What ergodicity means for you.

This paper explores the relation between within-person and between-person research designs using the concept of ergodicity from statistical mechanics in physics. We demonstrate the consequences of ergodicity using several real data examples from previously published studies. We then create several simulated examples that illustrate the independence of within-person processes from between-person differences, and pair these examples with analytic results that reinforce our conclusions. Finally, we discuss the plausibility of ergodicity being the general rule rather than the exception for social and behavioral processes, address common arguments against heeding the implications of ergodicity for behavioral research, and offer several possible solutions.

Products of Variables in Structural Equation Models.

A general method is introduced in which variables that are products of other variables in the context of a structural equation model (SEM) can be decomposed into the sources of variance due to the multiplicands. The result is a new category of SEM which we call a Products of Variables Model (PoV). Some useful and practical features of PoV models include estimation of interactions between latent variables, latent variable moderators, manifest moderators with missing values, and manifest or latent squared terms. Expected means and covariances are analytically derived for a simple product of two variables and it is shown that the method reproduces previously published results for this special case. It is shown algebraically that using centered multiplicands results in an unidentified model, but if the multiplicands have non-zero means, the result is identified. The method has been implemented in OpenMx and Ωnyx and is applied in five extensive simulations.

State Space Mixture Modeling: Finding People with Similar Patterns of Change.

Increasingly, behavioral scientists encounter data where several individuals were measured on multiple variables over numerous occasions. Many current methods combine these data, assuming all individuals are randomly equivalent. An extreme alternative assumes no one is randomly equivalent. We propose state space mixture modeling as one possible compromise. State space mixture modeling assumes that unknown groups of people exist who share the same parameters of a state space model, and simultaneously estimates both the state space parameters and group membership. The goal is to find people that are undergoing similar change processes over time. The present work demonstrates state space mixture modeling on a simulated data set, and summarizes the results from a large simulation study. The illustration shows how the analysis is conducted, whereas the simulation provides evidence of its general validity and applicability. In the simulation study, sample size had the greatest influence on parameter estimation and the dimension of the change process had the greatest impact on correctly grouping people together, likely due to the distinctiveness of their patterns of change. State space mixture modeling offers one of the best-performing methods for simultaneously drawing conclusions about individual change processes while also analyzing multiple people.

Characterizing affect dynamics with a damped linear oscillator model: Theoretical considerations and recommendations for individual-level applications.

People show stable differences in the way their affect fluctuates over time. Within the general framework of dynamical systems, the damped linear oscillator (DLO) model has been proposed as a useful approach to study affect dynamics. The DLO model can be applied to repeated measures provided by a single individual, and the resulting parameters can capture relevant features of the person's affect dynamics. Focusing on negative affect, we provide an accessible interpretation of the DLO model parameters in terms of emotional lability, resilience, and vulnerability. We conducted a Monte Carlo study to test the DLO model performance under different empirically relevant conditions in terms of individual characteristics and sampling scheme. We used state-space models in continuous time. The results show that, under certain conditions, the DLO model is able to accurately and efficiently recover the parameters underlying the affective dynamics of a single individual. We discuss the results and the theoretical and practical implications of using this model, illustrate how to use it for studying psychological phenomena at the individual level, and provide specific recommendations on how to collect data for this purpose. We also provide a tutorial website and computer code in R to implement this approach. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Estimation of nonlinear mixed-effects continuous-time models using the continuous-discrete extended Kalman filter.

