Epidemiologic Methods ; Models, Statistical

Models, Statistical ; Propensity Score

Models, Statistical ; Propensity Score

Models, Statistical ; Propensity Score

Statistical methods are widely used for the analysis of data obtained from experimental or observational studies. We examined the statistical methods used for papers published in the Journal of Korean Academy of Rehabilitation Medicine in 1996. The issues included the mean, coefficient of variation, t-test, chi-square test, ANOVA and type IV error. The mean and the standard deviation in descriptive statistics and the t-test in inferential statistics were most frequently used. Statistical methods should incorporate statistical modelling and the needed assumptions. Trials of new statistical models and methods are recommended.
Models, Statistical ; Rehabilitation*

Computer Simulation ; Models, Statistical

OBJECTIVETo analyze binary classification repeated measurement data with generalized estimating equations (GEE) and generalized linear mixed models (GLMMs) using SPSS19.0.

METHODSGEE and GLMMs models were tested using binary classification repeated measurement data sample using SPSS19.0.

RESULTS AND CONCLUSIONCompared with SAS, SPSS19.0 allowed convenient analysis of categorical repeated measurement data using GEE and GLMMs.

The null hypothesis significance test method is popular in biological and medical research. Many researchers have used this method for their research without exact knowledge, though it has both merits and shortcomings. Readers will know its shortcomings, as well as several complementary or alternative methods, as such the estimated effect size and the confidence interval.
Biostatistics ; Confidence Intervals ; Models, Statistical

OBJECTIVES: The purpose of this study was to investigate the effects of query expansion algorithms for MEDLINE retrieval within a pseudo-relevance feedback framework. METHODS: A number of query expansion algorithms were tested using various term ranking formulas, focusing on query expansion based on pseudo-relevance feedback. The OHSUMED test collection, which is a subset of the MEDLINE database, was used as a test corpus. Various ranking algorithms were tested in combination with different term re-weighting algorithms. RESULTS: Our comprehensive evaluation showed that the local context analysis ranking algorithm, when used in combination with one of the reweighting algorithms - Rocchio, the probabilistic model, and our variants - significantly outperformed other algorithm combinations by up to 12% (paired t-test; p < 0.05). In a pseudo-relevance feedback framework, effective query expansion would be achieved by the careful consideration of term ranking and re-weighting algorithm pairs, at least in the context of the OHSUMED corpus. CONCLUSIONS: Comparative experiments on term ranking algorithms were performed in the context of a subset of MEDLINE documents. With medical documents, local context analysis, which uses co-occurrence with all query terms, significantly outperformed various term ranking methods based on both frequency and distribution analyses. Furthermore, the results of the experiments demonstrated that the term rank-based re-weighting method contributed to a remarkable improvement in mean average precision.
Information Storage and Retrieval ; Models, Statistical

No abstract available.
Statistics as Topic ; Models, Statistical ; Data Interpretation, Statistical ; Sampling Studies ; Biometry