A flexible Tool for Model Building: the Relevant Transformation of the Inputs Network Approach (RETINA)

A new method, called relevant transformation of the inputs network approach (RETINA) is proposed as a tool for model building and selection. It is designed to improve some of the shortcomings of neural networks. It has the flexibility of neural network models, the concavity of the likelihood in the weights of the usual likelihood models, and the ability to identify a parsimonious set of attributes that are likely to be relevant for predicting out of sample outcomes. RETINA expands the range of models by considering transformations of the original inputs; splits the sample in three disjoint subsamples, sorts the candidate regressors by a saliency feature, chooses the models in subsample 1, uses subsample 2 for parameter estimation and subsample 3 for cross-validation. It is modular, can be used as a data exploratory tool and is computationally feasible in personal computers. In tests on simulated data, it achieves high rates of successes when the sample size or the R2 are large enough. As our experiments show, it is superior to alternative procedures such as the non negative garrote and forward and backward stepwise regression.

Flexible Tool for Model Building: the Relevant Transformation of the Inputs Network Approach (RETINA)

A new method, called Relevant Transformation of the Inputs Network Approach (RETINA) isproposed as a tool for model building. It is designed around flexibility (with nonlinear transformations of the predictors of interest), selective search within the range of possible models, out-of-sample forecasting ability and computational simplicity. In tests on simulated data, it shows both a high rate of successful retrieval of the DGP which increases with the sample size and a good performance relative to other alternative procedures. A telephone service demand model is built to show how the procedure applies on real data.Relevant Transformation of the Inputs Network Approach (RETINA), Economics models

The Adolescent Cardio-Renal Intervention Trial (AdDIT): retinal vascular geometry and renal function in adolescents with type 1 diabetes

Aims/hypothesis We examined the hypothesis that elevation in urinary albumin creatinine ratio (ACR) in adolescents with type 1 diabetes is associated with abnormal retinal vascular geometry (RVG) phenotypes. Methods A cross-sectional study at baseline of the relationship between ACR within the normoalbuminuric range and RVG in 963 adolescents aged 14.4 ± 1.6 years with type 1 diabetes (median duration 6.5 years) screened for participation in AdDIT. A validated algorithm was used to categorise log10 ACR into tertiles: upper tertile ACR was defined as ‘high-risk’ for future albuminuria and the lower two tertiles were deemed ‘low-risk’. RVG analysis, using a semi-automated computer program, determined retinal vascular calibres (standard and extended zones) and tortuosity. RVG measures were analysed continuously and categorically (in quintiles: Q1–Q5) for associations with log10 ACR and ACR risk groups. Results Greater log10 ACR was associated with narrower vessel calibres and greater tortuosity. The high-risk group was more likely to have extended zone vessel calibres in the lowest quintile (arteriolar Q1 vs Q2–Q5: OR 1.67 [95% CI 1.17, 2.38] and venular OR 1.39 [0.98, 1.99]) and tortuosity in the highest quintile (Q5 vs Q1–Q4: arteriolar OR 2.05 [1.44, 2.92] and venular OR 2.38 [1.67, 3.40]). The effects of retinal vascular calibres and tortuosity were additive such that the participants with the narrowest and most tortuous vessels were more likely to be in the high-risk group (OR 3.32 [1.84, 5.96]). These effects were independent of duration, blood pressure, BMI and blood glucose control. Conclusions/interpretation Higher ACR in adolescents is associated with narrower and more tortuous retinal vessels. Therefore, RVG phenotypes may serve to identify populations at high risk of diabetes complications during adolescence and well before onset of clinical diabetes complications.This work was supported by the National Health and Medical Research Council of Australia (NHMRC 632521), JDRF (08-2007-902), Diabetes UK (DUK PO NO 2177 BDA:RD06/003341) and the British Heart Foundation

Improving the economic value of photographic screening for optical coherence tomography-detectable macular oedema : a prospective, multicentre, UK study

Peer reviewedPublisher PD

Features of age-related macular degeneration in the general adults and their dependency on age, sex, and smoking : Results from the German KORA study

Acknowledgments We thank all study participants for contributing to the KORA study.Peer reviewedPublisher PD

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach

Background: To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) “black-box” approaches, for automated diagnosis of Age-related Macular Degeneration (AMD). Methods: Data from healthy subjects and patients diagnosed with AMD or other retinal diseases were collected during routine visits via an Electronic Health Record (EHR) system. Patients’ attributes included demographics and, for each eye, presence/absence of major AMD-related clinical signs (soft drusen, retinal pigment epitelium, defects/ pigment mottling, depigmentation area, subretinal haemorrhage, subretinal fluid, macula thickness, macular scar, subretinal fibrosis). Interpretable techniques known as white box methods including logistic regression and decision trees as well as less interpreitable techniques known as black box methods, such as support vector machines (SVM), random forests and AdaBoost, were used to develop models (trained and validated on unseen data) to diagnose AMD. The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic (AUC) were used to assess performance. Results: Study population included 487 patients (912 eyes). In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of (0.92), followed by SVM and decision trees (0.90). All machine learning models identified soft drusen and age as the most discriminating variables in clinicians’ decision pathways to diagnose AMD. C Conclusions: Both black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support