Many intensive longitudinal measurements are collected at irregularly spaced time intervals, and involve complex, possibly nonlinear and heterogeneous patterns of change. Effective modelling of such change processes requires continuous-time differential equation models that may be nonlinear and include mixed effects in the parameters. One approach of fitting such models is to define random effect variables as additional latent variables in a stochastic differential equation (SDE) model of choice, and use estimation algorithms designed for fitting SDE models, such as the continuous-discrete extended Kalman filter (CDEKF) approach implemented in the dynr R package, to estimate the random effect variables as latent variables. However, this approach's efficacy and identification constraints in handling mixed-effects SDE models have not been investigated. In the current study, we analytically inspect the identification constraints of using the CDEKF approach to fit nonlinear mixed-effects SDE models; extend a published model of emotions to a nonlinear mixed-effects SDE model as an example, and fit it to a set of irregularly spaced ecological momentary assessment data; and evaluate the feasibility of the proposed approach to fit the model through a Monte Carlo simulation study. Results show that the proposed approach produces reasonable parameter and standard error estimates when some identification constraint is met. We address the effects of sample size, process noise variance, and data spacing conditions on estimation results.

Simulated nonlinear genetic and environmental dynamics of complex traits.

Genetic studies of complex traits often show disparities in estimated heritability depending on the method used, whether by genomic associations or twin and family studies. We present a simulation of individual genomes with dynamic environmental conditions to consider how linear and nonlinear effects, gene-by-environment interactions, and gene-by-environment correlations may work together to govern the long-term development of complex traits and affect estimates of heritability from common methods. Our simulation studies demonstrate that the genetic effects estimated by genome wide association studies in unrelated individuals are inadequate to characterize gene-by-environment interaction, while including related individuals in genome-wide complex trait analysis (GCTA) allows gene-by-environment interactions to be recovered in the heritability. These theoretical findings provide an explanation for the "missing heritability" problem and bridge the conceptual gap between the most common findings of GCTA and twin studies. Future studies may use the simulation model to test hypotheses about phenotypic complexity either in an exploratory way or by replicating well-established observations of specific phenotypes.

Conversion Total Hip Arthroplasty in the Era of Bundled Care Payments: Impacts on Costs of Care.

BACKGROUND: Conversion hip arthroplasty is defined as a patient who has had prior open or arthroscopic hip surgery with or without retained hardware that is removed and replaced with arthroplasty components. Currently, it is classified under the same diagnosis-related group as primary total hip arthroplasty (THA); however, it frequently requires a higher cost of care. METHODS: A retrospective study of 228 conversion THA procedures in an orthopaedic specialty hospital was performed. Propensity score matching was used to compare the study group to a cohort of 510 primary THA patients by age, body mass index, sex, and American Society of Anesthesiologists score. These matched groups were compared based on total costs, implants used, operative times, length of stay (LOS), readmissions, and complications. RESULTS: Conversion THA incurred 25% more mean total costs compared to primary THA (P < .05), longer lengths of surgery (154 versus 122 minutes), and hospital LOS (2.1 versus 1.56 days). A subgroup analysis showed a 57% increased cost for cephalomedullary nail conversion, 34% increased cost for sliding hip screw, 33% for acetabular open reduction and internal fixation conversion, and 10% increased costs in closed reduction and percutaneous pinning conversions (all P < .05). There were 5 intraoperative complications in the conversion group versus none in the primary THA group (P < .01), with no statistically significant difference in readmissions. CONCLUSION: Conversion THA is significantly more costly than primary THA and has longer surgical times and greater LOS. Specifically, conversion THA with retained implants had the greatest impact on cost.

Two Filtering Methods of Forecasting Linear and Nonlinear Dynamics of Intensive Longitudinal Data.

With the advent of new data collection technologies, intensive longitudinal data (ILD) are collected more frequently than ever. Along with the increased prevalence of ILD, more methods are being developed to analyze these data. However, relatively few methods have yet been applied for making long- or even short-term predictions from ILD in behavioral settings. Applications of forecasting methods to behavioral ILD are still scant. We first establish a general framework for modeling ILD and then extend that frame to two previously existing forecasting methods: these methods are Kalman prediction and ensemble prediction. After implementing Kalman and ensemble forecasts in free and open-source software, we apply these methods to daily drug and alcohol use data. In doing so, we create a simple, but nonlinear dynamical system model of daily drug and alcohol use and illustrate important differences between the forecasting methods. We further compare the Kalman and ensemble forecasting methods to several simpler forecasts of daily drug and alcohol use. Ensemble forecasts may be more appropriate than Kalman forecasts for nonlinear dynamical systems models, but further forecasting evaluation methods must be put into practice.

