Modeling normative kinetic perimetry isopters using mixed-effects quantile regression

Kinetic perimetry is used to quantify visual field size/sensitivity. Clinically, perimetry can be used to diagnose and monitor ophthalmic and neuro-ophthalmic disease. Normative data are integral to the interpretation of these findings. However, there are few computational developments that allow clinicians to collect and analyze normative data from kinetic perimeters. In this article we describe an approach to fitting kinetic responses using linear quantile mixed models. Analogously to traditional linear mixed-effects models for the mean, linear quantile mixed models account for repeated measurements taken from the same individual, but differently from linear mixed-effects models, they are more flexible as they require weaker distributional assumptions and allow for quantile-specific inference. Our approach improves on parametric alternatives based on normal assumptions. We introduce the R package kineticF, a freely available and open-access resource for the analysis of perimetry data. Our proposed approach can be used to analyze normative data from further studies

Nonlinear growth generates age changes in the moments of the frequency distribution: the example of height in puberty

Higher moments of the frequency distribution of child height and weight change with age, particularly during puberty, though why is not known. Our aims were to confirm that height skewness and kurtosis change with age during puberty, to devise a model to explain why, and to test the model by analyzing the data longitudinally. Heights of 3245 Christ's Hospital School boys born during 1927-1956 were measured twice termly from 9 to 20 years (n = 129 508). Treating the data as independent, the mean, standard deviation (SD), skewness, and kurtosis were calculated in 40 age groups and plotted as functions of age t. The data were also analyzed longitudinally using the nonlinear random-effects growth model H( t) = h( t - epsilon) + alpha, with H( t) the cross-sectional data, h( t) the individual mean curve, and epsilon and alpha subject-specific random effects reflecting variability in age and height at peak height velocity (PHV). Mean height increased monotonically with age, while the SD, skewness, and kurtosis changed cyclically with, respectively, 1, 2, and 3 turning points. Surprisingly, their age curves corresponded closely in shape to the first, second, and third derivatives of the mean height curve. The growth model expanded as a Taylor series in e predicted such a pattern, and the longitudinal analysis showed that adjusting for age at PHV on a multiplicative scale largely removed the trends in the higher moments. A nonlinear growth process where subjects grow at different rates, such as in puberty, generates cyclical changes in the higher moments of the frequency distribution

Modelling normative kinetic perimetry isopters using mixed-effects quantile regression

Kinetic perimetry is used to quantify visual field size/sensitivity. Clinically, perimetry can be used to diagnose and monitor ophthalmic and neuro-ophthalmic disease. Normative data are integral to the interpretation of these findings. However, there are few computational developments that allow clinicians to collect and analyze normative data from kinetic perimeters. In this article we describe an approach to fitting kinetic responses using linear quantile mixed models. Analogously to traditional linear mixed-effects models for the mean, linear quantile mixed models account for repeated measurements taken from the same individual, but differently from linear mixed-effects models, they are more flexible as they require weaker distributional assumptions and allow for quantile-specific inference. Our approach improves on parametric alternatives based on normal assumptions. We introduce the R package kineticF, a freely available and open-access resource for the analysis of perimetry data. Our proposed approach can be used to analyze normative data from further studies

Local dependence in bivariate copulae with beta marginals

The local dependence function (LDF) describes changes in the correlation structure of continuous bivariate random variables along their range. Bivariate density functions with Beta marginals can be used to model jointly a wide variety of data with bounded outcomes in the (0,1) range, e.g. proportions. In this paper we obtain expressions for the LDF of bivariate densities constructed using three different copula models (Frank, Gumbel and Joe) with Beta marginal distributions, present examples for each, and discuss an application of these models to analyse data collected in a study of marks obtained on a statistics exam by postgraduate students

Is amblyopia associated with school readiness and cognitive performance during early schooling? Findings from the Millennium Cohort Study

Background: Amblyopia is a neurodevelopmental condition causing reduced vision, for which programmes of whole population child vision screening exist throughout the world. There is an ongoing debate about the value of screening due to the lack of evidence about meaningful functional impacts of amblyopia. Our objective was to determine whether amblyopia is associated with school readiness and early cognitive performance. / Methods and findings: Data from the prospective Millennium Cohort Study of children born in the United Kingdom in 2000–01 and followed-up to age 7 years (n = 13,967). Using parental self-report on eye conditions and treatment coded by clinical reviewers, participants were grouped into no eye conditions, strabismus alone, refractive amblyopia, or strabismic/mixed (refractive plus strabismic) amblyopia. The outcomes were poor school readiness using Bracken School Readiness Assessment 10 points) irrespective of whether treatment had already started. The age-related cognitive trajectories of children with amblyopia did not differ from those without any eye conditions for either NV (p = 0.62) or PC (p = 0.51). These associations are at population rather than individual level, so it might be that some individuals with amblyopia did experience significant adverse outcomes that are not captured by summary statistics. / Conclusions: Amblyopia is not significantly associated with adverse cognitive performance and trajectories in early schooling and there is no evidence that this is due to a mediating effect of treatment. Although amblyopia combined with strabismus is associated with poor school readiness, this is not translated into poor cognitive performance. These novel findings may explain the lack of association reported between amblyopia and educational outcomes in adult life and suggest that the impact of amblyopia on education is not of itself a justification for whole population child vision screening aimed at detecting this disorder

Does having amblyopia affect school readiness and cognitive performance?

Background: Amblyopia is a neurodevelopmental condition causing reduced vision, for which international programmes of whole population child vision screening exist. There is an ongoing debate about the value of screening due to the lack of evidence about meaningful functional impacts of amblyopia and the extent to which these can be mitigated by treatment. / Objective(s): To determine whether amblyopia is associated with school readiness and early cognitive performance. / Method(s): Data from the prospective Millennium Cohort Study of children born in the United Kingdom in 2000-01 and followed-up to age 7 years (n=13,967). Using parental self-report on eye conditions and treatment coded by clinical reviewers, participants were grouped into no eye conditions, strabismus alone, refractive amblyopia, or strabismic/mixed (refractive plus strabismic) amblyopia. The outcomes were poor school readiness using Bracken School Readiness Assessment 10 points) compared to those without any eye conditions, irrespective of whether the treatment had already started. The age-related cognitive trajectories of children with amblyopia did not differ from those without any eye conditions for Naming Vocabulary (p=0.62) and Pattern Construction (p=0.51). / Conclusions: Amblyopia does not appear to affect cognitive performance and trajectories in early schooling and there is no evidence that this is due to a mediating effect of treatment. Although amblyopia combined with strabismus is associated with poor school readiness, this is not translated to poor cognitive performance. These novel findings may explain the lack of associations between amblyopia and educational outcomes in adult life and suggest that the impact of amblyopia on education may not of itself be a justification for population screening aimed at detecting this disorder

Reference range: Which statistical intervals to use?

Reference ranges, which are data-based intervals aiming to contain a pre-specified large proportion of the population values, are powerful tools to analyse observations in clinical laboratories. Their main point is to classify any future observations from the population which fall outside them as atypical and thus may warrant further investigation. As a reference range is constructed from a random sample from the population, the event ‘a reference range contains (100 P)% of the population’ is also random. Hence, all we can hope for is that such event has a large occurrence probability. In this paper we argue that some intervals, including the P prediction interval, are not suitable as reference ranges since there is a substantial probability that these intervals contain less than (100 P)% of the population, especially when the sample size is large. In contrast, a (P,γ) tolerance interval is designed to contain (100 P)% of the population with a pre-specified large confidence γ so it is eminently adequate as a reference range. An example based on real data illustrates the paper’s key points