Search

Abstract Background How to select variables and identify functional forms for continuous variables is a key concern when creating a multivariable model. Ad hoc ‘traditional’ approaches to variable selection have been in use for at least 50 years. Similarly, methods for determining functional forms for continuous variables were first suggested many years ago. More recently, many alternative approaches to address these two challenges have been proposed, but knowledge of their properties and meaningful comparisons between them are scarce. To define a state of the art and to provide evidence-supported guidance to researchers who have only a basic level of statistical knowledge, many outstanding issues in multivariable modelling remain. Our main aims are to identify and illustrate such gaps in the literature and present them at a moderate technical level to the wide community of practitioners, researchers and students of statistics. Methods We briefly discuss general issues in building descriptive regression models, strategies for variable selection, different ways of choosing functional forms for continuous variables and methods for combining the selection of variables and functions. We discuss two examples, taken from the medical literature, to illustrate problems in the practice of modelling. Results Our overview revealed that there is not yet enough evidence on which to base recommendations for the selection of variables and functional forms in multivariable analysis. Such evidence may come from comparisons between alternative methods. In particular, we highlight seven important topics that require further investigation and make suggestions for the direction of further research. Conclusions Selection of variables and of functional forms are important topics in multivariable analysis. To define a state of the art and to provide evidence-supported guidance to researchers who have only a basic level of statistical knowledge, further comparative research is required.

Post-COVID syndrome, defined as symptoms persisting for more than twelve weeks after the diagnosis of COVID-19, has been recognised as a new clinical entity in the context of SARS-CoV-2 infection. This study was conducted to characterise the burden and predictors for post-COVID-19 syndrome in the local population. It was a community-based web-survey study conducted in Norfolk, East England, UK. We sent the survey to patients with confirmed COVID-19 infection by real-time polymerase chain reaction by December 6th, 2020. Questions related to the pre-COVID and post-COVID level of symptoms and further healthcare use. Baseline characteristics were collected from the primary care records. Logistic regression analysis was conducted to establish predictors for post-COVID-19 syndrome and further healthcare utilisation. Of 6,318 patients, survey responses were obtained from 1,487 participants (23.5%). Post-COVID-19 syndrome symptoms were experienced by 774 (52.1%) respondents. Male sex compared to female sex was a factor protective of post-COVID symptoms; relative risk (RR) 0.748, 95% confidence interval (CI), 0.605-0.924. Body mass index was associated with a greater risk of developing post-COVID-19 symptoms (RR 1.031, 95% CI, 1.016-1.047, for 1 kg/m2). A total of 378 (25.4%) people used further health services after their index COVID-19 infection, of whom 277 (73.2%) had post-COVID symptoms. Male sex was negatively associated with the use of further health services (RR 0.618, 95% CI, 0.464-0.818) whereas BMI was positively associated (RR 1.027, 95% CI, 1.009-1.046). Overall, post-COVID-19 symptoms increased the probability of using health services with RR 3.280, 95% CI, 2.540-4.262. This survey of a large number of people previously diagnosed with COVID-19 across East England shows a high prevalence of self-reported post-COVID-19 syndrome. Female sex and BMI were associated with an increased risk of post-COVID-19 syndrome and further utilisation of healthcare.

Abstract Background With progress on both the theoretical and the computational fronts the use of spline modelling has become an established tool in statistical regression analysis. An important issue in spline modelling is the availability of user friendly, well documented software packages. Following the idea of the STRengthening Analytical Thinking for Observational Studies initiative to provide users with guidance documents on the application of statistical methods in observational research, the aim of this article is to provide an overview of the most widely used spline-based techniques and their implementation in R. Methods In this work, we focus on the R Language for Statistical Computing which has become a hugely popular statistics software. We identified a set of packages that include functions for spline modelling within a regression framework. Using simulated and real data we provide an introduction to spline modelling and an overview of the most popular spline functions. Results We present a series of simple scenarios of univariate data, where different basis functions are used to identify the correct functional form of an independent variable. Even in simple data, using routines from different packages would lead to different results. Conclusions This work illustrate challenges that an analyst faces when working with data. Most differences can be attributed to the choice of hyper-parameters rather than the basis used. In fact an experienced user will know how to obtain a reasonable outcome, regardless of the type of spline used. However, many analysts do not have sufficient knowledge to use these powerful tools adequately and will need more guidance.

