Comparative effectiveness of cognitive behavioural therapy, modafinil, and their combination for treating fatigue in multiple sclerosis (COMBO-MS): a randomised, statistician-blinded, parallel-arm trial.

Background: Fatigue is one of the most disabling symptoms reported by people with multiple sclerosis. Although behavioural and pharmacological interventions might be partly beneficial, their combined effects have not been evaluated for multiple sclerosis fatigue, or examined with sufficient consideration of characteristics that might affect treatment response. In this comparative effectiveness research trial, we compared the effectiveness of cognitive behavioural therapy (CBT), modafinil, and their combination for treating multiple sclerosis fatigue.

Missing Data Analysis.

Methods for handling missing data in clinical psychology studies are reviewed. Missing data are defined, and a taxonomy of main approaches to analysis is presented, including complete-case and available-case analysis, weighting, maximum likelihood, Bayes, single and multiple imputation, and augmented inverse probability weighting. Missingness mechanisms, which play a key role in the performance of alternative methods, are defined.

ASSESSING SELECTION BIAS IN REGRESSION COEFFICIENTS ESTIMATED FROM NONPROBABILITY SAMPLES WITH APPLICATIONS TO GENETICS AND DEMOGRAPHIC SURVEYS.

Selection bias is a serious potential problem for inference about relationships of scientific interest based on samples without well-defined probability sampling mechanisms. Motivated by the potential for selection bias in: (a) estimated relationships of polygenic scores (PGSs) with phenotypes in genetic studies of volunteers and (b) estimated differences in subgroup means in surveys of smartphone users, we derive novel measures of selection bias for estimates of the coefficients in linear and probit regression models fitted to nonprobability samples, when aggregate-level auxiliary data are available for the selected sample and the target population. The measures arise from normal pattern-mixture models that allow analysts to examine the sensitivity of their inferences to assumptions about nonignorable selection in these samples.

A simulation study of diagnostics for selection bias.

A non-probability sampling mechanism arising from non-response or non-selection is likely to bias estimates of parameters with respect to a target population of interest. This bias poses a unique challenge when selection is 'non-ignorable', i.e.

Bayesian sensitivity analyses for longitudinal data with dropouts that are potentially missing not at random: A high dimensional pattern-mixture model.

Randomized clinical trials with outcome measured longitudinally are frequently analyzed using either random effect models or generalized estimating equations. Both approaches assume that the dropout mechanism is missing at random (MAR) or missing completely at random (MCAR). We propose a Bayesian pattern-mixture model to incorporate missingness mechanisms that might be missing not at random (MNAR), where the distribution of the outcome measure at the follow-up time , conditional on the prior history, differs across the patterns of missing data.

Oliceridine Exhibits Improved Tolerability Compared to Morphine at Equianalgesic Conditions: Exploratory Analysis from Two Phase 3 Randomized Placebo and Active Controlled Trials.

Introduction: In the management of postoperative acute moderate-to-severe pain, opioids remain an important component. However, conventional opioids have a narrow therapeutic index and are associated with dose-limiting opioid-related adverse events (ORAEs) that can result in worse patient outcomes. Oliceridine, a new intravenous µ-opioid receptor agonist, is shown in nonclinical studies to be biased for G protein signaling (achieving analgesia) with limited recruitment of β-arrestin (associated with ORAEs).

Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework.

Missing data are ubiquitous in medical research. Although there is increasing guidance on how to handle missing data, practice is changing slowly and misapprehensions abound, particularly in observational research. Importantly, the lack of transparency around methodological decisions is threatening the validity and reproducibility of modern research.

A comparison of parametric propensity score-based methods for causal inference with multiple treatments and a binary outcome.

We consider comparative effectiveness research (CER) from observational data with two or more treatments. In observational studies, the estimation of causal effects is prone to bias due to confounders related to both treatment and outcome. Methods based on propensity scores are routinely used to correct for such confounding biases.

Measures of the Degree of Departure from Ignorable Sample Selection.

With the current focus of survey researchers on "big data" that are not selected by probability sampling, measures of the degree of potential sampling bias arising from this nonrandom selection are sorely needed. Existing indices of this degree of departure from probability sampling, like the R-indicator, are based on functions of the propensity of inclusion in the sample, estimated by modeling the inclusion probability as a function of auxiliary variables. These methods are agnostic about the relationship between the inclusion probability and survey outcomes, which is a crucial feature of the problem.

