Healthy biological systems exhibit complex patterns of variability that can be described by mathematical chaos. Heart rate variability (HRV) consists of changes in the time intervals between consecutive heartbeats called interbeat intervals (IBIs). A healthy heart is not a metronome. The oscillations of a healthy heart are complex and constantly changing, which allow the cardiovascular system to rapidly adjust to sudden physical and psychological challenges to homeostasis. This article briefly reviews current perspectives on the mechanisms that generate 24 h, short-term (~5 min), and ultra-short-term (<5 min) HRV, the importance of HRV, and its implications for health and performance. The authors provide an overview of widely-used HRV time-domain, frequency-domain, and non-linear metrics. Time-domain indices quantify the amount of HRV observed during monitoring periods that may range from ~2 min to 24 h. Frequency-domain values calculate the absolute or relative amount of signal energy within component bands. Non-linear measurements quantify the unpredictability and complexity of a series of IBIs. The authors survey published normative values for clinical, healthy, and optimal performance populations. They stress the importance of measurement context, including recording period length, subject age, and sex, on baseline HRV values. They caution that 24 h, short-term, and ultra-short-term normative values are not interchangeable. They encourage professionals to supplement published norms with findings from their own specialized populations. Finally, the authors provide an overview of HRV assessment strategies for clinical and optimal performance interventions.

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An Overview of Heart Rate Variability Metrics and Norms.

Preface 1. Introduction 2. The concept of risk 3. Overview of count response models 4. Methods of estimation and assessment 5. Assessment of count models 6. Poisson regression 7. Overdispersion 8. Negative binomial regression 9. Negative binomial regression: modeling 10. Alternative variance parameterizations 11. Problems with zero counts 12. Censored and truncated count models 13. Handling endogeneity and latent class models 14. Count panel models 15. Bayesian negative binomial models Appendix A. Constructing and interpreting interactions Appendix B. Data sets and Stata files References Index.

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Negative Binomial Regression

Cardiovascular disease (CVD), which comprises heart attacks (myocardial infarction), angina and strokes, is ranked as the number one cause of mortality and is a major cause of morbidity world wide. High blood cholesterol is linked to CVD events and is an important risk factor. Reducing high blood cholesterol, is thus an important way to reduce the chances of suffering a CVD event. Statins - cholesterol lowering drugs - (e.g. simvastatin, pravastatin, atorvastatin) are the first-choice treatments. Since the early statin randomised controlled trials were reported in the 1990s, several reviews of the effects of statins have been published highlighting their benefits particularly in people with a past history of CVD. Benefits include a reduction in CVD events. Statins have also been shown to reduce the risk of a first event in otherwise healthy individuals at high risk of CVD (primary prevention) but information on possible hazards has not been reported fully. The aim of this updated systematic review is to assess the effects, both in terms of benefits and harms of statins, for the primary prevention of CVD. We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE and EMBASE until 2011. We found 18 randomised controlled trials with 19 trial arms (56,934 patients) dating from 1994 to 2008. All were randomised control trials comparing statins with usual care or placebo. The mean age of the participants was 57 years (range 28 - 97 years), 60.3% were men, and of the eight trials that reported on ethnicity, 85.9 % were Caucasian. Duration of treatment was a minimum one year and with follow-up of a minimum of six months. All-cause mortality and fatal and non-fatal CVD events were reduced with the use of statins as was the need for revascularisation (the restoration of an adequate blood supply to the heart) by means of surgery (coronary artery bypass graft ) or by angioplasty (PTCA). Of 1000 people treated with a statin for five years, 18 would avoid a major CVD event which compares well with other treatments used for preventing cardiovascular disease. Taking statins did not increase the risk of serious adverse effects such as cancer. Statins are likely to be cost-effective in primary prevention.

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Statins for the primary prevention of cardiovascular disease.

Taking into account a continuous exposure in regression models by using categorization, when non-linear dose-response associations are expected, have been widely criticized. As one alternative, restricted cubic spline (RCS) functions are powerful tools (i) to characterize a dose-response association between a continuous exposure and an outcome, (ii) to visually and/or statistically check the assumption of linearity of the association, and (iii) to minimize residual confounding when adjusting for a continuous exposure. Because their implementation with SAS® software is limited, we developed and present here an SAS macro that (i) creates an RCS function of continuous exposures, (ii) displays graphs showing the dose-response association with 95 per cent confidence interval between one main continuous exposure and an outcome when performing linear, logistic, or Cox models, as well as linear and logistic-generalized estimating equations, and (iii) provides statistical tests for overall and non-linear associations. We illustrate the SAS macro using the third National Health and Nutrition Examination Survey data to investigate adjusted dose-response associations (with different models) between calcium intake and bone mineral density (linear regression), folate intake and hyperhomocysteinemia (logistic regression), and serum high-density lipoprotein cholesterol and cardiovascular mortality (Cox model).

