Proportional Hazards Assumption Research Articles

There is More to Length of Survival than Survival: Endpoints in Oncology Trials.

Progression free survival as a primary endpoint for comparative trials does not fully capture the therapeutic risk/benefit ratio. Additionally, summarization of the treatment effect via a hazard ratio is problematic when the proportional hazards assumption is violated. Restricted mean survival time metrics may address these challenges but have other limitations.

The use and abuse of survival analysis and Kaplan-Meier curves in surgical trials.

Survival analysis based on Cox regression and Kaplan-Meier curves, initially devised for oncology trials, have frequently been used in other contexts where fundamental statistical assumptions (such as a constant hazard ratio) are not satisfied. This is almost always the case in trials that compare surgery with medical management. We review a trial that compared extracranial-intracranial bypass surgery (EC-IC bypass) with medical management (MM) of patients with symptomatic occlusion of the carotid or middle cerebral artery, where it was claimed that surgery was of no benefit. We discuss a hypothetical study and review other neurovascular trials which have also used survival analysis to compare results. The trial comparing EC-IC bypass and MM did not satisfy the fundamental proportional hazard assumption necessary for valid analyses. This was also the case for two prior EC-IC bypass trials, as well as for other landmark neurovascular studies, such as the trials comparing endarterectomy with MM for carotid stenoses, or for the trial that compared intervention and MM for unruptured brain arteriovenous malformations. While minor deviations may have little effect on large trials, it may be impossible to show the benefits of surgery when trial size is small and deviations large. Survival analyses are inappropriate in RCTs comparing surgery with conservative management, unless survival is calculated after the postoperative period. Alternative ways to compare final clinical outcomes, using for example a fixed follow-up period, should be planned for preventive surgical trials that compare intervention with conservative management.

Non-proportional hazards model with a PVF frailty term: application with a melanoma dataset

Survival data analysis often uses the Cox proportional hazards (PH) model. This model is widely applied due to its straightforward interpretation of the hazard ratio under the assumption that the hazard rates for two subjects remain constant over time. However, in several randomized clinical trials with long-term survival data comparing two new treatments, it is frequently observed that Kaplan-Meier plots exhibit crossing survival curves. This violation of the PH assumption of the Cox PH model can not be applied to evaluate the treatment's effect on survival. This paper introduces a novel long-term survival model with non-PH that incorporates a frailty term into the hazard function. This model allows us to examine the effect of prognostic factors on survival and quantify the degree of unobservable heterogeneity. The model parameters are estimated using the maximum likelihood estimation procedure, and we evaluate the performance of the proposed models through simulation studies. Additionally, we demonstrate the applicability of our approach by fitting the models to a real skin cancer dataset.

Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features

BackgroundSemiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients’ survival analysis.MethodsTraining (n = 411) and testing (n = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic.ResultsAlthough the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis.ConclusionsThe Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival.Trial registrationClinicalTrials.gov NCT03543215, https://clinicaltrials.gov/, date of registration: 30th June 2017.

The impact of maternal smoking during pregnancy and the age of smoking initiation on incident dementia: A prospective cohort study.

The impact of early-life tobacco exposure on dementia has remained unknown. Using the UK Biobank, the associations of maternal smoking during pregnancy (MSDP) and age of smoking initiation (ASI) with the onset time of all-cause dementia were estimated with accelerated failure time models. The effects of MSDP and ASI on brain structure and their genetic correlation to Alzheimer's disease (AD) were analyzed. A Mendelian randomization (MR) analysis was conducted. The time ratios for smokers starting in childhood, adolescence, and adulthood (vs never smokers) were 0.87 (0.76 to 0.99), 0.92 (0.88 to 0.96), and 0.95 (0.89 to 1.01). MSDP and smoking in adolescence altered many brain regions, including the hippocampus. In genetic analysis, MSDP was genetically and causally linked to AD, and a younger ASI was genetically correlated to a higher AD risk. Early-life smoking accelerated dementia onset and was genetically correlated to AD. MSDP demonstrated genetic and causal linkage to AD risks. Unlike the commonly used Cox proportional hazards model, this article uses a parametric survival analysis method - the accelerated failure model - to explore the relationship between exposure to onset time. It can be used as an alternative method when the proportional hazards assumption is not met. Genetic analyses including genetic correlation study and MR analysis and brain structure analyses were conducted to support our findings and explore the potential mechanisms. The study reveals the relationship between different smoking initiation periods and the onset time of dementia and shows that earlier smoking exposure has a more significant impact on dementia. It emphasizes the importance of preventing early smoking. In the future, more research focusing on the relationship between early exposure and dementia is called for to provide more detailed prevention measures for dementia that cover all age groups.

