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Mendelian randomization uses genetic variants to make causal inferences about a modifiable exposure. Subject to a genetic variant satisfying the instrumental variable assumptions, an association between the variant and outcome implies a causal effect of the exposure on the outcome. Complications arise with a binary exposure that is a dichotomization of a continuous risk factor (for example, hypertension is a dichotomization of blood pressure). This can lead to violation of the exclusion restriction assumption: the genetic variant can influence the outcome via the continuous risk factor even if the binary exposure does not change. Provided the instrumental variable assumptions are satisfied for the underlying continuous risk factor, causal inferences for the binary exposure are valid for the continuous risk factor. Causal estimates for the binary exposure assume the causal effect is a stepwise function at the point of dichotomization. Even then, estimation requires further parametric assumptions. Under monotonicity, the causal estimate represents the average causal effect in 'cornpliers', individuals for whom the binary exposure would be present if they have the genetic variant and absent otherwise. Unlike in randomized trials, genetic compliers are unlikely to be a large or representative subgroup of the population. Under homogeneity, the causal effect of the exposure on the outcome is assumed constant in all individuals; rarely a plausible assumption. We here provide methods for causal estimation with a binary exposure (although subject to all the above caveats). Mendelian randomization investigations with a dichotomized binary exposure should be conceptualized in terms of an underlying continuous variable.

Deciding which concepts should be described in causal language and which should not

The goal of this study was to examine what happens to established associations between attention deficit hyperactivity disorder (ADHD) symptoms and cortical surface and thickness regions once we apply inverse probability of censoring weighting (IPCW) to address potential selection bias. Moreover, we illustrate how different factors that predict participation contribute to potential selection bias. Participants were 9‐ to 11‐year‐old children from the Generation R study ( N  = 2707). Cortical area and thickness were measured with magnetic resonance imaging (MRI) and ADHD symptoms with the Child Behavior Checklist. We examined how associations between ADHD symptoms and brain morphology change when we weight our sample back to either follow‐up (ages 9–11), baseline (cohort at birth), or eligible (population of Rotterdam at time of recruitment). Weights were derived using IPCW or raking and missing predictors of participation used to estimate weights were imputed. Weighting analyses to baseline and eligible increased beta coefficients for the middle temporal gyrus surface area, as well as fusiform gyrus cortical thickness. Alternatively, the beta coefficient for the rostral anterior cingulate decreased. Removing one group of variables used for estimating weights resulted in the weighted regression coefficient moving closer to the unweighted regression coefficient. In addition, we found considerably different beta coefficients for most surface area regions and all thickness measures when we did not impute missing covariate data. Our findings highlight the importance of using inverse probability weighting (IPW) in the neuroimaging field, especially in the context of mental health‐related research. We found that including all variables related to exposure‐outcome in the IPW model and combining IPW with multiple imputations can help reduce bias. We encourage future psychiatric neuroimaging studies to define their target population, collect information on eligible but not included participants and use inverse probability of censoring weighting (IPCW) to reduce selection bias.

INTRODUCTION Adverse effects of monoclonal antibodies against amyloid beta are common, and may affect validity of randomized controlled trials (RCTs) through unblinding of participants. METHODS We used observations from published phase 3 RCTs in Alzheimer's disease to calculate the magnitude of unblinding effects on cognition that would be required to explain observed cognitive benefits in RCTs. RESULTS In trials of lecanemab, aducanumab, and donanemab, incidence of amyloid‐related imaging abnormalities with active treatment ranged from 22% to 44%, the vast majority of which presumably led to unblinding. Effects of unblinding on the Clinical Dementia Rating Sum of Boxes required to fully explain observed drug effects ranged from 1.1 point (95% confidence interval: 0.2–2.0) with aducanumab, to 3.3 points (2.1–4.4) with donanemab and 3.7 points (2.0–5.6) with lecanemab. Infusion‐related reactions were common, with potential unblinding effects particularly for lecanemab. Similar patterns were observed for the Alzheimer's Disease Assessment Scale Cognitive subscale. DISCUSSION Psychological treatment effects due to unblinding may explain a substantial share of observed treatment effects in RCTs.

ObjectivesClinical practice guidelines are designed to guide clinical practice and often make causal claims when making recommendations. Sometimes, guidelines make or require stronger causal claims than supplied in the original studies, a phenomenon we call ‘causal language jump’. We aimed to evaluate the strength of expressed causation in guidelines and the evidence they reference to assess the pattern of jumps, taking diabetes as an illustrative example.DesignThis is a systematic evaluation of guidelines and original studies cited by them, using scoping review design with deviations.Data sourceRandomly sampled 300 guideline statements (narrative sentences describing evidence to support recommendations) from four selected diabetes guidelines.Eligibility criteriaThe eligible guidelines should focus on non-pharmacological treatments or preventive strategies for adult type 2 diabetes mellitus management and related complications. The eligible action recommendations and guideline statements should intend to support non-pharmacological treatments or preventive strategies of type 2 diabetes or in a general diabetic context.Data extraction and synthesisWe rated the causation strength in the statements and the dependence on causation in recommendations supported by these statements using existing scales. Among the causal statements, the cited original studies were similarly assessed. We then evaluated jumps by checking if the causal claims in guideline statements were stronger than in original studies, and if the causation-dependence in guideline recommendations was stronger than supplied in guideline statements. We also assessed how well they report target trial emulation (TTE) components as a proxy for reliability.ResultsOf the 300 statements, 114 (38.0%) were causal, and 76 (66.7%) expressed strong causation. 27.2% (31/114) of causal guideline statements stated stronger causation than any of their references and demonstrated ‘causal language jump’; 34.9% (29/83) of guideline recommendations required stronger causation than provided in statements. Of the 53 eligible studies for TTE rating, most did not report treatment assignment and causal contrast in detail. The prevalence of these jumps could be partially attributed to the suboptimal use of causal and associational words.ConclusionsCausal language jumps were common among diabetes guidelines. While these jumps are sometimes inevitable, they should always be justified by good causal inference practices.

