Interval-censoring occurs in survival analysis when the time until an event of interest is not known precisely (and instead, only is known to fall into a particular interval). Such censoring commonly is produced when periodic assessments (usually clinical or laboratory examinations) are used to assess if the event has occurred. My objectives were to raise awareness about interval-censoring including its existence, the potential ramifications of ignoring its existence, the different types of interval-censored data, and the analytical methods to analyze such data (including availability in standard statistical software). Asynchronous interval-censored survival analysis was demonstrated by parametric evaluation of risk factors for the time to first detected shedding of Salmonella muenster (identified by repeated periodic fecal cultures) for a herd of dairy cows. These results were compared with those from survival analyses which ignored or approximated the interval-censoring. Ignoring or approximating the asynchronous interval-censoring in the survival analysis generally resulted in the risk factors' regression coefficients having the same signs and a decrease (often >50%) in their absolute size. All the standard errors from the three methods of approximating the interval-censoring were <40% of their interval-censored counterparts. The conclusions drawn from the asynchronous interval-censored analysis versus those from the approximations varied dramatically. (The general conclusion from the approximations was that none of the risk factors for this example warranted further consideration.) That ignoring or approximating the left- and interval-censored nature of the dependent variable resulted in biased results was consistent with the literature. In the currently available asynchronous interval-censored models, the inclusion of time-dependent covariates that vary continuously is awkward. Statistical models for the semi-parametric estimation of asynchronous interval-censored survival analysis are not generally available in standard statistical software.
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http://dx.doi.org/10.1016/s0167-5877(03)00103-x | DOI Listing |
Arch Ital Urol Androl
January 2025
Department of Urology, School of Medicine, Shiraz University of Medical Sciences, Shiraz.
Objectives: This research aimed to compare the prostate cancer (PCa) features, survival rate, and functional outcomes after open suprapubic Radical Prostatectomy (RP) between younger men (≤ 55 years) and older men (> 55 years).
Methods: In this retrospective cohort study, we studied 134 patients with clinically localized PCa who underwent RP at our centers between 2011 and 2019, with 26 (19.40%) patients aged ≤ 55.
Accurate survival prediction of patients with long-bone metastases is challenging, but important for optimizing treatment. The Skeletal Oncology Research Group (SORG) machine learning algorithm (MLA) has been previously developed and internally validated to predict 90-day and 1-year survival. External validation showed promise in the United States and Taiwan.
View Article and Find Full Text PDFJ Clin Orthop Trauma
February 2025
Trauma and Orthopaedics East and North Hertfordshire NHS Trust Lister Hospital, Stevenage, UK.
Background: There has been an increasing interest in elbow hemiarthroplasty to circumvent the problems with total elbow arthroplasty for comminuted distal humerus fractures in the elderly. The primary aim of the study is to assess the mid-term clinical and radiological outcomes of patients undergoing TEA and hemiarthroplasty for distal humerus fractures.
Methods: Retrospective analysis of data for patients undergoing hemiarthroplasty for distal humerus fractures (OTA- C3 Comminuted total articular fractures) was done.
Front Immunol
January 2025
Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Background: Leukocytes play an important role in inflammatory response after a traumatic brain injury (TBI). We designed this study to identify TBI phenotypes by clustering blood levels of various leukocytes.
Methods: TBI patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were included.
Front Immunol
January 2025
Department of Clinical Pharmaceutics, Nagoya City University Graduate School of Medical Sciences, Nagoya, Aichi, Japan.
Introduction: Immune-related adverse events (irAEs) induced by immune checkpoint inhibitors are difficult to predict and can lead to severe events. Although it is important to develop strategies for the early detection of severe irAEs, there is a lack of evidence on irAEs associated with ipilimumab plus nivolumab therapy for metastatic renal cell carcinoma (RCC). Therefore, this study aimed to investigate the association between eosinophil and severe irAEs in patients receiving ipilimumab plus nivolumab therapy for RCC.
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