A constrained optimum adaptive design for dose finding in early phase clinical trials.

Recently, interest has grown in the development of dose-finding methods that consider both toxicity and efficacy as endpoints. Along with responses on these, the incorporation of pharmacokinetic (PK) data can be beneficial in terms of patients' safety and can also increase the efficiency of the design for finding the best dose for the next phase. In this paper, the maximum concentration (Cmax) is used as the PK measure guiding the dose selection. The ethically attractive approach, which is based on the probability of efficacy, is used as a dose optimisation criterion. At each stage of an adaptive trial, that dose is selected for which the criterion is maximised, subject to the constraints imposed on the Cmax and the probability of toxicity. The inter-patient variability of the PK model parameters is considered, and population D-optimal sampling time points for measuring the concentration of a drug in the blood are calculated. The method is illustrated with a one-compartment PK model with first-order absorption, with the parameters being assumed to be random. The Cox model for bivariate binary responses is employed to model the dose-response outcomes. The results of a simulation study for several plausible dose-response scenarios show a significant gain in the efficiency of the design, as well as a reduction in the proportion of toxic responses.

Limitations of using COX proportional hazards model in cardiovascular research.

The article by Zhao et al. titled "Associations of Triglyceride-Glucose (TyG) Index with Chest Pain Incidence and Mortality among the U.S. Population" provides valuable insights into the positive correlation between the TyG index and chest pain incidence, as well as a nonlinear relationship with mortality. However, the use of the COX proportional hazards model in their analysis presents several limitations. The assumption of constant hazard ratios over time may not hold, potentially leading to biased estimates. The model's struggle with time-dependent covariates and the possibility of residual confounding are notable concerns. Additionally, the study's subgroup analyses might suffer from reduced statistical power, and potential interactions with other metabolic markers were not explored. Considering these limitations, future research should adopt alternative approaches, such as time-varying covariate models, to provide a more comprehensive understanding of the relationship between the TyG index and cardiovascular outcomes.

Risk projection for time-to-event outcome from population-based case-control studies leveraging summary statistics from the target population.

Risk stratification based on prediction models has become increasingly important in preventing and managing chronic diseases. However, due to cost- and time-limitations, not every population can have resources for collecting enough detailed individual-level information on a large number of people to develop risk prediction models. A more practical approach is to use prediction models developed from existing studies and calibrate them with relevant summary-level information of the target population. Many existing studies were conducted under the population-based case-control design. Gail et al. (J Natl Cancer Inst 81:1879-1886, 1989) proposed to combine the odds ratio estimates obtained from case-control data and the disease incidence rates from the target population to obtain the baseline hazard function, and thereby the pure risk for developing diseases. However, the approach requires the risk factor distribution of cases from the case-control studies be same as the target population, which, if violated, may yield biased risk estimation. In this article, we propose two novel weighted estimating equation approaches to calibrate the baseline risk by leveraging the summary information of (some) risk factors in addition to disease-free probabilities from the targeted population. We establish the consistency and asymptotic normality of the proposed estimators. Extensive simulation studies and an application to colorectal cancer studies demonstrate the proposed estimators perform well for bias reduction in finite samples.

Analysis and prediction of long-term survival using a clinically applicable risk score based on the Electronic Health Record.

BACKGROUND: The long-term survival of a population assigned to a hospital can be essential to anticipate, manage, and provide appropriate hospital healthcare resources or lead preventive actions for high-risk mortality individuals. In this study, we discriminate which electronic health record variables are most relevant to predict the long-term survival of a population, and apply the results to identify high-risk mortality groups. MATERIALS AND METHODS: A prospective cohort study was conducted on a population of 113,403 individuals alive on July 1st, 2018 from the General Hospital of Castellón (Spain). Considering electronic health record patients' variables and survival days from the start date of the study, a Kaplan-Meier analysis and a multivariate Cox regression model were performed, and a risk score based on Cox coefficients was applied to predict survival over 3 years. RESULTS: All significant covariates from the Cox model (91.5% c-index) were associated with increased mortality risk. Using the proposed risk score, Kaplan-Meier curves show that survival probability in the 3rd year is 99.23% (95% confidence interval (CI) 99.18-99.29) for the low-risk, 91.21% (95% CI 90.67-91.76) for medium-risk, 76.52% (95% CI 75.59-77.46) for the high-risk, and 48.61 % (95% CI 46.85-50.36) for the very high-risk groups. DISCUSSION: The Cox model obtained is highly predictive, and it has been found that some electronic health record variables little studied to date, such as Clinical Risk Groups, have a strong impact on survival. Regarding clinical application, the proposed risk score is particularly useful for identifying high-risk subpopulations within a large population.

