Multiple imputation using Stata's -mi- command

Stata's -mi- command can be used to perform multiple-imputation analysis, including imputation, data management, and estimation. -mi impute- provides a number of univariate and multivariate imputation methods, including MVN data augmentation. -mi estimate- combines the estimation and pooling steps of the multiple-imputation procedure into one easy step. -mi- also provides an extensive ability to manage multiply-imputed data. I will give a brief overview of all of -mi-'s capabilities with emphasis on -mi impute- and -mi estimate-, and will also demonstrate examples of some of mi's unique data management features.

Semiparametric analysis of case-control genetic data in the presence of environmental factors

In the past decade, many statistical methods have been proposed for the analysis of case–control genetic data with an emphasis on haplotype-based disease association studies. Most of the methodology has concentrated on the estimation of genetic (haplotype) main effects. Most methods accounted for environmental and gene-environment interaction effects by utilizing prospective-type analyses that may lead to biased estimates when used with case–control data. Several recent publications addressed the issue of retrospective sampling in the analysis of case–control genetic data in the presence of environmental factors by developing new efficient semiparametric statistical methods. I present the new Stata command, haplologit, that implements efficient profile-likelihood semiparametric methods for fitting gene–environment models in the very important special cases of a) a rare disease, b) a single candidate gene in Hardy-Weinberg equilibrium, and c) independence of genetic and environmental factors.

Power analysis and sample-size determination in survival models with the new stpower command

Power analysis and sample-size determination are important components of a study design. In survival analysis, the power is directly related to the number of events observed in the study. The required sample size is therefore determined by the observed number of events. Survival data are commonly analyzed using the log-rank test or the Cox proportional hazards model. Stata 10’s new stpower command provides sample-size and power calculations for survival studies that use the log-rank test, the Cox proportional hazards model, and the parametric test comparing exponential hazard rates. It reports the number of events that must be observed in the study and accommodates unequal subject allocation between groups, nonuniform subject entry, and exponential losses to follow-up. This talk will demonstrate power, sample-size, and effect-size computations for different methods used to analyze survival data and for designs with recruitment periods and random censoring (administrative and loss to follow-up). It will also discuss building customized tables and producing graphs of power curves.

Haplotype analysis of case-control data using haplologit: New features

In haplotype-association studies, the risk of a disease is often determined not only by the presence of certain haplotypes but also by their interactions with various environmental factors. The detection of such interactions with case-control data is a challenging task and often requires very large samples. This prompted the development of more efficient estimation methods for analyzing case-control genetic data. The haplologit command implements efficient semiparametric methods, recently proposed in the literature, for fitting haplotype-environment models in the very important special cases of 1) a rare disease, 2) a single candidate gene in Hardy-Weinberg equilibrium, and 3) independence of genetic and environmental factors. In this presentation, I will describe new features of the haplologit command.

Chained equations and more in multiple imputation in Stata 12

I present the new Stata 12 command, mi impute chained, to perform multivariate imputation using chained equations (ICE), also known as sequential regression imputation. ICE is a flexible imputation technique for imputing various types of data. The variable-by-variable specification of ICE allows you to impute variables of different types by choosing the appropriate method for each variable from several univariate imputation methods. Variables can have an arbitrary missing-data pattern. By specifying a separate model for each variable, you can incorporate certain important characteristics, such as ranges and restrictions within a subset, specific to each variable. I also describe other new features in multiple imputation in Stata 12.

Multivariate Skew-t Distributions in Econometrics and Environmetrics

This dissertation is composed of three articles describing novel approaches for analysis and modeling using multivariate skew-normal and skew-t distributions in econometrics and environmetrics. In the first article we introduce the Heckman selection-t model. Sample selection arises often as a result of the partial observability of the outcome of interest in a study. In the presence of sample selection, the observed data do not represent a random sample from the population, even after controlling for explanatory variables. Heckman introduced a sample-selection model to analyze such data and proposed a full maximum likelihood estimation method under the assumption of normality. The method was criticized in the literature because of its sensitivity to the normality assumption. In practice, data, such as income or expenditure data, often violate the normality assumption because of heavier tails. We first establish a new link between sample-selection models and recently studied families of extended skew-elliptical distributions. This then allows us to introduce a selection-t model, which models the error distribution using a Student’s t distribution. We study its properties and investigate the finite-sample performance of the maximum likelihood estimators for this model. We compare the performance of the selection-t model to the Heckman selection model and apply it to analyze ambulatory expenditures. In the second article we introduce a family of multivariate log-skew-elliptical distributions, extending the list of multivariate distributions with positive support. We investigate their probabilistic properties such as stochastic representations, marginal and conditional distributions, and existence of moments, as well as inferential properties. We demonstrate, for example, that as for the log-t distribution, the positive moments of the log-skew-t distribution do not exist. Our emphasis is on two special cases, the log-skew-normal and log-skew-t distributions, which we use to analyze U.S. precipitation data. Many commonly used statistical methods assume that data are normally distributed. This assumption is often violated in practice which prompted the development of more flexible distributions. In the third article we describe two such multivariate distributions, the skew-normal and the skew-t, and present commands for fitting univariate and multivariate skew-normal and skew-t regressions in the statistical software package Stata

An Introduction to Survival Analysis Using Stata

An Introduction to Survival Analysis Using Stata, Third Edition is the ideal tutorial for professional data analysts who want to learn survival analysis for the first time or who are well versed in survival analysis but are not as dexterous in using Stata to analyze survival data. This text also serves as a valuable reference to those readers who already have experience using Stata’s survival analysis routines. The third edition has been updated for Stata 11, and it includes a new chapter on competing-risks analysis. This chapter describes the problems posed by competing events (events that impede the failure event of interest), and covers estimation of cause-specific hazards and cumulative incidence functions. Other enhancements include the handling of missing values by multiple imputation in Cox regression, a new-to-Stata-11 system for specifying categorical (factor) variables and their interactions, three additional diagnostic measures for Cox regression, and a more efficient syntax for obtaining predictions and diagnostics after Cox regression.Stata, survival analysis, hazard models

Features of Applying the Right to Suspension or Complete/ Partial Refusal to Fulfill a Duty in Case of Non-Fulfilment of the Counter Duty by the Other Party According to the Civil Legislation of Ukraine

The purpose of the research is to determine the scope and mechanism of applying the creditor’s right to suspension or partial/complete refusal to perform a duty in case of non-fulfilment of the counter duty by the debtor (obligations the specified right is applied to and the mechanism of its implementation).  Main content. The legal nature of the right to suspension or complete/partial refusal to fulfil a duty has been clarified: the author concluded that this is a way to protect civil rights and interests. Attention is drawn to the fact that the studied right can be applied in case of non-fulfilment of a negative obligation by the other party. Conclusions. The right to suspension or refusal to fulfil a duty shall be applied to obligations when their fulfilment is not simultaneous, but a certain procedure for performing parties’ duties is provided for. This right can be applied in case of other party’s non-fulfilment of its main duty within the obligation, non-fulfilment of an auxiliary duty does not entitle the other party to suspend or refuse its duty