A survey of variable selection methods in two Chinese epidemiology journals

<p>Abstract</p> <p>Background</p> <p>Although much has been written on developing better procedures for variable selection, there is little research on how it is practiced in actual studies. This review surveys the variable selection methods reported in two high-ranking Chinese epidemiology journals.</p> <p>Methods</p> <p>Articles published in 2004, 2006, and 2008 in the Chinese Journal of Epidemiology and the Chinese Journal of Preventive Medicine were reviewed. Five categories of methods were identified whereby variables were selected using: A - bivariate analyses; B - multivariable analysis; e.g. stepwise or individual significance testing of model coefficients; C - first bivariate analyses, followed by multivariable analysis; D - bivariate analyses or multivariable analysis; and E - other criteria like prior knowledge or personal judgment.</p> <p>Results</p> <p>Among the 287 articles that reported using variable selection methods, 6%, 26%, 30%, 21%, and 17% were in categories A through E, respectively. One hundred sixty-three studies selected variables using bivariate analyses, 80% (130/163) via multiple significance testing at the 5% alpha-level. Of the 219 multivariable analyses, 97 (44%) used stepwise procedures, 89 (41%) tested individual regression coefficients, but 33 (15%) did not mention how variables were selected. Sixty percent (58/97) of the stepwise routines also did not specify the algorithm and/or significance levels.</p> <p>Conclusions</p> <p>The variable selection methods reported in the two journals were limited in variety, and details were often missing. Many studies still relied on problematic techniques like stepwise procedures and/or multiple testing of bivariate associations at the 0.05 alpha-level. These deficiencies should be rectified to safeguard the scientific validity of articles published in Chinese epidemiology journals.</p

Developing a predictive tool for psychological well-being among Chinese adolescents in the presence of missing data

<p>Abstract</p> <p>Background</p> <p>Multi-dimensional behavioral rating scales like the CBCL and YSR are available for diagnosing psychosocial maladjustment in adolescents, but these are unsuitable for large-scale usage since they are time-consuming and their many sensitive questions often lead to missing data. This research applies multiple imputation to tackle the effects of missing data in order to develop a simple questionnaire-based predictive instrument for psychosocial maladjustment.</p> <p>Methods</p> <p>Questionnaires from 2919 Chinese sixth graders in 21 schools were collected, but 86% of the students were missing one or more of the variables for analysis. Fifteen (10 training, 5 validation) samples were imputed using multivariate imputation chain equations. A ten-variable instrument was constructed by applying stepwise variable selection algorithms to the training samples, and its predictive performance was evaluated on the validation samples.</p> <p>Results</p> <p>The instrument had an AUC of 0.75 (95% CI: 0.73 to 0.78) and a calibration slope of 0.98 (95% CI: 0.86 to 1.09). The prevalence of psychosocial maladjustment was 18%. If a score of > 1 was used to define a negative test, then 80% of the students would be classified as negative. The resulting test had a diagnostic odds ratio of 5.64 (95% CI: 4.39 to 7.24), with negative and positive predictive values of 88% and 43%, and negative and positive likelihood ratios of 0.61 and 3.41, respectively.</p> <p>Conclusions</p> <p>Multiple imputation together with internal validation provided a simple method for deriving a predictive instrument in the presence of missing data. The instrument's high negative predictive value implies that in populations with similar prevalences of psychosocial maladjustment test-negative students can be confidently excluded as being normal, thus saving 80% of the resources for confirmatory psychological testing.</p

Evaluation of Student Pharmacists’ Attitudes and Perceptions of Hormonal Contraception Prescribing in Indiana

Community pharmacists’ scope of practice is expanding to include hormonal contraceptive prescribing. Prior to introducing statewide legislation, it is important to assess the perceptions of future pharmacists. A cross-sectional survey was distributed to 651 third- and fourth-year professional students enrolled at three colleges of pharmacy in Indiana. Data were collected between September and October 2019 to assess students’ attitudes about prescribing hormonal contraceptives, readiness to prescribe, perceived barriers, and desire for additional training. In total, 20.9% (n = 136) students responded. Most (89%, n = 121) believe that pharmacist-prescribed hormonal contraceptives would be beneficial to women in Indiana, and 91% (n = 124) reported interest in providing this service. Liability, personal beliefs, and religious beliefs were the most commonly cited perceived barriers. Most students felt they received adequate teaching on hormonal contraceptive methods (90%, n = 122) and hormonal contraceptive counseling (79%, n = 107); only 5% (n = 7) felt ready to provide the service at the time of survey completion. Student pharmacists in their final two years of pharmacy school are interested in prescribing hormonal contraceptives and believe that this service would be beneficial. This expansion of pharmacy practice would likely be supported by future pharmacists who feel the service could provide benefit to women seeking hormonal contraceptives in Indiana

