Uncertainty Analysis of Time-Integrated Activity Coefficient in Single-Time-Point Dosimetry Using Bayesian Fitting Method

Purpose

Calculation of the uncertainty of the individual time-integrated activity coefficient (TIACs) is desirable in molecular radiotherapy. However, the calculation of TIAC's uncertainty in single-time-point (STP) method has never been reported in the literature. This study presents a method based on the Bayesian fitting (BF) to calculate the standard deviation (SD) of individual TIACs in the STP dosimetry.

Methods

Biokinetic data of 177Lu-DOTATATE in kidneys were obtained from PMID33443063. BF methods with extended objective function, which optimize the fitting using prior knowledge of the function's parameters, were used. Reference TIACs (rTIACs) were calculated by fitting a mono-exponential function to the all-time-point data. The goodness of fit was checked based on the visual inspection and the coefficient of variations (CV) of the fitted parameters < 0.5. BF with relative (BFr) and absolute-based (BFa) variance methods were used to obtain the calculated TIACs (cTIACs) from the STP dosimetry. Performance of the STP method was obtained by calculating the relative deviation (RD) between cTIACs and rTIACs.

Results

Visual inspection showed a good fit for all patients with CV of fitted parameters less than 50%. The mean ± SD of cTIAC's %RD were 7.0 ± 25.2 for BFr and 2.6 ± 8.9 for BFa. The range of %CV of the individual cTIAC's SD for BFr and BFa methods was 36-78% and 22-33%, respectively, while the %CV of the rTIAC SD was 0.8-49%.

Conclusion

We introduce the BF method to calculate the SD of individual TIACs in STP dosimetry. The presented method might be used as an alternative method for uncertainty analysis in STP dosimetry.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13139-024-00851-8.

Shifted Mixture Model Using Weibull, Lognormal, and Gamma Distributions

The objective of this investigation is to provide framework to construct a threefold mixture model and its shifted version using Weibull, lognormal, and gamma distributions. The proposed models are examined by establishing the statistical and reliability indices. The parameter estimation using the maximum likelihood estimation method (MLE) and expectation-maximization has been proposed. The usefulness of the shifted mixture models by fitting them into the actual data set has revealed. The goodness-of-fit tests are used to compare the mixture models for the real-life data. Based on statistical testing, it is established that for small data set, shifted mixture model is the best fitted model in comparison with other single and mixed mixture distributions.

Advances in nonparametric item response theory for scale construction in quality-of-life research

We introduce the special section on nonparametric item response theory (IRT) in Quality of Life Research. Starting from the well-known Rasch model, we provide a brief overview of nonparametric IRT models and discuss the assumptions, the properties, and the investigation of goodness of fit. We provide references to more detailed texts to help readers getting acquainted with nonparametric IRT models. In addition, we show how the rather diverse papers in the special section fit into the nonparametric IRT framework. Finally, we illustrate the application of nonparametric IRT models using data from a questionnaire measuring activity limitations in walking. The real-data example shows the quality of the scale and its constituent items with respect to dimensionality, local independence, monotonicity, and invariant item ordering.

Incremental Model Fit Assessment in the Case of Categorical Data: Tucker-Lewis Index for Item Response Theory Modeling

The Tucker-Lewis index (TLI; Tucker & Lewis, 1973), also known as the non-normed fit index (NNFI; Bentler & Bonett, 1980), is one of the numerous incremental fit indices widely used in linear mean and covariance structure modeling, particularly in exploratory factor analysis, tools popular in prevention research. It augments information provided by other indices such as the root-mean-square error of approximation (RMSEA). In this paper, we develop and examine an analogous index for categorical item level data modeled with item response theory (IRT). The proposed Tucker-Lewis index for IRT (TLIRT) is based on Maydeu-Olivares and Joe's (2005) [Formula: see text] family of limited-information overall model fit statistics. The limited-information fit statistics have significantly better Chi-square approximation and power than traditional full-information Pearson or likelihood ratio statistics under realistic situations. Building on the incremental fit assessment principle, the TLIRT compares the fit of model under consideration along a spectrum of worst to best possible model fit scenarios. We examine the performance of the new index using simulated and empirical data. Results from a simulation study suggest that the new index behaves as theoretically expected, and it can offer additional insights about model fit not available from other sources. In addition, a more stringent cutoff value is perhaps needed than Hu and Bentler's (1999) traditional cutoff criterion with continuous variables. In the empirical data analysis, we use a data set from a measurement development project in support of cigarette smoking cessation research to illustrate the usefulness of the TLIRT. We noticed that had we only utilized the RMSEA index, we could have arrived at qualitatively different conclusions about model fit, depending on the choice of test statistics, an issue to which the TLIRT is relatively more immune.