Actuarial Analysis of Survival after Breast Cancer Diagnosis among Lithuanian Females.

Breast cancer is the most common cause of mortality due to cancer for women both in Lithuania and worldwide. The chances of survival after diagnosis differ significantly depending on the stage of disease at the time of diagnosis and other factors. One way to estimate survival is to construct a Kaplan-Meier estimate for each factor value separately. However, in cases when it is impossible to observe a large number of patients (for example, in the case of countries with lower numbers of inhabitants), dividing the data into subsets, say, by stage at diagnosis, may lead to results where some subsets contain too few data, thus causing the results of a Kaplan-Meier (or any other) method to become statistically incredible. The problem may become even more acute if researchers want to use more risk factors, such as stage at diagnosis, sex, place of living, treatment method, etc. Alternatively, Cox models can be used to analyse survival data with covariates, and they do not require the data to be divided into subsets according to chosen risks factors (hazards). We estimate the chances of survival for up to 5 years after a breast cancer diagnosis for Lithuanian females during the period of 1995-2016. Firstly, we construct Kaplan-Meier estimates for each stage separately; then, we apply a (stratified) Cox model using stage, circumstance of diagnosis, and year of diagnosis as (potential) hazards. Some directions of further research are provided in the last section of the paper.

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.

Screening of miRNAs as prognostic biomarkers and their associated hub targets across Hepatocellular carcinoma using survival-based bioinformatics approach.

BACKGROUND: The hepatocellular carcinoma (HCC) incident rate is gradually increasing yearly despite all the research and efforts taken by scientific communities and governing bodies. Approximately 90% of all liver cancer cases belong to HCC. Usually, HCC patients approach the treatment in the late stages of this malignancy which becomes the primary cause of high mortality rate. The knowledge about molecular pathogenesis of HCC is limited and needs more attention from researchers to identify the driver genes and miRNAs, which causes to translate this information into clinical practice. Therefore, the key regulators identification of miRNA-mRNA regulatory network is essential to identify HCC-associated genes. METHODOLOGY: We extracted microRNA (miRNA) and messenger RNA (mRNA) expression datasets of normal and tumor HCC patient samples from UCSC Xena followed by identifying differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs). Univariate and multivariate cox-proportional hazard models were utilized to identify DEMs having significant association with overall survival (OS). Kaplan-Meier (KM) plotter was used to validate the presence of prognostic DEMs. A risk-score model was used to evaluate the effectiveness of KM-plotter validated DEMs combination on risk of samples. Target DEGs of prognostic miRNAs were identified via sources such as miRTargetLink and miRWalk followed by their validation in an external microarray cohort and enrichment analysis. RESULTS: 562 DEGs and 388 DEMs were identified followed by seven prognostic miRNAs (i.e., miR-19a, miR-19b, miR-30d-5p, miR-424-5p, miR-3677-5p, miR-3913-5p, miR-7705) post univariate, multivariate, risk-score model evaluation and KM-plotter analyses. ANLN, MRO, CPEB3 were their targets and were also validated in GSE84005 dataset. CONCLUSIONS: The findings of this study decipher that most significant miRNAs and their identified target genes have association with apoptosis, inflammation, cell cycle regulation and cancer-related pathways, which appear to contribute to HCC pathogenesis and therefore, the discovery of new targets.

