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Interaction analysis is a critical component of clinical and public health research and represents a key topic in precision health and medicine. In applied settings, however, interaction assessment is usually limited to the test of a product term in a regression model and to the presentation of results stratified by levels of additional covariates. Stratification of results often relies on categorizing or making linearity assumptions for continuous covariates, with substantial loss of precision and of relevant information. In time-to-event analysis, moreover, interaction assessment is often limited to the multiplicative hazard scale by inclusion of a product term in a Cox regression model, disregarding the clinically relevant information that is captured by the absolute risk scale. In this paper we present a user-friendly procedure, based on the prediction of individual absolute risks from the Cox model, for the estimation and presentation of interactive effects on both the multiplicative and additive scales in survival analysis. We describe how to flexibly incorporate interactions with continuous covariates, which potentially operate in a nonlinear fashion, provide software for replicating our procedure, and discuss different approaches to deriving CIs. The presented approach will allow clinical and public health researchers to assess complex relationships between multiple covariates as they relate to a clinical endpoint, and to provide a more intuitive and precise depiction of the results in applied research papers focusing on interaction and effect stratification. [ABSTRACT FROM AUTHOR]

BACKGROUND: Cardiovascular trials often use a composite end point and a time-to-first event model. We sought to compare edoxaban versus warfarin using the win ratio, which offers data complementary to time-to-first event analysis, emphasizing the most severe clinical events. METHODS: ENGAGE AF-TIMI 48 (Effective Anticoagulation With Factor Xa Next Generation in Atrial Fibrillation-Thrombolysis in Myocardial Infarction 48) was a double-blind, randomized trial in which patients with atrial fibrillation were assigned 1:1:1 to a higher dose edoxaban regimen (60/30 mg daily), a lower dose edoxaban regimen (30/15 mg daily), or warfarin. In an exploratory analysis, we analyzed the trial outcomes using an unmatched win ratio approach. The win ratio for each edoxaban regimen was the total number of edoxaban wins divided by the number of warfarin wins for the following ranked clinical outcomes: 1: death; 2: hemorrhagic stroke; 3: ischemic stroke/systemic embolic event/epidural or subdural bleeding; 4: noncerebral International Society on Thrombosis and Haemostasis major bleeding; and 5: cardiovascular hospitalization. RESULTS: 21 105 patients were randomized to higher dose edoxaban regimen (N=7035), lower dose edoxaban regimen (N=7034), or warfarin (N=7046), yielding >49 million pairs for each treatment comparison. The median age was 72 years, 38% were women, and 59% had prior vitamin K antagonist use. The win ratio was 1.11 (95% CI, 1.05-1.18) for higher dose edoxaban regimen versus warfarin and 1.11 (95% CI, 1.05-1.18) for lower dose edoxaban regimen versus warfarin. The favorable impacts of edoxaban on death (34% of wins) and cardiovascular hospitalization (41% of wins) were the major contributors to the win ratio. Results consistently favored edoxaban in subgroups based on creatine clearance and dose reduction at baseline, with heightened benefit among those without prior vitamin K antagonist use. CONCLUSIONS: In a win ratio analysis of the ENGAGE AF-TIMI 48 trial, both dose regimens of edoxaban were superior to warfarin for the net clinical outcome incorporating ischemic and bleeding events. As the win ratio emphasizes the most severe clinical events, this analysis supports the superiority of edoxaban over warfarin in patients with atrial fibrillation. [ABSTRACT FROM AUTHOR]

Motivation Very low-depth sequencing has been proposed as a cost-effective approach to capture low-frequency and rare variation in complex trait association studies. However, a full characterization of the genotype quality and association power for very low-depth sequencing designs is still lacking. Results We perform cohort-wide whole-genome sequencing (WGS) at low depth in 1239 individuals (990 at 1× depth and 249 at 4× depth) from an isolated population, and establish a robust pipeline for calling and imputing very low-depth WGS genotypes from standard bioinformatics tools. Using genotyping chip, whole-exome sequencing (75× depth) and high-depth (22×) WGS data in the same samples, we examine in detail the sensitivity of this approach, and show that imputed 1× WGS recapitulates 95.2% of variants found by imputed GWAS with an average minor allele concordance of 97% for common and low-frequency variants. In our study, 1× further allowed the discovery of 140 844 true low-frequency variants with 73% genotype concordance when compared to high-depth WGS data. Finally, using association results for 57 quantitative traits, we show that very low-depth WGS is an efficient alternative to imputed GWAS chip designs, allowing the discovery of up to twice as many true association signals than the classical imputed GWAS design. Availability and implementation The HELIC genotype and WGS datasets have been deposited to the European Genome-phenome Archive (https://www.ebi.ac.uk/ega/home): EGAD00010000518; EGAD00010000522; EGAD00010000610; EGAD00001001636, EGAD00001001637. The peakplotter software is available at https://github.com/wtsi-team144/peakplotter , the transformPhenotype app can be downloaded at https://github.com/wtsi-team144/transformPhenotype. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

