Preliminary Cost Variance Modeling to Compare Autologous Intraovarian Platelet-Rich Plasma vs. Standard Hormone Replacement Therapy for Menopause Management

Background: Menopause symptoms and hormone replacement therapy (HRT) are among the most common reasons patients seek gynecological advice. Although at least half of all women in developed countries will take HRT during their lifetime, the treatment is not without risk and guidance on HRT is mixed. Greater awareness of negative HRT health effects from extended use has piqued interest in ‘safer options’. Menopause reversal with autologous ovarian platelet-rich plasma (OPRP) has brought this restorative approach forward for consideration, but appropriateness and cost-effectiveness require examination. Methods: HRT and OPRP data from USA were projected to compare cumulative 1yr patient costs using stochastic Monte Carlo modeling. Results: Mean±SD cost-to-patient for HRT including initial consult plus pharmacy refills was estimated at about $576±246/yr. While OPRP included no pharmacy component, an estimated 4 visits over 1yr for OPRP maintenance entailed ultrasound, phlebotomy/sample processing, surgery equipment, and incubation/laboratory expense, yielding mean±SD cost for OPRP at $8,710±4,911/yr (p<0.0001 vs. HRT, by t-test). Upper-bound estimates for annual HRT and OPRP costs were $1,341 and $22,232, respectively. Conclusions: While HRT and OPRP may have similar efficacy and safety for menopause therapy, they diverge sharply in cost-effectiveness. Most patients would likely find OPRP too complex, invasive, and expensive to be competitive vs. HRT. Although OPRP is an interesting and cautiously useful technique for selected menopause patients reluctant to use HRT, repurposing this infertility treatment for wider use appears inefficient compared to standard HRT currently available.

Putting Variation into Variance: Modeling Between-Study Heterogeneity in Meta-Analysis

We shed much needed light upon a critical assumption that is oft-overlooked---or not considered at all---in random-effects meta-analysis.Namely, that between-study variance is constant across \emph{all} studies which implies they are from the \emph{same} population. Yet it is not hard to imagine a situation where there are several and not merely one population of studies, perhaps differing in their between-study variance (i.e., heteroskedasticity). The objective is to then make inference, given that there are variations in heterogeneity. There is an immediate problem, however, in that modeling heterogeneous variance components is not straightforward to do in a general way. To this end, we propose novel methodology, termed Bayesian location-scale meta-analysis, that can accommodate moderators for both the overall effect (location) and the between-study variance (scale). After introducing the model, we then extend heterogeneity statistics, prediction intervals, and hierarchical shrinkage, all of which customarily assume constant heterogeneity, to include variations therein. With these new tools in hand, we go to work demonstrating that quite literally \emph{everything} changes when between-study variance is not constant across studies. The changes were not small and easily passed the interocular trauma test---the importance hits right between the eyes. Such examples include (but are not limited to) inference on the overall effect, a compromised predictive distribution, and improper shrinkage of the study-specific effects. Further, we provide an illustrative example where heterogeneity was not considered a mere nuisance to show that modeling variance for its own sake can provide unique inferences, in this case into discrimination across nine countries. The discussion includes several ideas for future research. We have implemented the proposed methodology in the {\tt R} package \textbf{blsmeta}.

Realized Variance Modeling: Decoupling Forecasting from Estimation*

This article evaluates the in-sample fit and out-of-sample forecasts of various combinations of realized variance models and functions delivering estimates (estimation criteria). Our empirical findings highlight that: independently of the econometrician’s forecasting loss (FL) function, certain estimation criteria perform significantly better than others; the simple ARMA modeling of the log realized variance generates superior forecasts than the Heterogeneous Autoregressive (HAR) family, for any of the FL functions considered; the (2, 1) parameterizations with negative lag-2 coefficient emerge as the benchmark specifications generating the best forecasts and approximating long-range dependence as does the HAR family.

The Impact of Organizational Citizenship Behaviors on Employee Performance

The aim of this research is to test the relationship and impact of organizational citizenship behaviors as a separate variable in the performance of employees as a response variable at the headquarters of the Ministry of Water Resources centrally funded in Baghdad governorate. Based on the importance of the subject of research in public organizations and the importance of organizations investigated for the society, the analytical descriptive approach was adopted in the completion of this research. The research included all of the ministry except the certificates of intermediate and endowment. The data were collected from 255 respondents who represent the research sample exclusively and comprehensively. By adopting the questionnaire which included (75) paragraphs, Personal interviews and field observations were used as assistance tools in their collection. The study also adopted the program (Amos V.18, Spss V.21) with the adoption of methods of descriptive statistics (natural distribution test, global empirical analysis, variance modeling, arithmetic mean, percentages, standard deviation, relative importance, diffraction coefficient, Pearson correlation coefficient , And the simple regression coefficient) to test its hypotheses. The main findings of the research, which showed the validity of the hypotheses, show that the behavior of citizenship in the Ministry of Water Resources headquarters affects directly and increase this effect indirectly in the performance of workers.

Process and Variance Modeling

Models are an important component of research design that serve as intermediaries between theories and data, often directing decisions about methods and statistics. This article discusses the basic differences and assumptions associated with process and variance models as a way of introducing the four articles contained within this special issue of Family Business Review on “Process and Variance Methods.” Specifically, we highlight three key issues regarding modeling—time and causality, measurement and operationalization, and model specification—making specific ties to the challenges often associated with family business research.