Recurrence/prognosis estimation using a molecularly positive surgical margin-based model calls for alternative curative strategies in pIIIA/N2 NSCLC.

Stage pIIIA/N2 non-small cell lung cancer (NSCLC) is primarily treated by complete surgical resection combined with neoadjuvant/adjuvant therapies. However, up to 40% of patients experience tumor recurrence. Here, we studied 119 stage pIIIA/N2 NSCLC patients who received complete surgery plus adjuvant chemotherapy (CT) or chemoradiotherapy (CRT). The paired tumor and resection margin samples were analyzed using next-generation sequencing (NGS). Although all patients were classified as negative resection margins by histologic methods, NGS revealed that 47.1% of them had molecularly positive surgical margins. Patients who tested positive for NGS-detected residual tumors had significantly shorter disease-free survival (DFS) (P = 0.002). Additionally, metastatic lymph node ratio, erb-b2 receptor tyrosine kinase 2 (ERBB2) mutations, and SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily a, member 4 (SMARCA4) mutations were also independently associated with DFS. We used these four features to construct a COX model that could effectively estimate recurrence risk and prognosis. Notably, mutational profiling through broad-panel NGS could more sensitively detect residual tumors than the conventional histologic methods. Adjuvant CT and adjuvant CRT exhibited no significant difference in eliminating locoregional recurrence risk for stage pIIIA/N2 NSCLC patients with molecularly positive surgical margins.

Mediational pathways between aggregate genetic liability and nonfatal suicide attempt: A Swedish population-based cohort.

Despite recent progress in the genetics of suicidal behavior, the pathway by which genetic liability increases suicide attempt risk is unclear. We investigated the mediational pathways from family/genetic risk for suicide attempt (FGRSSA) to suicide attempt by considering the roles of psychiatric illnesses. In a Swedish cohort, we evaluated time to suicide attempt as a function of FGRSSA and the mediational effects of alcohol use disorder, drug use disorder, attention-deficit/hyperactivity disorder, major depression, anxiety disorder, bipolar disorder, and non-affective psychosis. Analyses were conducted by sex in three age periods: 15-25 years (Nfemales = 850,278 and Nmales = 899,366), 26-35 years (Nfemales = 800,189 and Nmales = 861,774), and 36-45 years (Nfemales = 498,285 and Nmales = 535,831). The association between FGRSSA and suicide attempt was mediated via psychiatric disorders. The highest mediation effects were observed for alcohol use disorder in males (15-25 years, HRtotal = 1.60 [1.59; 1.62], mediation = 14.4%), drug use disorder in females (25-36 years, HRtotal = 1.46 [1.44; 1.49], mediation = 11.2%), and major depression (25-36 years) in females (HRtotal = 1.46 [1.44; 1.49], mediation = 7%) and males (HRtotal = 1.50 [1.47;1.52], mediation = 4.7%). While the direct effect of FGRSSA was higher at ages of 15-25, the mediation via psychiatric disorders was more prominent in later adulthood. Our study informs about the psychiatric illnesses via which genetic liability operates to impact suicide attempt risk, with distinct contributions according to age and sex.

Incidence of T2DM and the role of baseline glycaemic status as a determinant in a metropolitan population in northern Madrid (Spain).

AIM: To estimate the incidence of T2DM and assess the effect of pre-T2DM (isolated impaired fasting glucose [iIFG], isolated impaired glucose tolerance [iIGT] or both) on progress to T2DM in the adult population of Madrid. METHODS: Population-based cohort comprising 1,219 participants (560 normoglycaemic and 659 preT2DM [418 iIFG, 70 iIGT or 171 IFG-IGT]). T2DM was defined based on fasting plasma glucose or HbA1c or use of glucose-lowering medication. We used a Cox model with normoglycaemia as reference category. RESULTS: During 7.26 years of follow-up, the unadjusted incidence of T2DM was 11.21 per 1000 person-years (95 %CI, 9.09-13.68) for the whole population, 5.60 (3.55-8.41) for normoglycaemic participants and 16.28 (12.78-20.43) for pre-T2DM participants. After controlling for potential confounding factors, the baseline glycaemic status was associated with higher primary effect on developing T2DM was iIGT (HR = 3.96 [95 %CI, 1.93-8.10]) and IFG-IGT (3.42 [1.92-6.08]). The HR for iIFG was 1.67 (0.96-2.90). Obesity, as secondary effect, was strongly significantly associated (HR = 2.50 [1.30-4.86]). CONCLUSIONS: Our incidence of T2DM is consistent with that reported elsewhere in Spain. While baseline iIGT and IFG-IGT behaved a primary effect for progression to T2DM, iIFG showed a trend in this direction.