A key challenge in the analysis of cancer genomes is the identification of driver genes from the vast number of mutations present in a cohort of patients. DOTS-Finder is a new tool that allows the detection of driver genes through the sequential application of functional and frequentist approaches, and is specifically tailored to the analysis of few tumor samples. We have identified driver genes in the genomic data of 34 tumor types derived from existing exploratory projects such as The Cancer Genome Atlas and from studies investigating the usefulness of genomic information in the clinical settings. DOTS-Finder is available at https://cgsb.genomics.iit.it/wiki/projects/DOTS-Finder/. [ABSTRACT FROM AUTHOR]

Aims Materials and Methods Results Conclusions To develop a clinical risk model to identify individuals at higher risk of developing new‐onset diabetes and who might benefit more from weight loss pharmacotherapy.A total of 21 143 patients without type 2 diabetes at baseline from two TIMI clinical trials of stable cardiovascular patients were divided into a derivation (~2/3) and validation (~1/3) cohort. The primary outcome was new‐onset diabetes. Twenty‐seven candidate risk variables were considered, and variable selection was performed using multivariable Cox regression. The final model was evaluated for discrimination and calibration, and for its ability to identify patients who experienced a larger benefit from the weight loss medication lorcaserin in terms of risk of new‐onset diabetes.During a median (interquartile range) follow‐up of 2.3 (1.8–2.7) years, new‐onset diabetes occurred in 1013 patients (7.7%). The final model included five independent predictors (glycated haemoglobin, fasting glucose, age, body mass index, and triglycerides/high‐density lipoprotein). The clinical risk model showed good discrimination (Harrell's C‐indices 0.802, 95% confidence interval [CI] 0.788–0.817 and 0.807, 95% CI 0.788–0.826) in the derivation and validation cohorts. The calibration plot demonstrated adequate calibration (2.5‐year area under the curve was 81.2 [79.1–83.5]). While hazard ratios for new‐onset diabetes with a weight‐loss therapy were comparable across risk groups (annual risks of <1%, 1%–5%, and >5%), there was a sixfold gradient in absolute risk reduction from lowest to highest risk group (p = 0.027).The developed clinical risk model effectively predicts new‐onset diabetes, with potential implications for personalized patient care and therapeutic decision making. [ABSTRACT FROM AUTHOR]