Chronic Obstructive Pulmonary Disease Mortality in Bladder Cancer Patients: A SEER-Based Competing Risk Analysis.

This study was designed to evaluate risk of mortality from chronic obstructive pulmonary disease (COPD) in patients with bladder cancer (BC). Data on patients diagnosed with BC by pathology between 2000 and 2016 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Based on reference data from the general population, the standardized mortality rate (SMR) is calculated. Nelson-Aalen cumulative hazard curves were used for assessment of the risk of COPD mortality in BC patients. Multivariable competing risk models were conducted. The proportional hazards assumption was tested using Schoenfeld residuals, which were scaled and plotted over time for each risk factor. A total of 237,563 BC patients were identified for further analysis from the SEER database, 5,198 of these patients experienced COPD mortality; the overall SMR for COPD mortality in BC patients was 1.58 (95% CI: 1.54-1.63). Age, race, year of diagnosis, histologic type, summary stage, surgery, marital status, college education level, and median household income independently predicted COPD mortality in BC patients. In comparison to the general population, the risk of COPD mortality is significantly higher in patients with BC. Pre-identification of high-risk groups and respiratory care provisions are important measures to effectively improve survival in this group of patients.

Modelling Determinants of Time to Age at First Marriage Among Nigeria Women: Stratified-Extended Cox Model

The time to age at first marriage is the duration of time between birth until the age at which women began to live with their first partner. This study seeks to apply the Stratified-Extended Cox model (SE-Cox) which addresses the limitations of Cox proportional Hazard model and combines the features of both Stratified Cox and Extended Cox in a single model. The data on age at first marriage of women aged 15-49 from the Nigeria Demographic and Health Survey conducted in 2018 was used. It was observed that Highest educational qualification level, religion, birth cohort and age at first sex violates the Cox proportional hazard assumptions. The Highest educational qualification and religion are assumed to be strata while birth cohort and age at first sex are assumed to be time-varying covariates. The study showed that region, location of residence, wealth index, respondent age at first sex and birth cohort are significant factors that affects the age at first marriage.

A transformer model for cause-specific hazard prediction

BackgroudModelling discrete-time cause-specific hazards in the presence of competing events and non-proportional hazards is a challenging task in many domains. Survival analysis in longitudinal cohorts often requires such models; notably when the data is gathered at discrete points in time and the predicted events display complex dynamics. Current models often rely on strong assumptions of proportional hazards, that is rarely verified in practice; or do not handle sequential data in a meaningful way. This study proposes a Transformer architecture for the prediction of cause-specific hazards in discrete-time competing risks. Contrary to Multilayer perceptrons that were already used for this task (DeepHit), the Transformer architecture is especially suited for handling complex relationships in sequential data, having displayed state-of-the-art performance in numerous tasks with few underlying assumptions on the task at hand.ResultsUsing synthetic datasets of 2000–50,000 patients, we showed that our Transformer model surpassed the CoxPH, PyDTS, and DeepHit models for the prediction of cause-specific hazard, especially when the proportional assumption did not hold. The error along simulated time outlined the ability of our model to anticipate the evolution of cause-specific hazards at later time steps where few events are observed. It was also superior to current models for prediction of dementia and other psychiatric conditions in the English longitudinal study of ageing cohort using the integrated brier score and the time-dependent concordance index. We also displayed the explainability of our model’s prediction using the integrated gradients method.ConclusionsOur model provided state-of-the-art prediction of cause-specific hazards, without adopting prior parametric assumptions on the hazard rates. It outperformed other models in non-proportional hazards settings for both the synthetic dataset and the longitudinal cohort study. We also observed that basic models such as CoxPH were more suited to extremely simple settings than deep learning models. Our model is therefore especially suited for survival analysis on longitudinal cohorts with complex dynamics of the covariate-to-outcome relationship, which are common in clinical practice. The integrated gradients provided the importance scores of input variables, which indicated variables guiding the model in its prediction. This model is ready to be utilized for time-to-event prediction in longitudinal cohorts.

Interaction of physical activity and low-level air pollution on cardiovascular disease: a prospective cohort study in UK Biobank.

