Statistical Models Commons^™

Nbr2 Stata Ado-Do Files, Joseph Hilbe

Joseph M Hilbe

No abstract provided.

Comparing Paired Vs. Non-Paired Statistical Methods Of Analyses When Making Inferences About Absolute Risk Reductions In Propensity-Score Matched Samples., Peter C. Austin

Peter Austin

Propensity-score matching allows one to reduce the effects of treatment-selection bias or confounding when estimating the effects of treatments when using observational data. Some authors have suggested that methods of inference appropriate for independent samples can be used for assessing the statistical significance of treatment effects when using propensity-score matching. Indeed, many authors in the applied medical literature use methods for independent samples when making inferences about treatment effects using propensity-score matched samples. Dichotomous outcomes are common in healthcare research. In this study, we used Monte Carlo simulations to examine the effect on inferences about risk differences (or absolute risk …

Optimal Caliper Widths For Propensity-Score Matching When Estimating Differences In Means And Differences In Proportions In Observational Studies., Peter C. Austin

Peter Austin

In a study comparing the effects of two treatments, the propensity score is the probability of assignment to one treatment conditional on a subject's measured baseline covariates. Propensity-score matching is increasingly being used to estimate the effects of exposures using observational data. In the most common implementation of propensity-score matching, pairs of treated and untreated subjects are formed whose propensity scores differ by at most a pre-specified amount (the caliper width). There has been a little research into the optimal caliper width. We conducted an extensive series of Monte Carlo simulations to determine the optimal caliper width for estimating differences …

A Tutorial And Case Study In Propensity Score Analysis: An Application To Estimating The Effect Of In-Hospital Smoking Cessation Counseling On Mortality, Peter C. Austin

Peter Austin

Propensity score methods allow investigators to estimate causal treatment effects using observational or nonrandomized data. In this article we provide a practical illustration of the appropriate steps in conducting propensity score analyses. For illustrative purposes, we use a sample of current smokers who were discharged alive after being hospitalized with a diagnosis of acute myocardial infarction. The exposure of interest was receipt of smoking cessation counseling prior to hospital discharge and the outcome was mortality with 3 years of hospital discharge. We illustrate the following concepts: first, how to specify the propensity score model; second, how to match treated and …

Errata Negative Binomial Regression 1st Edition 1st Print, Joseph Hilbe

Joseph M Hilbe

Errata for the first edition and printing of Negative Binomal Regression, August 2007. Many of the items listed here were corrected in the 2008 second printing.

Wavelet-Based Functional Linear Mixed Models: An Application To Measurement Error–Corrected Distributed Lag Models, Elizabeth J. Malloy, Jeffrey S. Morris, Sara D. Adar, Helen Suh, Diane R. Gold, Brent A. Coull

Jeffrey S. Morris

Frequently, exposure data are measured over time on a grid of discrete values that collectively define a functional observation. In many applications, researchers are interested in using these measurements as covariates to predict a scalar response in a regression setting, with interest focusing on the most biologically relevant time window of exposure. One example is in panel studies of the health effects of particulate matter (PM), where particle levels are measured over time. In such studies, there are many more values of the functional data than observations in the data set so that regularization of the corresponding functional regression coefficient …

Members’ Discoveries: Fatal Flaws In Cancer Research, Jeffrey S. Morris

Jeffrey S. Morris

A recent article published in The Annals of Applied Statistics (AOAS) by two MD Anderson researchers—Keith Baggerly and Kevin Coombes—dissects results from a highly-influential series of medical papers involving genomics-driven personalized cancer therapy, and outlines a series of simple yet fatal flaws that raises serious questions about the veracity of the original results. Having immediate and strong impact, this paper, along with related work, is providing the impetus for new standards of reproducibility in scientific research.

Statistical Contributions To Proteomic Research, Jeffrey S. Morris, Keith A. Baggerly, Howard B. Gutstein, Kevin R. Coombes

Jeffrey S. Morris

Proteomic profiling has the potential to impact the diagnosis, prognosis, and treatment of various diseases. A number of different proteomic technologies are available that allow us to look at many proteins at once, and all of them yield complex data that raise significant quantitative challenges. Inadequate attention to these quantitative issues can prevent these studies from achieving their desired goals, and can even lead to invalid results. In this chapter, we describe various ways the involvement of statisticians or other quantitative scientists in the study team can contribute to the success of proteomic research, and we outline some of the …

Informatics And Statistics For Analyzing 2-D Gel Electrophoresis Images, Andrew W. Dowsey, Jeffrey S. Morris, Howard G. Gutstein, Guang Z. Yang

