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Common Odds Ratio (common + odds_ratio)

Distribution by Scientific Domains
Medical Sciences40%

Selected Abstracts

Applying the Liu-Agresti Estimator of the Cumulative Common Odds Ratio to DIF Detection in Polytomous Items

JOURNAL OF EDUCATIONAL MEASUREMENT, Issue 4 2003
Randall D. Penfield
Liu and Agresti (1996) proposed a Mantel and Haenszel-type (1959) estimator of a common odds ratio for several 2 × J tables, where the J columns are ordinal levels of a response variable. This article applies the Liu-Agresti estimator to the case of assessing differential item functioning (DIF) in items having an ordinal response variable. A simulation study was conducted to investigate the accuracy of the Liu-Agresti estimator in relation to other statistical DIF detection procedures. The results of the simulation study indicate that the Liu-Agresti estimator is a viable alternative to other DIF detection statistics. [source]

An evidence-based assessment of the clinical guidelines for replanted avulsed teeth.

DENTAL TRAUMATOLOGY, Issue 2 2009
Part II: prescription of systemic antibiotics
The principles of evidence-based dentistry can be used to assess whether this is the best approach based on currently-available evidence. The objective of this study was to use the principles of evidence-based dentistry to answer the PICO question: (P) for a replanted avulsed permanent tooth, (I) is prescribing SAT, (C) compared with not prescribing SAT, (O) associated with an increased likelihood of successful periodontal healing after tooth replantation? Materials and methods:, A literature search was performed across four internet databases (Ovid Medline, Cochrane Library, PubMed, ISI Web of Science), for relevant citations (n = 35 702). Limiting citations to those in English and removing duplicates produced a set of titles (n = 14 742) that were sieved according to evidence-based dentistry principles. Relevant titles were selected for abstract assessment (n = 782), identifying papers for examination (n = 74). Inclusion criteria were applied and three papers (326 total teeth) met the final criteria for meta-analysis. Results:, Meta-analyses found no statistically significant difference between prescribing or not prescribing antibiotics for acceptable periodontal healing without progressive root resorption (common odds ratio = 0.90, SE = 0.29, 95% confidence intervals = 0.51,1.58). Conclusion:, The evidence for an association between prescribing SAT and an increased likelihood of acceptable periodontal healing outcome is inconclusive. This investigation of antibiotic use as defined in the clinical guidelines indicates there is inconclusive clinical evidence from studies of replanted avulsed human teeth to either contradict or support the guideline. Pending future research to the contrary, dentists are recommended to follow current guidelines in prescribing SAT when replanting avulsed teeth. [source]

Applying the Liu-Agresti Estimator of the Cumulative Common Odds Ratio to DIF Detection in Polytomous Items

JOURNAL OF EDUCATIONAL MEASUREMENT, Issue 4 2003
Randall D. Penfield
Liu and Agresti (1996) proposed a Mantel and Haenszel-type (1959) estimator of a common odds ratio for several 2 × J tables, where the J columns are ordinal levels of a response variable. This article applies the Liu-Agresti estimator to the case of assessing differential item functioning (DIF) in items having an ordinal response variable. A simulation study was conducted to investigate the accuracy of the Liu-Agresti estimator in relation to other statistical DIF detection procedures. The results of the simulation study indicate that the Liu-Agresti estimator is a viable alternative to other DIF detection statistics. [source]

Anti-HCV and HCV-RNA prevalence and clinical correlations in cases with non-Hodgkin's lymphoma

AMERICAN JOURNAL OF HEMATOLOGY, Issue 2 2003
Semra Paydas
Abstract Hepatitis C virus (HCV) is an RNA virus in the Flaviviridae family. It displays lymphotropism in addition to hepatotropism and extrahepatic manifestations are very well known. There are many studies showing an association between HCV infection and non-Hodgkin's lymphomas (NHL). In this study the evidence for HCV infection was studied in cases with NHL. To this end, anti-HCV antibody and HCV-RNA were screened in serum samples of cases with NHL using third-generation ELISA and RT-PCR. Anti-HCV antibody was studied in 223 patients and was found to be positive in 18 cases (8.1%). Anti-HCV antibody positivity was compared with our blood bank / blood donor population. There was an important increased risk of HCV infection,the common odds ratio was 34.56 and corrected odds ratio was 19.07. HCV-RNA was studied in 67 of 223 serum samples. HCV-RNA was found to be positive in 21 of 67 samples (31.3%). When compared with clinico-demographic parameters for anti-HCV and HCV-RNA, including age, nodal status, and grade (in evaluable cases), except age in cases with or without HCV-RNA, we did not find an important correlation with HCV status and clinical findings (P = 0.155; 0.442; 0.288 for anti-HCV and 0.027; 0,558; 0.126, respectively). These results suggest that HCV infection may be an important risk factor for lymphomagenesis and HCV-RNA is more useful for the detection of HCV infection in these immunosuppressed cases. Simultaneous detection of anti-HCV and HCV-RNA will be more informative in this population. Am. J. Hematol. 74:89,93, 2003. © 2003 Wiley-Liss, Inc. [source]

AN EVALUATION OF NON-ITERATIVE METHODS FOR ESTIMATING THE LINEAR-BY-LINEAR PARAMETER OF ORDINAL LOG-LINEAR MODELS

AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 3 2009
Eric J. Beh
Summary Parameter estimation for association and log-linear models is an important aspect of the analysis of cross-classified categorical data. Classically, iterative procedures, including Newton's method and iterative scaling, have typically been used to calculate the maximum likelihood estimates of these parameters. An important special case occurs when the categorical variables are ordinal and this has received a considerable amount of attention for more than 20 years. This is because models for such cases involve the estimation of a parameter that quantifies the linear-by-linear association and is directly linked with the natural logarithm of the common odds ratio. The past five years has seen the development of non-iterative procedures for estimating the linear-by-linear parameter for ordinal log-linear models. Such procedures have been shown to lead to numerically equivalent estimates when compared with iterative, maximum likelihood estimates. Such procedures also enable the researcher to avoid some of the computational difficulties that commonly arise with iterative algorithms. This paper investigates and evaluates the performance of three non-iterative procedures for estimating this parameter by considering 14 contingency tables that have appeared in the statistical and allied literature. The estimation of the standard error of the association parameter is also considered. [source]