Proportional Hazards Assumption Research Articles

PATH-44. CLINICAL AND RADIOGRAPHIC PREDICTORS OF PROGNOSIS IN ASTROCYTOMA, IDH-MUTANT, WHO GRADE 4

Abstract BACKGROUND Astrocytoma, IDH-mutant, WHO grade 4, are classified as a separate entity in the 2021 WHO classification of central nervous system tumors and have been poorly characterized in the literature. In this study, we report on the clinical outcomes in a large cohort of newly diagnosed grade 4 IDH-mutant astrocytoma. METHODS We retrospectively identified adult patients with astrocytoma, IDH-mutant, WHO grade 4, treated for their initial diagnosis at Dana-Farber Cancer Institute and Massachusetts General Hospital between 2010 and 2021. Clinical, molecular, and radiological characteristics were recorded, and their association with overall survival (OS) and progression-free survival (PFS) was measured by a log-rank test. If a proportional hazard assumption was violated, we compared the median OS and PFS between groups by the bootstrap method instead. RESULTS We identified 140 patients, with a median age at diagnosis of 37.9 years (range: 19-87) and male predominance (60.7%). MGMT promoter was methylated in 51.4%, and CDKN2A/B homozygous deletion was present in 40 cases (28.6%). Most tumors showed enhancement on the preoperative scan (n=108; 77.1%). Seventy percent of patients had subtotal resection, 25.7% had a gross total resection, and 4.3% had a biopsy. Most patients received standard chemoradiation followed by adjuvant temozolomide (n=103; 73.6%). The median OS was 6.9 years (95% CI: 4.5-9.2), and the median PFS was 3.3 years (95% CI: 2.1-4.4). OS and PFS were not associated with MGMT promoter methylation or the presence of enhancement on the preoperative scan. CDKN2A/B homozygous deletion was associated with worse OS (5.5 vs. 7.8 years, log-rank test-based p-value=0.036) but had no association with PFS (3.4 vs 2.6 years, bootstrap method-based p-value=0.488). CONCLUSIONS In our cohort of astrocytoma, IDH-mutant, WHO Grade 4, CDKN2A/B homozygous deletion had a negative prognostic impact, whereas MGMT promoter methylation status and enhancement on the preoperative scan did not affect survival outcomes.

Delayed Separation of Kaplan-Meier Curves is Commonly Observed in Studies of Advanced/Metastatic Solid Tumors Treated with Anti-PD-(L)1 Therapy: Systematic Review and Meta-Analysis.

Immune checkpoint inhibitor (ICI) Kaplan-Meier (KM) curves often show delayed survival benefit followed by long-term survival in a subgroup of patients. Such outcomes can violate the proportional hazards assumption, leading to a loss of statistical power. We aimed to determine common trends in delayed separation to inform future ICI clinical trials. A literature search was performed using Trialtrove® to identify phase III trials of antiprogrammed cell death (ligand)-1 (anti-PD-[L]1) agents in locally advanced/metastatic solid tumors published between January 2010 and September 2021. The frequency of delayed separation of overall survival (OS) and progression-free survival (PFS) KM curves, correlation between duration of delayed separation in OS/PFS KM curves, and correlation between duration of delayed separation in OS/PFS KM curves with corresponding hazard ratios (HRs) were assessed in all-comer and PD-L1 enriched populations. Eighty-five studies with OS/PFS KM curves were identified. Most studies showed delayed separation of OS (> 67.9%) and PFS (> 54.5%) KM curves. The correlation between the duration of delayed separation in OS/PFS KM curves was strongest in the PD-L1 enriched population (adjusted R2 = 0.66). No correlation was seen between the duration of delayed separation of OS KM curves and OS HR. A modest correlation was seen between the duration of delayed separation of PFS KM curves and PFS HR in all-comer and PD-L1 enriched populations (adjusted R2 = 0.24 and 0.31, respectively). Delayed separation of KM curves was common in clinical trials of anti-PD-(L)1 agents. Understanding delayed separation is key to clinical study designs and assessing outcomes with ICIs.

