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A Commensurate Prior Model With Random Effects for Survival and Competing Risk Outcomes to Accommodate Historical Controls.
Pharm Stat
January 2025
Division of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
Clinical trials (CTs) often suffer from small sample sizes due to limited budgets and patient enrollment challenges. Using historical data for the CT data analysis may boost statistical power and reduce the required sample size. Existing methods on borrowing information from historical data with right-censored outcomes did not consider matching between historical data and CT data to reduce the heterogeneity.
View Article and Find Full Text PDFBayesian Sample Size Calculation in Small n, Sequential Multiple Assignment Randomized Trials (snSMART).
Pharm Stat
January 2025
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
A recent study design for clinical trials with small sample sizes is the small n, sequential, multiple assignment, randomized trial (snSMART). An snSMART design has been previously proposed to compare the efficacy of two dose levels versus placebo. In such a trial, participants are initially randomized to receive either low dose, high dose or placebo in stage 1.
View Article and Find Full Text PDFUrinary lipoarabinomannan for diagnosis of Tuberculosis in an HIV-negative population: A scoping review.
Afr J Prim Health Care Fam Med
December 2024
Department of Internal Medicine, Prince Mshiyeni Memorial Hospital, Durban.
Background: Tuberculosis (TB) remains a leading cause of mortality in low-resource settings and poses a diagnostic challenge in human immunodeficiency virus (HIV)-negative populations because of limitations in traditional diagnostic methods such as sputum smear microscopy (SSM) and sputum Xpert Ultra. There is a lack of effective, non-invasive diagnostic options for TB diagnosis in HIV-negative populations. This scoping review explores the potential of urinary lipoarabinomannan (ULAM) as a point-of-care diagnostic tool for Mycobacterium tuberculosis (MTB) in HIV-negative individuals.
View Article and Find Full Text PDFMachine learning approaches for risk prediction after percutaneous coronary intervention: a systematic review and meta-analysis.
Eur Heart J Digit Health
January 2025
Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, USA.
Aims: Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and peri-procedural planning. Traditional risk models have demonstrated a modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy.
View Article and Find Full Text PDFMachine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis.
Eur Heart J Digit Health
January 2025
School of Life Course & Population Sciences, King's College London, SE1 1UL London, UK.
Cardiovascular disease (CVD) remains a major cause of mortality in the UK, prompting the need for improved risk predictive models for primary prevention. Machine learning (ML) models utilizing electronic health records (EHRs) offer potential enhancements over traditional risk scores like QRISK3 and ASCVD. To systematically evaluate and compare the efficacy of ML models against conventional CVD risk prediction algorithms using EHR data for medium to long-term (5-10 years) CVD risk prediction.
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