SARFIMA model prediction for infectious diseases: application to hemorrhagic fever with renal syndrome and comparing with SARIMA

Background

The early warning model of infectious diseases plays a key role in prevention and control. This study aims to using seasonal autoregressive fractionally integrated moving average (SARFIMA) model to predict the incidence of hemorrhagic fever with renal syndrome (HFRS) and comparing with seasonal autoregressive integrated moving average (SARIMA) model to evaluate its prediction effect.

Methods

Data on notified HFRS cases in Weifang city, Shandong Province were collected from the official website and Shandong Center for Disease Control and Prevention between January 1, 2005 and December 31, 2018. The SARFIMA model considering both the short memory and long memory was performed to fit and predict the HFRS series. Besides, we compared accuracy of fit and prediction between SARFIMA and SARIMA which was used widely in infectious diseases.

Results

Model assessments indicated that the SARFIMA model has better goodness of fit (SARFIMA (1, 0.11, 2)(1, 0, 1)₁₂: Akaike information criterion (AIC):-631.31; SARIMA (1, 0, 2)(1, 1, 1)₁₂: AIC: − 227.32) and better predictive ability than the SARIMA model (SARFIMA: root mean square error (RMSE):0.058; SARIMA: RMSE: 0.090).

Conclusions

The SARFIMA model produces superior forecast performance than the SARIMA model for HFRS. Hence, the SARFIMA model may help to improve the forecast of monthly HFRS incidence based on a long-range dataset.

Introducing the coupled stepwise areal constraining and Mahalanobis distance: a promising MCDM-based probabilistic model for landfill site selection

This study sets out to propose a new ensemble of probabilistic spatial modeling and multi-criteria decision-making comprised of stepwise areal constraining and Mahalanobis distance algorithms in order to assess areal suitability for landfilling. The Ardak watershed was selected as the study area due to encountering several cases of open garbage dumps and uncontrolled landfills which are one of the main sources of river water pollution in the upstream of the Ardak dam. The results revealed that the proposed algorithm successfully assists in inventory-irrespective probabilistic modeling of landfill siting which is mainly indebted to the role of areal constraining in providing training and validation samples for the Mahalanobis distance model. The latter also showed a robust pattern recognition results from which a discernible differentiation of the area was attained while the spatial dependencies between the environmental factors were taken into account. Mahalanobis distance also gave an outstanding performance in terms of goodness of fit (area under the success rate 89.367) and prediction power (area under the success rate 89.252). Based on a five-point scale classification scheme, about 2.7% and 2.6% of the study area, respectively, have high and very high suitability for landfilling, while the remaining area is shared between very low-to-moderate suitability classes. According to the current trail of literature regarding landfill site selection which mostly relies on mere areal filtering, a probabilistic model would give invaluable inferences regarding the pattern of suitability/susceptibility of the area of interest and causative role of the influential factors. Graphical Abstract.

The Italian ICD-11 field trial: clinical utility of diagnostic guidelines for schizophrenia and related disorders

Background

The 11th revision of the International Classification of Diseases and Related Disorders (ICD-11) has been released. In order to test the clinical consistency and the clinical utility of the proposed guidelines the World Health Organization (WHO) has carried out the Ecological Implementation Field Studies in various countries. In this paper the results of the Italian field trials on the clinical utility of the ICD-11 diagnostic guideline concerning schizophrenia and related disorders will be presented.

Methods

In Italy, field trials have been carried out at the Department of Psychiatry of the University of Campania “L. Vanvitelli”. All patients showing any psychotic symptom and referring to the outpatient and inpatient units have been recruited. Patients were interviewed by two clinicians with whom they had not had any prior clinical contact. At the end of each interview, clinicians were asked to complete 12 questions about the clinical utility of the diagnostic guidelines as applied to each patient.

