https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.2517-6161.1977.tb01600.x

Maximum likelihood from incomplete data via the EM algorithm

G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.

/pdf/statistical-power-analyses-using-g-power-3-1-tests-for-dnxl4mlxpv.pdf

Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.

/pdf/missing-data-our-view-of-the-state-of-the-art-3xnwc88wmd.pdf

Missing data: Our view of the state of the art.

The metabolic syndrome has received increased attention in the past few years. This statement from the American Heart Association (AHA) and the National Heart, Lung, and Blood Institute (NHLBI) is intended to provide up-to-date guidance for professionals on the diagnosis and management of the metabolic syndrome in adults.

The metabolic syndrome is a constellation of interrelated risk factors of metabolic origin— metabolic risk factors —that appear to directly promote the development of atherosclerotic cardiovascular disease (ASCVD).1 Patients with the metabolic syndrome also are at increased risk for developing type 2 diabetes mellitus. Another set of conditions, the underlying risk factors , give rise to the metabolic risk factors. In the past few years, several expert groups have attempted to set forth simple diagnostic criteria to be used in clinical practice to identify patients who manifest the multiple components of the metabolic syndrome. These criteria have varied somewhat in specific elements, but in general they include a combination of both underlying and metabolic risk factors.

The most widely recognized of the metabolic risk factors are atherogenic dyslipidemia, elevated blood pressure, and elevated plasma glucose. Individuals with these characteristics commonly manifest a prothrombotic state and a pro-inflammatory state as well. Atherogenic dyslipidemia consists of an aggregation of lipoprotein abnormalities including elevated serum triglyceride and apolipoprotein B (apoB), increased small LDL particles, and a reduced level of HDL cholesterol (HDL-C). The metabolic syndrome is often referred to as if it were a discrete entity with a single cause. Available data suggest that it truly is a syndrome, ie, a grouping of ASCVD risk factors, but one that probably has more than one cause. Regardless of cause, the syndrome identifies individuals at an elevated risk for ASCVD. The magnitude of the increased risk can vary according to which components of the syndrome are …

https://www.lipidcenter.com/pdf/metabolic.pdf

Diagnosis and Management of the Metabolic Syndrome An American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement

XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

BMDP statistical software

Alternative formulations of Levene's test statistic for equality of variances are found to be robust under nonnormality. These statistics use more robust estimators of central location in place of the mean. They are compared with the unmodified Levene's statistic, a jackknife procedure, and a χ2 test suggested by Layard which are all found to be less robust under nonnormality.

Robust Tests for the Equality of Variances

OBJECTIVE Early diagnosis of distal symmetric sensorimotor polyneuropathy, a common complication of diabetes, may decrease patient morbidity by allowing for potential therapeutic interventions. We have designed an outpatient program to facilitate diagnosis of diabetic neuropathy. RESEARCH DESIGN AND METHODS Patients are initially administered a brief questionnaire and screening examination, designated the Michigan Neuropathy Screening Instrument (MNSI). Diabetic neuropathy is confirmed in patients with a positive assessment by a quantitative neurological examination coupled with nerve conduction studies, designated the Michigan Diabetic Neuropathy Score (MDNS). In this study, 56 outpatients with confirmed type I or II diabetes were administered the standardized quantitative components required to diagnose and stage diabetic neuropathy according to the San Antonio Consensus Statement (1) and the Mayo Clinic protocol (2). These same patients were then assessed with the MNSI and the MDNS. RESULTS Of 29 patients with a clinical MNSI score > 2, 28 had neuropathy. Twenty-eight patients with an MDNS of ≥ 7 had neuropathy, while 21 non-neuropathic patients had a score ≤ 6. Of 35 patients with diabetic neuropathy, 34 had ≥ 2 abnormal nerve conductions. Twenty-one normal patients and one patient with neuropathy had ≤ 1 abnormal nerve conduction. CONCLUSIONS The results indicate that the MNSI is a good screening tool for diabetic neuropathy and that the MDNS coupled with nerve conductions provides a simple means to confirm this diagnosis.

Morton B. Brown

Papers

BMDP statistical software

Robust Tests for the Equality of Variances

A Practical Two-Step Quantitative Clinical and Electrophysiological Assessment for the Diagnosis and Staging of Diabetic Neuropathy

BMDP-77, Biomedical Computer Programs, P-Series

Prevalence of Urinary Incontinence and Other Urological Symptoms in the Noninstitutionalized Elderly