Mendelian randomization (MR) is an increasingly popular approach to estimating causal effects. Although the assumptions underlying MR cannot be verified, they imply certain constraints, the instrumental inequalities, which can be used to falsify the MR conditions. However, the instrumental inequalities are rarely applied in MR. We aimed to explore whether the instrumental inequalities could detect violations of the MR conditions in case studies analyzing the effect of commonly studied exposures on coronary artery disease risk. Using 1077 single nucleotide polymorphisms (SNPs), we applied the instrumental inequalities to MR models for the effects of vitamin D concentration, alcohol consumption, C-reactive protein (CRP), triglycerides, high-density lipoprotein (HDL) cholesterol, and low-density lipoprotein (LDL) cholesterol on coronary artery disease in the UK Biobank. For their relevant exposure, we applied the instrumental inequalities to MR models proposing each SNP as an instrument individually, and to MR models proposing unweighted allele scores as an instrument. We did not identify any violations of the MR assumptions when proposing each SNP as an instrument individually. When proposing allele scores as instruments, we detected violations of the MR assumptions for 5 of 6 exposures. Within our setting, this suggests the instrumental inequalities can be useful for identifying violations of the MR conditions when proposing multiple SNPs as instruments, but may be less useful in determining which SNPs are not instruments. This work demonstrates how incorporating the instrumental inequalities into MR analyses can help researchers to identify and mitigate potential bias.

Context: Policies legislating paid leave from work for new parents, and to attend to individual and family illness, are common across Organisation for Economic Co-operation and Development (OECD) countries. However, there exists no comprehensive review of their potential impacts on economic, social, and health outcomes. Methods: We conducted a systematic review of the peer-reviewed literature on paid leave and socioeconomic and health outcomes. We reviewed 5,538 abstracts and selected 85 published papers on the impact of parental leave policies, 22 papers on the impact of medical leave policies, and 2 papers that evaluated both types of policies. We synthesized the main findings through a narrative description; a meta-analysis was precluded by heterogeneity in policy attributes, policy changes, outcomes, and study designs. Findings: We were able to draw several conclusions about the impact of parental leave policies. First, extensions in the duration of paid parental leave to between 6 and 12 months were accompanied by attendant increases in leave-taking and longer durations of leave. Second, there was little evidence that extending the duration of paid leave had negative employment or economic consequences. Third, unpaid leave does not appear to confer the same benefits as paid leave. Fourth, from a population health perspective, increases in paid parental leave were consistently associated with better infant and child health, particularly in terms of lower mortality rates. Fifth, paid paternal leave policies of adequate length and generosity have induced fathers to take additional time off from work following the birth of a child. How medical leave policies for personal or family illness influence health has not been widely studied. Conclusions: There is substantial quasi-experimental evidence to support expansions in the duration of job-protected paid parental leave as an instrument for supporting women's labor force participation, safeguarding women's incomes and earnings, and improving child survival. This has implications, in particular, for countries that offer shorter durations of job-protected paid leave or lack a national paid leave entitlement altogether.

Physical activity and sedentary behaviors have been linked to a variety of general health benefits and problems. However, few studies have examined how physical activity during childhood is related to brain development, with the majority of work to date focusing on cardio-metabolic health. This study examines the association between physical activity and screen time with white matter microstructure in the general pediatric population. In a sample of 2532 children (10.12 ± 0.58 years; 50.04% boys) from the Generation R Study, a population-based cohort in Rotterdam, the Netherlands, we assessed physical activity and screen time using parent-reported questionnaires. Magnetic resonance imaging of white matter microstructure was conducted using diffusion tensor imaging. Total physical activity was positively associated with global fractional anisotropy (β = 0.057, 95% CI = 0.016, 0.098, p = 0.007) and negatively associated with global mean diffusivity (β = −0.079, 95% CI = −0.120, −0.038, p < 0.001), two commonly derived scalar measures of white matter microstructure. Two components of total physical activity, outdoor play and sport participation, were positively associated with global fractional anisotropy (β = 0.041, 95% CI=(0.000, 0.083), p = 0.047; β = 0.053, 95% CI=(0.010, 0.096), p = 0.015, respectively) and inversely associated with global mean diffusivity (β = −0.074, 95% CI= (−0.114, −0.033), p < 0.001; β = −0.043, 95% CI=(-0.086, 0.000), p = 0.049, respectively). No associations were observed between screen time and white matter microstructure (p > 0.05). This study provides new evidence that physical activity is modestly associated with white matter microstructure in children. In contrast, complementing other recent evidence on cognition, screen time was not associated with white matter microstructure. Causal inferences from these modest associations must be interpreted cautiously in the absence of longitudinal data. However, these data still offer a promising avenue for future work to explore to what extent physical activity may promote healthy white matter development. •Higher levels of physical activity were associated with greater white matter microstructure in children.•Outdoor play and sport participation were specifically related to white matter microstructure.•No association was observed between screen time and white matter microstructure.