Factors affecting clinical pregnancy rates after IUI for the treatment of unexplained infertility and mild male subfertility

Objective: The aim of the present retrospective study was to evaluate intrauterine insemination (IUI) clinical experiences and to define the variables for predicting success. Material and Methods: The present study was an observational trial performed in a private IVF center on subfertile couples who had applied for treatment between 2002 and 2012, in which the data of 503 IUI cases were retrospectively reviewed. Couples who had been diagnosed with unexplained and mild male subfertility were included. The primary outcome measure was the clinical pregnancy rate in an attempt to form a predictive model for the odds of a clinical pregnancy. Recorded parameters were used to determine the prediction model. Results: Utilizing univariate logistic regression analysis, clinical pregnancy was positively associated with the duration of infertility (OR=1.09, p=0.089), secondary infertility (OR=1.77, p=0.050), and +4 sperm motility after preparation (OR=1.03, p=0.091). Following an adjustment analysis involving a multivariate logistic regression, clinical pregnancy was still found to positively associate with secondary infertility (OR=2.51, p=0.008). Conclusion: IUI success in secondary infertile couples who were in the unexplained infertility and mild male subfertility groups was higher than that in primary infertile couples, and the chances of pregnancy increased as sperm numbers with +4 motility increased. It is difficult to concomitantly evaluate all these parameters and to determine a predictive parameter in IUI independent from other factors.Publisher's Versio

Bayesian Unit Root Test İn Stochastic Volatility Models With Correlated Errors And Autoregressive Model Of Order P

The purpose of this project is to develop a Bayesian Markov Chain Monte Carlo (MCMC) based unit-root testing procedure to test for nonstationarity in the extended Stochastic Volatility (eSV) Model which we describe below. A basic SV (bSV) model specifies that i) conditional mean equation is modeled as a nonlinear stochastic function of unobserved volatilities, ii) the logarithm of the unobserved volatility follows a log-normal autoregressive model of order 1, iii) the innovations in the volatility model and the innovations in the conditional mean equation are independent. The bSV model has been used for many financial series such as stock indices and exchange rates. In the presence of nonstationary volatility, the past shocks on the current volatility remain persistent and this creates an uncertain business environment. Frequentist and Bayesian unit root tests have been developed to test for nonstationarity of volatility in bSV models (see 2.a). However it has been observed that bSV is too restrictive for many financial series. We extend the model (eSV) to allow i) higher order log-normal autoregressive model for conditional volatility, ii) leverage effect which means nonzero correlation between the innovations in the volatility model and innovations in the conditional mean equation. This project will develop a Bayesian MCMC unit root testing methodology in regard to testing nonstationarity of volatilities in eSV. Also we will design a Monte Carlo sampling experiment to evaluate the performances of unit root tests with bSV and eSV in the presence of model misspecifications. This project will extend the Bayesian MCMC unit root testing procedure (2008) to the eSV model that allows correlated errors and a general autoregressive model for log volatility. Already high dimension of the parameter space in bSV will be higher with the join of the parameter representing the leverage effect and the parameters of the higher order autoregressive model for volatilities in eSV. The project will develop an MCMC algorithm to overcome the above mentioned curse of dimensionality in estimating the model parameters. The project will uncover how sensitive the Bayesian approach for unit root testing in this model to the prior belief about the covariance matrix of the mean and volatility innovations and amount of test's robustness to misspecified autoregressive models. It will also assess the performance of the test by estimating the likelihood of false negatives and false positives by designing an extensive Monte Carlo experiment

Analysis of correlated circular and extremal data with a flexible cylindrical distribution

In this article, we introduce a flexible cylindrical distribution for modeling and analysis of dependent extremal and directional observations. The distribution can be used to investigate the connection between two related phenomena, such as the daily fastest wind speed and its direction. The proposed model is applicable for the analysis of a wide variety of cylindrical data, including datasets with asymmetrically distributed directional observations. The model enjoys the advantages of interpretable model parameters, known marginal and conditional distributions, and a practical test for independence. Our simulation study shows that maximum likelihood estimators of the model parameters maintain desired finite sample properties. The distribution is then used to characterize the joint behavior of atmospheric variables in the context of wildfires or bushfires

Performances of Bayesian model selection criteria for generalized linear models with non-ignorably missing covariates

This article deals with model comparison as an essential part of generalized linear modelling in the presence of covariates missing not at random (MNAR). We provide an evaluation of the performances of some of the popular model selection criteria, particularly of deviance information criterion (DIC) and weighted L (WL) measure, for comparison among a set of candidate MNAR models. In addition, we seek to provide deviance and quadratic loss-based model selection criteria with alternative penalty terms targeting directly the MNAR models. This work is motivated by the need in the literature to understand the performances of these important model selection criteria for comparison among a set of MNAR models. A Monte Carlo simulation experiment is designed to assess the finite sample performances of these model selection criteria in the context of interest under different scenarios for missingness amounts. Some naturally driven DIC and WL extensions are also discussed and evaluated

Modeling mixed clustered outcome data with missing variables

Projenin amaci asagida siralanan karakteristiklere sahip ve biyolojiden genetige sosyolojiden psikolojiye kadar pek cok alanda karsimiza cikan veri turlerini analiz etmek icin bir model ve yontem gelistirmek. Sozkonusu verilerin ozellikleri 1) Denekler ortak bir takım durumlara birlikte mağruz kaldığı için bu deneklerden elde edilen veriler ilişkili (böylece bu ilişkili denekler birer küme (cluster) oluşturuyorlar), 2) Her deneğe dair birden fazla cevap verisi varr ve bu korelasyonlu cevap değişkenlerinin kimi sürekli kimi kesikli rassal değişken, 3) Çeşitli sebeplerden dolayı bu response verilerden bazilari missing