To investigate theassociations of physical activity (PA), low-level air pollution, and interaction on cardiovascular diseases (CVD) incidence based on the UK Biobank. PA was measured by the International Physical Activity Questionnaire and five air pollutants were estimated using Land Use Regression. All association estimates were based on Cox regression. Dose-response relationship was explored by restricted cubic spline, while multiplicative and additive interaction were examined by Pinteraction and relative excess risk due to interaction (RERI). As deviating proportional hazards assumption, we analyzed data as follow-up < 4years and ≥ 4years, separately. PA with 1000-4000 Metabolic Equivalent Task (MET) min/week showed the strongest protective impact on CVD incidence, while only low-level nitrogen dioxides (NO2) showed negative impact among five air pollutants and was considered for further analysis. Multiplicative interaction between PA and NO2 was observed during ≥ 4years follow-up (Pinteraction = 0.049) while not during < 4years (Pinteraction = 0.290). Positive additive interactions were found for high PA and low NO2 (< 20 μg/m3) group (RERI: 0.07, 95% confidence intervals: 0.02-0.11) during < 4years, and for moderate PA with NO2 at 40- μg/m3 (0.07, 0.02-0.13) and < 20 μg/m3 (0.07, 0.02-0.12), while high PA showed similar results with NO2 at 40-, 20- and < 20 μg/m3 during ≥ 4years. PA about 1000-4000 METs min/week showed the lowest CVD risk. Possibility of interaction with PA and NO2 is more likely to present with the increase in follow-up duration. We call for the optimal thresholds of PA, and exploring interaction thoroughly by considering types of PA.

Association between blood pressure and new onset of chronic kidney disease in non-diabetic Japanese adults: A population-based longitudinal study from 1998 to 2023

Background and aimsHypertension is a risk factor for developing chronic kidney disease (CKD). Studies of adult participants in the USA reported that hypertension increased the risk of developing CKD even in the non-diabetic population. However, studies in non-diabetic populations are limited and additional studies in other races are required. This study aimed to examine the relationship between hypertension and the development of CKD in non-diabetic Asian adults. Methods and resultsIn this longitudinal study, non-diabetic Japanese adults who took annual checkups from 1998 to 2023 were included. CKD was defined as <60 mL/min/1.73 m2, and hypertension was classified into four levels according to the guidelines of the American College of Cardiology/American Heart Association. The Weibull accelerated failure time model was selected because the proportional hazards assumption was violated. Of the 7363 (men: 40.3%) people in the final cohort, 2498 (men: 40.1%) developed CKD after a mean follow-up of 7.99 years. Elevated blood pressure (BP) and hypertension stage 2 had a 9% (95% confidence interval [CI]: 1%–16%) and 11% (95% CI: 5%–17%) shorter survival time to CKD onset, respectively, than normal BP. Hypertension stage 1 also had a shorter survival to CKD onset by point estimate, but all 95% CIs crossed 1 in all models. ConclusionsIn a relatively healthy Asian population without diabetes, controlling BP to an appropriate range reduces the risk of developing CKD.

Causal interpretation of the hazard ratio in randomized clinical trials.

Although the hazard ratio has no straightforward causal interpretation, clinical trialists commonly use it as a measure of treatment effect. We review the definition and examples of causal estimands. We discuss the causal interpretation of the hazard ratio from a two-arm randomized clinical trial, and the implications of proportional hazards assumptions in the context of potential outcomes. We illustrate the application of these concepts in a synthetic model and in a model of the time-varying effects of COVID-19 vaccination. We define causal estimands as having either an individual-level or population-level interpretation. Difference-in-expectation estimands are both individual-level and population-level estimands, whereas without strong untestable assumptions the causal rate ratio and hazard ratio have only population-level interpretations. We caution users against making an incorrect individual-level interpretation, emphasizing that in general a hazard ratio does not on average change each individual's hazard by a factor. We discuss a potentially valid interpretation of the constant hazard ratio as a population-level causal effect under the proportional hazards assumption. We conclude that the population-level hazard ratio remains a useful estimand, but one must interpret it with appropriate attention to the underlying causal model. This is especially important for interpreting hazard ratios over time.