Beyond hazard ratios: appropriate statistical methods for quantifying the clinical effectiveness of immune-oncology therapies – the example of the Netherlands

BackgroundThe Dutch Committee for the Evaluation of Oncological Drugs evaluates the effectiveness of new oncological treatments. The committee compares survival endpoints to the so-called PASKWIL-2023 criteria for palliative treatments, which define if treatment effects are considered clinically relevant. A positive recommendation depends on whether the median overall survival (OS) is below or above 12 months in the comparator arm. If the former applies, an OS benefit of at least 12 weeks, and a hazard ratio (HR) smaller than 0.7 are required. If the latter applies, an OS or progression free survival (PFS) benefit of at least 16 weeks, and an HR smaller than 0.7 are required. Nonetheless, the median survival time may not be reached and the proportional hazards (PH) assumption, quantified by the HR, is likely violated for immuno-oncology (IO) therapies, deeming these criteria inappropriate.MethodsWe conducted a systematic literature review to identify statistical methods used to represent the clinical effectiveness of IO therapies based on trial data. We searched MEDLINE and EMBASE databases from inception to August 31, 2022, limited to English papers. Methodological studies, randomized controlled trials, and discussion papers recognising key issues of survival data analysis of IO therapies were eligible for inclusion.ResultsA total of 1,035 unique references were identified. After full paper screening, 17 publications were included in the review. Additionally, 43 papers were identified through ‘snowballing’. We conclude that the current PASKWIL-2023 criteria are methodologically incorrect under non-PH. In that case, single summary statistics fail to capture the treatment effect and any measure should be interpreted in combination with the Kaplan-Meier curves. We recommend ’parameter-free’ measures, such as the difference in restricted mean survival time, avoiding assumptions on the underlying survival.ConclusionsThe HR is commonly used to assess treatment effectiveness, without investigating the validity of the PH assumption. This happens with the application of the PASKWIL-2023 criteria for palliative oncology treatments, which can only be valid under a PH setting. Under non-PH, alternative treatment effect measures are suggested. We propose a step-by-step approach supporting the choice of the most appropriate methods to quantify treatment effectiveness that can be used to redefine the PASKWIL-2023 criteria, or similar criteria in other clinical areas.

Respective impact of bleeding and ischemic complications of mortality after percutaneous coronary interventions: substudy from the FRANCE-PCI registry

Abstract Background Patients undergoing percutaneous coronary interventions (PCI) remain at high risk of both bleeding and ischemic events, both burdened with high morbimortality. The respective impact of both type of complications in contemporary PCI, however, remains unclear. Purpose To evaluate and compare the respective impact of ischemic and bleeding complications on mortality after PCI Methods The FRANCE PCI is an ongoing registry of consecutive French patients undergoing PCI and monitored for one year thereafter. Patients presenting with complications after the index PCI were categorized as ischemic complications in case of stent thrombosis, myocardial infarction, stroke or unplanned revascularization, as hemorrhagic complications in case of bleeding BARC ≥3 or as ischemic and hemorrhagic when both types of events occurred in the same patients. The impact of ischemic and hemorrhagic complications on mortality was evaluated separately for the index hospitalization and the 12-months follow-up after hospital discharge, using multivariate logistic model and Cox proportional hazards model, respectively. The multivariate Cox model was developed using complications as time-dependent variables and estimating the hazard ratios (HRs) over 3 periods of time (0 to 8 weeks, 8 to 24 weeks, and 24 to 52 weeks). Consequently, the proportional hazard assumption, tested using the Schoenfeld test, was met for each period. Multiple imputations were performed for missing data on covariates. Results A total of 54,599 patients, with a median age of 69 [60.0-78.0] years, 75.0% male, underwent PCI in 30 centers between 2014 and 2020, in an elective setting for 47% of the cases. During the index hospitalization, an ischemic and hemorrhagic complications occurred in 610 (1.1% 95%CI 1.0-1.2) and 531 (1.0% 95%CI 0.9-1.1) patients, respectively, while the mortality rate was 2.1% 95%CI 2.0-2.2. The association between ischemic, hemorrhagic and ischemic and hemorrhagic events with mortality was aOR 8.4 95%CI 6.4-10.9, aOR 10.3 95%CI 8.1-13.2 and aOR 13.2 95%CI 7.0-24.7, respectively. Among the 51,738 patients alive and not lost-to-follow-up after discharge, an ischemic and hemorrhagic complications occurred in 2,727 (5.3% 95%CI 5.1-5.5) and 1,022 (2.0% 95%CI 1.9-2.1) patients, respectively, while the mortality incidence was 3.8% 95%CI 3.6-4.0. The associations between ischemic, hemorrhagic and ischemic and hemorrhagic events with mortality according to the periods of time are detailed in Table 1 and Figure 1. Conclusion Both ischemic and hemorrhagic complications after PCI are associated with increased risk of mortality. The risk is particularly increased during the early period after the index PCI and diminishes over time, albeit faster after ischemic complications alone, compared to hemorrhagic events alone or the association of ischemic and hemorrhagic complications.

