The Existence of Maximum Likelihood Estimates for the Binary Response Logistic Regression Model

The existence of maximum likelihood estimates for the binary response logistic regression model depends on the configuration of the data points in your data set. There are three mutually exclusive and exhaustive categories for the configuration of data points in a data set: Complete Separation, Quasi-Complete Separation, and Overlap. For this paper, a binary response logistic regression model is considered. A 2 x 2 tabular presentation of the data set to be modeled is provided for each of the three categories mentioned above. In addition, the paper will present an example of a data set whose data points have a linear dependency. Both unconditional maximum likelihood estimation (asymptotic inference) and exact conditional estimation (exact inference) will be considered and contrasted in terms of results. The statistical software package SAS will be used for the binary response logistic regression modeling

An Example of How to Write the Statistical Section of a Bioequivalence Study Protocol for FDA Review

This paper provides a detailed example of how one should write the statistical section of a bioequivalence study protocol for FDA review. Three forms of bioequivalence are covered: average bioequivalence (ABE), population bioequivalence (PBE) and individual bioequivalence (IBE). The method of analysis is based on Jones and Kenward (2003) and a modification of their SAS Macro is provided

Adjustment to the McNemar’s Test for the Analysis of Clustered Matched-Pair Data

This paper presents how one can adjust the McNemar’s test for the analysis of clustered matched-pair data. A McNemar’s-like table for K clusters of matched-pair data is used

The Calculation of the 97.5% Upper Confidence Bound: Application to Clustered Binary Data in a Binomial Non-Inferiority Two-Sample Trial.

This paper will discuss the analysis of a cluster randomized binomial non-inferiority two-sample trial. The determination of the intra-cluster correlation coefficient (ICC) and its use in the calculation of the 97.5% upper confidence bound for delta, the true difference in binomial proportions between the active control and the experimental treatment groups, will be outlined

Assessment of Sample Size and Power for the Analysis of Clustered Matched-Pair Data

This paper outlines how one can determined the sample size or power of a study design that is based on clustered matched-pair data. Detailed examples are provided

The Design and Sample Size Requirement for a Cluster Randomized Non-Inferiority Trial with Two Binary Co-Primary Outcomes.

This paper will discuss the design and sample size requirement for a cluster randomized non-inferiority trial with two binary co-primary outcomes. A hypothetical study (the EXAMPLE Trial) will be considered. Lets assume the EXAMPLE Trial will consist of two separate binomial non-inferiority two-sample trials. Trial 1: the Coronary Artery Disease known population (co-primary 1) and Trial 2: the Coronary Artery Disease unknown population (co-primary 2). A physician-month cluster randomization scheme will be used. That is, for each trial (trial 1 and trial 2) every month for a 12-month period, each physician participating in the EXAMPLE Trial will be allocated a randomized cluster of size 10. The physician will need to consent and enroll 10 patients each month for the 12-month period for each trial (trial 1 and trial 2). Each cluster will be specific to a treatment group (either EXPERIMENTAL or CONTROL). The design and sample size method discussed by Bland (2003) and Donner and Klar (2000) will be used. The EXAMPLE Trial will be declared a success if statistical significance is demonstrated at the pre-specified nominal alpha-level for both co-primary outcomes

Creating partnerships for capacity building in developing countries - the experience of the World Bank

The authors discuss a variety of experiences in a number of transition, and developing countries to build institutional capacity for economics education. A flexible approach met with some success. The approach uses partnerships that combine the often different needs of a number of private donors, with the World Bank on the supply side. Much of the success was due to adopting each effort to the individual country situation. The authors also provide a brief summary of five academic institutions, and four research networks in Europe, Africa, Asia, and Latin America.Public Health Promotion,Health Monitoring&Evaluation,Agricultural Knowledge&Information Systems,Decentralization,ICT Policy and Strategies,ICT Policy and Strategies,Health Monitoring&Evaluation,Agricultural Knowledge&Information Systems,Tertiary Education,Scientific Research&Science Parks

The Assessment of the Degree of Concordance Between the Observed Values and the Predicted Values of a Mixed-Effect Model Using “Method of Comparison” Techniques

In this paper, we present a methodology for determining the degree of concordance between observed and model-based predicted values of a mixed-effect model. In particular, we will compare the degree to which observed and model-based predicted values agree by using ‘method of comparison’ techniques. We will also present the results of the concordance correlation coefficient (CCC)

The Analysis of Pixel Intensity (Myocardial Signal Density) Data: The Quantification of Myocardial Perfusion by Imaging Methods.

This paper described a number of important issues in the analysis of pixel intensity data, as well as approaches for dealing with these. We particularly emphasized the issue of clustering, which may be ubiquitous in studies of pixel intensity data. Clustering can take many forms, e.g., measurements of different sections of a heart or repeated measurements of the same research participant. Clustering typically has the effect of increasing variance estimates. When one fails to account for clustering, variance estimates may be unrealistically small, resulting in spurious significance. We illustrated several possible approaches to account for clustering, including adjusting standard errors for design effects and modeling the covariance structure within clusters using mixed models. These methods offer great flexibility for dealing with a wide variety of research designs and include the capability for adjusting for covariates and different case weights. Similar methods can be used to account for clustering in both superiority and equivalence analyses. In situations where clustering affects the true cluster mean, µ, but not the difference between measures of the mean, it is possible that clustering will have a much greater impact on superiority analyses than on equivalence analyses

The Psychological Determinants of Occupational and Non-Occupational Risk-Taking Among Law Enforcement Officers

The goal of this study was to identify and statistically examine the psychological determinants of risk-taking among law enforcement officers. This study was conceptualized and designed on a rather simple premise that risk-taking in one\u27s leisure would have a dramatic and predominant influence on the grouping of subjects into definable personality trait categories. The suspicion regarding these categories was that subjects who engaged in risk-taking in their leisure time would be distinctively different from all other emerging groups, with regard to the 16 PF Cattell factors. It was also suspected that this leisure time risk-taking group\u27s personality profile would be split between a well adjusted group, who would be high in the personality traits of control and independence; and a less well adjusted group, who would have a pathological or marginal personality trait profile. In total, four hundred and fourteen (414) law enforcement officers\u27 Leisure Time Questionnaires and Cattell 16 PF Questionnaires were analyzed. The subjects were from a total of one hundred and forty-five (145) different law enforcement agencies from thirty-three (33) different States in the United States. The anonymous Leisure Time Questionnaire was designed to collect biographical information about the subjects and arranged the leisure time activities in alphabetical order, in an attempt to mask the risk-taking activities evaluation. The activities listed include all popular leisure-time activities that have been identified by the insurance industry, to which is attached an additional insurance premium. This questionnaire also included questions that evaluated occupational autonomy and discretion, and a fantasy leisure time question that elicited responses that were not dependent on the availability of free time or money. The Cattell Sixteen Personality Factor questionnaire was also administered to this sample and provided scores in twenty-six (26) personality trait categories. Although the original hypotheses of this study, were not largely supported, there were significant findings between the general population and law enforcement officers, within three (3) occupational law enforcement groups, and within six (6) law enforcement occupational/risk-taking groups; which are displayed in twenty (20) tables and nine (9) figures