Publications by authors named "Judith J M Rijnhart"

Single-Case Experimental Designs (SCEDs), or N-of-1 trials, are commonly used to estimate intervention effects in many disciplines including in the treatment of youth mental health problems. SCEDs consist of repeated measurements of an outcome over time for a single case (e.g.

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Despite previous warnings against the use of the difference-in-coefficients method for estimating the indirect effect when the outcome in the mediation model is binary, the difference-in-coefficients method remains readily used in a variety of fields. The continued use of this method is presumably because of the lack of awareness that this method conflates the indirect effect estimate and non-collapsibility. In this paper, we aim to demonstrate the problems associated with the difference-in-coefficients method for estimating indirect effects for mediation models with binary outcomes.

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Article Synopsis
  • The study investigates how health behaviors like smoking, physical inactivity, and alcohol use may mediate the relationship between depression, anxiety, and different types of cancer, including lung and breast cancer.
  • Utilizing data from 18 cohorts with a total of 319,613 participants, the researchers performed two-stage meta-analyses to analyze these associations and calculate the mediating effects.
  • Results showed that smoking and physical inactivity significantly mediated links between depression, anxiety, and lung cancer, highlighting the importance of smoking cessation programs for individuals dealing with mental health issues.
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Moderation analysis is used to study under what conditions or for which subgroups of individuals a treatment effect is stronger or weaker. When a moderator variable is categorical, such as assigned sex, treatment effects can be estimated for each group resulting in a treatment effect for males and a treatment effect for females. If a moderator variable is a continuous variable, a strategy for investigating moderated treatment effects is to estimate conditional effects (i.

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Introduction: We evaluated determinants associated with care partner outcomes along the Alzheimer's disease (AD) stages.

Methods: We included = 270 care partners of amyloid-positive patients in the pre-dementia and dementia stages of AD. Using linear regression analysis, we examined determinants of four care partner outcomes: informal care time, caregiver distress, depression, and quality of life (QoL).

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Background: Confounding is a common issue in epidemiological research. Commonly used confounder-adjustment methods include multivariable regression analysis and propensity score methods. Although it is common practice to assess the linearity assumption for the exposure-outcome effect, most researchers do not assess linearity of the relationship between the confounder and the exposure and between the confounder and the outcome before adjusting for the confounder in the analysis.

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Introduction: We studied life satisfaction across Alzheimer's disease (AD) stages and studied mobility and meaningful activities as mediators of the associations between these AD stages and life satisfaction.

Methods: In this cross-sectional study, we included = 269 amyloid-positive patients with subjective cognitive decline (SCD), mild cognitive impairment (MCI), and AD dementia from the Amsterdam Dementia Cohort. Life satisfaction was measured with the satisfaction with life scale.

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Background: Quality of life (QoL) is an important outcome from the perspective of patients and their caregivers, in both dementia and pre-dementia stages. Yet, little is known about the long-term changes in QoL over time. We aimed to compare the trajectories of QoL between amyloid-positive and amyloid-negative SCD or MCI patients and to evaluate QoL trajectories along the Alzheimer's disease (AD) continuum of cognitively normal to dementia.

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Objective: Traditional methods to deal with non-linearity in regression analysis often result in loss of information or compromised interpretability of the results. A recommended but underutilized method for modeling non-linear associations in regression models is spline functions. We explain spline functions in a non-mathematical way and illustrate the application and interpretation to an empirical data example.

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Objectives: Longitudinal mediation effects can be estimated with mixed effects models. Mixed effects models are versatile, as they accommodate the estimation of contemporaneous, lagged, time-independent, and time-dependent effects. However, the inclusion of time lags and time interactions in mixed effects models for longitudinal mediation analysis has received little attention.

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Background: Patients and caregivers express a desire for accurate prognostic information about time to institutionalization and mortality. Previous studies predicting institutionalization and mortality focused on the dementia stage. However, Alzheimer's disease (AD) is characterized by a long pre-dementia stage.