An Overview of Introductory and Advanced Survival Analysis Methods in Clinical Applications: Where Have we Come so far?

The time-to-event relationship for survival modeling is considered when designing a study in clinical trials. However, because time-to-event data are mostly not normally distributed, survival analysis uses non-parametric data processing and analysis methods, mainly Kaplan-Meier (KM) estimation models and Cox proportional hazards (CPH) regression models. At the same time, the log-rank test can be applied to compare curves from different groups. However, resorting to conventional survival analysis when fundamental assumptions, such as the Cox PH assumption, are not met can seriously affect the results, rendering them flawed. Consequently, it is necessary to examine and report more sophisticated statistical methods related to the processing of survival data, but at the same time, able to adequately respond to the contemporary real problems of clinical applications. On the other hand, the frequent misinterpretation of survival analysis methodology, combined with the fact that it is a complex statistical tool for clinicians, necessitates a better understanding of the basic principles underlying this analysis to effectively interpret medical studies in making treatment decisions. In this review, we first consider the basic models and mechanisms behind survival analysis. Then, due to common errors arising from the inappropriate application of conventional models, we revise more demanding statistical extensions of survival models related to data manipulation to avoid wrong results. By providing a structured review of the most representative statistical methods and tests covering contemporary survival analysis, we hope this review will assist in solving problems that arise in clinical applications.

Identifying contributing factors and locations of pedestrian severe crashes using hazard-based duration model.

Pedestrian safety remains a significant concern, with the growing number of severe pedestrian crashes resulting in substantial human and economic costs. Previous research into pedestrian crashes has extensively analyzed the influences of weather, lighting, and pedestrian demographics. However, these studies often overlook the critical spatial variables that contribute to pedestrian crashes. Our study aims to explore these overlooked spatial variables by examining the distance pedestrians travel before encountering a severe crash. This approach provides a supplementary perspective in safety analysis, emphasizing the importance of pedestrian movement patterns. The model considers various factors that may influence pedestrian traveled distance before being involved in a severe crash, such as weather conditions, lighting conditions, and pedestrian demographics. Ohio's pedestrian-involved crashes were gathered and analyzed as a case study. The results indicated that 50 % of fatal pedestrian crashes occurred within 0.84 miles of the pedestrians' residences. Moreover, it was shown that factors including lighting condition, pedestrian age, drug toxication, and the location at impact significantly influence the pedestrians traveled distance. These findings provide valuable insights into the spatial distribution of pedestrian crashes and shed light on the factors contributing to their severity. By understanding these relationships, policymakers and urban planners can design targeted interventions such as improving street lighting, implementing traffic calming measures, and developing safety awareness campaigns for specific age groups, to enhance pedestrian safety and reduce the incidence of severe crashes.

Effect of continuous hypertonic saline infusion on clinical outcomes in patients with traumatic brain injury.