Periarticular screws: what's in and what's out of the joint?

Periarticular hardware placement can be challenging and a source of angst for orthopaedic surgeons due to fear of penetrating the articular surface and causing undue harm to the joint. In recent years, many surgeons have turned to computed tomography (CT) and other intraoperative or postoperative modalities to determine whether hardware is truly extraarticular in areas of complex anatomy. Yet, these adjuncts are expensive, time consuming, and often unnecessary given the advancement in understanding of intraoperative fluoroscopy. We present a review article with the goal of empowering surgeons to leave the operating room, with fluoroscopy alone, assured that all hardware is beneath the articular surface that is being worked on. By understanding a simple concept, surgeons can extrapolate the information in this article to any joint and bony surface in the body. While targeted at both residents and surgeons who may not have completed a trauma fellowship, this review can benefit all orthopaedic surgeons alike.

The Analytic Identification of Variance Component Models Common to Behavior Genetics.

Many behavior genetics models follow the same general structure. We describe this general structure and analytically derive simple criteria for its identification. In particular, we find that variance components can be uniquely estimated whenever the relatedness matrices that define the components are linearly independent (i.e., not confounded). Thus, we emphasize determining which variance components can be identified given a set of genetic and environmental relationships, rather than the estimation procedures. We validate the identification criteria with several well-known models, and further apply them to several less common models. The first model distinguishes child-rearing environment from extended family environment. The second model adds a gene-by-common-environment interaction term in sets of twins reared apart and together. The third model separates measured-genomic relatedness from the scanner site variation in a hypothetical functional magnetic resonance imaging study. The computationally easy analytic identification criteria allow researchers to quickly address model identification issues and define novel variance components, facilitating the development of new research questions.

Multilevel Modeling in Classical Twin and Modern Molecular Behavior Genetics.

For more than a decade, it has been known that many common behavior genetics models for a single phenotype can be estimated as multilevel models (e.g., van den Oord 2001; Guo and Wang 2002; McArdle and Prescott 2005; Rabe-Hesketh et al. 2007). This paper extends the current knowledge to (1) multiple phenotypes such that the method is completely general to the variance structure hypothesized, and (2) both higher and lower levels of nesting. The multi-phenotype method also allows extended relationships to be considered (see also, Bard et al. 2012; Hadfield and Nakagawa 2010). The extended relationship model can then be continuously expanded to merge with the case typically seen in the molecular genetics analyses of unrelated individuals (e.g., Yang et al. 2011). We use the multilevel form of behavior genetics models to fit a multivariate three level model that allows for (1) child level variation from unique environments and additive genetics, (2) family level variation from additive genetics and common environments, and (3) neighborhood level variation from broader geographic contexts. Finally, we provide R (R Development Core Team 2020) functions and code for multilevel specification of several common behavior genetics models using OpenMx (Neale et al. 2016).

Combining Structural-Equation Modeling with Genomic-Relatedness-Matrix Restricted Maximum Likelihood in OpenMx.

There is a long history of fitting biometrical structural-equation models (SEMs) in the pregenomic behavioral-genetics literature of twin, family, and adoption studies. Recently, a method has emerged for estimating biometrical variance-covariance components based not upon the expected degree of genetic resemblance among relatives, but upon the observed degree of genetic resemblance among unrelated individuals for whom genome-wide genotypes are available-genomic-relatedness-matrix restricted maximum-likelihood (GREML). However, most existing GREML software is concerned with quickly and efficiently estimating heritability coefficients, genetic correlations, and so on, rather than with allowing the user to fit SEMs to multitrait samples of genotyped participants. We therefore introduce a feature in the OpenMx package, "mxGREML", designed to fit the biometrical SEMs from the pregenomic era in present-day genomic study designs. We explain the additional functionality this new feature has brought to OpenMx, and how the new functionality works. We provide an illustrative example of its use. We discuss the feature's current limitations, and our plans for its further development.

