Bayesian Techniques in Predicting Frailty among Community-Dwelling Older Adults in the Netherlands

Background Frailty is a syndrome that is defined as an accumulation of deficits in physical, psychological, and social domains. On a global scale, there is an urgent need to create frailty-ready healthcare systems due to the healthcare burden that frailty confers on systems and the increased risk of falls, healthcare utilization, disability, and premature mortality. Several studies have been conducted to develop prediction models for predicting frailty. Most studies used logistic regression as a technique to develop a prediction model. One area that has experienced significant growth is the application of Bayesian techniques, partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. Objective We compared ten different Bayesian networks as proposed by ten experts in the field of frail elderly people to predict frailty with a choice from ten dichotomized determinants for frailty. Methods We used the opinion of ten experts who could indicate, using an empty Bayesian network graph, the important predictors for frailty and the interactions between the different predictors. The candidate predictors were age, sex, marital status, ethnicity, education, income, lifestyle, multimorbidity, life events, and home living environment. The ten Bayesian network models were evaluated in terms of their ability to predict frailty. For the evaluation, we used the data of 479 participants that filled in the Tilburg Frailty indicator (TFI) questionnaire for assessing frailty among community-dwelling older people. The data set contained the aforementioned variables and the outcome "frail". The model fit of each model was measured using the Akaike information criterion (AIC) and the predictive performance of the models was measured using the area under the curve (AUC) of the receiver operator characteristic (ROC). The AUCs of the models were validated using bootstrapping with 100 repetitions. The relative importance of the predictors in the models was calculated using the permutation feature importance algorithm (PFI). Results The ten Bayesian networks of the ten experts differed considerably regarding the predictors and the connections between the predictors and the outcome. However, all ten networks had corrected AUCs >0.700. Evaluating the importance of the predictors in each model, "diseases or chronic disorders" was the most important predictor in all models (10 times). The predictors "lifestyle" and "monthly income" were also often present in the models (both 6 times). One or more diseases or chronic disorders, an unhealthy lifestyle, and a monthly income below 1800 euro increased the likelihood of frailty. Conclusions Although the ten experts all made different graphs, the predictive performance was always satisfying (AUCs >0.700). While it is true that the predictor importance varied all the time, the top three of the predictor importance consisted of "diseases or chronic disorders", "lifestyle" and "monthly income". All in all, asking for the opinion of experts in the field of frail elderly to predict frailty with Bayesian networks may be more rewarding than a data-driven forecast with Bayesian networks because they have expert knowledge regarding interactions between the different predictors.

Multiple ECG segments denoising autoencoder model

OBJECTIVES

Denoising autoencoder (DAE) with a single hidden layer of neurons can recode a signal, i.e., converting the original signal into a noise-reduced signal. The DAE approach has shown a good performance in denoising bio-signals, like electrocardiograms (ECG). In this paper, we study the effect of correlated, uncorrelated and jittered datasets on the performance of the DAE model.

METHODS

Vectors of multiple concatenated ECG segments of simultaneously recorded Einthoven recordings I, II, III are considered to establish the following dataset cases: (1) correlated, (2) uncorrelated, and (3) jittered. We consider our previous work in finding the optimal number of hidden neurons receiving the input signal with respect to signal quality and computational burden by applying Akaike's information criterion. To evaluate DAE, these datasets are corrupted with six types of noise, namely mix noise (MX), motion artifact noise (MA), electrode movement (EM), baseline wander (BW), Gaussian white noise (GWN) and high-frequency noise (HFN), to simulate real case scenario. Spectral analysis is used to study the effects of noise whose power spectrum may overlap with the power spectrum of the wanted signal on DAE performance.

RESULTS

The simulation results show (a) that the number of hidden neurons to denoise multiple correlated ECG is much lower than for jittered signals, (b) QRS-complex based ECG alignment preferable, (c) noises with slightly overlapping power spectrum, like BW and HFN, can be easily removed with sufficient number of neurons, while the noise with completely overlapping spectrum, like GWN, requires a very low-dimensional and thus coarser reduction to recover the signal.

CONCLUSIONS

The performance of DAE model in terms of signal-to-noise ratio improvement and the required number of hidden neurons can be improved by utilizing the correlation among simultaneous Einthoven I, II, III records.

Modeling the recovery time of patients with coronavirus disease 2019 using an accelerated failure time model

Objective

To identify factors associated with recovery time from coronavirus disease 2019 (COVID-19).

Methods

In this retrospective study, data for patients with COVID-19 were obtained between 21 June and 30 August 2020. An accelerated failure time (AFT) model was used to identify covariates associated with recovery time (days from hospital admission to discharge). AFT models with different distributions (exponential, log-normal, Weibull, generalized gamma, and log-logistic) were generated. Akaike’s information criterion (AIC) was used to identify the most suitable model.

Results

A total of 730 patients with COVID-19 were included (92.5% recovered and 7.5% censored). Based on its low AIC value, the log-logistic AFT model was the best fit for the data. The covariates affecting length of hospital stay were oxygen saturation, lactate dehydrogenase, neutrophil-lymphocyte ratio, D-dimer, ferritin, creatinine, total leucocyte count, age > 80 years, and coronary artery disease.

Conclusions

The log-logistic AFT model accurately described the recovery time of patients with COVID-19.