Results

Fourteen clinicians and 100 patients have been involved. The ICD-11 clinical guidelines were perceived as easy to use, with an adequate goodness of fit, clear and understandable and with an adequate level of details and specificity to describe the essential features of the diagnoses. Clinicians rated very positively their usefulness in describing the threshold between patient’s disorder and normality. Despite still very positive, the guidelines have been perceived as less useful to select a treatment, to assess patients’ prognosis and to communicate with other mental health professionals.

Conclusions

The 11th revision of the chapter on Mental, Behavioural and Neurodevelopmental Disorders has made substantive changes to the conceptualization of mental disorders which could have impacted on their reliability and clinical utility. Results of the Italian field studies, in line with those reported by the international sample, highlight that ICD-11 has been rated as highly clinically useful by participating clinician, more than the ICD-10. This could be considered a good reason to be optimistic about the implementation of the ICD-11 among global clinicians.

Trial registration The study has been approved by the Ethical Review Board of the University of Campania “L. Vanvitelli” (N. 416, 2016)

Model-based goodness-of-fit tests for the ordered stereotype model

This paper presents two new model-based goodness-of-fit tests for the ordered stereotype model applied to an ordinal response variable. The proposed tests are based on the Lipsitz test, which partitions the subjects into G groups following the popular Hosmer-Lemeshow test for binary data. The tests construct an alternative model where group effects are added into the null model. If the model fits the data well then the null model is correct, and there should be no group effects. One of the main advantages of the ordered stereotype model is that it allows us to determine a new uneven spacing of the ordinal response categories, dictated by the data. The two proposed tests use this new adjusted spacing. One test uses the form of the original ordered stereotype model, and the other uses an ordinary linear model. We demonstrate the performance of both tests under a variety of scenarios. Finally, the results of the application in three examples are presented.

Dataset on growth curves of Boer goats fitted by ten non-linear functions

Data on the description of growth of female Boer goats from the Mexican national breeding flock are presented. Goat meat is highly appreciated for the preparation of traditional dishes of Mexican cuisine, and its demand is on the rise. Boer goats are of relatively recent arrival in Mexico and the size of the performance-recorded flock has been increasing steadily in the last ten years. Repeated measures of body weight at different ages from birth to adulthood of Boer goats are scarce. When available, such data can be used to describe the growth pattern and the meat production potential of goat meat breeds such as the Boer. This paper presents data on estimators of growth curve parameters, plots of average predicted growth curves, plots of residuals on age, and data on goodness of fit statistics of ten non-linear functions fitted to describe the growth curve of Boer goats.

On assessing binary regression models based on ungrouped data

Assessing a binary regression model based on ungrouped data is a commonly encountered but very challenging problem. Although tests, such as Hosmer-Lemeshow test and le Cessie-van Houwelingen test, have been devised and widely used in applications, they often have low power in detecting lack of fit and not much theoretical justification has been made on when they can work well. In this article, we propose a new approach based on a cross-validation voting system to address the problem. In addition to a theoretical guarantee that the probabilities of type I and II errors both converge to zero as the sample size increases for the new method under proper conditions, our simulation results demonstrate that it performs very well.

Peer Influence, Peer Selection and Adolescent Alcohol Use: a Simulation Study Using a Dynamic Network Model of Friendship Ties and Alcohol Use

While studies suggest that peer influence can in some cases encourage adolescent substance use, recent work demonstrates that peer influence may be on average protective for cigarette smoking, raising questions about whether this effect occurs for other substance use behaviors. Herein, we focus on adolescent drinking, which may follow different social dynamics than smoking. We use a data-calibrated Stochastic Actor-Based (SAB) Model of adolescent friendship tie choice and drinking behavior to explore the impact of manipulating the size of peer influence and selection effects on drinking in two school-based networks. We first fit a SAB Model to data on friendship tie choice and adolescent drinking behavior within two large schools (n = 2178 and n = 976) over three time points using data from the National Longitudinal Study of Adolescent to Adult Health. We then alter the size of the peer influence and selection parameters with all other effects fixed at their estimated values and simulate the social systems forward 1000 times under varying conditions. Whereas peer selection appears to contribute to drinking behavior similarity among adolescents, there is no evidence that it leads to higher levels of drinking at the school level. A stronger peer influence effect lowers the overall level of drinking in both schools. There are many similarities in the patterning of findings between this study of drinking and previous work on smoking, suggesting that peer influence and selection may function similarly with respect to these substances.

