ArticleBiostatistics & epidemiology2017
Multi-state models and missing covariate data: Expectation-Maximization algorithm for likelihood estimation.
Article in Biostatistics & epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Facilitating Exercise Habit Formation among Cardiac Rehabilitation Patients: A Randomized Controlled Pilot Trial.International journal of environmental research and public health · 2021Trial
- Recommendations on Methods for Assessing Multimorbidity Changes Over Time: Aligning the Method to the Purpose.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024Review
- Invasive Phenoprofiling of Acute-Myocardial-Infarction-Related Cardiogenic Shock.Journal of clinical medicine · 2023Article
- Estimating Population-Based Recurrence Rates of Colorectal Cancer over Time in the United States.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020Article
- Mental health selection: common mental disorder and migration between multiple states of deprivation in a UK cohort.BMJ open · 2020Article
- A Two-Day Acceptance and Commitment Therapy (ACT) Workshop Increases Presence and Work Functioning in Healthcare Workers.Frontiers in psychiatry · 2020Article
- Long-term effects of an e-learning course on patient safety: A controlled longitudinal study with medical students.PloS one · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Multi-state models have been widely used to analyze longitudinal event history data obtained in medical and epidemiological studies. The tools and methods developed recently in this area require completely observed data. However, missing data within variables of interest is very common in practice, and it has been an issue in applications. We propose a type of EM algorithm, which handles missingness within multiple binary covariates efficiently, for multi-state model applications. Simulation studies show that the EM algorithm performs well for both missing completely at random (MCAR) and missing at random (MAR) covariate data. We apply the method to a longitudinal aging and cognition study dataset, the Klamath Exceptional Aging Project (KEAP), whose data were collected at Oregon Health & Science University and integrated into the Statistical Models of Aging and Risk of Transition (SMART) database at the University of Kentucky.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.