Statistics in Medicine

Hybrid controlled trials (HCTs) incorporate real-world data into randomized controlled trials (RCTs) by augmenting the internal control arm with patients receiving the same treatment in routine care. Beyond increasing power, HCTs may improve recruitment by supporting unequal randomization ratios that increase patient access to experimental treatments. However, HCT validity is threatened by bias from unmeasured confounding due to lack of randomization of external controls, leading to outcome non-...

Cox proportional hazard regressions are frequently employed to develop prognostic models for time-to-event data, considering both patient-specific and disease-specific characteristics. In high-dimensional clinical modeling, these biological features can exhibit high collinearity due to inter-feature relationships, potentially causing instability and numerical issues during estimation without regularization. For rare diseases such as acute myeloid leukemia (AML), the sparsity and scarcity of data...

Epigenetic clocks estimate biological age from DNA methylation patterns at CpG sites, providing robust predictions of mortality and morbidity risk. "Blue zones"--regions of exceptional longevity--offer a unique opportunity to investigate how biological aging diverges from chronological age. However, standard clocks are typically trained on large, heterogeneous datasets, reflecting average population trends rather than region-specific dynamics. Using data from the Costa Rican Longevity and Health...

BackgroundVaccines can prevent severe disease by preventing infection or by reducing progression among those who become infected. Vaccine effectiveness against progression given infection is often used to quantify this second mechanism, but it conditions on infection, which is itself affected by vaccination. As a result, this estimand lacks a clear causal interpretation and may behave non-intuitively over time. MethodsWe introduce a conceptual framework that models protection against infection ...

Clinical deployment of foundation models requires decision policies that operate under explicit error budgets, such as a cap on false-positive clinical calls. Strong average accuracy alone does not guarantee safety: errors can concentrate among patients selected for action, leading to harm and inefficient use of healthcare resources. Here we introduce SO_SCPLOWTRATC_SCPLOWCP, a stratified conformal framework that turns foundation model predictions into decision-ready outputs through error-contro...

BackgroundHealthcare utilization forecasting systems are often derived from static, annualized market share assumptions that fail to represent real-world treatment dynamics. Such approaches systematically misestimate future utilization by ignoring longitudinal treatment sequencing, discontinuation with surveillance, recurrence-driven re-entry, and provider adoption dynamics. ObjectiveThis study proposes a reusable, governance-driven health informatics forecasting framework designed to generate ...

Drug repurposing offers the opportunity to identify promising drug targets efficiently using existing data, but there are currently limitations to these efforts; there is a particular need for versatile, but rigorous high-throughput approaches. As such, we developed a flexible, high-throughput, Mendelian randomization (MR)-based drug repurposing pipeline with three stages: 1) MR-based identification, 2) MR-based validation and prioritization, and 3) application. This pipeline can be applied to a...

BackgroundTyphoid fever incidence estimates are central to policy decisions on vaccine introduction and investments in non-vaccine prevention and control but are often unavailable. We explored whether prevalence metrics from sentinel studies of community-onset bloodstream infections could accurately predict local Salmonella Typhi (S. Typhi) incidence. MethodsUsing a previous systematic review (January 2018-December 2024), we identified studies reporting both typhoid incidence and prevalence of ...

AO_SCPLOWBSTRACTC_SCPLOWLongitudinal healthcare surveys frequently contain inconsistencies in self-reported onset ages, where participants report different ages for the same condition between enrollment and follow-up surveys. We propose two methods to handle this challenge. First, we introduce a procedure that aggregates inconsistency patterns to construct participant-level reliability scores, enabling researchers to stratify participants and prioritize analysis on high-reliability cohorts. Seco...

BackgroundThe Most Favored Nation (MFN) policy is a mechanism that incorporates foreign prices to determine the maximum allowable net price for any branded drug within US government-funded healthcare. Two proposed rules, the Global Benchmark for Efficient Drug Pricing ("GLOBE") (90 Fed. Reg. 60,244) for Medicare Part B and the Guarding US Medicare Against Rising Drug Costs ("GUARD") (90 Fed. Reg. 60,338) for Medicare Part D, invoke the Center for Medicare and Medicaid Innovation Centers payment ...

