Analysis Of Unbalanced Data Research Articles

Influence of chronic obstructive pulmonary disease on hospital outcomes of percutaneous coronary interventions in patients with acute coronary syndrome

To evaluate the impact of chronic obstructive pulmonary disease (COPD) on hospital outcomes of percutaneous coronary interventions (PCI) in patients with acute coronary syndrome (ACS). A cohort prospective study of the COPD effect on mortality and coronary microvascular obstruction (CMVO, no-reflow) development after PCI in ACS was carried out. 626 patients admitted in 2019-2020 were included, 418 (67%) - men, 208 (33%) - women. Median age - 63 [56; 70] years. Myocardial infarction with ST elevation identified in 308 patients (49%), CMVO - in 59 (9%) patients (criteria: blood flow <3 grade according to TIMI flow grade; perfusion <2 points according to Myocardial blush grade; ST segment resolution <70%). 13 (2.1%) patients died. Based on the questionnaire "Chronic Airways Diseases, A Guide for Primary Care Physicians, 2005", 2 groups of patients were identified: 197 (31%) with COPD (≥17 points) and 429 (69%) without COPD (<17 points). Groups were compared on unbalanced data (÷2 Pearson, Fisher exact test). The propensity score was calculated, and a two-way logistic regression analysis was performed. The data were balanced by the Kernel "weighting" method, logistic regression analysis was carried out using "weighting" coefficients. Results as odds ratio (OR) and 95% confidence interval. The conducted research allowed us to obtain the following results, depending on the type of analysis: 1) analysis of unbalanced data in patients with COPD: OR death 3.60 (1.16-11.12); p=0.03; OR CMVO 0.65 (0.35-1.22); p=0,18; 2) two-way analysis with propensity score: OR death 3.86 (1.09-13.74); p=0.04; OR CMVO 0.61 (0.31-1.19); p=0.15; 3) regression analysis with "weight" coefficients: OR death 12.49 (2.27-68.84); p=0.004; OR CMVO 0.63 (0.30-1.33); p=0.22. The presence of COPD in patients with ACS undergoing PCI increases mortality and does not affect the incidence of CMVO.

English

In breeding programs, obtaining breeding lines was important, and first selfing selection generations is common. In addition, an analytic approach through mixed models can lead to more success in genotype selection because it lends flexibility in analysis of unbalanced data and provides more precise genotypic values in regard to progenies evaluated. The goal of this study was to make early selection (in the S2 generation) of progenies evaluated in top crosses, using a mixed-model approach. Five hundred S2 progenies were plant derived from three populations using selection intensity (40%), which were crossbreeding with three testers. The hybrids obtained, together with control treatments, were set up in five experiments in Brazil: three in Minas Gerais, one in Santa Catarina, and one in Parana, which evaluated grain yield. The REML method was used for calculation of variance components, and means were predicted through BLUP. The BLUPs of general combining ability (GCA) and specific combining ability (SCA) were also predicted, and the Spearman correlation coefficients among BLUPs were estimated. The dominance effects had a bigger influence on yield expression, as seen from wider amplitude in SCA values. There was an 86% coincidence considering strategy in which selection index was carried out within three populations, in relation to selection by the overall value of GCA. Considering superior hybrids, the progenies of population C exceeded the quantity of hybrids expected by 24.6%, whereas there was a reduction of 30.8% for A and 20% for B. The hybrids that exhibited the highest BLUP means were derived from crosses between progenies of population C together with the tester LE84. The low correlation among testers, both for SCA and for BLUPs, indicates that there is progeny per tester interaction. Key words: Zea mays, combining ability, mixed models, development of breeding lines.

Analysis of Unbalanced Data by Mixed Linear Models Using the mixed Procedure of the SAS System

AbstractUnbalanced data are a common problem in plant research based on designed experiments. Such data are often conveniently analysed using linear mixed models. Recent developments in mixed model theory have been implemented in major packages. This paper describes the use of the mixed procedure of the SAS System for the analysis of designed experiments. Special emphasis is given to the specification of options as depending on the assumed mixed model and on the unbalancedness in the data. In addition, we consider a compact representation of multiple comparisons for unbalanced data (letter display). Two small data sets are used to exemplify the methods.

