present in the literature, spanning from keeping all random effects (maximal models), which produces several singularity and convergence issues, to removing random effects until the best fit is found, with the risk of inflating Type I error (reduced models). However, defining the random structure to fit a nonsingular and convergent model is not straightforward. Moreover, the lack of a standard approach may lead the researcher to make decisions that potentially inflate Type I errors. After reviewing LMMs, we introduce a step-by-step approach to avoid convergence and singularity issues and control for Type I error inflation during model reduction of fully crossed experimental designs. Specifically, we propose the use of complex random intercepts (CRIs) when maximal models are overparametrized. CRIs are multiple random intercepts that represent the residual variance of categorical fixed effects within a given grouping factor. We validated CRIs and the proposed procedure by extensive simulations and a real-case application. We demonstrate that CRIs can produce reliable results and require less computational resources. Moreover, we outline a few criteria and recommendations on how and when scholars should reduce overparametrized models. Overall, the proposed procedure provides clear solutions to avoid overinflated results using LMMs in psychology and neuroscience.

A comparison of methods for specifying optimal random effects structures

Using Monte Carlo simulations, this study compared the performance of various approaches to the specification of random effects structures in linear mixed effects models (LMMs), including the minimal approach, the maximal approach, the forward search, the backward search, and the all-possible structures approach. The results showed that if the predictor of interest is at the within-cluster level or involves a cross-level interaction, the maximal approach, the best-path forward search, and the best-path backward search are all desirable methods. If the predictor of interest is at the cluster level, it is not essential to specify random slopes of Level-1 predictors. In addition, it is important to specify random slopes of within-cluster control variables, as they can increase the statistical power for testing the main within-cluster variables, especially when the sample size is small and the variance of the random slope of the control variable is large.

Are your random effects normal? A simulation study of methods for estimating whether subjects or items come from more than one population by examining the distribution of random effects in mixed-effects logistic regression.

With mixed-effects regression models becoming a mainstream tool for every psycholinguist, there has become an increasing need to understand them more fully. In the last decade, most work on mixed-effects models in psycholinguistics has focused on properly specifying the random-effects structure to minimize error in evaluating the statistical significance of fixed-effects predictors. The present study examines a potential misspecification of random effects that has not been discussed in psycholinguistics: violation of the single-subject-population assumption, in the context of logistic regression. Estimated random-effects distributions in real studies often appear to be bi- or multimodal. However, there is no established way to estimate whether a random-effects distribution corresponds to more than one underlying population, especially in the more common case of a multivariate distribution of random effects. We show that violations of the single-subject-population assumption can usually be detected by assessing the (multivariate) normality of the inferred random-effects structure, unless the data show quasi-separability, i.e., many subjects or items show near-categorical behavior. In the absence of quasi-separability, several clustering methods are successful in determining which group each participant belongs to. The BIC difference between a two-cluster and a one-cluster solution can be used to determine that subjects (or items) do not come from a single population. This then allows the researcher to define and justify a new post hoc variable specifying the groups to which participants or items belong, which can be incorporated into regression analysis.

A framework to characterise the reproducibility of meta-analysis results with its application to direct oral anticoagulants in the acute treatment of venous thromboembolism.

The number of meta-analyses of aggregate data has dramatically increased due to the facility of obtaining data from publications and the development of free, easy-to-use, and specialised statistical software. Even when meta-analyses include the same studies, their results may vary owing to different methodological choices. Assessment of the replication of meta-analysis provides an example of the variation of effect 'naturally' observed between multiple research projects. Reproducibility of results has mostly been reported using graphical descriptive representations. A quantitative analysis of such results would enable (i) breakdown of the total observed variability with quantification of the variability generated by the replication process and (ii) identification of which variables account for this variability, such as methodological quality or the statistical analysis procedures used. These variables might explain systematic mean differences between results and dispersion of the results. To quantitatively characterise the reproducibility of meta-analysis results, a bivariate linear mixed-effects model was developed to simulate both mean results and their corresponding uncertainty. Results were assigned to several replication groups, those assessing the same studies, outcomes, treatment indication and comparisons classified in the same replication group. A nested random effect structure was used to break down the total variability within each replication group and between these groups to enable calculation of an intragroup correlation coefficient and quantification of reproducibility. Determinants of variability were investigated by modelling both mean and variance parameters using covariates. The proposed model was applied to the example of meta-analyses evaluating direct oral anticoagulants in the acute treatment of venous thromboembolism.

A machine learning approach for clustered data

Artificial neural networks (NNs) are a machine learning algorithm that have been used as a convenient alternative of conventional statistical models, such as regression in prediction and classification because of their capability of modeling complex relationships between dependent and independent variables without a priori assumptions about the model form and variable distributions. However, traditional NNs cannot incorporate dependencies of data with a clustering or nesting structure involved in longitudinal studies and cluster sampling. This research is intended to fill this literature gap by integrating the random-effects structure into NNs to account for within-cluster correlations. The proposed NN method incorporating random effects (NNRE) is trained by minimizing the cost function using the backpropagation algorithm combined with the quasi-Newton and gradient descent algorithms. Model overfitting is controlled by using the L 2 regularization method. The trained NNRE model is evaluated for prediction accuracy by using the leaving-one-out cross-validation for both simulated and real data. Prediction accuracy is compared between NNRE and two existing models, the conventional generalized linear mixed model (GLIMMIX) and the generalized neural network mixed model (GNMM), using simulations and real data. Results show that the proposed NNRE results in higher accuracy than both the GLIMMIX and GNMM.