Mendelian randomization has emerged as a transformative approach for inferring causal relationships between risk factors and disease outcomes. However, applying Mendelian randomization to disease progression - a critical step in validating pharmacological targets - is hampered by index event bias. This form of selection bias occurs because analyses of disease progression are necessarily restricted to individuals who have already experienced the disease event. Here, we present a comprehensive eva...

BackgroundClinical risk prediction models are typically evaluated by discrimination (area under the receiver operating characteristic curve, AUC), with calibration receiving less attention. We developed a multi-timeframe diabetes prediction framework emphasizing calibration and used synthetic data validation to investigate whether good discrimination guarantees good calibration. MethodsWe generated 500,000 synthetic patients using published epidemiological parameters from QDiabetes-2018, FINDRI...

Background & AimsLiver cancer primarily develops in patients with chronic liver disease (CLD), yet most cases are diagnosed at an advanced stage with poor prognosis. While clinical surveillance of patients with CLD generates extensive longitudinal data, its unstructured free-text nature hinders large-scale research. To unlock this real-world evidence, we developed a scalable framework using open-source Large Language Models (LLMs) to transform unstructured clinical text into structured data. Me...

BackgroundRoutinely collected health data are increasingly used to generate real-world evidence for therapeutic decision-making. Yet, stakeholders, including clinicians, pharmaceutical industry representatives, patient advocacy groups, and statisticians, prioritize different aspects of data quality, analysis, and interpretation. Without explicit consideration of these perspectives, analyses risk being fragmented, misaligned with end-user needs, or lacking transparency. MethodsWe developed a sta...

The UK Biobank (UKB) Brain Imaging cohort contains data from almost 100,000 subjects and has yielded invaluable understanding of the links between the brain and health outcomes and lifestyles. Much of the understanding of these links has come from exploring the association between Imaging Derived Phenotypes (IDPs) and other variables that are unrelated to brain imaging, so called non-Imaging Derived Phenotypes (nIDPs). When performing analysis of this kind, it is very important to control for we...

While the prostate-specific antigen (PSA) test is a widely used prostate cancer screening tool, its application remains controversial. Opportunistic PSA testing generates complex data in which testing intensities, PSA levels, and prostate cancer diagnosis are interdependent. Conventional analyses rarely model these processes jointly. The objective of this study was to develop a population-based joint model to analyse PSA dynamics, retesting patterns, and prostate cancer risk. We used the Stockho...

BackgroundNo randomized clinical trial comparing the most established new modalities of treatment for patients with localized prostate cancer has been published, and there is scarce comparative effectiveness research assessing Patient-Reported Outcome Measures (PROMs). Objectiveto compare the impact of active surveillance, robot-assisted radical prostatectomy (RARP), Intensity-modulated radiotherapy (IMRT), and real-time brachytherapy on patients, through PROMs, from pre-treatment to five years...

BackgroundThe Global Youth Tobacco Survey (GYTS) is widely used to monitor tobacco use among adolescents worldwide. However, inconsistent analytical approaches particularly in handling complex survey designs and predictor selection limit comparability across countries, survey waves, and software platforms. Although much of the GYTS literature relies on proprietary tools such as SAS and SPSS, practical and transparent guidance on implementing reproducible, theory-informed analyses remains limited...

ObjectivesDigital biomarkers offer scalable screening for type 2 diabetes, yet adoption is stalled by uncertainty regarding economic viability. This study evaluates the cost-effectiveness and budget impact of digital screening compared to opportunistic screening from a Swiss payer perspective. MethodsA probabilistic Markov cohort model was developed to simulate at-risk Swiss adults (age [≥]45, BMI [≥]25 kg/m{superscript 2}) over a 40-year horizon. The model incorporates a digital attritio...

BackgroundQuadruple therapy, comprising an angiotensin receptor-neprilysin inhibitor (ARNI), {beta}-blocker, mineralocorticoid receptor antagonist (MRA), and sodium-glucose cotransporter 2 inhibitor (SGLT2i), is guideline-recommended for heart failure with reduced ejection fraction (HFrEF). However, uptake in Singapore remains low. This study evaluated the cost-effectiveness of scaling up quadruple therapy from the current 30% uptake to realistic (80%) and stretch (100%) targets. MethodsWe deve...