Purpose Of Review: Clinical trials to evaluate the supportive and palliative care treatments have some different missing data concerns than the other clinical trials. This study reviews the literature on missing data as it may apply to these trials.
Recent Findings: Prevention of missing data through study design and conduct is a recent area of focus. Missing data can be minimized by simplifying trial participation for patients, their caregivers, and trialists. Run-in periods with active drug or collecting data from observer (proxy) respondents may complicate a trial but may be used to address some specific concerns. Many analyses can accommodate data missing because of nonresponse by multiple imputation, using carefully chosen imputation models. Analysis of trials evaluating end-of-life care should distinguish between missing data and truncation because of death.
Summary: Likely patterns for missing data should be discussed when planning a clinical trial, as modifications to trial design can minimize missing data while still addressing study aims. Many statistical analysis methods are available to accommodate missing data, but robustness of study conclusions to assumptions about mechanisms underlying the missingness should be evaluated by sensitivity analyses.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1097/SPC.0b013e328358441d | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Identifying predictors of nosocomial pneumonia in trauma patients admitted to a level-1 trauma center.
Arch Orthop Trauma Surg
December 2024
Department of Surgery, University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht, The Netherlands.
Background: Nosocomial pneumonia is common in trauma patients and associated with an adverse prognosis. We recently externally validated and recalibrated an existing formula to predict nosocomial pneumonia risk. Identifying more potential predictors could aid in a more accurate prediction of nosocomial pneumonia risk in level-1 trauma patients.
View Article and Find Full Text PDFMissing Fundamental Nursing Care: What's the Extent of Missed Oral Care? A Cross-Sectional Study.
Nurs Rep
December 2024
Department of Translational Medicine, University of Piemonte Orientale, Via P. Solaroli, 17, 28100 Novara, Italy.
Background: The Fundamentals of Care framework emphasizes a patient-centered approach that prioritizes the nurse-patient relationship and care environment to meet patients' basic needs, including oral hygiene. Recognized as crucial for preventing systemic health problems, oral care neglect is a global concern. Studies identify missed oral care as a widespread issue, contributing to significant patient safety risks.
View Article and Find Full Text PDFEnhancing Transcriptomic Insights into Neurological Disorders Through the Comparative Analysis of Shapley Values.
Curr Issues Mol Biol
November 2024
Systems Biology Unit, Department of Experimental Biology, Faculty of Experimental Sciences, University of Jaén, 23071 Jaén, Spain.
Neurological disorders such as Autism Spectrum Disorder (ASD), Schizophrenia (SCH), Bipolar Disorder (BD), and Major Depressive Disorder (MDD) affect millions of people worldwide, yet their molecular mechanisms remain poorly understood. This study describes the application of the Comparative Analysis of Shapley values (CASh) to transcriptomic data from nine datasets associated with these complex disorders, demonstrating its effectiveness in identifying differentially expressed genes (DEGs). CASh, which combines Game Theory with Bootstrap resampling, offers a robust alternative to traditional statistical methods by assessing the contribution of each gene in the broader context of the complete dataset.
View Article and Find Full Text PDFMulti-Task Deep Neural Networks for Irregularly Sampled Multivariate Clinical Time Series.
Proc (IEEE Int Conf Healthc Inform)
June 2024
College of Medicine, University of Florida, Gainesville, FL, USA.
Multivariate clinical time series data, such as those contained in Electronic Health Records (EHR), often exhibit high levels of irregularity, notably, many missing values and varying time intervals. Existing methods usually construct deep neural network architectures that combine recurrent neural networks and time decay mechanisms to model variable correlations, impute missing values, and capture the impact of varying time intervals. The complete data matrices thus obtained from the imputation task are used for downstream risk prediction tasks.
View Article and Find Full Text PDFIdentification of key factors for malnutrition diagnosis in chronic gastrointestinal diseases using machine learning underscores the importance of GLIM criteria as well as additional parameters.
Front Nutr
December 2024
Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germany.
Introduction: Disease-related malnutrition is common but often underdiagnosed in patients with chronic gastrointestinal diseases, such as liver cirrhosis, short bowel and intestinal insufficiency, and chronic pancreatitis. To improve malnutrition diagnosis in these patients, an evaluation of the current Global Leadership Initiative on Malnutrition (GLIM) diagnostic criteria, and possibly the implementation of additional criteria, is needed.
Aim: This study aimed to identify previously unknown and potentially specific features of malnutrition in patients with different chronic gastrointestinal diseases and to validate the relevance of the GLIM criteria for clinical practice using machine learning (ML).
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!