A mixed-model approach for estimating drivers of microbiota community composition and differential taxonomic abundance.

Next-generation sequencing (NGS) and metabarcoding approaches are increasingly applied to wild animal populations, but there is a disconnect between the widely applied generalized linear mixed model (GLMM) approaches commonly used to study phenotypic variation and the statistical toolkit from community ecology typically applied to metabarcoding data. Here, we describe the suitability of a novel GLMM-based approach for analyzing the taxon-specific sequence read counts derived from standard metabarcoding data. This approach allows decomposition of the contribution of different drivers to variation in community composition (e.g., age, season, individual) via interaction terms in the model random-effects structure. We provide guidance to implementing this approach and show how these models can identify how responsible specific taxonomic groups are for the effects attributed to different drivers. We applied this approach to two cross-sectional data sets from the Soay sheep population of St. Kilda. GLMMs showed agreement with dissimilarity-based approaches highlighting the substantial contribution of age and minimal contribution of season to microbiota community compositions, and simultaneously estimated the contribution of other technical and biological factors. We further used model predictions to show that age effects were principally due to increases in taxa of the phylum Bacteroidetes and declines in taxa of the phylum Firmicutes. This approach offers a powerful means for understanding the influence of drivers of community structure derived from metabarcoding data. We discuss how our approach could be readily adapted to allow researchers to estimate contributions of additional factors such as host or microbe phylogeny to answer emerging questions surrounding the ecological and evolutionary roles of within-host communities. IMPORTANCE NGS and fecal metabarcoding methods have provided powerful opportunities to study the wild gut microbiome. A wealth of data is, therefore, amassing across wild systems, generating the need for analytical approaches that can appropriately investigate simultaneous factors at the host and environmental scale that determine the composition of these communities. Here, we describe a generalized linear mixed-effects model (GLMM) approach to analyze read count data from metabarcoding of the gut microbiota, allowing us to quantify the contributions of multiple host and environmental factors to within-host community structure. Our approach provides outputs that are familiar to a majority of field ecologists and can be run using any standard mixed-effects modeling packages. We illustrate this approach using two metabarcoding data sets from the Soay sheep population of St. Kilda investigating age and season effects as worked examples.

Ordinal categorical response with baseline response and skew-normal random effect: an application to schizophrenia data

The normal is a popular random effect distribution perceived to identify the correlation between the baseline or initial response and subsequent responses in the analysis of longitudinal data. In this article, we apply more flexible skew-normal random effects in modeling initial responses for the ordinal longitudinal data which include normal as a member. Furthermore, the correlation structure between the baseline response and the consequent responses is studied. Our results indicate that the skew-normal random effects produce the best results in modeling initial conditions for longitudinal ordinal data. Various types of simulation studies are applied to investigate the properties of the proposed model in different scenarios. We found that the baseline observation includes the pre-sample information of the process affecting the structure of random effects and explanatory variables of the proposed model. The developed model is illustrated as an application to the Schizophrenia Collaborative Study data.

First steps into the pupillometry multiverse of developmental science

Pupillometry has been widely implemented to investigate cognitive functioning since infancy. Like most psychophysiological and behavioral measures, it implies hierarchical levels of arbitrariness in preprocessing before statistical data analysis. By means of an illustrative example, we checked the robustness of the results of a familiarization procedure that compared the impact of audiovisual and visual stimuli in 12-month-olds. We adopted a multiverse approach to pupillometry data analysis to explore the role of (1) the preprocessing phase, that is, handling of extreme values, selection of the areas of interest, management of blinks, baseline correction, participant inclusion/exclusion and (2) the modeling structure, that is, the incorporation of smoothers, fixed and random effects structure, in guiding the parameter estimation. The multiverse of analyses shows how the preprocessing steps influenced the regression results, and when visual stimuli plausibly predicted an increase of resource allocation compared with audiovisual stimuli. Importantly, smoothing time in statistical models increased the plausibility of the results compared to those nested models that do not weigh the impact of time. Finally, we share theoretical and methodological tools to move the first steps into (rather than being afraid of) the inherent uncertainty of infant pupillometry.

