Childhood Bradycardia Associates With Atrioventricular Conduction Defects in Older Age: A Longitudinal Birth Cohort Study

Background This study explored the association between childhood bradycardia and later‐life cardiac phenotype using longitudinal data from the 1946 National Survey of Health and Development (NSHD) birth cohort. Methods and Results Resting heart rate was recorded at 6 and 7 years of age to provide the bradycardia exposure defined as a childhood resting heart rate <75 bpm. Three outcomes were studied: (1) echocardiographic data at 60 to 64 years of age, consisting of ejection fraction, left ventricular mass index, myocardial contraction fraction index, and E/e′; (2) electrocardiographic evidence of atrioventricular or ventricular conduction defects by 60 to 64 years of age; and (3) all‐cause and cardiovascular mortality. Generalized linear models or Cox regression models were used, and adjustment was made for relevant demographic and health‐related covariates, and for multiple testing. Mixed generalized linear models and fractional polynomials were used as sensitivity analyses. One in 3 older adults with atrioventricular conduction defects had been bradycardic in childhood, with defects being serious (Mobitz type II second‐degree atrioventricular block or higher) in 12%. In fully adjusted models, childhood bradycardia was associated with 2.91 higher odds of atrioventricular conduction defects (95% CI, 1.59–5.31; P =0.0005). Associations persisted in random coefficients mixed generalized linear models (odds ratio, 2.50; 95% CI, 1.01–4.31). Fractional polynomials confirmed a linear association between the log odds of atrioventricular conduction defects at 60 to 64 years of age and resting heart rate at 7 years of age. There was no association between bradycardia in childhood and mortality outcomes or with echocardiographic parameters and ventricular conduction defects in older age. Conclusions Longitudinal birth cohort data indicate that childhood bradycardia trebles the odds of having atrioventricular conduction defects in older age, 88% of which are benign. In addition, it does not influence mortality or heart size and function. Future research should concentrate on identifying children at risk.

Not straightforward: modelling non-linearity in training load and injury research

ObjectivesTo determine whether the relationship between training load and injury risk is non-linear and investigate ways of handling non-linearity.MethodsWe analysed daily training load and injury data from three cohorts: Norwegian elite U-19 football (n=81, 55% male, mean age 17 years (SD 1)), Norwegian Premier League football (n=36, 100% male, mean age 26 years (SD 4)) and elite youth handball (n=205, 36% male, mean age 17 years (SD 1)). The relationship between session rating of perceived exertion (sRPE) and probability of injury was estimated with restricted cubic splines in mixed-effects logistic regression models. Simulations were carried out to compare the ability of seven methods to model non-linear relationships, using visualisations, root-mean-squared error and coverage of prediction intervals as performance metrics.ResultsNo relationships were identified in the football cohorts; however, a J-shaped relationship was found between sRPE and the probability of injury on the same day for elite youth handball players (p<0.001). In the simulations, the only methods capable of non-linear modelling relationships were the quadratic model, fractional polynomials and restricted cubic splines.ConclusionThe relationship between training load and injury risk should be assumed to be non-linear. Future research should apply appropriate methods to account for non-linearity, such as fractional polynomials or restricted cubic splines. We propose a guide for which method(s) to use in a range of different situations.

Estimation of marginal effects for models with alternative variable transformations

margins is a powerful postestimation command that allows the estimation of marginal effects for official and community-contributed commands, with well-defined predicted outcomes (see predict). While the use of factor-variable notation allows one to easily estimate marginal effects when interactions and polynomials are used, estimation of marginal effects when other types of transformations such as splines, logs, or fractional polynomials are used remains a challenge. In this article, I describe how margins‘s capabilities can be extended to analyze other variable transformations using the command f_able.

Bayesian splines versus fractional polynomials in network meta-analysis

Background Network meta-analysis (NMA) provides a powerful tool for the simultaneous evaluation of multiple treatments by combining evidence from different studies, allowing for direct and indirect comparisons between treatments. In recent years, NMA is becoming increasingly popular in the medical literature and underlying statistical methodologies are evolving both in the frequentist and Bayesian framework. Traditional NMA models are often based on the comparison of two treatment arms per study. These individual studies may measure outcomes at multiple time points that are not necessarily homogeneous across studies. Methods In this article we present a Bayesian model based on B-splines for the simultaneous analysis of outcomes across time points, that allows for indirect comparison of treatments across different longitudinal studies. Results We illustrate the proposed approach in simulations as well as on real data examples available in the literature and compare it with a model based on P-splines and one based on fractional polynomials, showing that our approach is flexible and overcomes the limitations of the latter. Conclusions The proposed approach is computationally efficient and able to accommodate a large class of temporal treatment effect patterns, allowing for direct and indirect comparisons of widely varying shapes of longitudinal profiles.

Inference about Time-dependent Prognostic Accuracy Measures in the Presence of Competing Risks

Background: Evaluating a candidate marker or developing a model for predicting risk of future conditions is one of the major goals in medicine. However, model development and assessment for a time-to-event outcome may be complicated in the presence of competing risks. In this manuscript, we propose local and global estimators of cause-specific AUC for right-censored survival times in the presence of competing risks. Methods: The local estimator - cause-specific weighted mean rank (cWMR) - is a local average of time-specific observed cause-specific AUCs within a neighborhood of given time t. The global estimator - cause-specific fractional polynomials (cFPL) - is based on modelling the cause-specific AUC as a function of t through fractional polynomials. Results: We investigated the performance of the proposed cWMR and cFPL estimators through simulation studies and real-life data analysis. The estimators perform well in small samples, have minimal bias and appropriate coverage. Conclusions: The local estimator cWMR and the global estimator cFPL will provide computationally efficient options for assessing the prognostic accuracy of markers for time-to-event outcome in the presence of competing risks in many practical settings.