Jeffrey S. Morris

Whilst recent progress in ‘shotgun’ peptide separation by integrated liquid chromatography and mass spectrometry (LC/MS) has enabled its use as a sensitive analytical technique, proteome coverage and reproducibility is still limited and obtaining enough replicate runs for biomarker discovery is a challenge. For these reasons, recent research demonstrates the continuing need for protein separation by two-dimensional gel electrophoresis (2-DE). However, with traditional 2-DE informatics, the digitized images are reduced to symbolic data though spot detection and quantification before proteins are compared for differential expression by spot matching. Recently, a more robust and automated paradigm has emerged where gels are directly …

Bayesian Random Segmentationmodels To Identify Shared Copy Number Aberrations For Array Cgh Data, Veerabhadran Baladandayuthapani, Yuan Ji, Rajesh Talluri, Luis E. Nieto-Barajas, Jeffrey S. Morris

Jeffrey S. Morris

Array-based comparative genomic hybridization (aCGH) is a high-resolution high-throughput technique for studying the genetic basis of cancer. The resulting data consists of log fluorescence ratios as a function of the genomic DNA location and provides a cytogenetic representation of the relative DNA copy number variation. Analysis of such data typically involves estimation of the underlying copy number state at each location and segmenting regions of DNA with similar copy number states. Most current methods proceed by modeling a single sample/array at a time, and thus fail to borrow strength across multiple samples to infer shared regions of copy number aberrations. …

Participation And Engagement In Sport: A Double Hurdle Approach For The United Kingdom, Babatunde Buraimo, Brad Humphreys, Rob Simmons

Dr Babatunde Buraimo

This paper uses pooled cross-section data from four waves of the United Kingdom’s Taking Part Survey, 2005 to 2009, in order to investigate determinants of probability of participation and levels of engagement in sports. The two rival modelling approaches considered here are the double-hurdle approach and the Heckman sample selection model. The Heckman model proves to be deficient in several key respects. The double-hurdle approach offers more reliable estimates than the Heckman sample selection model, at least for this particular survey. The distinction is more than just statistical nuance as there are substantive differences in qualitative results from the two …

Creation Of Synthetic Discrete Response Regression Models, Joseph Hilbe

Joseph M Hilbe

The development and use of synthetic regression models has proven to assist statisticians in better understanding bias in data, as well as how to best interpret various statistics associated with a modeling situation. In this article I present code that can be easily amended for the creation of synthetic binomial, count, and categorical response models. Parameters may be assigned to any number of predictors (which are shown as continuous, binary, or categorical), negative binomial heterogeneity parameters may be assigned, and the number of levels or cut points and values may be specified for ordered and unordered categorical response models. I …

Statistical Criteria For Selecting The Optimal Number Of Untreated Subjects Matched To Each Treated Subject When Using Many-To-One Matching On The Propensity Score, Peter C. Austin

Peter Austin

Propensity-score matching is increasingly being used to estimate the effects of treatments using observational data. In many-to-one (M:1) matching on the propensity score, M untreated subjects are matched to each treated subject using the propensity score. The authors used Monte Carlo simulations to examine the effect of the choice of M on the statistical performance of matched estimators. They considered matching 1–5 untreated subjects to each treated subject using both nearest-neighbor matching and caliper matching in 96 different scenarios. Increasing the number of untreated subjects matched to each treated subject tended to increase the bias in the estimated treatment effect; …

The Performance Of Different Propensity-Score Methods For Estimating Differences In Proportions (Risk Differences Or Absolute Risk Reductions) In Observational Studies, Peter C. Austin

Peter Austin

Propensity score methods are increasingly being used to estimate the effects of treatments on health outcomes using observational data. There are four methods for using the propensity score to estimate treatment effects: covariate adjustment using the propensity score, stratification on the propensity score, propensity-score matching, and inverse probability of treatment weighting (IPTW) using the propensity score. When outcomes are binary, the effect of treatment on the outcome can be described using odds ratios, relative risks, risk differences, or the number needed to treat. Several clinical commentators suggested that risk differences and numbers needed to treat are more meaningful for clinical …

Modeling Future Record Performances In Athletics, Joseph Hilbe

Joseph M Hilbe

No abstract provided.