Non-additive (dominance) effects of genetic variants associated with refractive error and myopia

Genome-wide association studies (GWAS) have revealed that the genetic contribution to certain complex diseases is well-described by Fisher’s infinitesimal model in which a vast number of polymorphisms each confer a small effect. Under Fisher’s model, variants have additive effects both across loci and within loci. However, the latter assumption is at odds with the common observation of dominant or recessive rare alleles responsible for monogenic disorders. Here, we searched for evidence of non-additive (dominant or recessive) effects for GWAS variants known to confer susceptibility to the highly heritable quantitative trait, refractive error. Of 146 GWAS variants examined in a discovery sample of 228,423 individuals whose refractive error phenotype was inferred from their age-of-onset of spectacle wear, only 8 had even nominal evidence (p < 0.05) of non-additive effects. In a replication sample of 73,577 individuals who underwent direct assessment of refractive error, 1 of these 8 variants had robust independent evidence of non-additive effects (rs7829127 within ZMAT4, p = 4.76E−05) while a further 2 had suggestive evidence (rs35337422 in RD3L, p = 7.21E−03 and rs12193446 in LAMA2, p = 2.57E−02). Accounting for non-additive effects had minimal impact on the accuracy of a polygenic risk score for refractive error (R2 = 6.04% vs. 6.01%). Our findings demonstrate that very few GWAS variants for refractive error show evidence of a departure from an additive mode of action and that accounting for non-additive risk variants offers little scope to improve the accuracy of polygenic risk scores for myopia

Multiallelic copy number variation in the complement component 4A (C4A) gene is associated with late-stage age-related macular degeneration (AMD)

Peer reviewedPublisher PD

Analysis of oxygenation and other risk factors of retinopathy of prematurity in preterm babies

Maintaining adequate and stable blood oxygen level is important for preterm babies to avoid the risk of brain, lung and retinal injury such as retinopathy of prematurity (ROP). However, wide disparities in policies and practices of oxygenation in preterm babies exist among neonatal care providers as it is still unclear which best method of monitoring and what features of oxygen measurements are important to clinician’s interpretations for assessing preterm babies at risk of developing severe ROP or unstable health condition. This thesis consists of two projects: NZ-ROP that examines multiple factors of severe ROP including summary statistics (mean, standard deviation (SD), coefficient of variation (CV) and desaturation) for oxygen saturation (OS) features in very extreme preterm babies, and NZ-LP that investigates the efficacy of some of these statistics for health monitoring of late preterm babies. The OS data in NZ-ROP were recorded using modified oximeters that have offsets and inherent software artefact, both of which mask the actual saturation for certain OS ranges and may complicate the choice of methods in the analyses. Therefore, novel algorithms involving linear and quadratic interpolations are developed, implemented on the New Zealand data, and validated using the data of a UK preterm baby, as recorded from offsets and non-offsets oximeters. For all data sets, the algorithms produced saturation distributions that were very close to those obtained from the non-offset oximeter. The algorithms perform within the recommended standards of commercial oximeters currently used in the clinical practice. ROP is a multifactorial disease, with oxygenation fluctuations as one of the key contributors. The all-subsets logistic regression, robust and generalised additive statistical modelling, along with a model averaging approach, are applied in NZ-ROP to determine the relationship of variability and level of OS with severe ROP, and the extent of contribution of various clinical predictors to the severity of this eye disease. Desaturation, as a measure of OS variability, has the strongest association with severe ROP among all OS statistics, in particular, the risk of severe ROP is almost three times higher in babies that exhibit greater occurrences of desaturation episodes. Additionally, this study identifies longer periods of ventilation support, frequent desaturation events, extreme prematurity and low birth weight as the most important factors that substantially exacerbate the severity of ROP, and therefore signify babies’ underlying condition of being severely ill. Persistent cardiorespiratory instabilities prior to hospital discharge may expose preterm babies to a greater risk of neuro-developmental impairments. In NZ-LP, the statistical summaries of mean, SD and CV are computed from the OS measurements of healthy stable and unstable babies, and the performance of these statistics in detecting the unstable babies is evaluated using an extremeness index for outlying data and a hierarchical clustering technique. With SD and CV, the clinically unstable babies were very well separated from the group of stable babies, wherein, the separation was even more apparent with the use of CV. These suggest that measures of variability could be better than saturation level for highlighting babies’ underlying instability due to immature physiological systems, but the combination of variability and level through the CV are believed to be even better. Identification and summarisation of useful OS features quantitatively hold great promise for improved monitoring of oxygenation instability and diagnosis of severe ROP for preterm babies