Publications by authors named "S PENEV"

Article Synopsis
  • Multiple imputation and maximum likelihood estimation are two common methods for handling missing data, but improper multiple imputation can act similarly to a stochastic expectation-maximization approach.
  • The article suggests that traditional model selection tools like Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC) can help effectively choose the best imputation model, which is crucial to avoid bias in analysis.
  • Simulations show that not only can incorrect imputation lead to biased parameter estimates, but also overfitting the imputation model can have negative effects, highlighting the need for careful model selection in imputation strategies.
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Purpose: Since the development of antipsychotic drugs in the 1950s, a variety of studies and case reports have been published that suggest an association between exposure to typical antipsychotics and venous thromboembolisms (VTE). Therefore, when starting treatment with antipsychotics, especially low-potency typical antipsychotics and clozapine, health-care providers must account for the patient's existing VTE risk factors.

Design/methodology/approach: In this case report, the authors describe the development of a pulmonary embolism associated with use of chlorpromazine in the treatment of an acute manic episode in a 51-year-old female patient with bipolar disorder type 1.

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A procedure for testing mean collinearity in multidimensional spaces is outlined, which is applicable in settings with missing data and can be used when examining group mean differences. The approach is based on non-linear parameter restrictions and is developed within the framework of latent variable modelling. The method provides useful information about the constellation of multiple response centroids in the populations studied, and is illustrated with an example.

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This paper is concerned with the reliability of weighted combinations of a given set of dichotomous measures. Maximal reliability for such measures has been discussed in the past, but the pertinent estimator exhibits a considerable bias and mean squared error for moderate sample sizes. We examine this bias, propose a procedure for bias correction, and develop a more accurate asymptotic confidence interval for the resulting estimator.

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