Random year intercepts in mixed models help to assess uncertainties in insect population trends

Abstract An increasing number of studies is investigating insect population trends based on time series data. However, the available data is often subject to temporal pseudoreplication. Inter‐annual variability of environmental conditions and strong fluctuations in insect abundances can impede reliable trend estimation. Temporal random effect structures in regression models have been proposed as solution for this issue, but remain controversial. We investigated trends in ground beetle abundance across 24 years using generalised linear mixed models. We fitted four models: A base model, a model featuring a random year intercept, a model featuring basic weather parameters, and a model featuring both random year intercept and weather parameters. We then performed a simple sensitivity analysis to assess the robustness of the four models with respect to influential years, also testing for possible spurious baseline and snapshot effects. The model structure had a significant impact on the overall magnitude of the estimated trends. However, we found almost no difference among the models in how the removal of single years (sensitivity analysis) relatively affected trend coefficients. The two models with a random year intercept yielded significantly larger confidence intervals and their p‐values were more sensitive during sensitivity analysis. Significant differences of the model with random year intercept and weather parameters to all other models suggest that the random year effects and specific weather effects are rather additive than interchangeable. We conclude that random year intercepts help to produce more reliable and cautious uncertainty measures for insect population trends. Moreover, they might help to identify influential years in sensitivity analyses more easily. We recommend random year intercepts in addition to any variables representing temporally variable environmental conditions, such as weather variables.

Multivariate generalized linear mixed models for underdispersed count data

Researchers are often interested in understanding the relationship between a set of covariates and a set of response variables. To achieve this goal, the use of regression analysis, either linear or generalized linear models, is largely applied. However, such models only allow users to model one response variable at a time. Moreover, it is not possible to directly calculate from the regression model a correlation measure between the response variables. In this article, we employed the Multivariate Generalized Linear Mixed Models framework, which allows the specification of a set of response variables and calculates the correlation between them through a random effect structure that follows a multivariate normal distribution. We used the maximum likelihood estimation framework to estimate all model parameters using Laplace approximation to integrate out the random effects. The derivatives are provided by automatic differentiation. The outer maximization was made using a general-purpose algorithm such as PORT and Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS). We delimited this problem by studying count response variables with the following distributions: Poisson, negative binomial, Conway-Maxwell-Poisson (COM-Poisson), and double Poisson. While the first distribution can model only equidispersed data, the second models equi and overdispersed, and the third and fourth models all types of dispersion (i.e. including underdispersion). The models were implemented on software R with package TMB, based on C++ templates. Besides the full specification, models with simpler structures in the covariance matrix were considered (fixed and common variance, and ρ set to 0) and fixed dispersion. These models were applied to a dataset from the National Health and Nutrition Examination Survey, where two response variables are underdispersed and one can be considered equidispersed that were measured at 1281 subjects. The double Poisson full model specification overcame the other three competitors considering three goodness-of-fit measures: Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and maximized log-likelihood. Consequently, it estimated parameters with smaller standard error and a greater number of significant correlation coefficients. Therefore, the proposed model can deal with multivariate count responses and measures the correlation between them taking into account the effects of the covariates.

An application of mixed-effects model to evaluate the role of age and size on radial variation in wood specific gravity in teak (Tectona grandis)

To test whether radial variation of wood specific gravity (WSG) is controlled by tree age or tree size in teak (Tectona grandis L.f) plantation trees, opposing different-length pith-to-bark strips which represents the differential lateral growth rate was compared using mixed-effects model which considers the heterogeneity of variances and dependency in the data to gain insight into the stochastic processes that govern the wood formation process. Various models were tested in devising an appropriate radial WSG model. Models that accounted for serial correlation in WSG data performed better than the simple structure that assumes zero correlation between measurements. The autoregressive plus random tree effect structure performed better in describing the radial variation pattern. The variability of the data related to random fluctuations during tree development and the wood formation process is modeled by the autoregressive parameter revealing the intrinsic complexity of wood formation. Since they cannot be attributed to observed factors, models should consider temporal or serial correlations when assessing wood quality. The results revealed that tree age is a decisive factor in controlling the WSG of wood, while tree size is statistically less important. Furthermore, the core wood production period varies with the growth rate. It is shown that the core wood area decreased with slow growth. Findings presented here appear to provide the first demonstration of radial variation in WSG with respect to growth rate and age for planted teak growing in Ghana.

No decrease in perseverance and performance on cognitive tasks in Danish cohort of hormonal contraceptive users.

Cognitive consequences of hormonal contraceptives (HCs) are largely underexplored, despite the popularity of use. This study investigates the association between perseverance during cognitively challenging tasks and the use of HCs among Danish women. We hypothesised that women using HCs show decreased perseverance across tasks compared to their naturally cycling counterparts. We further hypothesised that HC using women would show decreased performance as a measure of accuracy (i.e. more incorrect answers) compared to naturally cycling women. The study used a cross-sectional repeated measures design, consisting of a Danish version of the Anagram Persistence Task and the Hagen Matrices Test, followed by an extensive survey documenting menstrual and HC history for each participant. The study was conducted online. Data processing was conducted on data from 129 participants. The former hypothesis was analysed through multilevel regression with a nested random effects structure on log-transformed data. The latter hypothesis was analysed through a multilevel generalised linear model with a nested random effects structure using the binomial family. No support was found for either of the hypotheses.