https://eve.vet-alfort.fr/mod/resource/view.php?id=20109

Dose‐response analyses using restricted cubic spline functions in public health research

Christensen, R. (2002), Plane Answers to Complex Questions: The Theory of Linear Models (3rd ed.), New York: Springer-Verlag. Crocker, D. C. (1980), Review of Linear Regression Analysis, by G. A. F. Seber, Technometrics, 22, 130. Datta, B. N. (1995), Numerical Linear Algebra and Applications, Paci c Grove, CA: Brooks/Cole. Draper, N. R. (2002), “Applied Regression Analysis Bibliography Update 2000–2001,” Communications in Statistics: Theory and Methods, 2051– 2075. Golub, G. H., and Van Loan, C. F. (1996), Matrix Computations (3rd ed.), Baltimore, MD: Johns Hopkins University Press. Graybill, F. A. (2000), Theory and Application of the Linear Model, Paci c Grove, CA: Brooks/Cole. Hocking, R. R. (2003), Methods and Applications of Linear Models: Regression and the Analysis of Variance (2nd ed.), New York: Wiley. Porat, B. (1993), Digital Processing of Random Signals, Englewood Cliffs, NJ: Prentice-Hall. Ravishanker, N., and Dey, D. K. (2002), A First Course in Linear Model Theory, Boca Raton, FL: Chapman and Hall/CRC. White, H. (1984), Asymptotic Theory for Econometricians, Orlando, FL: Academic Press.

Generalized Estimating Equations

Medical researchers often need to compare two methods of measurement, or a new method with an established one, to determine whether these two methods can be used interchangeably or the new method can replace the established one. – 6 In most of these situations, the ‘true’ value of the measured quantity is unknown. In a series of articles, Bland and Altman – 9 advocated the use of a graphical method to plot the difference scores of two measurements against the mean for each subject and argued that if the new method agrees sufficiently well with the old, the old may be replaced. Here the idea of agreement plays a crucial role in method comparison studies. There are numerous published clinical and laboratory studies evaluating agreement between two measurement methods using Bland–Altman analysis. The original Bland–Altman publication has been cited on more than 11 500 occasions—compelling evidence of its importance in medical research. The Bland–Altman method calculates the mean difference between two methods of measurement (the ‘bias’), and 95% limits of agreement as the mean difference (2 SD) [or more precisely (1.96 SD)]. It is expected that the 95% limits include 95% of differences between the two measurement methods. The plot is commonly called a Bland–Altman plot and the associated method is usually called the Bland–Altman method. The Bland–Altman method can even include estimation of confidence intervals for the bias and limits of agreement, but these are often omitted in research papers. The presentation of the 95% limits of agreement is for visual judgement of how well two methods of measurement agree. The smaller the range between these two limits the better the agreement is. The question of how small is small depends on the clinical context: would a difference between measurement methods as extreme as that described by the 95% limits of agreement meaningfully affect the interpretation of the results? Repeated measurements for each subject are often used in clinical research. Two recent articles in the British Journal of Anaesthesia use such a design. 6 When repeated measures data are available, it is desirable to use all the data to compare the two methods. However, the original Bland–Altman method was developed for two sets of measurements done on one occasion (i.e. independent data), and so this approach is not suitable for repeated measures data. However, as a naı̈ve analysis, it may be used to explore the data because of the simplicity of the method. Examples of the misuse of agreement estimation for repeated measures data can be found readily in the anaesthetic literature: Opdam and colleagues did repeated measurements of cardiac output in six subjects, but incorrectly analysed and plotted 251 paired data sets using the standard Bland–Altman technique. Niedhart and colleagues compared a processed EEG device’s electrode placement on each side of the head in 12 subjects, but analysed and plotted 22 860 paired data sets. Such examples of incorrect use are widespread in the anaesthetic and critical care literature. Bland and Altman have provided a modification for analysing repeated measures under stable or changing conditions, where repeated data were collected over a period of time. As an alternative, we propose using random effects models for this purpose.

I. Using the Bland–Altman method to measure agreement with repeated measures

The generalized estimating equation (GEE) approach is a widely used statistical method in the analysis of longitudinal data in clinical and epidemiolog- ical studies. It is an extension of the generalized linear model (GLM) method to correlated data such that valid standard errors of the parameter estimates can be drawn. Unlike the GLM method, which is based on the maximum likelihood the- ory for independent observations, the GEE method is based on the quasilikelihood theory and no assumption is made about the distribution of response observations. Therefore, Akaike's information criterion, a widely used method for model selec- tion in GLM, is not applicable toGEE directly. However, Pan (Biometrics 2001; 57: 120{125) proposed a model-selection method for GEE and termed it quasilike- lihood under the independence model criterion. This criterion can also be used to select the best-working correlation structure. From Pan's methods, I developed a general Stata program, qic, that accommodates all the distribution and link functions and correlation structures available in Stata version 9. In this paper, I introduce this program and demonstrate how to use it to select the best working correlation structure and the best subset of covariates through two examples in longitudinal studies.

