scholarly journals Randomization Inference with Stata: A Guide and Software

2017 ◽  
Vol 17 (3) ◽  
pp. 630-651 ◽  
Author(s):  
Simon Heß
Keyword(s):  
Social Sciences ◽  
Laboratory Experiments ◽  
Applied Economics ◽  
Small Samples ◽  
Randomization Tests ◽  
P Values ◽  
Software Packages ◽  
Individual Program ◽  
Randomization Inference ◽  
Economic Journal

Randomization inference or permutation tests are only sporadically used in economics and other social sciences—this despite a steep increase in randomization in field and laboratory experiments that provide perfect experimental setups for applying randomization inference. In the context of causal inference, such tests can handle problems often faced by applied researchers, including issues arising in the context of small samples, stratified or clustered treatment assignments, or nonstandard randomization techniques. Standard statistical software packages have either no implementation of randomization tests or very basic implementations. Whenever researchers use randomization inference, they regularly code individual program routines, risking inconsistencies and coding mistakes. In this article, I show how randomization inference can best be conducted in Stata and introduce a new command, ritest, to simplify such analyses. I illustrate this approach's usefulness by replicating the results in Fujiwara and Wantchekon (2013, American Economic Journal: Applied Economics 5: 241–255) and running simulations. The applications cover clustered and stratified assignments, with varying cluster sizes, pairwise randomization, and the computation of nonapproximate p-values. The applications also touch upon joint hypothesis testing with randomization inference.

MS2/Cardio: Towards a Multi-Service Medical Software for Cardiology

1992 ◽  
Vol 31 (01) ◽  
pp. 18-28 ◽  
Author(s):  
C. Combi ◽  
G. Pozzi ◽  
R. Rossi ◽  
F. Pinciroli
Keyword(s):  
Statistical Processing ◽  
Clinical Information ◽  
Daily Practice ◽  
Management Unit ◽  
Record Management ◽  
Medical Software ◽  
Software Packages ◽  
Ambulatory Patients ◽  
Patient Database ◽  
Individual Program

Abstract:Many clinics are interested to use software packages in daily practice, but lack of integration of such packages seriously limits their scope. In practice this often entails switching between programs and interrupting the run of an individual program. A multi-task approach would not solve this problem as it would not eliminate the need to input the same data many times, as often occurs when using separate packages. The construction of a Multi-Service Medical Software package (MSx2) is described, which was also developed as an example of practical integration of some clinically relevant functions. The package runs on a personal computer in an MS-DOS environment and integrates a time-oriented medical record management unit (TOMRU) for data of ambulatory patients, and a drug information management unit (DIMU) concerning posology, content, effects, and possible interactions. Of the possible database configurations allowed by MSx2, the cardiology patient database (MSx2/C) and hypertensive patient database (MSx2/H) were developed and described here. Clinical information to be included in the configurations was obtained after discussion and consensus of clinical practitioners. MSx2/C was distributed to several hundred clinical centers during computerized courses to train future users. MSx2 can easily transfer patient data to statistical processing packages.

scholarly journals RL-SKAT: An exact and efficient score test for heritability and set tests

2017 ◽  
Author(s):  
Regev Schweiger ◽  
Omer Weissbrod ◽  
Elior Rahmani ◽  
Martina Müller-Nurasyid ◽  
Sonja Kunze ◽  
...  
Keyword(s):  
Variance Component ◽  
Association Studies ◽  
Score Test ◽  
Small Samples ◽  
Phenotypic Variance ◽  
Test Statistic ◽  
Entire Genome ◽  
P Values ◽  
Continuous Response ◽  
Efficient Score Test

AbstractTesting for the existence of variance components in linear mixed models is a fundamental task in many applicative fields. In statistical genetics, the score test has recently become instrumental in the task of testing an association between a set of genetic markers and a phenotype. With few markers, this amounts to set-based variance component tests, which attempt to increase power in association studies by aggregating weak individual effects. When the entire genome is considered, it allows testing for the heritability of a phenotype, defined as the proportion of phenotypic variance explained by genetics. In the popular score-based Sequence Kernel Association Test (SKAT) method, the assumed distribution of the score test statistic is uncalibrated in small samples, with a correction being computationally expensive. This may cause severe inflation or deflation of p-values, even when the null hypothesis is true. Here, we characterize the conditions under which this discrepancy holds, and show it may occur also in large real datasets, such as a dataset from the Wellcome Trust Case Control Consortium 2 (n=13,950) study, and in particular when the individuals in the sample are unrelated. In these cases the SKAT approximation tends to be highly over-conservative and therefore underpowered. To address this limitation, we suggest an efficient method to calculate exact p-values for the score test in the case of a single variance component and a continuous response vector, which can speed up the analysis by orders of magnitude. Our results enable fast and accurate application of the score test in heritability and in set-based association tests. Our method is available in http://github.com/cozygene/RL-SKAT.

Randomization tests for small samples: an application for genetic expression data

2003 ◽  
Vol 52 (3) ◽  
pp. 365-376 ◽  
Author(s):  
Gary L. Gadbury ◽  
Grier P. Page ◽  
Moonseong Heo ◽  
John D. Mountz ◽  
David B. Allison
Keyword(s):  
Small Samples ◽  
Expression Data ◽  
Randomization Tests ◽  
Genetic Expression

scholarly journals Structural Equation Modeling (SEM)-Based Meta-Analysis

2021 ◽  
Author(s):  
Mike W.-L. Cheung
Keyword(s):  
Social Sciences ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Meta Analysis ◽  
Equation Model ◽  
Equation Modeling ◽  
Future Directions ◽  
Software Packages ◽  
Meta Analyses

Structural equation modeling (SEM) and meta-analysis are two popular techniques in the behavioral, medical, and social sciences. They have their own research communities, terminologies, models, software packages, and even journals. This chapter introduces SEM-based meta-analysis, an approach to conduct meta-analyses using the SEM framework. By conceptualizing studies in a meta-analysis as subjects in a structural equation model, univariate, multivariate, and three-level meta-analyses can be fitted as structural equation models using definition variables. We will review fixed-, random-, and mixed-effects models using the SEM framework. Examples will be used to illustrate the procedures using the metaSEM and OpenMx packages in R. This chapter closes with a discussion of some future directions for research.

scholarly journals Geographic Information Systems and Applied Economics: An Initial Discussion of Potential Applications and Contributions

1994 ◽  
Vol 23 (2) ◽  
pp. 140-149 ◽  
Author(s):  
Richard Taupier ◽  
Cleve Willis
Keyword(s):  
Social Sciences ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Geographic Information ◽  
Applied Economics ◽  
Gis Technology ◽  
Potential Applications

Geographic Information Systems (GIS) are becoming increasingly important to virtually all of the natural and social sciences. Applied economists will find that GIS can make valuable contributions to many of the problems with which they are concerned. Moreover, a great deal of the science behind GIS technology would benefit from the contributions of applied economists. This paper presents some initial suggestions for the ways in which GIS may be important to economics and the GIS related issues concerning which applied economists could provide useful insights.

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