Acute kidney injury as a risk factor for heart failure hospitalisation after major non-cardiac surgery

Abstract Aim The importance of acute kidney injury (AKI) after major non-cardiac surgery is incompletely understood. The aim of this study was to evaluate the association of AKI with short-term complications (e.g. perioperative myocardial injury (PMI); acute heart failure (AHF)), and long-term adverse outcomes (e.g. readmission for AHF, all-cause mortality). Methods The prospective observational international multi-center BASEL PMI study screened inpatients aged 40-85 at high cardiovascular risk undergoing non-cardiac surgery at tertiary hospitals to detect asymptomatic PMI. This secondary analysis focused on AKI, with prospective adjudication of AKI, PMI and AHF by blinded experts. Results In 11'133 patient cases, the incidence of AKI within 7 days was 11.1%, classified as KDIGO stage one (68.3%), two (15.4%) and three (16.3%). Pre- and perioperative determinants for AKI were perioperative myocardial infarction / injury (PMI, OR 3.63), the need for intra-operative blood transfusion (OR 1.48), chronic heart failure (CHF, OR 1.42), surgery duration (OR 1.37), male sex (OR 1.27), preoperative GFR (OR 0.80) and preoperative hemoglobin concentration (OD 0.84). Median time to occurrence of AKI was 2 days (IQR 1-4), similar to PMI (median 1, IQR 1-2) and in contrast to AHF (median 4, IQR 2-9). 41% of all AKI patients experienced additionally a PMI. In multivariable Cox regression analysis, AKI (aHR 1.76, 95% CI 1.51-2.06, p < 0.001) and PMI (aHR 1.21, 95% CI 1.02-1.45, p = 0.025) were both independently associated with prediction of one-year all-cause mortality after adjustment for each other and confounding factors, whereas AHF was marginally not (aHR 1.28, 95% CI 0.98-1.68, p = 0.070). However, testing the assumed proportional hazards assumption, the impact of AKI, PMI and AHF on mortality depended significantly on time. AKI had significantly increased short- and long-term mortality, in contrast to PMI and AHF with just elevated short-term mortality. The coincidence of AKI and PMI resulted in an additive effect on overall mortality. Importantly, patients with AKI showed more in-hospital AHF (aHR 2.08, 95% CI 1.49-2.91) within 30 days and higher hospitalization rates for HF within one year (aHR 1.40, 95% CI 1.01-1.98, p = 0.046) than patients without AKI, particularly patients without known heart failure (aHR 1.65, 95% CI 1.05-2.60, p = 0.029). Conclusion AKI is a common complication, occurring in 11% after non-cardiac surgery. The occurrence of AKI was associated with increased risks for in-hospital AHF, hospitalizations for heart failure and all-cause mortality. Notably, AKI patients without known history of heart failure faced hospitalizations for de novo heart failure. The occurrence of AKI unveiling de novo heart failure highlights the importance of systematic monitoring for AKI after surgery and gives clinicians a window of opportunity to take action at an early stage.

Treatment Effect Measures Under Nonproportional Hazards.

'Treatment effect measures under nonproportional hazards' by Snapinn etal. (Pharmaceutical Statistics, 22, 181-193) recently proposed some novel estimates of treatment effect for time-to-event endpoints. In this note, we clarify three points related to the proposed estimators that help to elucidate their properties. We hope that their work, and this commentary, will motivate further discussion concerning treatment effect measures that do not require the proportional hazards assumption.

