Analyzing propensity matched zero-inflated count outcomes in observational studies

Determining the effectiveness of different treatments from observational data, which are characterized by imbalance between groups due to lack of randomization, is challenging. Propensity matching is often used to rectify imbalances among prognostic variables. However, there are no guidelines on how appropriately to analyze group matched data when the outcome is a zero-inflated count. In addition, there is debate over whether to account for correlation of responses induced by matching and/or whether to adjust for variables used in generating the propensity score in the final analysis. The aim of this research is to compare covariate unadjusted and adjusted zero-inflated Poisson models that do and do not account for the correlation. A simulation study is conducted, demonstrating that it is necessary to adjust for potential residual confounding, but that accounting for correlation is less important. The methods are applied to a biomedical research data set.     

Decision theory,linear partial information and statistical dominance

The paper deals with decision making under conditions of linear partial information, i.e. when probabilities of states of nature are not known precisely, but are subject to linear constraints. Conditions ensuring strict and weak statistical dominance of one strategy over another are derived. It is also shown that weak dominance in terms of payoffs is equivalent to weak dominance in terms of regrets. The new results are more general than those obtained by Fishburn and by Kmietowicz and Pearman for weak and strict ranking of probabilities, and include them as special cases. The new results can be employed in practical decision making in several ways.     

Long-term low-dose dehydroepiandrosterone replacement therapy in aging males with partial androgen deficiency

Dehydroepiandrosterone (DHEA) and dehydroepiandrosterone sulfate (DHEAS) age-related withdrawal is very likely to be involved in the aging process and the onset of age-related diseases, giving rise to the question of whether preventing or compensating the decline of these steroids may have endocrine and clinical benefits. The aim of the present trial was to evaluate the endocrine, neuroendocrine and clinical consequences of a long-term (1 year), low-dose (25?mg/day) replacement therapy in a group of aging men who presented the clinical characteristics of partial androgen deficiency (PADAM). Circulating DHEA, DHEAS, androstenedione, total testosterone and free testosterone, dihydrotestosterone (DHT), progesterone, 17-hydroxyprogesterone, allopregnanolone, estrone, estradiol, sex hormone binding globulin (SHBG), cortisol, follicle stimulating hormone (FSH), luteinizing hormone (LH), growth hormone (GH) and insulin-like growth factor 1 (IGF-1) levels were evaluated monthly to assess the endocrine effects of the therapy, while β-endorphin values were used as a marker of the neuroendocrine effects. A Kupperman questionnaire was performed to evaluate the subjective symptoms before and after treatment.

The results showed a great modification of the endocrine profile; with the exception of cortisol levels, which remained unchanged, DHEA, DHEAS, androstenedione, total and free testosterone, DHT, progesterone, 17-hydroxyprogesterone, estrone, estradiol, GH, IGF-1 and β-endorphin levels increased significantly with respect to baseline values, while FSH, LH and SHBG levels showed a significant decrease. The Kupperman score indicated a progressive improvement in mood, fatigue and joint pain.

In conclusion, the present study demonstrates that 25?mg/day of DHEA is able to cause significant changes in the hormonal profile and clinical symptoms and can counteract the age-related decline of endocrine and neuroendocrine functions. Restoring DHEA levels to young adult values seems to benefit the age-related decline in physiological functions but, however promising, placebo-controlled trials are required to confirm these preliminary results.     

Speeding through the frat house: a qualitative exploration of nonmedical ADHD stimulant use in fraternities

Qualitative methods were used to investigate the use of nonmedical Attention Deficit Hyperactivity Disorder (ADHD) stimulants by fraternity members. The primary goal of the study was to determine students' levels of understanding and motivations for use of these Schedule II controlled substances. Seventy-nine in-depth interviews were conducted. Key findings highlighted how: a) easy it was for subjects to obtain stimulants; b) little health information nonmedical users have about stimulants; c) academic stress created a fertile context for stimulant use; and d) a small number of prescribed users supply the vast majority of nonmedical users with their stimulants. Most nonmedical users claimed to primarily use ADHD stimulants in periods of high academic stress and believed that they not only reduced fatigue, but also increased reading comprehension, interest, cognition, and memory. These qualitative data have supplied a rich and complex understanding behind nonmedical ADHD stimulant use among fraternity members.     

Forensic psychiatric nursing. Discursive practices and the emergence of a specialty

1. While some authors argue that specialization is a threat to holistic nursing practice, others suggest that specialization is not only desirable and inevitable but should follow a rigorous process, including specific educational curriculum and examination procedures. 2. Two opposing views regarding forensic nursing are found in contemporary nursing literature. Some authors resist the discourse of specialization in nursing, while the discursive practices of others (e.g., leaders in forensic psychiatric nursing) could reflect a desire for power. 3. Using a poststructuralist approach, the controversy that has accompanied the development of forensic nursing through discursive practices is analyzed.     

Latent class based multiple imputation approach for missing categorical data

In this paper we propose a latent class based multiple imputation approach for analyzing missing categorical covariate data in a highly stratified data model. In this approach, we impute the missing data assuming a latent class imputation model and we use likelihood methods to analyze the imputed data. Via extensive simulations, we study its statistical properties and make comparisons with complete case analysis, multiple imputation, saturated log-linear multiple imputation and the Expectation–Maximization approach under seven missing data mechanisms (including missing completely at random, missing at random and not missing at random). These methods are compared with respect to bias, asymptotic standard error, type I error, and 95% coverage probabilities of parameter estimates. Simulations show that, under many missingness scenarios, latent class multiple imputation performs favorably when jointly considering these criteria. A data example from a matched case–control study of the association between multiple myeloma and polymorphisms of the Inter-Leukin 6 genes is considered.