Additive and Dose-Dependent Effect of Rituximab on Pediatric Patients’ Outcomes Concurrently Receiving Immunosuppressive Chemotherapy, A Prospective Cohort Study

Background: Rituximab (RTX) is approved for treating CD20-positive B-cell malignancies, including non-Hodgkin lymphoma, when used alongside chemotherapy. It has the potential to interact with and alter the host immune system, putting patients at a heightened risk of infection. Therefore, the use of RTX necessitates a meticulous assessment of infectious risks based on the latest evidence. Objectives: We conducted a prospective investigation into infectious complications and mortality among children undergoing RTX treatment over the observation period of the study. Methods: In this observational cohort study, we included 61 pediatric patients treated with RTX for malignancy and immune thrombocytopenic purpura (ITP), as well as 122 cancer patients who had never received RTX (the unexposed group). These patients were prospectively monitored for febrile neutropenia (FN), bloodstream infection (BSI), invasive fungal infection (IFI), and mortality. All infectious complications were documented starting from the initial dose of RTX and continuing for at least six months following the last dose. Logistic regression and Cox regression analyses, with consideration for the proportional hazards assumption, were utilized to evaluate the impact of covariates on mortality and infection-related outcomes. Results: Infectious complications were observed in 52.5% of children treated with RTX. These complications were notably more prevalent in children with malignancy compared to those with chronic ITP (89.5% versus 10.5%, respectively). RTX was found to be associated with an increased likelihood of mortality in children with malignancy (OR [95% CI]: 1.54 [0.41, 5.69]). According to Cox regression analysis, RTX was linked to a higher risk of IFIs, death, FN events, and BSIs over a 36-month observation period (4.33 [1.21, 15.52], 3.26 [1.008, 10.59], 1.68 [0.83, 3.41], and 1.59 [0.27, 9.17], respectively). The total dose of RTX administered was also associated with adverse patient outcomes, with the odds of infectious events and death increasing in the second, third, and fourth quartiles of the total RTX dose administered. Furthermore, the estimated one-year and two-year survival rates for cancer patients treated with RTX were 77% and 58%, respectively. Conclusions: RTX treatment, when used concurrently with immunosuppressive chemotherapy for hematologic malignancies in children, showed additive and dose-dependent effects on clinical outcomes.

Comparison of methods for handling outliers in Cox regression model

The Cox regression analysis is used to determine the relationship between a dependent variable and covariates in survival analysis involving censored data. The proportional hazards assumption is one of the most important assumptions of Cox regression. Outliers may have a strong influence on the Cox regression model’s parameter estimates and lead to violation of the proportional hazard assumption. Therefore, having outliers in the data set is a problem for researchers. In this case, robust estimations are commonly used to infer the parameters in a more robust way. However, we explore a new approach consisting of considering an outlier as missing data and replacing it by the multiple imputation method. The aim of this study is to compare these two methods through simulation. Furthermore, an analysis of a lung cancer data set is considered for illustration. According to the results of the study carried out based on simulated data sets and a real data set, the multiple imputation method, which is a missing data analysis method, solves the problem of outliers better than the robust estimation method, as the outcome is closer to the results obtained through original data.

Methods for non-proportional hazards in clinical trials: A systematic review.

For the analysis of time-to-event data, frequently used methods such as the log-rank test or the Cox proportional hazards model are based on the proportional hazards assumption, which is often debatable. Although a wide range of parametric and non-parametric methods for non-proportional hazards has been proposed, there is no consensus on the best approaches. To close this gap, we conducted a systematic literature search to identify statistical methods and software appropriate under non-proportional hazard. Our literature search identified 907 abstracts, out of which we included 211 articles, mostly methodological ones. Review articles and applications were less frequently identified. The articles discuss effect measures, effect estimation and regression approaches, hypothesis tests, and sample size calculation approaches, which are often tailored to specific non-proportional hazard situations. Using a unified notation, we provide an overview of methods available. Furthermore, we derive some guidance from the identified articles.

Spatial-temporal Bayesian accelerated failure time models for survival endpoints with applications to prostate cancer registry data

Prostate cancer is the most common cancer after non-melanoma skin cancer and the second leading cause of cancer deaths in US men. Its incidence and mortality rates vary substantially across geographical regions and over time, with large disparities by race, geographic regions (i.e., Appalachia), among others. The widely used Cox proportional hazards model is usually not applicable in such scenarios owing to the violation of the proportional hazards assumption. In this paper, we fit Bayesian accelerated failure time models for the analysis of prostate cancer survival and take dependent spatial structures and temporal information into account by incorporating random effects with multivariate conditional autoregressive priors. In particular, we relax the proportional hazards assumption, consider flexible frailty structures in space and time, and also explore strategies for handling the temporal variable. The parameter estimation and inference are based on a Monte Carlo Markov chain technique under a Bayesian framework. The deviance information criterion is used to check goodness of fit and to select the best candidate model. Extensive simulations are performed to examine and compare the performances of models in different contexts. Finally, we illustrate our approach by using the 2004-2014 Pennsylvania Prostate Cancer Registry data to explore spatial-temporal heterogeneity in overall survival and identify significant risk factors.