BackgroundOlympic weightlifting requires technical skills, explosive power, strength, and coordination. Weightlifters can be competitive within a range of morphological characteristics due to competition body weight classes. To date no studies have examined when sex differences arise in weightlifting and the impact of body mass on performances at different ages.ObjectivesTo examine when sex-related differences emerge, to quantify the influence of body mass on performances at different ages, and to estimate the age at peak performance.MethodsCompetitions results from USA Weightlifting National Championships, Youth, Junior, and Senior from 2014 to 2019 were collected for weightlifters aged 6 to 30.ResultsAt age 10 the median total weight lifted was 51kg and 54kg, respectively, for girls and boys. From age 10 to 12 a gender gap emerges with a sex difference of 11.7% at age 14 at 55kg body mass. At age 25 the sex-related performance difference is smaller for lighter athletes (23.6% at 69kg body mass) and larger for heavier athletes (29.9% at 81kg body mass). The median peak age for men is 26.5 years (95% CI: 25.7, 27.3) and for women 25.9 years (95% CI: 24.7, 27.3).ConclusionWe quantified the impact of body weight and age and sex differences for youth and young adults, ages 6 to 30 years old, participating in national level Olympic weightlifting competitions in the United States. Body weight at younger ages has less impact on performance compared to older ages, and boys and girls perform similarly. When reaching the ages typically associated with the onset of puberty, boys' performances rapidly increase and the gap between genders widens. Women achieve peak performance at a similar age than men. Such results may help to establish progression trajectories for talented athletes and inform coaches, athletes and national governing bodies.

A total of 3,782 performance results for male and female weightlifters, ages 14–30 from 123 countries, from Youth, Junior, and Senior World Championships and Olympic Games 2013–2017 were used to estimate the age at peak performance in Olympic weightlifting and quantify performance development from adolescence to adulthood. The age at peak performance was estimated for men and women globally and for different geographic regions. Overall, male and female weightlifters achieve their peak performance in weightlifting at similar ages. The median peak age is 26.0 years (95% CI: 24.9, 27.1) for men and 25.0 years (95% CI: 23.9, 27.4) for women, at the 90th percentile of performances. The median peak age was 26.3 years for men (95% CI: 24.5, 29.6) and 26.4 years for women (95% CI: 24.5, 29.6), at the 50th percentile. It is a novel finding that the age at peak performance varies for male and female athletes from different geographic regions (Western Europe, Eastern Europe, Middle East, Far East, North- and South America). For some regions men reach peak performance at a younger age than women, while this relationship is reversed for other regions. A possible explanation could be that socio-economic factors influence the pool of available athletes and thus may under- or overestimate the true peak age. Unlike in track and field where the discipline might determine specific body types, weightlifters at all ages compete in body weight classes, enabling us to compare performance levels and annual rate of change for athletes of different body mass. We quantified increases in performance in Olympic weightlifting for male and female adolescents. Sex-specific differences arise during puberty, boys outperform girls, and there is a rapid increase in their performance levels before the further growth slows down. The largest annual rate of increase in the total weight lifted was achieved between 16 and 17 years of age for both sexes with lower body mass and between 21 and 22 years with higher body mass. Such new information may help to establish progression trajectories for young athletes.

Multicenter phase II/III clinical trials are large‐scale operations that often include hundreds of recruiting centers in several countries. Therefore, the operational aspects of a trial must be thoroughly planned and closely monitored to ensure better oversight and study conduct. Predicting patient recruitment plays a pivotal role in trial monitoring as it informs how many people are expected to be recruited on a given day. As such, study teams may rely on predictions to assess progress and detect any deviations from the original plan that might put the study and potentially, patients at risk. Recruitment predictions are often based on a Poisson‐Gamma model that assumes that centers have a constant recruitment rate throughout the trial. The model has useful features and has extensively been used, yet its main assumption is often unrealistic. A nonhomogeneous Poisson process has been recently proposed that can incorporate time‐varying recruitment rates. In this work, we predict patient recruitment using both approaches and assess the impact of said assumption in a real‐world setting where studies may not necessarily have constant center‐specific recruitment rates. The paper showcases experience from modeling recruitment in trials sponsored by AstraZeneca between 2005 and 2018. In these data, there is evidence of time‐varying recruitment rates. The predictive performance of models that account for both constant and time‐varying recruitment effects is presented. Following a descriptive analysis of data, we assess model performance and investigate the impact of model misspecification. [ABSTRACT FROM AUTHOR]

In this work, we develop 'quantile foliation' to predict outcomes for one explanatory variable based on two covariates and varying quantiles. This is an extension of quantile sheets. Data from World Championships in Olympic weightlifting with athletes aged 13 to 90 are used to study performances across the life span. Weightlifters of all ages compete in body weight classes, and we study performance development for adolescents, age at peak performance and decline for Masters athletes who are 35 years or older. In prior studies, weightlifting performances were compared with a body mass adjustment formula developed using world records. Although intended for elite athletes with highest performances, this formula was applied to weightlifters of all ages, and age factors for Masters were estimated based on these body mass adjustments. A comparison of youth athletes' performances for different body mass has not been done. With quantile foliation, it is possible to examine age-associated patterns of performance increase for youth and to study the decline after reaching the peak performance. This can be done for athletes with different body mass and different performance levels as measured by quantiles. R code and example data are available as supplementary materials. [ABSTRACT FROM AUTHOR]