Osmotic therapy has been recognized as an important treatment option for patients with traumatic brain injury (TBI). Nevertheless, the effect of hypertonic saline (HTS) remains unknown, as findings are primarily based on a large database. This study aimed to elucidate the effect of HTS on the clinical outcomes of patients with TBI admitted to the intensive care unit (ICU). We retrospectively identified patients with moderate-to-severe TBI from two public databases: Medical Information Mart for Intensive Care (MIMIC)-IV and eICU Collaborative Research Database (eICU-CRD). A marginal structural Cox model (MSCM) was used, with time-dependent variates designed to reflect exposure over time during ICU stay. Trajectory modeling based on the intracranial pressure evolution pattern allowed for the identification of subgroups. Overall, 130 (6.65%) of 1955 eligible patients underwent HTS. MSCM indicated that the HTS significantly associated with higher infection complications (e.g., urinary tract infection (HR 1.88, 95% CI 1.26-2.81, p = 0.002)) and increased ICU LOS (HR 2.02, 95% CI 1.71-2.40, p < 0.001). A protective effect of HTS on GCS was found in subgroups with medium and low intracranial pressure. Our study revealed no significant difference in mortality between patients who underwent HTS and those who did not. Increased occurrence rates of infection and electrolyte imbalance are inevitable outcomes of continuous HTS infusion. Although the study suggests slight beneficial effects, including better neurological outcomes, these results warrant further validation.

Efficient estimation of Cox model with random change point.

In clinical studies, the risk of a disease may dramatically change when some biological indexes of the human body exceed some thresholds. Furthermore, the differences in individual characteristics of patients such as physical and psychological experience may lead to subject-specific thresholds or change points. Although a large literature has been established for regression analysis of failure time data with change points, most of the existing methods assume the same, fixed change point for all study subjects. In this paper, we consider the situation where there exists a subject-specific change point and two Cox type models are presented. The proposed models also offer a framework for subgroup analysis. For inference, a sieve maximum likelihood estimation procedure is proposed and the asymptotic properties of the resulting estimators are established. An extensive simulation study is conducted to assess the empirical performance of the proposed method and indicates that it works well in practical situations. Finally the proposed approach is applied to a set of breast cancer data.

Combining Clinical With Cognitive or Magnetic Resonance Imaging Data for Predicting Transition to Psychosis in Ultra High-Risk Patients: Data From the PACE 400 Cohort.

BACKGROUND: Multimodal modeling that combines biological and clinical data shows promise in predicting transition to psychosis in individuals who are at ultra-high risk. Individuals who transition to psychosis are known to have deficits at baseline in cognitive function and reductions in gray matter volume in multiple brain regions identified by magnetic resonance imaging. METHODS: In this study, we used Cox proportional hazards regression models to assess the additive predictive value of each modality-cognition, cortical structure information, and the neuroanatomical measure of brain age gap-to a previously developed clinical model using functioning and duration of symptoms prior to service entry as predictors in the Personal Assessment and Crisis Evaluation (PACE) 400 cohort. The PACE 400 study is a well-characterized cohort of Australian youths who were identified as ultra-high risk of transitioning to psychosis using the Comprehensive Assessment of At Risk Mental States (CAARMS) and followed for up to 18 years; it contains clinical data (from N = 416 participants), cognitive data (n = 213), and magnetic resonance imaging cortical parameters extracted using FreeSurfer (n = 231). RESULTS: The results showed that neuroimaging, brain age gap, and cognition added marginal predictive information to the previously developed clinical model (fraction of new information: neuroimaging 0%-12%, brain age gap 7%, cognition 0%-16%). CONCLUSIONS: In summary, adding a second modality to a clinical risk model predicting the onset of a psychotic disorder in the PACE 400 cohort showed little improvement in the fit of the model for long-term prediction of transition to psychosis.

The Southern European Atlantic diet and all-cause and cause-specific mortality: a European multicohort study.