Time to Death and Its Predictors among Neonates Hospitalized with Sepsis in Eastern Ethiopia.

Although neonatal sepsis is a major public health problem contributing to 30-50% of neonatal deaths in low- and middle-income countries, data on predictors of time to death are limited in Eastern Ethiopia. This study is aimed at determining predictors of time to death among neonates with sepsis admitted in public hospitals in Eastern Ethiopia. An institutional-based retrospective cohort study was conducted among 415 neonates admitted to referral hospitals in Eastern Ethiopia with sepsis from January 1, 2021, to December 31, 2021. Data were collected from medical records by using structured checklist and entered using EpiData 3.1 and analyzed using Stata 17. The Kaplan-Meier curves and log-rank tests were used to describe survival experience among different categories. The proportional hazard assumption and goodness of fit for the Cox regression model were checked. The Cox regression model was used to identify the significant predictors. Hazard ratios (HRs) with 95% confidence intervals (CI) were calculated. Finally, statistical significance was set at a p value < 0.05 in the Cox regression analysis. Of the 415 neonates with neonatal sepsis, 71 (17.1%) (95% CI: 13.60-21.08) died at discharge, with a median time to death of 14 days. The overall incidence rate of mortality was 36.5 per 1000 neonate days. Low birthweight (AHR = 2.50; 95% CI: 1.15-5.44), maternal age ≥ 35 years (AHR = 3.17; 95% CI: 1.11, 9.04), low fifth-minute Apgar score (AHR: 2.32; 95% CI: 1.30-4.14), and late initiation of breastfeeding (AHR = 4.82; 95% CI: 1.40-16.65) were independent predictors of mortality among neonates with sepsis. Almost one in five neonates with sepsis died at discharge. Low birthweight, maternal age ≥ 35 years, low fifth-minute Apgar score, and late initiation of breastfeeding were predictors of mortality.

Statistical primer: individual patient data meta-analysis and meta-analytic approaches in case of non-proportional hazards.

Individual patient data (IPD) meta-analyses build upon traditional (aggregate data) meta-analyses by collecting IPD from the individual studies rather than using aggregated summary data. Although both traditional and IPD meta-analyses produce a summary effect estimate, IPD meta-analyses allow for the analysis of data to be performed as a single dataset. This allows for standardization of exposure, outcomes, and analytic methods across individual studies. IPD meta-analyses also allow the utilization of statistical methods typically used in cohort studies, such as multivariable regression, survival analysis, propensity score matching, uniform subgroup and sensitivity analyses, better management of missing data, and incorporation of unpublished data. However, they are more time-intensive, costly, and subject to participation bias. A separate issue relates to the meta-analytic challenges when the proportional hazards assumption is violated. In these instances, alternative methods of reporting time-to-event estimates, such as restricted mean survival time should be used. This statistical primer summarizes key concepts in both scenarios and provides pertinent examples.

Predictors of Attrition in the Pathways to Desistance Study: Testing for Differential Salience Across Time

There is limited research examining predictors of attrition in the Pathways to Desistance study. There is also a paucity of research examining time-varying effects of predictors of attrition. Changes in characteristics across a study period may lead to the emergence of increases/decreases in attrition risk across time. The Pathways to Desistance dataset (11 waves) was analyzed. Cox proportional hazard modeling was used to examine effects of time-stable and time-varying predictors of attrition in four domains: sociodemographic, mental health, behavioral, and psychosocial. The proportional hazards assumption was tested to determine if effects of predictors varied. Results indicated that sociodemographic and psychosocial predictors were most relevant for understanding differential attrition. Effects of age, peers, and gender on attrition risk varied across time.