Pilot study of duloxetine for treatment of aromatase inhibitor‐associated musculoskeletal symptoms

BACKGROUND: Approximately 50% of postmenopausal women with hormone receptor‐positive early stage breast cancer treated with an aromatase inhibitor (AI) develop musculoskeletal symptoms. Standard analgesics are relatively ineffective. Duloxetine is a serotonin norepinephrine reuptake inhibitor with proven efficacy for treatment of multiple chronic pain states. The authors investigated the hypothesis that duloxetine is efficacious for treatment of AI‐associated musculoskeletal symptoms. METHODS: The authors performed a single‐arm, open‐label phase 2 study of duloxetine in postmenopausal women with breast cancer who developed new or worsening pain after treatment with an AI for at least 2 weeks. Patients were treated with duloxetine for 8 weeks (30 mg for 7 days, then 60 mg daily). The primary endpoint was a 30% decrease in average pain score over 8 weeks, and secondary outcomes included change in average and worst pain, pain interference, depression, sleep quality, and hot flashes. Statistical analysis was done with t tests for paired data. RESULTS: Twenty‐one of 29 evaluable patients (72.4%) achieved at least a 30% decrease in average pain, and 18 of 23 patients (78.3%) who completed protocol‐directed treatment continued duloxetine. The mean percentage reduction in average pain severity between baseline and 8 weeks was 60.9% (95% confidence interval [CI], 48.6%‐73.1%), and in maximum pain severity it was 59.9% (95% CI, 47.0‐72.7%). The most common adverse events were grade 1 or 2 fatigue, xerostomia, nausea, and headache. CONCLUSIONS: Duloxetine appears to be effective and well tolerated for treatment of AI‐associated musculoskeletal symptoms. Future randomized, placebo‐controlled studies are warranted. Cancer 2011;. © 2011 American Cancer Society. Bothersome musculoskeletal symptoms affect about half of women with early stage breast cancer treated with aromatase inhibitors. In this pilot clinical trial, treatment with duloxetine appeared to significantly improve pain and functioning, and was relatively well tolerated.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89530/1/26230_ftp.pd

Comparing Principal Component Analysis with Maximum Likelihood in Ordination

25 pages, 1 article*Comparing Principal Component Analysis with Maximum Likelihood in Ordination* (Lynn, Henry S.; McCulloch, Charles E.) 25 page

A Critique of Maximum Likelihood Ordination Using Principal Components Analysis

18 pages, 1 article*A Critique of Maximum Likelihood Ordination Using Principal Components Analysis* (Lynn, Henry S.; McCulloch, Charles E.) 18 page

Non-native vascular flora of the Arctic : Taxonomic richness, distribution and pathways

We present a comprehensive list of non-native vascular plants known from the Arctic, explore their geographic distribution, analyze the extent of naturalization and invasion among 23 subregions of the Arctic, and examine pathways of introductions. The presence of 341 non-native taxa in the Arctic was confirmed, of which 188 are naturalized in at least one of the 23 regions. A small number of taxa (11) are considered invasive; these plants are known from just three regions. In several Arctic regions there are no naturalized non-native taxa recorded and the majority of Arctic regions have a low number of naturalized taxa. Analyses of the non-native vascular plant flora identified two main biogeographic clusters within the Arctic: American and Asiatic. Among all pathways, seed contamination and transport by vehicles have contributed the most to non-native plant introduction in the Arctic.Peer reviewe

American Cancer Society/American Society of Clinical Oncology Breast Cancer Survivorship Care Guideline

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136493/1/caac21319_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136493/2/caac21319-sup-0001-suppinfo1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136493/3/caac21319.pd