Errors in Statistical Inference Under Model Misspecification: Evidence, Hypothesis Testing, and AIC

The methods for making statistical inferences in scientific analysis have diversified even within the frequentist branch of statistics, but comparison has been elusive. We approximate analytically and numerically the performance of Neyman-Pearson hypothesis testing, Fisher significance testing, information criteria, and evidential statistics (Royall, 1997). This last approach is implemented in the form of evidence functions: statistics for comparing two models by estimating, based on data, their relative distance to the generating process (i.e., truth) (Lele, 2004). A consequence of this definition is the salient property that the probabilities of misleading or weak evidence, error probabilities analogous to Type 1 and Type 2 errors in hypothesis testing, all approach 0 as sample size increases. Our comparison of these approaches focuses primarily on the frequency with which errors are made, both when models are correctly specified, and when they are misspecified, but also considers ease of interpretation. The error rates in evidential analysis all decrease to 0 as sample size increases even under model misspecification. Neyman-Pearson testing on the other hand, exhibits great difficulties under misspecification. The real Type 1 and Type 2 error rates can be less, equal to, or greater than the nominal rates depending on the nature of model misspecification. Under some reasonable circumstances, the probability of Type 1 error is an increasing function of sample size that can even approach 1! In contrast, under model misspecification an evidential analysis retains the desirable properties of always having a greater probability of selecting the best model over an inferior one and of having the probability of selecting the best model increase monotonically with sample size. We show that the evidence function concept fulfills the seeming objectives of model selection in ecology, both in a statistical as well as scientific sense, and that evidence functions are intuitive and easily grasped. We find that consistent information criteria are evidence functions but the MSE minimizing (or efficient) information criteria (e.g., AIC, AICc, TIC) are not. The error properties of the MSE minimizing criteria switch between those of evidence functions and those of Neyman-Pearson tests depending on models being compared.

On the testing of Hardy-Weinberg proportions and equality of allele frequencies in males and females at biallelic genetic markers

Standard statistical tests for equality of allele frequencies in males and females and tests for Hardy‐Weinberg equilibrium are tightly linked by their assumptions. Tests for equality of allele frequencies assume Hardy‐Weinberg equilibrium, whereas the usual chi‐square or exact test for Hardy‐Weinberg equilibrium assume equality of allele frequencies in the sexes. In this paper, we propose ways to break this interdependence in assumptions of the two tests by proposing an omnibus exact test that can test both hypotheses jointly, as well as a likelihood ratio approach that permits these phenomena to be tested both jointly and separately. The tests are illustrated with data from the 1000 Genomes project.

A Small-Sample Choice of the Tuning Parameter in Ridge Regression

We propose new approaches for choosing the shrinkage parameter in ridge regression, a penalized likelihood method for regularizing linear regression coefficients, when the number of observations is small relative to the number of parameters. Existing methods may lead to extreme choices of this parameter, which will either not shrink the coefficients enough or shrink them by too much. Within this "small-n, large-p" context, we suggest a correction to the common generalized cross-validation (GCV) method that preserves the asymptotic optimality of the original GCV. We also introduce the notion of a "hyperpenalty", which shrinks the shrinkage parameter itself, and make a specific recommendation regarding the choice of hyperpenalty that empirically works well in a broad range of scenarios. A simple algorithm jointly estimates the shrinkage parameter and regression coefficients in the hyperpenalized likelihood. In a comprehensive simulation study of small-sample scenarios, our proposed approaches offer superior prediction over nine other existing methods.

An integrated pharmacokinetic and pharmacodynamic study of a new drug of abuse, methylone, a synthetic cathinone sold as "bath salts"

INTRODUCTION

Methylone (3,4-methylenedioxymethcathinone) is a new psychoactive substance and an active ingredient of "legal highs" or "bath salts". We studied the pharmacokinetics and locomotor activity of methylone in rats at doses equivalent to those used in humans.

MATERIAL AND METHODS

Methylone was administered to male Sprague-Dawley rats intravenously (10mg/kg) and orally (15 and 30 mg/kg). Plasma concentrations and metabolites were characterized by LC/MS and LC-MS/MS fragmentation patterns. Locomotor activity was monitored for 180-240 min.

RESULTS

Oral administration of methylone induced a dose-dependent increase in locomotor activity in rats. The plasma concentrations after i.v. administration were described by a two-compartment model with distribution and terminal elimination phases of α=1.95 h(-1) and β=0.72 h(-1). For oral administration, peak methylone concentrations were achieved between 0.5 and 1h and fitted to a flip-flop model. Absolute bioavailability was about 80% and the percentage of methylone protein binding was of 30%. A relationship between methylone brain levels and free plasma concentration yielded a ratio of 1.42 ± 0.06, indicating access to the central nervous system. We have identified four Phase I metabolites after oral administration. The major metabolic routes are N-demethylation, aliphatic hydroxylation and O-methylation of a demethylenate intermediate.

DISCUSSION

Pharmacokinetic and pharmacodynamic analysis of methylone showed a correlation between plasma concentrations and enhancement of the locomotor activity. A contribution of metabolites in the activity of methylone after oral administration is suggested. Present results will be helpful to understand the time course of the effects of this drug of abuse in humans.