Estimation and utilisation of glutenin gene effects from the analysis of unbalanced data from wheat breeding programs

Glutenins are a major determinant of dough characteristics in wheat. These proteins are determined by genes at 6 loci (Glu genes), with multiple alleles present in most breeding programs. This study was conducted to determine whether estimates of allele effects for the important dough rheological characters, maximum dough resistance (Rmax) and dough extensibility, could be determined from aggregated data from southern Australian wheat breeding programs using statistical techniques appropriate for unbalanced data. From a 2-stage analysis of 3226 samples of 1926 cultivars and breeding lines, estimates of Rmax and extensibility effects were obtained, first for the lines, and then for 31 glutenin alleles. Glutenin genes did not determine flour protein concentration, and this character was used as a covariate. Rankings of the estimates of Rmax for the alleles were similar to the relative scores for dough strength reported from previous studies, providing strong evidence that the analysis of a large, unbalanced data set from applied wheat breeding programs can provide reliable estimates. All 2-way interactions between loci were present for 18 of the alleles. Analyses including interactions showed that epistasis was important for both Rmax and extensibility, especially between the Glu-B1 locus coding for high molecular weight glutenins and the Glu-A3 and Glu-B3 loci coding for low molecular weight glutenins. Because of the complexity of these interactions, similar values of Rmax and extensibility were predicted for diverse combinations of alleles. This implied that the practical application of glutenin genes in applied wheat breeding would be greatly enhanced by computer software which can predict dough rheology characteristics from glutenin allele classifications.

Analysis of Unbalanced Data from an Experiment with Random Block Effects and Unequally Spaced Factor Levels

Analysis of Unbalanced Data from an Experiment with Random Block Effects and Unequally Spaced Factor Levels

Analysis of Unbalanced Data from an Experiment with Random Block Effects and Unequally Spaced Factor Levels

We examine a 32 factorial experiment that was run on each of 12 days with extra replications of the middle point. Usually, the literature on the analysis of factorial experiments performed in blocks treats the blocks as a fixed effect. Data collected from this experiment, however, were analyzed by treating the block effect as random. The combination of quantitative factor levels and random block effects provides an opportunity to discuss issues that are not commonly encountered in the literature on mixed model analyses of data. In particular, we: (a) discuss modeling the interaction between blocks and regression coefficients; (b) discuss the effect of coding on the estimation of degrees of freedom (df) of the fixed terms; (c) present an oddity with estimating the intercept df; and (d) discuss inference on the mean of the responses. How the experiment may have been run and its consequences are also discussed.

ANALYSIS OF UNBALANCED MIXED MODEL DATA: Traditional ANOVA Versus Contemporary Methods

Analysis of unbalanced data and analysis of mixed model data are important topics of statistical discussion. Analysis of unbalanced data with fixed effects gives rise to the different types of sums of squares in analysis of variance. Mixed model riata raises issues of determining appropriate error terms for test statistics and standard errors Clf estimates. The situation is even more difficult when the two topics occur together, resulting in unbalanced mixed model data. These problems have plagued users ofPROC GLM in the SAS System. Now, with PROC MIXED available, some of the problems are resolved while others remain. This paper gives an overview of two areas of difficulty in analysis of variance using PROC GLM, and describes which problems carry over to PROC MIXED, and which are essentially solved with PROC MIXED.

Sweeping, alignment and the analysis of unbalanced data

The terms sweeping and alignment refer to the same process. Sweeping/alignment is used by data analysts as a technique for describing the effects of a model factor (e.g., treatments in a randomized block design) after the effects of nuisance parameters (e.g., blocks) have been removed from the data. In this paper sweeping/alignment is used as the basis for developing tests of factors in unbalanced experimental design models. Formulas are presented for treatment effects in randomized block designs with missing observations, and for interaction and main effects in unbalanced two-way factorial designs with empty cells.