Emotional and interpersonal mechanisms in community SSRI treatment of social anxiety disorder.

BACKGROUND: Affective and interpersonal behavioural patterns characteristic of social anxiety disorder show improvement during treatment with serotonin agonists (e.g., selective serotonin reuptake inhibitors), commonly used in the treatment of social anxiety disorder. The present study sought to establish whether, during community psychopharmacological treatment of social anxiety disorder, changes in positive or negative affect and agreeable or quarrelsome behaviour mediate improvement in social anxiety symptom severity or follow from it. METHODS: Adults diagnosed with social anxiety disorder (n = 48) recorded their interpersonal behaviour and affect naturalistically in an event-contingent recording procedure for 1-week periods before and during the first 4 months of treatment with paroxetine. Participants and treating psychiatrists assessed the severity of social anxiety symptoms monthly. A multivariate latent change score framework examined temporally lagged associations of change in affect and interpersonal behaviour with change in social anxiety symptom severity. RESULTS: Elevated agreeable behaviour and positive affect predicted greater subsequent reduction in social anxiety symptom severity over the following month of treatment. Elevated negative affect, but not quarrelsome behaviour, predicted less subsequent reduction in symptom severity. LIMITATIONS: Limitations included limited assessment of extreme behaviour (e.g., violence) that may have precluded examining the efficacy of paroxetine because of the lack of a placebo control group. CONCLUSION: The present study suggests that interpersonal behaviour and affect may be putative mechanisms of action for serotonergic treatment of social anxiety disorder. Prosocial behaviour and positive affect increase during serotonergic treatment of social anxiety disorder. Specifically, modulating agreeable behaviour, positive affect and negative affect in individuals' daily lives may partially explain and refine clinical intervention.

Intramedullary Nail Fixation for the Treatment of Pathologic Humeral Shaft Fractures.

The goal of this study was to report the clinical outcomes of pathologic humeral shaft fractures treated with reamed or unreamed intramedullary nail fixation in an era of longer patient survival. A retrospective review was conducted of all patients who underwent intramedullary nail fixation performed by a single surgeon for pathologic humeral shaft fractures at a Level I trauma center from 2009 to 2017. Of the 25 patients who were identified, 9 were excluded. Groups were categorized according to whether they underwent reamed or unreamed fixation, and they were evaluated for evidence of union, complications, and reoperation. Of the patients, 11 underwent an unreamed procedure and 5 underwent a reamed procedure. Mean length of follow-up was 51.5 weeks. Of the patients who participated, 12 (75%) showed evidence of union and 2 patients (12.5%) showed evidence of nonunion, with no statistical difference between the groups. Five patients (31.3%) had complications. One nonunion occurred in the reamed group and did not require reoperation. In the unreamed group, complications consisted of 1 delayed union, 1 nonunion treated with revision intramedullary nail fixation, and 2 cases of disease progression that required reoperation. Intramedullary nail fixation of pathologic humeral shaft fractures achieves rates of union parallel to those seen with fixation in a healthy population. The length of follow-up in the current study was longer than the life expectancy reported by previous authors, which can be attributed to improvements in the treatment of cancer. The current authors argue that unreamed fixation is the optimal technique because it yields similar outcomes to a reamed approach and is faster and potentially safer. [Orthopedics. 2020;43(5):e389-e398.].

A Method of Correcting Estimation Failure in Latent Differential Equations with Comparisons to Kalman Filtering.

