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Assessing erectile neurogenic dysfunction from heart rate variability through a Generalized Linear Mixed Model framework.

Abstract：

BACKGROUND:The low (LF) vs. high (HF) frequency energy ratio, computed from the spectral decomposition of heart beat intervals, has become a major tool in cardiac autonomic system control and sympatho-vagal balance studies. The (statistical) distributions of response variables designed from ratios of two quantities, such as the LF/HF ratio, are likely to non-normal, hence preventing e.g., from a relevant use of the t-test. Even using a non-parametric formulation, the solution may be not appropriate as the test statistics do not account for correlation and heteroskedasticity, such as those that can be observed when several measures are taken from the same patient. OBJECTIVES:The analyses for such type of data require the application of statistical models which do not assume a priori independence. In this spirit, the present contribution proposes the use of the Generalized Linear Mixed Models (GLMMs) framework to assess differences between groups of measures performed over classes of patients. METHODS:Statistical linear mixed models allow the inclusion of at least one random effect, besides the error term, which induces correlation between observations from the same subject. Moreover, by using GLMM, practitioners could assume any probability distribution, within the exponential family, for the data, and naturally model heteroskedasticity. Here, the sympatho-vagal balance expressed as LF/HF ratio of patients suffering neurogenic erectile dysfunction under three different body positions was analyzed in a case-control protocol by means of a GLMM under gamma and Gaussian distributed responses assumptions. RESULTS:The gamma GLMM model was compared with the normal linear mixed model (LMM) approach conducted using raw and log transformed data. Both raw GLMM gamma and log transformed LMM allow better inference for factor effects, including correlations between observations from the same patient under different body position compared to the raw LMM. The gamma GLMM provides a more natural distribution assumption of a response expressed as a ratio. CONCLUSIONS:A gamma distribution assumption intrinsically models quadratic relationships between the expected value and the variance of the data avoiding prior data transformation. SAS and R source code are available on request.

journal_name

Comput Methods Programs Biomed

journal_title

Computer methods and programs in biomedicine

authors

Fernández EA,Souza Neto EP,Abry P,Macchiavelli R,Balzarini M,Cuzin B,Baude C,Frutoso J,Gharib C

doi

10.1016/j.cmpb.2009.11.001

subject

Has Abstract

pub_date

2010-07-01 00:00:00

pages

49-56

issue

eissn

0169-2607

issn

1872-7565

pii

S0169-2607(09)00291-0

journal_volume

pub_type

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Assessing erectile neurogenic dysfunction from heart rate variability through a Generalized Linear Mixed Model framework.