Robustness and Balance in the Mixed Model

When balanced data are analyzed by a robust method, different weights are applied to data points depending on their apparent status as outliers. This results in the effective unbalancing of the data. Concepts developed for the analysis of unbalanced data can help to suggest robust methods of analysis for balanced data.

Analysis of Unbalanced Data: A Pre-Program Introduction.

Analysis of Unbalanced Data: A Pre-Program Introduction.

Analysis of Unbalanced Data: A Pre-Program Introduction.

Analysis of Unbalanced Data: A Pre-Program Introduction.

Serial P-values

Abstract When a collection of hypotheses is to be tested it is necessary to maintain a bound on the simultaneous Type I error rate. Serial P-values are used to define a serial test that does provide such a bound. Moreover, serial P-values are meaningful in the context of multiple tests, with or without the ‘rejection-confirmation’ decisions. The method is particularly suited to the analysis of unbalanced data, especially contingency tables.

Review of Analysis of Unbalanced Data: A Pre-Program Introduction.

Review of Analysis of Unbalanced Data: A Pre-Program Introduction.

Analysis of Variance Techniques for Unbalanced Data

Increasing interest has been expressed, particularly by social scientists, in the analysis of unbalanced data occurring in models not of full rank, i.e., linear models where the numbers of observations in the subclasses are unequal. The various discussions of this problem that have appeared in the psychological and educational literature differ depending upon the intent of the author(s). For example, some writers (Gourlay, 1955; Steinhorst & Miller, 1969; Tsao, 1942, 1946; Williams, 1972) have examined and at times compared different types of solutions, including approximate solutions, for the unbalanced case. Other writers (Overall & Spiegel, 1969; Rawlings, 1972) have focused upon the difficulty which arises in interpreting the results of unbalanced data analyses because the estimable functions involved in tests of hypotheses are not orthogonal. Though not specifically addressed to the unbalanced case but important in this regard is the work of Bottenberg and Ward (1960), Cohen (1968), Hurst (1970), and Jennings (1967) that demonstrates the equivalence of linear regression and the fixed-effects analysis of variance by the use of regression on dummy variables. This diversity in purpose, combined with the relative narrowness of the individual efforts, has resulted in a fragmented treatment of the problem of unbalanced data and in some cases confusion and controversy regarding methodology, e.g., the role of constraints in obtaining a solution for models not of full rank or the calculation of reductions in sums of squares (Overall & Spiegel, 1973; Rawlings, 1973; Smith, 1973). In response, at least one writer (Joe, 1971) has been prompted to call for a greater understanding and more detailed presentation of the models employed. Some of the more important issues encountered in the analysis of unbalanced data will be examined here within a single framework. These issues are broadly defined as (1) solutions to the normal equations for linear models where the incidence or design matrix is not of full column rank, (2) estimable functions of the parameters and tests of hypotheses, and (3) reductions in sums of squares and interpretation of results. Because of the increased availability of computer hardware and sophisticated least squares routines, the various approximate solutions for analyzing unbalanced data are not considered here.

Hypothesis Testing in Linear Models (Eisenhart Model I)

The results from an analysis of balanced data are frequently summarized in an analysis of variance (AOV) table. Each sum of squares (SS) in the AOV table is uniquely associated with testing a particular hypothesis in the linear model. These hypotheses are well known and cause no confusion among statisticians as to what is being tested. Results from an analysis of unbalanced data, however, cannot be uniquely summarized in an AOV table, and, consequently, statisticians are often confused about the hypotheses being tested. Some statisticians prefer an orthogonal partitioning of the SS (paralleling the balanced case) as the appropriate analysis; others prefer various forms of nonorthogonal analyses. The purpose of this paper is to show (and, hopefully, clarify) the hypotheses that are being tested in various unbalanced AOV tables.