Lrm Revision To Ch 2.1, Joseph Hilbe

Joseph M Hilbe

Rewording of part Ch 2.1 of Logistic Regression Models

Logistic Regression Using R, Joseph Hilbe

Joseph M Hilbe

R code and output for examples in Logistic Regression Models, Chapman & Hall/CRC (2009)

Balance Diagnostics For Comparing The Distribution Of Baseline Covariates Between Treatment Groups In Propensity-Score Matched Samples, Peter C. Austin

Peter Austin

The propensity score is a subject’s probability of treatment, conditional on observed baseline covariates. Conditional on the true propensity score, treated and untreated subjects have similar distributions of observed baseline covariates. Propensity-score matching is a popular method of using the propensity score in the medical literature. Using this approach, matched sets of treated and untreated subjects with similar values of the propensity score are formed. Inferences about treatment effect made using propensity-score matching are valid only if, in the matched sample, treated and untreated subjects have similar distributions of measured baseline covariates. In this paper we discuss the following methods …

Gone In 60 Seconds: The Absorption Of News In A High-Frequency Betting Market, Babatunde Buraimo, David Peel, Rob Simmons

Dr Babatunde Buraimo

This paper tests for efficiency in a betting market that offers high-frequency data, the Betfair betting exchange for wagering on outcomes of English Premier League soccer matches. We find clear evidence of rapid adjustment of prices to large disturbances (news). Full adjustment takes place within a one minute interval after the news. This suggests that this particular wagering market is not just efficient at pre-match prices but is also efficient in the face of events within games.

The Black Swan: Praise And Criticism, Peter H. Westfall, Joseph M. Hilbe

Joseph M Hilbe

No abstract provided.

A Review Of Limdep 9.0 And Nlogit 4.0, Joseph Hilbe

Joseph M Hilbe

No abstract provided.

Mathematica 5.2: A Review, Joseph Hilbe

Joseph M Hilbe

No abstract provided.

A Review Of Stata 9.0, Joseph Hilbe

Joseph M Hilbe

No abstract provided.

Stochastic Convergence Among European Economies, Mauro Costantini, Claudio Lupi

Claudio Lupi

The aim of this paper is to test the stochastic convergence in real per capita GDP for 15 European countries using non−stationary panel data approaches over the period 1950−2003. Cross−sectional dependence is assumed due to the existence of strong linkages among European economies. However, tests derived under the assumption of cross−sectional independence are also carried out for completeness and comparison. We also split the whole sample into two sub−periods (1950−1976, 1977−2003) in order to take into account the effects of the first oil crisis (1973−1974) and to evaluate the robustness of the statistical analysis. Our results offer little support to …

Unemployment Scarring In High Unemployment Regions, Claudio Lupi, Patrizia Ordine

Claudio Lupi

This paper investigates the effect of individual unemployment experiences on re-employment wages. The empirical analysis is carried out on a panel of Italian individuals. The main result is that while in the northern regions the effect is similar to the one estimated for the UK, in the southern area of the country the impact is not significant. We link this result to the particular socio-economic environment in which the unemployment spells are experienced. We argue that this might be due to the fact that in a high unemployment environment individual unemployment experiences are perceived as "normal" and do not necessarily …

Testing For Asymmetry In Economic Time Series Using Bootstrap Methods, Claudio Lupi, Patrizia Ordine

Claudio Lupi

In this paper we show that phase-scrambling bootstrap offers a natural framework for asymmetry testing in economic time series. A comparison with other bootstrap schemes is also sketched. A Monte Carlo analysis is carried out to evaluate the size and power properties of the phase-scrambling bootstrap-based test.

Poicen.Sas : Censored Poisson Regression, Joseph Hilbe, Gordon Johnston

Joseph M Hilbe

SAS Macro to estimate censored Poisson data, using method of Hilbe. See Hilbe, Joseph M (2011), Negative Binomial Regression, 2nd ed (Cambridge University Press)

Generalized Linear Models: Software Implementation And The Structure Of A General Power-Link Based Glm Algorithm, Joseph Hilbe

Joseph M Hilbe

Generalized linear modeling (GLM) is currently undergoing a renaissance. The number of software packages offering GLM capability grows each year and as a partial consequence one finds an increased number of research endeavors being modeled using GLM methodology. On the other hand, there have likewise been an increasing number of requests to vendors by users of statistical packages to include GLM facilities amid other offerings. The overall effect has been a near 300 percent increase in GLM programs over the past four years.

I shall discuss the nature of generalized linear models followed by an examination of how they have …

Direct Tests Of The Rational Expectations Hypothesis: A Study Of Italian Entrepreneurs’ Inflationary Expectations (1980-1988), Claudio Lupi

Claudio Lupi

The primary concern of this paper is to test the rational expectations hypothesis for Italian entrepreneurs' inflationary expectations between 1980 and 1988 using monthly observed expectations. Particular care is devoted to analyzing the problems arising when multiperiod expectations and a nonwhite noise measurement error in the expectations series are considered. The empirical analysis is carried out using cross correlations on ARIMA residuals and transfer function models. This technique seems to be particularly appealing for rationality testing.

The Pseudo-Problem Of Induction, Joseph Hilbe

Joseph M Hilbe

Paper I delivered at the IVth International Congress for Logic, Methodology, and Philosophy of Science held in Bucharest, Romania in 1971.