Studies have used the latent differential equation (LDE) model to estimate the parameters of damped oscillation in various phenomena, but it has been shown that correct, non-zero parameter estimates are only obtained when the latent series exhibits little or no process noise. Consequently, LDEs are limited to modeling deterministic processes with measurement error rather than those with random behavior in the true latent state. The reasons for these limitations are considered, and a piecewise deterministic approximation (PDA) algorithm is proposed to treat process noise outliers as functional discontinuities and obtain correct estimates of the damping parameter. Comprehensive, random-effects simulations were used to compare results with those obtained using a state-space model (SSM) based on the Kalman filter. The LDE with the PDA algorithm (LDEPDA) successfully recovered the simulated damping parameter under a variety of conditions when process noise was present in the latent state. The LDEPDA had greater precision and accuracy than the SSM when estimating parameters from data with sparse jump discontinuities, but worse performance for diffusion processes overall. All three methods were applied to a sample of postural sway data. The basic LDE estimated zero damping, while the LDEPDA and SSM estimated moderate to high damping. The SSM estimated the smallest standard errors for both frequency and damping parameter estimates.

dynr.mi: An R Program for Multiple Imputation in Dynamic Modeling.

Assessing several individuals intensively over time yields intensive longitudinal data (ILD). Even though ILD provide rich information, they also bring other data analytic challenges. One of these is the increased occurrence of missingness with increased study length, possibly under non-ignorable missingness scenarios. Multiple imputation (MI) handles missing data by creating several imputed data sets, and pooling the estimation results across imputed data sets to yield final estimates for inferential purposes. In this article, we introduce dynr.mi(), a function in the R package, Dynamic Modeling in R (dynr). The package dynr provides a suite of fast and accessible functions for estimating and visualizing the results from fitting linear and nonlinear dynamic systems models in discrete as well as continuous time. By integrating the estimation functions in dynr and the MI procedures available from the R package, Multivariate Imputation by Chained Equations (MICE), the dynr.mi() routine is designed to handle possibly non-ignorable missingness in the dependent variables and/or covariates in a user-specified dynamic systems model via MI, with convergence diagnostic check. We utilized dynr.mi() to examine, in the context of a vector autoregressive model, the relationships among individuals' ambulatory physiological measures, and self-report affect valence and arousal. The results from MI were compared to those from listwise deletion of entries with missingness in the covariates. When we determined the number of iterations based on the convergence diagnostics available from dynr.mi(), differences in the statistical significance of the covariate parameters were observed between the listwise deletion and MI approaches. These results underscore the importance of considering diagnostic information in the implementation of MI procedures.

Responding to a 100-Year-Old Challenge from Fisher: A Biometrical Analysis of Adult Height in the NLSY Data Using Only Cousin Pairs.

In 1918, Fisher suggested that his research team had consistently found inflated cousin correlations. He also commented that because a cousin sample with minimal selection bias was not available the cause of the inflation could not be addressed, leaving this inflation as a challenge still to be solved. In the National Longitudinal Survey of Youth (the NLSY79, the NLSY97, and the NLSY-Children/Young Adult datasets), there are thousands of available cousin pairs. Those in the NLSYC/YA are obtained approximately without selection. In this paper, we address Fisher's challenge using these data. Further, we also evaluate the possibility of fitting ACE models using only cousin pairs, including full cousins, half-cousins, and quarter-cousins. To have any chance at success in such a restricted kinship domain requires an available and highly-reliable phenotype; we use adult height in our analysis. Results provide a possible answer to Fisher's challenge, and demonstrate the potential for using cousin pairs in a stand-alone analysis (as well as in combination with other biometrical designs).

What's for dynr: A Package for Linear and Nonlinear Dynamic Modeling in R.

Intensive longitudinal data in the behavioral sciences are often noisy, multivariate in nature, and may involve multiple units undergoing regime switches by showing discontinuities interspersed with continuous dynamics. Despite increasing interest in using linear and nonlinear differential/difference equation models with regime switches, there has been a scarcity of software packages that are fast and freely accessible. We have created an R package called dynr that can handle a broad class of linear and nonlinear discrete- and continuous-time models, with regime-switching properties and linear Gaussian measurement functions, in C, while maintaining simple and easy-to-learn model specification functions in R. We present the mathematical and computational bases used by the dynr R package, and present two illustrative examples to demonstrate the unique features of dynr.