The power Lomax distribution with an application to bladder cancer data

A three-parameters continuous distribution, namely, Power Lomax distribution (POLO) is proposed and studied for remission times of bladder cancer data. POLO distribution accommodate both inverted bathtub and decreasing hazard rate. Several statistical and reliability properties are derived. Point estimation via method of moments and maximum likelihood and the interval estimation are also studied. The simulation schemes are calculated to examine the bias and mean square error of the maximum likelihood parameter estimators. Finally, a real data application about the remission time of bladder cancer is used to illustrate the usefulness of the proposed distribution in modelling real data application. The characteristics of the fitting data using the proposed distribution are compared with known extensions of Lomax distribution. The comparison showed that the POLO distribution outfit most well-known extensions of Lomax distribution.

"Generality of mis-fit"? The real-life difficulty of matching scales in an interconnected world

A clear understanding of processes at multiple scales and levels is of special significance when conceiving strategies for human-environment interactions. However, understanding and application of the scale concept often differ between administrative-political and ecological disciplines. These mirror major differences in potential solutions whether and how scales can, at all, be made congruent. As a result, opportunities of seeking "goodness-of-fit" between different concepts of governance should perhaps be reconsidered in the light of a potential "generality of mis-fit." This article reviews the interdisciplinary considerations inherent in the concept of scale in its ecological, as well as administrative-political, significance and argues that issues of how to manage "mis-fit" should be awarded more emphasis in social-ecological research and management practices. These considerations are exemplified by the case of reindeer husbandry in Fennoscandia. Whilst an indigenous small-scale practice, reindeer husbandry involves multi-level ecological and administrative-political complexities-complexities that we argue may arise in any multi-level system.

Goodness of fit between prenatal maternal sleep and infant sleep: Associations with maternal depression and attachment security

The current study prospectively examined the ways in which goodness of fit between maternal and infant sleep contributes to maternal depressive symptoms and the mother-child relationship across the first years of life. In a sample of 173 mother-child dyads, maternal prenatal sleep, infant sleep, maternal depressive symptoms, and mother-child attachment security were assessed via self-report, actigraphy, and observational measures. Results suggested that a poor fit between mothers' prenatal sleep and infants' sleep at 8 months (measured by sleep diary and actigraphy) was associated with maternal depressive symptoms at 15 months. Additionally, maternal depression mediated the association between the interplay of mother and infant sleep (measured by sleep diary) and mother-child attachment security at 30 months. Findings emphasize the importance of the match between mother and infant sleep on maternal wellbeing and mother-child relationships and highlight the role of mothers' perceptions of infant sleep.

The Infinitesimal Jackknife and Moment Structure Analysis Using Higher Order Moments

Mean corrected higher order sample moments are asymptotically normally distributed. It is shown that both in the literature and popular software the estimates of their asymptotic covariance matrices are incorrect. An introduction to the infinitesimal jackknife is given and it is shown how to use it to correctly estimate the asymptotic covariance matrices of higher order sample moments. Another advantage in using the infinitesimal jackknife is the ease with which it may be used when stacking or sub-setting estimators. The estimates given are used to test the goodness of fit of a non-linear factor analysis model. A computationally accelerated form for infinitesimal jackknife estimates is given.

Problems in detecting misfit of latent class models in diagnostic research without a gold standard were shown

OBJECTIVES

The objective of this study was to evaluate the performance of goodness-of-fit testing to detect relevant violations of the assumptions underlying the criticized "standard" two-class latent class model. Often used to obtain sensitivity and specificity estimates for diagnostic tests in the absence of a gold reference standard, this model relies on assuming that diagnostic test errors are independent. When this assumption is violated, accuracy estimates may be biased: goodness-of-fit testing is often used to evaluate the assumption and prevent bias.