AIMS: The Southern European Atlantic diet (SEAD) is the traditional dietary pattern of northwestern Spain and northern Portugal, but it may resemble that of central, eastern, and western European countries. The SEAD has been found associated with lower risk of myocardial infarction and mortality in older adults, but it is uncertain whether this association also exists in other European populations and if it is similar as that found in its countries of origin. METHODS AND RESULTS: We conducted a prospective analysis of four cohorts with 35 917 subjects aged 18-96 years: ENRICA (Spain), HAPIEE (Czechia and Poland), and Whitehall II (United Kingdom). The SEAD comprised fresh fish, cod, red meat and pork products, dairy, legumes and vegetables, vegetable soup, potatoes, whole-grain bread, and moderate wine consumption. Associations were adjusted for sociodemographic variables, energy intake, lifestyle, and morbidity. After a median follow-up of 13.6 years (range = 0-15), we recorded 4 973 all-cause, 1 581 cardiovascular, and 1 814 cancer deaths. Higher adherence to the SEAD was associated with lower mortality in the pooled sample. Fully adjusted hazard ratios and 95% confidence interval per 1-standard deviation increment in the SEAD were 0.92 (0.89, 0.95), 0.91 (0.86, 0.96), and 0.94 (0.89, 0.99) for all-cause, cardiovascular, and cancer mortality, respectively. The association of the SEAD with all-cause mortality was not significantly different between countries [Spain = 0.93 (0.88, 0.99), Czechia = 0.94 (0.89,0.99), Poland = 0.89 (0.85, 0.93), United Kingdom = 0.98 (0.89, 1.07); P for interaction = 0.16]. CONCLUSION: The SEAD was associated with lower all-cause, cardiovascular, and cancer mortality in southern, central, eastern, and western European populations. Associations were of similar magnitude as those found for existing healthy dietary patterns.

In this study of 35 917 subjects from southern, central, eastern, and western European countries, the Southern European Atlantic diet (traditional dietary pattern of northwestern Spain and northern Portugal) was associated with lower 13.6-year mortality from any cause, cardiovascular disease, and cancer. The associations of the Southern European Atlantic diet with lower mortality were not significantly different between countries (Spain, Czechia, Poland, and the United Kingdom). Study associations were similar as those found for existing healthy dietary patterns, suggesting that rather different diets could confer comparable benefits on health.

Diagnostics for a two-stage joint survival model.

A two-stage joint survival model is used to analyse time to event outcomes that could be associated with biomakers that are repeatedly collected over time. A Two-stage joint survival model has limited model checking tools and is usually assessed using standard diagnostic tools for survival models. The diagnostic tools can be improved and implemented. Time-varying covariates in a two-stage joint survival model might contain outlying observations or subjects. In this study we used the variance shift outlier model (VSOM) to detect and down-weight outliers in the first stage of the two-stage joint survival model. This entails fitting a VSOM at the observation level and a VSOM at the subject level, and then fitting a combined VSOM for the identified outliers. The fitted values were then extracted from the combined VSOM which were then used as time-varying covariate in the extended Cox model. We illustrate this methodology on a dataset from a multi-centre randomised clinical trial. A multi-centre trial showed that a combined VSOM fits the data better than an extended Cox model. We noted that implementing a combined VSOM, when desired, has a better fit based on the fact that outliers are down-weighted.

Factors Affecting Survival of Patients with Cervical Cancer.

Background: Cervical cancer is the fourth leading cause of cancer-related death among women worldwide. We aimed to identify the factors affecting the survival rate of cervical cancer patients, as these factors are vital for preventing the progression and effective treatment of cancer. Methods: In this retrospective cohort study, 254 patients with cervical cancer who were registered in The Kerman Population-Based Cancer Registry (KPBCR) between 2012 and 2022 and whose status was known to be alive or dead were enrolled. Since the proportional hazard assumption was not established for the type of treatment, the extended Cox model was used to determine the variables influencing the survival of the patients. Results: The mean survival time of the patients was 91.28 ± 3.02 months. The results of fitting the extended Cox model showed that the risk of death increases by 1.02 per year of age at diagnosis (HR=1.02; 95% CI: 1.00, 1.04). Moreover, for a one-unit increase in body mass index (BMI), the risk of death increased by 0.93 (HR=0.93; 95% CI: 0.88, 0.98). The risk of death in patients with disease stages III&IV was 3.08 times that of patients with disease stages I&II (HR=3.08; 95% CI: 1.05, 9.03). The risk of death in patients receiving at least one of the radiotherapy and chemotherapy treatments after 18 months was 7.11 times that of patients undergoing surgery (HR=7.11; 95% CI: 1.69, 29.91). Conclusion: The age of diagnosis, BMI, disease stage, and type of treatment significantly affect the survival of patients. Thus, raising women's awareness of periodical examinations and early diagnosis can reduce the risk of death and prevent cervical cancer progression.