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QIC program and model selection in GEE analyses

The Generalized Estimating Equations (GEE) method is one of the most commonly used statistical methods for the analysis of longitudinal data in epidemiological studies. A working correlation structure for the repeated measures of the outcome variable of a subject needs to be specified by this method. However, statistical criteria for selecting the best correlation structure and the best subset of explanatory variables in GEE are only available recently because the GEE method is developed on the basis of quasi-likelihood theory. Maximum likelihood based model selection methods, such as the widely used Akaike Information Criterion (AIC), are not applicable to GEE directly. Pan (2001) proposed a selection method called QIC which can be used to select the best correlation structure and the best subset of explanatory variables. Based on the QIC method, we developed a computing program to calculate the QIC value for a range of different distributions, link functions and correlation structures. This program was written in Stata software. In this article, we introduce this program and demonstrate how to use it to select the most parsimonious model in GEE analyses of longitudinal data through several representative examples.

Selection of Working Correlation Structure and Best Model in GEE Analyses of Longitudinal Data

Although previous studies have investigated beliefs about back pain in clinical and employed populations, there is a paucity of data examining the beliefs of the broader community. We aimed to characterize the beliefs that community-dwelling women have about back pain and its consequences, and to determine whether those with varying levels of pain intensity and disability differ in their beliefs. 542 community-dwelling women, aged 24 to 80 years, were recruited from a research database. Participants completed a self-administered questionnaire that included detailed demographic information, the Chronic Pain Grade Questionnaire (CPG) and the Back Beliefs Questionnaire (BBQ). The CPG examined individuals' levels of pain intensity and disability, and the BBQ investigated their beliefs about back pain and its consequences. 506 (93.4%) women returned the study questionnaire. The mean (SD) BBQ score for the cohort was 30.7 (6.0), indicating generally positive beliefs about back pain. However, those women with high intensity pain and high level disability had a mean (SD) score of 28.5 (5.7) and 24.8 (5.7) respectively, which reflects greater negativity about back pain and its consequences. There was an association between negative beliefs and high pain intensity (OR = 0.94 (95% CI: 0.90, 0.99), p = 0.01) and high level disability (OR = 0.93 (95% CI: 0.89, 0.97), p = 0.001), after adjusting for confounders. This study highlights that although women living in the community were generally positive about back pain, subgroups of women with high pain intensity and high level disability were identified who had more pessimistic views. While a causal relationship cannot be inferred from these cross-sectional data, the results suggest that negative beliefs individuals have about back pain may be predictive of chronic, disabling spinal pain.

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Negative beliefs about low back pain are associated with high pain intensity and high level disability in community-based women

Objective The aim of this study was to establish the impact of patient sex on the provision of analgesia by paramedics for patients reporting pain in the prehospital setting. Methods This retrospective cohort study of paramedic patient care records included all adult patients with a Glasgow Coma Score higher than 12 transported to hospital by ambulance in a major metropolitan area over a 7-day period in 2005. Data collected included demographics, patient report of pain and its type and severity, provision of analgesia by paramedics, and type of analgesia provided. The outcomes of interest were sex differences in the provision of analgesia. Data analysis was by descriptive statistics, χ 2 test, and logistic regression. Results Of the 3357 patients transported in the study period, 1766 (53%) reported pain; this forms the study sample. Fifty-two percent were female, median age was 61 years, and median initial pain score (on a 0-10 verbal numeric rating scale) was 6. Forty-five percent of patients reporting pain did not receive analgesia (791/1766) (95% confidence interval [CI], 43%-47%), with no significant difference between sexes ( P = .93). There were, however, significant sex differences in the type of analgesia administered, with males more likely to receive morphine (17%; 95% CI, 15%-20%) than females (13%; 95% CI, 11%-15%) ( P = .01). The difference remains significant when controlled for type of pain, age, and pain severity (odds ratio, 0.61, 95% CI, 0.44-0.84). Conclusion Sex is not associated with the rate of paramedic-initiated analgesia, but is associated with differences in the type of analgesia administered.

James JiSheng Cui

Papers

I. Using the Bland–Altman method to measure agreement with repeated measures

QIC program and model selection in GEE analyses

Selection of Working Correlation Structure and Best Model in GEE Analyses of Longitudinal Data

Negative beliefs about low back pain are associated with high pain intensity and high level disability in community-based women

The impact of patient sex on paramedic pain management in the prehospital setting