Memory and executive functioning are two important components of clinical neuropsychological (NP) practice and research. Multiple demographic factors are known to affect performance differentially on most NP tests, but adequate normative corrections, inclusive of race/ethnicity, are not available for many widely used instruments. This study compared demographic contributions for widely used tests of verbal and visual learning and memory (Brief Visual Memory TestRevised, Hopkins Verbal Memory Test-Revised), and executive functioning (Stroop Color and Word Test, Wisconsin Card Sorting Test-64) in groups of healthy Caucasians (n = 143) and African-Americans (n = 103). Demographic factors of age, education, gender, and race/ethnicity were found to be significant factors on some indices of all four tests. The magnitude of demographic contributions (especially age) was greater for African-Americans than Caucasians on most measures. New, demographically corrected T-score formulas were calculated for each race/ ethnicity. The rates of NP impairment using previously published normative standards significantly overestimated NP impairment in African-Americans. Utilizing the new demographic corrections developed and presented herein, NP impairment rates were comparable between the two race/ethnicities and unrelated to the other demographic characteristics (age, education,
SUMMARYWe study model selection for clustered data, when the focus is on cluster specific inference. Such data are often modelled using random effects, and conditional Akaike information was proposed in Vaida & Blanchard (2005) and used to derive an information criterion under linear mixed models. Here we extend the approach to generalized linear and proportional hazards mixed models. Outside the normal linear mixed models, exact calculations are not available and we resort to asymptotic approximations. In the presence of nuisance parameters, a profile conditional Akaike information is proposed. Bootstrap methods are considered for their potential advantage in finite samples. Simulations show that the performance of the bootstrap and the analytic criteria are comparable, with bootstrap demonstrating some advantages for larger cluster sizes. The proposed criteria are applied to two cancer datasets to select models when the clusterspecific inference is of interest.Some key words: Akaike information; Conditional likelihood; Effective degrees of freedom.
Perinatal immunization education improved the immunization status of infants, increased the women's knowledge on immunization and intention to vaccinate their infants.
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