Mismatch Repair Status and Surgical Outcomes in Localized Colorectal Cancer: A Nationwide Cohort Study

Objective: This study examined the association between deficient mismatch repair (dMMR) versus proficient MMR (pMMR) status and overall survival and disease-free survival in patients with localized colorectal cancer. Background: Several distinctions exist between patients with dMMR and pMMR colorectal cancer. However, the impact on prognosis is yet to be investigated in large-scale cohort studies. Methods: In this cohort study, we included patients who underwent curative-intent surgery for localized colorectal cancer between 2009 and 2020. Patients were identified in the Danish Colorectal Cancer Group database and patient-level data were extracted from 6 registry databases. After inclusion, patients with dMMR status were matched 1:1 to patients with pMMR status using an estimated propensity score. Results: After matching, 5994 patients were included. The patients had a median age of 74 years and a median follow-up of 4.1 years. There was no significant association between mismatch repair (MMR) status and overall survival (hazard ratio, 0.91; 95% confidence interval [CI], 0.81–1.03) or disease-free survival (hazard ratio, 0.89; 95% CI, 0.78–1.01). However, the restricted 5-year mean disease-free survival time, calculated due to violation of the proportional hazards assumption, showed a significant absolute difference of 0.13 years (95% CI, 0.03–0.23; P = 0.01) in favor of the dMMR group. Conclusions: No significant association with overall survival was found according to MMR status. dMMR status was, however, found to be associated with marginally improved disease-free survival compared to pMMR status in patients with localized colorectal cancer undergoing curative-intent surgery.

Geographically weighted accelerated failure time model for spatial survival data: application to ovarian cancer survival data in New Jersey

BackgroundIn large multiregional cohort studies, survival data is often collected at small geographical levels (such as counties) and aggregated at larger levels, leading to correlated patterns that are associated with location. Traditional studies typically analyze such data globally or locally by region, often neglecting the spatial information inherent in the data, which can introduce bias in effect estimates and potentially reduce statistical power.MethodWe propose a Geographically Weighted Accelerated Failure Time Model for spatial survival data to investigate spatial heterogeneity. We establish a weighting scheme and bandwidth selection based on quasi-likelihood information criteria. Theoretical properties of the proposed estimators are thoroughly examined. To demonstrate the efficacy of the model in various scenarios, we conduct a simulation study with different sample sizes and adherence to the proportional hazards assumption or not. Additionally, we apply the proposed method to analyze ovarian cancer survival data from the Surveillance, Epidemiology, and End Results cancer registry in the state of New Jersey.ResultsOur simulation results indicate that the proposed model exhibits superior performance in terms of four measurements compared to existing methods, including the geographically weighted Cox model, when the proportional hazards assumption is violated. Furthermore, in scenarios where the sample size per location is 20-25, the simulation data failed to fit the local model, while our proposed model still demonstrates satisfactory performance. In the empirical study, we identify clear spatial variations in the effects of all three covariates.ConclusionOur proposed model offers a novel approach to exploring spatial heterogeneity of survival data compared to global and local models, providing an alternative to geographically weighted Cox regression when the proportional hazards assumption is not met. It addresses the issue of certain counties' survival data being unable to fit the model due to limited samples, particularly in the context of rare diseases.

Cox Regression in Glioma Prognosis Analysis: Challenges with the Proportional Hazards Assumption and Coping Strategies. Comment on Ou Y, et al. "UBA2 as a Prognostic Biomarker and Potential Therapeutic Target in Glioma". Frontiers in Bioscience-Landmark. 2024; 29: 144.

Cox Regression in Glioma Prognosis Analysis: Challenges with the Proportional Hazards Assumption and Coping Strategies. Comment on Ou Y, et al. "UBA2 as a Prognostic Biomarker and Potential Therapeutic Target in Glioma". Frontiers in Bioscience-Landmark. 2024; 29: 144.

Prognostic modeling of hepatocellular carcinoma based on T-cell proliferation regulators: a bioinformatics approach.