Importance: Elevated lipoprotein(a) (Lp[a]) is a putative causal risk factor for atherosclerotic cardiovascular disease (ASCVD). There are conflicting data as to whether Lp(a) may increase cardiovascular risk only in the presence of concomitant inflammation., Objective: To investigate whether Lp(a) is associated with cardiovascular risk independent of high-sensitivity C-reactive protein (hs-CRP) in both primary and secondary prevention populations., Design, Setting, and Participants: This cohort study uses data from 3 distinct cohorts, 1 population-based cohort and 2 randomized clinical trials. Participants included individuals from the UK Biobank (data from 2006-2010) without prevalent ASCVD, participants in the FOURIER (TIMI 59) trial (data from 2013-2017) who had baseline Lp(a) and hs-CRP data, and participants in the SAVOR-TIMI 53 trial (data from 2010-2013) who had prevalent ASCVD and baseline values for Lp(a) and hs-CRP. The data analysis took place from November 2022 to November 2023., Exposure: Baseline plasma Lp(a), considered either as a continuous variable or dichotomized at 125 nmol/L., Main Outcomes and Measures: Risk of major adverse cardiovascular events (MACE) (composite of cardiovascular death, myocardial infarction [MI], or ischemic stroke), the individual MACE components, and peripheral artery disease (PAD)., Results: Among 357 220 individuals in the UK Biobank without prevalent ASCVD, 232 699 (65%) had low hs-CRP (<2 mg/L), and 124 521 (35%) had high hs-CRP (≥2 mg/L) values. In a Cox proportional hazard model adjusted for ASCVD risk factors, higher Lp(a) was associated with increased cardiovascular risk regardless of baseline hs-CRP value for MACE (hs-CRP ≥2 mg/L: hazard ratio [HR] per 50-nmol/L higher Lp[a], 1.05; 95% CI, 1.04-1.07; P < .001; for hs-CRP <2 mg/L: HR, 1.05; 95% CI, 1.04-1.07; P < .001; P = .80 for interaction), as well as MI, ischemic stroke, and PAD individually. Among 34 020 individuals in the FOURIER and SAVOR trials with baseline cardiometabolic disease, there were 17 643 (52%) with low and 16 377 (48%) with high baseline hs-CRP values. In Cox proportional hazard models using aggregated data from FOURIER and SAVOR, higher baseline Lp(a) was associated with increased cardiovascular risk regardless of baseline hs-CRP for MACE (hs-CRP ≥2 mg/L: HR per 50-nmol/L higher Lp[a], 1.02; 95% CI, 1.00-1.05; P = .04; hs-CRP <2 mg/L: HR, 1.05; 95% CI, 1.02-1.08; P < .001; P = .16 for interaction), MI, and PAD., Conclusions and Relevance: In this study, higher levels of Lp(a) were associated with MACE, MI, and PAD in both primary and secondary prevention populations regardless of baseline hs-CRP value.

Background: Venous thromboembolism (VTE) is a major cause of cardiovascular morbidity and mortality and has a known genetic contribution. We tested the performance of a genetic risk score for its ability to predict VTE in 3 cohorts of patients with cardiometabolic disease., Methods: We included patients from the FOURIER (Further Cardiovascular Outcomes Research With PCSK9 Inhibition in Patients With Elevated Risk), PEGASUS-TIMI 54 (Prevention of Cardiovascular Events in Patients With Prior Heart Attack Using Ticagrelor Compared to Placebo on a Background of Aspirin), and SAVOR-TIMI 53 (Saxagliptin Assessment of Vascular Outcomes Recorded in Patients with Diabetes Mellitus) trials (history of a major atherosclerotic cardiovascular event, myocardial infarction, and diabetes, respectively) who consented for genetic testing and were not on baseline anticoagulation. We calculated a VTE genetic risk score based on 297 single nucleotide polymorphisms with established genome-wide significance. Patients were divided into tertiles of genetic risk. Cox proportional hazards models were used to calculate hazard ratios for VTE across genetic risk groups. The polygenic risk score was compared with available clinical risk factors (age, obesity, smoking, history of heart failure, and diabetes) and common monogenic mutations., Results: A total of 29 663 patients were included in the analysis with a median follow-up of 2.4 years, of whom 174 had a VTE event. There was a significantly increased gradient of risk across VTE genetic risk tertiles ( P -trend <0.0001). After adjustment for clinical risk factors, patients in the intermediate and high genetic risk groups had a 1.88-fold (95% CI, 1.23-2.89; P =0.004) and 2.70-fold (95% CI, 1.81-4.06; P <0.0001) higher risk of VTE compared with patients with low genetic risk. In a continuous model adjusted for clinical risk factors, each standard deviation increase in the genetic risk score was associated with a 47% (95% CI, 29-68) increased risk of VTE ( P <0.0001)., Conclusions: In a broad spectrum of patients with cardiometabolic disease, a polygenic risk score is a strong, independent predictor of VTE after accounting for available clinical risk factors, identifying 1/3 of patients who have a risk of VTE comparable to that seen with established monogenic thrombophilia.