A joint Bayesian approach for the analysis of response measured at a primary endpoint and longitudinal measurements

Joint mixed modeling is an attractive approach for the analysis of a scalar response measured at a primary endpoint and longitudinal measurements on a covariate. In the standard Bayesian analysis of these models, measurement error variance and the variance/covariance of random effects are a priori modeled independently. The key point is that these variances cannot be assumed independent given the total variation in a response. This article presents a joint Bayesian analysis in which these variance terms are a priori modeled jointly. Simulations illustrate that analysis with multivariate variance prior in general lead to reduced bias (smaller relative bias) and improved efficiency (smaller interquartile range) in the posterior inference compared with the analysis with independent variance priors

A cluster tree based model selection approach for logistic regression classifier

Model selection methods are important to identify the best approximating model. To identify the best meaningful model, purpose of the model should be clearly pre-stated. The focus of this paper is model selection when the modelling purpose is classification. We propose a new model selection approach designed for logistic regression model selection where main modelling purpose is classification. The method is based on the distance between the two clustering trees. We also question and evaluate the performances of conventional model selection methods based on information theory concepts in determining best logistic regression classifier. An extensive simulation study is used to assess the finite sample performances of the cluster tree based and the information theoretic model selection methods. Simulations are adjusted for whether the true model is in the candidate set or not. Results show that the new approach is highly promising. Finally, they are applied to a real data set to select a binary model as a means of classifying the subjects with respect to their risk of breast cancer

Bayesian semiparametric models for nonignorable missing mechanisms in generalized linear models

Semiparametric models provide a more flexible form for modeling the relationship between the response and the explanatory variables. On the other hand in the literature of modeling for the missing variables, canonical form of the probability of the variable being missing (p) is modeled taking a fully parametric approach. Here we consider a regression spline based semiparametric approach to model the missingness mechanism of nonignorably missing covariates. In this model the relationship between the suitable canonical form of p (e.g. probit p) and the missing covariate is modeled through several splines. A Bayesian procedure is developed to efficiently estimate the parameters. A computationally advantageous prior construction is proposed for the parameters of the semiparametric part. A WinBUGS code is constructed to apply Gibbs sampling to obtain the posterior distributions. We show through an extensive Monte Carlo simulation experiment that response model coefficent estimators maintain better (when the true missingness mechanism is nonlinear) or equivalent (when the true missingness mechanism is linear) bias and efficiency properties with the use of proposed semiparametric missingness model compared to the conventional model

A test for detecting etiologic heterogeneity in epidemiological studies

Current statistical methods for analyzing epidemiological data with disease subtype information allow us to acquire knowledge not only for risk factor-disease subtype association but also, on a more profound account, heterogeneity in these associations by multiple disease characteristics (so-called etiologic heterogeneity of the disease). Current interest, particularly in cancer epidemiology, lies in obtaining a valid p-value for testing the hypothesis whether a particular cancer is etiologically heterogeneous. We consider the two-stage logistic regression model along with pseudo-conditional likelihood estimation method and design a testing strategy based on Rao's score test. An extensive Monte Carlo simulation study is carried out, false discovery rate and statistical power of the suggested test are investigated. Simulation results indicate that applying the proposed testing strategy, even a small degree of true etiologic heterogeneity can be recovered with a large statistical power from the sampled data. The strategy is then applied on a breast cancer data set to illustrate its use in practice where there are multiple risk factors and multiple disease characteristics of simultaneous concern

Kucuk hacimli orneklemlerde ikili regresyon model secim kriteri

Bu projede amac,ikili regresyon (İR) analizinin model karsılastırma ve secme asamasinda, ozellikle de kucuk hacimli orneklemlerde kullanılacak, yeni bir yöntem gelistirmektir. AIC gibi yaygın olarak kullanılan bilgi bazli kriterlerin, farkli (dogrusal ve dogrusal olmayan/ sabit etkili veya rassal ya da karma etkili) İR modellerinin karsılastırılması ve uygun olanın secilmesinde ozellikle de orneklem hacminin kucuk oldugu veri kumelerinde, dogru İR modelini yakalamasında problemler oldugu gozlenmistir. Bu projede, coklu İR model seciminde, iki kümelenme arası uzaklık ölçümünü baz alan bir yöntem gelistirilecektir. Yaptıgımız ilk simulasyon deneyleri, onerilen bu yontemin ozellikle de kucuk hacimli orneklerde, halihazirda kullanılan model karsilastirma yontemlerine kiyasla, gercek modele karsi daha duyarli oldugunu gostermistir. Yeni yöntemin asimptotik ve sonlu örneklem özellikleri araştırılacak, elde edilen bulgulara gore, coklu İR modellerinde kullanilacak yeni bir model karsilastirma stratejisi gelistirilecektir. Bu yeni yontemin etki ve performans analizi için kapsamlı bir Monte Carlo simülasyon çalışması yapılacaktır. Son olarak, Türkiye’deki kanser hastası kadınların kanser türleri ve risk faktorleri arasindaki iliskiyi en iyi sekilde yansıtan ve risk tahminlerinde kullanilabilecek bir modelin belirlenmesinde kullanilacaktir