The prognostic value and immune significance of T-cell proliferation regulators (TCRs) in hepatocellular carcinoma (HCC) have not been previously reported. This study aimed to develop a new prognostic model based on TCRs in patients with HCC. This study used The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) and International Cancer Genome Consortium-Liver Cancer-Riken, Japan (ICGC-LIRI-JP) datasets along with TCRs. Differentially expressed TCRs (DE-TCRs) were identified by intersecting TCRs and differentially expressed genes between HCC and non-cancerous samples. Prognostic genes were determined using Cox regression analysis and were used to construct a risk model for HCC. Kaplan-Meier survival analysis was performed to assess the difference in survival between high-risk and low-risk groups. Receiver operating characteristic curve was used to assess the validity of risk model, as well as for testing in the ICGC-LIRI-JP dataset. Additionally, independent prognostic factors were identified using multivariate Cox regression analysis and proportional hazards assumption, and they were used to construct a nomogram model. TCGA-LIHC dataset was subjected to tumor microenvironment analysis, drug sensitivity analysis, gene set variation analysis, and immune correlation analysis. The prognostic genes were analyzed using consensus clustering analysis, mutation analysis, copy number variation analysis, gene set enrichment analysis, and molecular prediction analysis. Among the 18 DE-TCRs, six genes (DCLRE1B, RAN, HOMER1, ADA, CDK1, and IL1RN) could predict the prognosis of HCC. A risk model that can accurately predict HCC prognosis was established based on these genes. An efficient nomogram model was also developed using clinical traits and risk scores. Immune-related analyses revealed that 39 immune checkpoints exhibited differential expression between the high-risk and low-risk groups. The rate of immunotherapy response was low in patients belonging to the high-risk group. Patients with HCC were further divided into cluster 1 and cluster 2 based on prognostic genes. Mutation analysis revealed that HOMER1 and CDK1 harbored missense mutations. DCLRE1B exhibited an increased copy number, whereas RAN exhibited a decreased copy number. The prognostic genes were significantly enriched in tryptophan metabolism pathways. This bioinformatics analysis identified six TCR genes associated with HCC prognosis that can serve as diagnostic markers and therapeutic targets for HCC.

Variation of All-Cause Mortality with Fat-Free Mass Index (FFMI) and Fat Mass Index (FMI) in Individuals with Asthma: Results from the NHANES Database Retrospective Cohort Study.

The relationship between fat-free mass index (FFMI), fat mass index (FMI), and mortality in patients with asthma remains unknown. This study aimed to examine the associations between FFMI and FMI and all-cause mortality in a cohort of American adults diagnosed with asthma. This study included 15,200 adults from NHANES. To assess mortality, we linked participant records to the National Death Index. FMI and FFMI were measured and evaluated using dual-energy X-ray absorptiometry (DXA). Survival differences across quintiles of FFMI and FMI were explored using Kaplan-Meier plots and log-rank tests, with the proportional hazards assumption assessed using Schoenfeld residuals. Cox proportional hazards regression models were used to estimate the hazard ratios (HRs) for mortality associated with FFMI and FMI, adjusting for potential confounders including age, sex, smoking status, physical activity, and other relevant factors. Additionally, stratified analyses based on theoretical considerations were conducted to identify subgroups of individuals exhibiting an elevated risk of mortality. This study also examined the nonlinear relationships between FFMI, FMI, and mortality using restricted cubic splines (RCS). After a median follow-up of 184 months, 12.11% of individuals had died. Kaplan-Meier plots revealed significant differences in all-cause mortality among patients with asthma across the FFMI and FMI quintiles. Specifically, individuals in the lowest FFMI quintile (Q1, 10.4-16.0, representing the range of FFMI values) exhibited a significantly increased risk of all-cause mortality (HR: 4.63; 95% CI: 1.59, 13.5; p < 0.01). Similarly, elevated risks of all-cause mortality were observed in the upper three quintiles of FMI, with Q3 (4.8-6.1) having an HR of 2.9 (95% CI: 1.20, 7.00; p < 0.05), Q4 (6.2-8.3) having an HR of 3.37 (95% CI: 1.41, 8.03; p < 0.01), and Q5 (8.4-22.8) having an HR of 4.6 (95% CI: 1.31, 16.2; p < 0.05). Moreover, the risk of all-cause mortality increased with increasing FMI and decreasing FFMI (p for non-linearity < 0.001 in both cases). Subgroup analyses further elucidated these associations across different categories. In examining the association between FMI and all-cause mortality among asthma patients across various subgroups, a heightened mortality risk found among males, individuals with medium education levels, medium income levels, and those who consume alcohol. The study shows that both high FMI and low FFMI are associated with increased mortality in patients with asthma. These findings underscore the critical role of FMI and FFMI in the health management of asthma patients. Therefore, it is recommended that clinicians proactively monitor and adjust these indices to improve patient prognosis and enhance health outcomes for individuals with asthma..

When and how to split the follow-up time in the analysis of epidemiological or clinical studies with follow-ups.