Background: The relationship between cholesterol levels and risk of venous thromboembolism (VTE) is uncertain. We set out to determine the effect of PCSK9 (proprotein convertase subtilisin/kexin type 9) inhibition on the risk of VTE, explore potential mechanisms, and examine the efficacy in subgroups with clinically and genetically defined risk., Methods: We performed a post hoc analysis of the FOURIER trial (Further Cardiovascular Outcomes Research With PCSK9 Inhibition in Subjects With Elevated Risk) testing whether evolocumab reduces the risk of VTE events (deep venous thrombosis or pulmonary embolism). Data from FOURIER and ODYSSEY OUTCOMES (Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment with Alirocumab) were then combined in a meta-analysis to assess the class effect of PCSK9 inhibition on the risk of VTE. We also analyzed baseline lipids in FOURIER to investigate potential mechanisms explaining the reduction in VTE with evolocumab. Last, an exploratory genetic analysis was performed in FOURIER to determine whether a VTE polygenic risk score could identify high-risk patients who would derive the greatest VTE reduction from evolocumab., Results: In FOURIER, the hazard ratio (HR) for VTE with evolocumab was 0.71 (95% CI, 0.50-1.00; P =0.05), with no effect in the 1st year (HR, 0.96 [95% CI, 0.57-1.62]) but a 46% reduction (HR, 0.54 [95% CI, 0.33-0.88]; P =0.014) beyond 1 year. A meta-analysis of FOURIER and ODYSSEY OUTCOMES demonstrated a 31% relative risk reduction in VTE with PCSK9 inhibition (HR, 0.69 [95% CI, 0.53-0.90]; P =0.007). There was no relation between baseline low-density lipoprotein cholesterol levels and magnitude of VTE risk reduction. In contrast, in patients with higher baseline lipoprotein(a) (Lp[a]) levels, evolocumab reduced Lp(a) by 33 nmol/L and risk of VTE by 48% (HR, 0.52 [95% CI, 0.30-0.89]; P =0.017), whereas, in patients with lower baseline Lp(a) levels, evolocumab reduced Lp(a) by only 7 nmol/L and had no effect on VTE risk ( P interaction 0.087 for HR; P heterogeneity 0.037 for absolute risk reduction). Modeled as a continuous variable, there was a significant interaction between baseline Lp(a) concentration and magnitude of VTE risk reduction ( P interaction =0.04). A polygenic risk score identified patients who were at >2-fold increased risk for VTE and who derived greater relative ( P interaction =0.04) and absolute VTE reduction ( P heterogeneity =0.009) in comparison with those without high genetic risk., Conclusions: PCSK9 inhibition significantly reduces the risk of VTE. Lp(a) reduction may be an important mediator of this effect, a finding of particular interest given the ongoing development of potent Lp(a) inhibitors.

Purpose: Trials that accrue participants on the basis of genetic biomarkers are a powerful means of testing targeted drugs, but they are often complicated by the rarity of the biomarker-positive population. Umbrella trials circumvent this by testing multiple hypotheses to maximize accrual. However, bigger trials have higher chances of conflicting treatment allocations because of the coexistence of multiple actionable alterations; allocation strategies greatly affect the efficiency of enrollment and should be carefully planned on the basis of relative mutation frequencies, leveraging information from large sequencing projects., Methods: We developed software named Precision Trial Drawer (PTD) to estimate parameters that are useful for designing precision trials, most importantly, the number of patients needed to molecularly screen (NNMS) and the allocation rule that maximizes patient accrual on the basis of mutation frequency, systematically assigning patients with conflicting allocations to the drug associated with the rarer mutation. We used data from The Cancer Genome Atlas to show their potential in a 10-arm imaginary trial of multiple cancers on the basis of genetic alterations suggested by the past Molecular Analysis for Personalised Therapy (MAP) conference. We validated PTD predictions versus real data from the SHIVA (A Randomized Phase II Trial Comparing Therapy Based on Tumor Molecular Profiling Versus Conventional Therapy in Patients With Refractory Cancer) trial., Results: In the MAP imaginary trial, PTD-optimized allocation reduces number of patients needed to molecularly screen by up to 71.8% (3.5 times) compared with nonoptimal trial designs. In the SHIVA trial, PTD correctly predicted the fraction of patients with actionable alterations (33.51% [95% CI, 29.4% to 37.6%] in imaginary v 32.92% [95% CI, 28.2% to 37.6%] expected) and allocation to specific treatment groups (RAS/MEK, PI3K/mTOR, or both)., Conclusion: PTD correctly predicts crucial parameters for the design of multiarm genetic biomarker-driven trials. PTD is available as a package in the R programming language and as an open-access Web-based app. It represents a useful resource for the community of precision oncology trialists. The Web-based app is available at https://gmelloni.github.io/ptd/shinyapp.html.