STUDY DESIGN AND SETTING

We investigate the performance of goodness-of-fit testing by Monte Carlo simulation. The simulation scenarios are based on three empirical examples.

RESULTS

Goodness-of-fit tests lack power to detect relevant misfit of the standard two-class latent class model at sample sizes that are typically found in empirical diagnostic studies. The goodness-of-fit tests that are based on asymptotic theory are not robust to the sparseness of data. A parametric bootstrap procedure improves the evaluation of goodness of fit in the case of sparse data.

CONCLUSION

Our simulation study suggests that relevant violation of the local independence assumption underlying the standard two-class latent class model may remain undetected in empirical diagnostic studies, potentially leading to biased estimates of sensitivity and specificity.

Modelling the effect of antimicrobial treatment on carriage of hospital pathogens with application to MRSA

Numerous studies have sought to assess the effectiveness of control measures aimed at reducing the spread of pathogens such as Methicillin-resistant Staphylococcus aureus (MRSA) in hospital settings. Far less is known about possible short-term effects of antibiotics and other antimicrobial treatments on pathogen carriage in patients. This paper is concerned with developing and applying methods for the analysis of detailed data on hospital patients which include information on patient treatments and screening tests for the pathogen in question. The carriage status (colonized, or not) of each patient is modelled as a Markov chain, and models for both perfect and imperfect test sensitivity are developed. Goodness-of-fit procedures based on simulation are also proposed. The methods are illustrated using both simulated data and data on MRSA.

Mathematical modeling of tuberculosis data of China

This paper concentrates on the tuberculosis data of China from January 2005 to December 2012. We set up a mathematical model to fit those data with the goodness of fit and obtain the optimal parameter values of the model. By the Chi-square test of the statistical inference, the optimal parameter values of the model are reasonable. We get the effective reproductive number of the disease for each year, and also investigate the preventive measures to control the tuberculosis.

A note on a nonparametric regression test through penalized splines

We examine a test of a nonparametric regression function based on penalized spline smoothing. We show that, similarly to a penalized spline estimator, the asymptotic power of the penalized spline test falls into a small- K or a large-K scenarios characterized by the number of knots K and the smoothing parameter. However, the optimal rate of K and the smoothing parameter maximizing power for testing is different from the optimal rate minimizing the mean squared error for estimation. Our investigation reveals that compared to estimation, some under-smoothing may be desirable for the testing problems. Furthermore, we compare the proposed test with the likelihood ratio test (LRT). We show that when the true function is more complicated, containing multiple modes, the test proposed here may have greater power than LRT. Finally, we investigate the properties of the test through simulations and apply it to two data examples.

Test the reliability of doubly robust estimation with missing response data

In statistical inference, one has to make sure that the underlying regression model is correctly specified otherwise the resulting estimation may be biased. Model checking is an important method to detect any departure of the regression model from the true one. Missing data are a ubiquitous problem in social and medical studies. If the underlying regression model is correctly specified, recent researches show great popularity of the doubly robust (DR) estimates method for handling missing data because of its robustness to the misspecification of either the missing data model or the conditional mean model, that is, the model for the conditional expectation of true regression model conditioning on the observed quantities. However, little work has been devoted to the goodness of fit test for DR estimates method. In this article, we propose a testing method to assess the reliability of the estimator derived from the DR estimating equation with possibly missing response and always observed auxiliary variables. Numerical studies demonstrate that the proposed test can control type I errors well. Furthermore the proposed method can detect departures from model assumptions in the marginal mean model of interest powerfully. A real dementia data set is used to illustrate the method for the diagnosis of model misspecification in the problem of missing response with an always observed auxiliary variable for cross-sectional data.

A Powerful Test for Multivariate Normality

This paper investigates a new test for normality that is easy for biomedical researchers to understand and easy to implement in all dimensions. In terms of power comparison against a broad range of alternatives, the new test outperforms the best known competitors in the literature as demonstrated by simulation results. In addition, the proposed test is illustrated using data from real biomedical studies.

