A Declaratory Model of Generalized Regression Neural Network (GRNN) for Estimating Sleep Apnea Index in the Elderly Suffering from Sleep Disturbance
Pages 112-119
Bingh Tang
DOI: http://dx.doi.org/10.6000/1929-6029.2016.05.02.5
Published: 02 June 206

Abstract: Objective: The main objective of this paper is to present a novel model for classifying senior patients into different apnea/hypopnea index (AHI) categories based on their clinical variables.

Methods and Materials: The proposed model is a generalized regression neural network (GRNN). Three important variables were first selected from the original 30 clinical variables. The GRNN was trained using 75 patients that were randomly selected from the total117 patients. The remaining 42 patients were used for testing GRNN model. The design parameter of the network, i.e., the spread of the radial basis function, was empirically optimized. To alleviate the model complexity, the original AHI values were dichotomized into two different groups, i.e., AHI>13 and AHI<=13. The use of GRNN for this application appear fairly novel, notwithstanding that there is a host of literatures on predicting obstructive sleep apnea (OSA) syndrome from demographic or other easy means to assess clinical variables.

Results: The proposed model has sensitivity and specificity of 95.7% and 50.0%, respectively, for the training cases, while 88.0% and 52.9%, respectively, for the testing cases.

Conclusion: The proposed neural network model has outperformed existing classification approaches in terms of classification accuracy and generalization, thus it can be potentially used in clinical applications, which would lead to a reduction of the necessity of in-laboratory nocturnal sleep studies.

The Method of Randomization for Cluster-Randomized Trials: Challenges of Including Patients with Multiple Chronic Conditions
Pages 2-7
Denise Esserman, Heather G. Allore and Thomas G. Travison
DOI: http://dx.doi.org/10.6000/1929-6029.2016.05.01.1
Published: 08 January 2016

Abstract: Cluster-randomized clinical trials (CRT) are trials in which the unit of randomization is not a participant but a group (e.g. healthcare systems or community centers). They are suitable when the intervention applies naturally to the cluster (e.g. healthcare policy); when lack of independence among participants may occur (e.g. nursing home hygiene); or when it is most ethical to apply an intervention to all within a group (e.g. school-level immunization). Because participants in the same cluster receive the same intervention, CRT may approximate clinical practice, and may produce generalizable findings. However, when not properly designed or interpreted, CRT may induce biased results.

CRT designs have features that add complexity to statistical estimation and inference. Chief among these is the cluster-level correlation in response measurements induced by the randomization. A critical consideration is the experimental unit of inference; often it is desirable to consider intervention effects at the level of the individual rather than the cluster. Finally, given that the number of clusters available may be limited, simple forms of randomization may not achieve balance between intervention and control arms at either the cluster- or participant-level.

In non-clustered clinical trials, balance of key factors may be easier to achieve because the sample can be homogenous by exclusion of participants with multiple chronic conditions (MCC). CRTs, which are often pragmatic, may eschew such restrictions. Failure to account for imbalance may induce bias and reducing validity. This article focuses on the complexities of randomization in the design of CRTs, such as the inclusion of patients with MCC, and imbalances in covariate factors across clusters.