Transporting survival of an HIV clinical trial to the external target populations

ABSTRACT Due to the heterogeneity of the randomized controlled trial (RCT) and external target populations, the estimated treatment effect from the RCT is not directly applicable to the target population. For example, the patient characteristics of the ACTG 175 HIV trial are significantly different from that of the three external target populations of interest: US early-stage HIV patients, Thailand HIV patients, and southern Ethiopia HIV patients. This paper considers several methods to transport the treatment effect from the ACTG 175 HIV trial to the target populations beyond the trial population. Most transport methods focus on continuous and binary outcomes; on the contrary, we derive and discuss several transport methods for survival outcomes: an outcome regression method based on a Cox proportional hazard (PH) model, an inverse probability weighting method based on the models for treatment assignment, sampling score, and censoring, and a doubly robust method that combines both methods, called the augmented calibration weighting (ACW) method. However, as the PH assumption was found to be incorrect for the ACTG 175 trial, the methods that depend on the PH assumption may lead to the biased quantification of the treatment effect. To account for the violation of the PH assumption, we extend the ACW method with the linear spline-based hazard regression model that does not require the PH assumption. Applying the aforementioned methods for transportability, we explore the effect of PH assumption, or the violation thereof, on transporting the survival results from the ACTG 175 trial to various external populations.

Abstract 3462: Mendelian randomization analysis of 27 cardiometabolic trait related polygenic scores identifies genetic risk for cardiometabolic and cardiovascular events in a large prospective study of breast cancer survivors

Abstract Background: CVD is the most common cause of non-cancer death in breast cancer patients. Due to cancer treatment-related cardiotoxicities, breast cancer survivors are at increased risk of cardiometabolic disease (CMD) and cardiovascular disease (CVD). In addition to treatment-related factors, behavioral risk factors such as diet, smoking, and obesity are shared between CVD and breast cancer. While clinical factors influencing the risk of CVD in breast cancer patients are well studied, the effects of genetic factors are less known. Methods: To investigate genetic factors contributing to risk of CMD and CVD in women with a history of breast cancer, we performed a Mendelian randomization analysis to explore the relationships between 27 polygenic scores (PGS) for CMD and CVD phenotypes selected from the PGS Catalog and literature and 13 incident CMD and CVD events in a large prospective cohort of 3,699 breast cancer survivors in the Pathways Study. Fine-Gray sub-distribution hazard ratios (HRs) and 95% confidence intervals (CIs) associated with one standard deviation of PGS were derived with adjustment for epidemiologic, clinical, and treatment factors. The time scale was defined as time since date of breast cancer diagnosis to first of incident event, health plan disenrollment, death, or end of study (12/31/2019). The proportional hazards assumption was tested using Schoenfeld residuals, and no violation was found. Results: We identified 19 PGS-CMD/CVD associations that met Bonferroni significance (P&amp;lt;1.17 × 10−4). Five PGS were associated with increased risk of dyslipidemia: apolipoprotein B (HR=1.52, 95% CI,1.38-1.65), LDL cholesterol (1.49, 1.36-1.63), total cholesterol (1.43, 1.31-1.56), triglyceride levels (1.24, 1.14-1.34), and coronary artery disease (1.22, 1.13-1.33). Four PGS were associated with increased risk of hypertension: systolic blood pressure (1.36, 1.25-1.48), diastolic blood pressure (1.21, 1.11-1.32), fasting glucose adjusted for BMI (1.19, 1.09-1.30), and hypertension (1.41, 1.29-1.54). PGS for atrial fibrillation was associated with increased risk of arrhythmia (1.40, 1.25-1.57) and any cardiovascular disease (1.22, 1.12-1.32). PGS for coronary artery disease and myocardial infarction were both associated with increased risk of ischemic heart disease (1.43, 1.21-1.70 and 1.38, 1.17-1.62, respectively). Conclusion: Our study provides strong evidence for the potential use of PGS to predict the risk of CVD and CMD in women with a history of breast cancer. Future studies may explore the use of PGS to identify women with breast cancer at high risk of CVD-related outcomes and strategies to mitigate this risk. Citation Format: Peter N. Fiorica, Haiyang Sheng, Cecile A. Laurent, Janise M. Roh, Valerie S. Lee, Alexa Zimbalist, Isaac J. Ergas, Qianqian Zhu, Richard K. Cheng, Eileen Rillamas-Sun, Carlos Iribarren, Jamal S. Rana, Mai Nguyen-Huynh, Dawn L. Hershman, Lawrence H. Kushi, Christine B. Ambrosone, Marilyn L. Kwan, Heather Greenlee, Song Yao. Mendelian randomization analysis of 27 cardiometabolic trait related polygenic scores identifies genetic risk for cardiometabolic and cardiovascular events in a large prospective study of breast cancer survivors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3462.