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Single case experimental designs (SCEDs) are used to test treatment effects in a wide range of fields and consist of repeated measurements for a single case throughout one or more baseline phases and throughout one or more treatment phases. Recently, mediation analysis has been applied to SCEDs. Mediation analysis decomposes the total treatment-outcome effect into a direct and indirect effect, and therefore aims to unravel the causal processes underlying treatment-outcome effects.

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Importance: Active participation in care by parents and zero separation between parents and their newborns is highly recommended during infant hospitalization in the neonatal intensive care unit (NICU).

Objective: To study the association of a family integrated care (FICare) model with maternal mental health at hospital discharge of their preterm newborn compared with standard neonatal care (SNC).

Design, Setting, And Participants: This prospective, multicenter cohort study included mothers with infants born preterm treated in level-2 neonatal units in the Netherlands (1 unit with single family rooms [the FICare model] and 2 control sites with standard care in open bay units) between May 2017 and January 2020 as part of the AMICA study (fAMily Integrated CAre in the neonatal ward).

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Background And Objectives: There is an urgent need to better understand frailty and its predisposing factors. Although numerous cross-sectional studies have identified various risk and protective factors of frailty, there is a limited understanding of longitudinal frailty progression. Furthermore, discrepancies in the methodologies of these studies hamper comparability of results.

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Importance: During newborn hospitalization in the neonatal unit, fathers often feel anxious and excluded from their child's caregiving and decision-making. Few studies and interventions have focused on fathers' mental health and their participation in neonatal care.

Objective: To study the association of a family integrated care (FICare) model (in single family rooms with complete couplet-care for the mother-newborn dyad) vs standard neonatal care (SNC) in open bay units with separate maternity care with mental health outcomes in fathers at hospital discharge of their preterm newborn and to study whether parent participation was a mediator of the association of the FICare model on outcomes.

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Frailty is associated with a higher risk of mortality, but not much is known about underlying pathways of the frailty-mortality association. In this study, we explore a wide range of possible mediators of the relation between frailty and mortality. Data were used from the Longitudinal Aging Study Amsterdam (LASA).

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Mediation analysis is an important statistical method in prevention research, as it can be used to determine effective intervention components. Traditional mediation analysis defines direct and indirect effects in terms of linear regression coefficients. It is unclear how these traditional effects are estimated in settings with binary variables.

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Background: Mediation analysis methodology underwent many advancements throughout the years, with the most recent and important advancement being the development of causal mediation analysis based on the counterfactual framework. However, a previous review showed that for experimental studies the uptake of causal mediation analysis remains low. The aim of this paper is to review the methodological characteristics of mediation analyses performed in observational epidemiologic studies published between 2015 and 2019 and to provide recommendations for the application of mediation analysis in future studies.

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There is an increasing awareness that replication should become common practice in empirical studies. However, study results might fail to replicate for various reasons. The robustness of published study results can be assessed using the relatively new multiverse-analysis methodology, in which the robustness of the effect estimates against data analytical decisions is assessed.

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An important recent development in mediation analysis is the use of causal mediation analysis. Causal mediation analysis decomposes the total exposure effect into causal direct and indirect effects in the presence of exposure-mediator interaction. However, in practice, traditional mediation analysis is still most widely used.

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Background: Confounding bias is a common concern in epidemiological research. Its presence is often determined by comparing exposure effects between univariable- and multivariable regression models, using an arbitrary threshold of a 10% difference to indicate confounding bias. However, many clinical researchers are not aware that the use of this change-in-estimate criterion may lead to wrong conclusions when applied to logistic regression coefficients.

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Background: Unidirectional studies suggest that the effects between cardiovascular disease, depressive symptoms and loneliness are reciprocal, but this has not been tested empirically. The aim was to study how cardiovascular morbidity, depressive symptoms and loneliness influence each other longitudinally.

Methods: Data from 2979 older adults from the Longitudinal Aging Study Amsterdam were analysed.

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