Indices of non-ignorable selection bias for proportions estimated from non-probability samples.

Rising costs of survey data collection and declining response rates have caused researchers to turn to non-probability samples to make descriptive statements about populations. However, unlike probability samples, non-probability samples may produce severely biased descriptive estimates due to selection bias. The paper develops and evaluates a simple model-based index of the potential selection bias in estimates of population proportions due to non-ignorable selection mechanisms.

Comparative Study of Confidence Intervals for Proportions in Complex Sample Surveys.

The most widespread method of computing confidence intervals (CIs) in complex surveys is to add and subtract the margin of error (MOE) from the point estimate, where the MOE is the estimated standard error multiplied by the suitable Gaussian quantile. This Wald-type interval is used by the American Community Survey (ACS), the largest US household sample survey. For inferences on small proportions with moderate sample sizes, this method often results in marked under-coverage and lower CI endpoint less than 0.

A randomized pragmatic trial of telephone-delivered cognitive behavioral-therapy, modafinil, and combination therapy of both for fatigue in multiple sclerosis: The design of the "COMBO-MS" trial.

Background: Fatigue is one of the most common and disabling chronic symptoms in multiple sclerosis (MS). Optimization of available treatments for MS-related fatigue has been stymied by lack of comparative effectiveness research that focuses on real-world treatment delivery methods and potential modification of treatment effect by other chronic MS symptoms or disability level. This report describes the design of a patient centered, comparative effectiveness trial of cognitive behavioral-therapy (CBT), modafinil, and combination therapy of both for fatigue in MS ("COMBO-MS").

Medical-encounter mental health diagnoses, non-fatal injury and polypharmacy indicators of risk for accident death in the US Army enlisted soldiers, 2004-2009.

Accidents are a leading cause of deaths in U.S. active duty personnel.

Correlation of Peripheral Immunity With Rapid Amyotrophic Lateral Sclerosis Progression.

Importance: Amyotrophic lateral sclerosis (ALS) has an immune component, but previous human studies have not examined immune changes over time.

Objectives: To assess peripheral inflammatory markers in participants with ALS and healthy control individuals and to track immune changes in ALS and determine whether these changes correlate with disease progression.

Design, Setting, And Participants: In this longitudinal cohort study, leukocytes were isolated from peripheral blood samples from 35 controls and 119 participants with ALS at the ALS Clinic of the University of Michigan, Ann Arbor, from June 18, 2014, through May 26, 2016.

Launching Effectiveness Research to Guide Practice in Neurosurgery: A National Institute Neurological Disorders and Stroke Workshop Report.

This workshop addressed challenges of clinical research in neurosurgery. Randomized controlled clinical trials (RCTs) have high internal validity, but often insufficiently generalize to real-world practice. Observational studies are inclusive but often lack sufficient rigor.

The treatment of missing data in a large cardiovascular clinical outcomes study.

Background: The potential impact of missing data on the results of clinical trials has received heightened attention recently. A National Research Council study provides recommendations for limiting missing data in clinical trial design and conduct, and principles for analysis, including the need for sensitivity analyses to assess robustness of findings to alternative assumptions about the missing data. A Food and Drug Administration advisory committee raised missing data as a serious concern in their review of results from the ATLAS ACS 2 TIMI 51 study, a large clinical trial that assessed rivaroxaban for its ability to reduce the risk of cardiovascular death, myocardial infarction or stroke in patients with acute coronary syndrome.

Analyzing clinical trial outcomes based on incomplete daily diary reports.

A case study is presented assessing the impact of missing data on the analysis of daily diary data from a study evaluating the effect of a drug for the treatment of insomnia. The primary analysis averaged daily diary values for each patient into a weekly variable. Following the commonly used approach, missing daily values within a week were ignored provided there was a minimum number of diary reports (i.

The extent and patterns of multiple chronic conditions in low-income children.

Background And Objectives: Little is known about the magnitude of multiple chronic conditions (MCC) in children. This study describes the prevalence of and patterns of comorbidities in children receiving Medicaid assistance.

Methods: Diagnoses from 5 years of Medicaid claims data were reviewed and identified 128,044 children with chronic conditions.