Ten-year predictors of major adverse cardiovascular events in patients without angina.

BACKGROUND: Longstanding cardiovascular risk factors cause major adverse cardiovascular events (MACE). Major adverse cardiovascular events prediction may improve outcomes. The aim was to evaluate the ten-year predictors of MACE in patients without angina. METHODS: Patients referred to Inkosi Albert Luthuli Hospital, Durban, South Africa, without typical angina from 2002 to 2008 were collected and followed up for MACE from 2009 to 2019. Survival time was calculated in months. Independent variables were tested with Cox proportional hazard models to predict MACE morbidity and MACE mortality. RESULTS: There were 525 patients; 401 (76.0%) were Indian, 167 (31.8%) had diabetes at baseline. At 10-year follow up 157/525 (29.9%) experienced MACE morbidity, of whom, 82/525 (15.6%) had MACE mortality. There were 368/525 (70.1%) patients censored, of whom 195/525 (37.1%) were lost to follow up. For MACE morbidity, mean and longest observation times were 102.2 and 201 months, respectively. Predictors for MACE morbidity were age (hazard ratio [HR] = 1.025), diabetes (HR = 1.436), Duke Risk category (HR = 1.562) and Ischaemic burden category (HR = 1.531). For MACE mortality, mean and longest observation times were 107.9 and 204 months, respectively. Predictors for MACE mortality were age (HR = 1.044), Duke Risk category (HR = 1.983), echocardiography risk category (HR = 2.537) and Ischaemic burden category (HR = 1.780). CONCLUSION: Among patients without typical angina, early ischaemia on noninvasive tests indicated microvascular disease and hyperglycaemia, predicting long-term MACE morbidity and MACE mortality.Contribution: Diabetes was a predictor for MACE morbidity but not for MACE mortality; patients lost to follow-up were possibly diabetic patients with MACE mortality at district hospitals. Early screening for ischaemia and hyperglycaemia control may improve outcomes.

Covariate balance-related propensity score weighting in estimating overall hazard ratio with distributed survival data.

BACKGROUND: When data is distributed across multiple sites, sharing information at the individual level among sites may be difficult. In these multi-site studies, propensity score model can be fitted with data within each site or data from all sites when using inverse probability-weighted Cox regression to estimate overall hazard ratio. However, when there is unknown heterogeneity of covariates in different sites, either approach may lead to potential bias or reduced efficiency. In this study, we proposed a method to estimate propensity score based on covariate balance-related criterion and estimate the overall hazard ratio while overcoming data sharing constraints across sites. METHODS: The proposed propensity score was generated by choosing between global and local propensity score based on covariate balance-related criterion, combining the global propensity score fitted in the entire population and the local propensity score fitted within each site. We used this proposed propensity score to estimate overall hazard ratio of distributed survival data with multiple sites, while requiring only the summary-level information across sites. We conducted simulation studies to evaluate the performance of the proposed method. Besides, we applied the proposed method to real-world data to examine the effect of radiation therapy on time to death among breast cancer patients. RESULTS: The simulation studies showed that the proposed method improved the performance in estimating overall hazard ratio comparing with global and local propensity score method, regardless of the number of sites and sample size in each site. Similar results were observed under both homogeneous and heterogeneous settings. Besides, the proposed method yielded identical results to the pooled individual-level data analysis. The real-world data analysis indicated that the proposed method was more likely to find a significant effect of radiation therapy on mortality compared to the global propensity score method and local propensity score method. CONCLUSIONS: The proposed covariate balance-related propensity score in multi-site distributed survival data outperformed the global propensity score estimated using data from the entire population or the local propensity score estimated within each site in estimating the overall hazard ratio. The proposed approach can be performed without individual-level data transfer between sites and would yield the same results as the corresponding pooled individual-level data analysis.