Exploring an optimal vector autoregressive model for multi-channel pulmonary sound data

The purpose of this study is to find a useful mathematical model for multi-channel pulmonary sound data. Vector auto-regressive (VAR) model schema is adopted and the best set of arguments, namely, the order and sample size of the model and the sampling rate of the data, is aimed to be determined. Both conventional prediction error criteria and a set of three new criteria which are derived specifically for pulmonary sound signals are used to evaluate the success of the model. In terms of these criteria, the second order 250-point model is selected to be the most descriptive VAR model for 14-channel pulmonary sound data. The preferred sampling rate is the original data acquisition rate, which is 9600 samples per second. The effect of normalization of the data with respect to the air flow is also examined. Six normalization schemes are implemented on the data prior to the model fit, and the resulting model parameters are examined in terms of the proposed criterion measures. It is concluded that normalization with respect to flow is not necessary prior to the VAR modeling of pulmonary sound data.

Characterizing changes in the excitability of corticospinal projections to proximal muscles of the upper limb

BACKGROUND

There has been an explosion of interest in methods of exogenous brain stimulation that induce changes in the excitability of human cerebral cortex. The expectation is that these methods may promote recovery of function following brain injury. To assess their effects on motor output, it is typical to assess the state of corticospinal projections from primary motor cortex to muscles of the hand, via electromyographic responses to transcranial magnetic stimulation. If a range of stimulation intensities is employed, the recruitment curves (RCs) obtained can, at least for intrinsic hand muscles, be fitted by a sigmoid function.

OBJECTIVE/HYPOTHESIS

To establish whether sigmoid fits provide a reliable basis upon which to characterize the input-output properties of the corticospinal pathway for muscles proximal to the hand, and to assess as an alternative the area under the (recruitment) curve (AURC).

METHODS

A comparison of the reliability of these measures, using RCs obtained for muscles that are frequently the targets of rehabilitation.

RESULTS

The AURC is an extremely reliable measure of the state of corticospinal projections to hand and forearm muscles, which has both face and concurrent validity. Construct validity is demonstrated by detection of widely distributed (across muscles) changes in corticospinal excitability induced by paired associative stimulation (PAS).

CONCLUSION(S)

The parameters derived from sigmoid fits are unlikely to provide an adequate means to assess the effectiveness of therapeutic regimes. The AURC can be employed to characterize corticospinal projections to a range of muscles, and gauge the efficacy of longitudinal interventions in clinical rehabilitation.

A characterization of multivariate normality through univariate projections

This paper introduces a new characterization of multivariate normality of a random vector based on univariate normality of linear combinations of its components.

Analysis of the rocuronium-vecuronium interaction on tetanic fade based on a generalized response surface model with varying relative potencies

BACKGROUND

Some studies have shown that rocuronium and vecuronium have additive, or synergistic effects on muscle relaxation based on the Loewe additivity. Therefore, we performed a fit of tetanic fade data to a generalized response surface model with varying relative potencies proposed by Kong and Lee (KLGRS) to evaluate the usefulness of KLGRS for capturing the interspersed drug interactions and to characterize the interaction between the two drugs.

METHODS

Left phrenic nerve-hemidiaphragms (Male Sprague-Dawley rats, 150-250 g) were mounted in Krebs solution. Supramaximal electrical stimulation (0.2 ms, rectangular) of 50 Hz for 1.9 s to the phrenic nerve evoked tetanic contractions that were measured with a force transducer. Each preparation was exposed to one of 4 vecuronium concentrations (0.0, 1.5, 2.5, and 3.0 microM), or one of 4 rocuronium concentrations (0.0, 3.0, 4.5, and 5.5 microM). Subsequently the adequate amount of rocuronium was added to a vecuronium bath and that of vecuronium was added to a rocuronium until an 80-90% increase in tetanic fade was achieved. We then fitted the modified KLGRS models to the above data, after which we selected the best model, based on 5 methods for determining goodness of fit. Using this method, we obtained the response surface, as well as contour plots for the response surface (i.e. isoboles), the polynomial function and the interaction index.

RESULTS

The model with the constant relative potency ratio and 8 parameters was found to best describe the results, and this model reflected well the characteristics of the raw data. In addition, the two drugs showed a synergistic interaction in almost every area and an antagonistic one in a very narrow area.

CONCLUSIONS

KLGRS was found to be a useful method of analyzing data describing interspersed drug interactions. The interaction between rocuronium and vecuronium was found to be synergistic.