In epidemiological or clinical studies with follow-ups, data tables generated and processed for statistical analysis are often of the "wide-format" type-consisting of one row per individual. However, depending on the situation and purpose of the study, they may need to be transformed into the "long-format" type-which allows for multiple rows per individual. This tutorial clarifies the typical situations wherein researchers are recommended to split follow-up times to generate long-format data tables. In such applications, the major analytical aims consist of (i) estimating the outcome incidence rates or their ratios between ≥ 2 groups, according to specific follow-up time periods; (ii) examining the interaction between the exposure status and follow-up time to assess the proportional hazards assumption in Cox models; (iii) dealing with time-varying exposures for descriptive or predictive purposes; (iv) estimating the causal effects of time-varying exposures while adjusting for time-varying confounders that may be affected by past exposures; and (v) comparing different time periods within the same individual in self-controlled case series analyses. This tutorial also discusses how to split follow-up times according to their purposes in practical settings, providing example codes in Stata, R, and SAS.

PERBANDINGAN MODEL REGRESI STRATIFIED COX DAN EXTENDED COX PADA ANALISIS SURVIVAL PENDERITA KANKER PAYUDARA

Breast cancer is a tumor that develops in the breast where cells in the mammary gland divide and develop uncontrollably. Breast cancer is the most common cancer that causes death in women among other cancers. This study aims to determine the factors that affect the survival of breast cancer patients from the METABRIC database. This study was analyzed using survival analysis method. The method that is often used is the Cox proportional hazard model where the proportional hazard assumption must be met. There is variable that do not meet the assumption so that the methods used are stratified Cox and extended Cox. The stratified Cox model overcomes variables that do not meet the assumption by stratifying variables that do not meet the assumption. The extended Cox model overcomes variables that do not meet the assumption by interacting the variables with a time function. The time functions used in this study are linear time functions and logarithmic time functions. Based on the smallest AIC value, the best model is the stratified Cox regression model without interaction. Factors that affect the survival of breast cancer patients from the METABRIC database are tumor size, chemotherapy, stage 1, stage 2, and type of surgery.

Factors Related to Bracket Bond Failure during Orthodontic Treatment: A Single-Centre Single-Operator Study.

This study aimed to investigate the influence of various patient-specific and bracket location-specific factors on bracket survival rates during comprehensive fixed appliance orthodontic treatment. A total of 197 patients (116 females, 81 males; mean age 16.3 years) having completed orthodontic treatment were included in this retrospective cohort study. Patients were treated using stainless steel non-self-ligating brackets, and the treatment duration was 23.7 months on average. The primary outcome was bracket bond failure. Potential predictors for bracket bond failure recorded included age, sex, oral hygiene, treatment duration, and several pre-treatment cephalometric characteristics such as overjet, overbite, and sagittal and vertical skeletal relationships. Factors associated with bracket failure were analysed with Cox regression, and proportional hazard assumptions were assessed using Kaplan-Meier tests. The overall failure rate was 4.4%. Bracket bond failure rates varied among tooth types and seemed to occur more on posterior teeth and on the right side of the arch. Bracket failure was more common in male patients and those with poor oral hygiene. Concerning dentofacial characteristics, bracket failure of anterior teeth was more common in those with an increased overjet and overbite.

Modified Cox Models: A Simulation Study on Different Survival Distributions, Censoring Rates, and Sample Sizes

The classical Cox model is the most popular procedure for studying right-censored data in survival analysis. However, it is based on the fundamental assumption of proportional hazards (PH). Modified Cox models, stratified and extended, have been widely employed as solutions when the PH assumption is violated. Nevertheless, prior comparisons of the modified Cox models did not employ comprehensive Monte-Carlo simulations to carry out a comparative analysis between the two models. In this paper, we conducted extensive Monte-Carlo simulation to compare the performance of the stratified and extended Cox models under varying censoring rates, sample sizes, and survival distributions. Our results suggest that the models’ performance at varying censoring rates and sample sizes is robust to the distribution of survival times. Thus, their performance under Weibull survival times was comparable to that of exponential survival times. Furthermore, we found that the extended Cox model outperformed other models under every combination of censoring, sample size and survival distribution.

PSVII-8 Survival analyses of the productive life in U.S. Katahdin ewes

Abstract Ewe productive life (PL), defined as the number of days between the first and last lambing, is a common indicator of longevity. This study aimed to analyze the PL of 10,276 Katahdin ewes, of which 69.3% were uncensored and 30.7% were right-censored records. Ewes were daughters of 1,188 sires and 6,312 dams, born between 1989 and 2021 in 58 flocks located across the U.S., and the National Sheep Improvement Program provided the data. Ewes with lambing interval and age at first lambing greater than 720 d and 1,095 d, respectively, were excluded. Moreover, flocks that did not report lambing records across at least 7 consecutive years or did not have an average of 20 or more lambing records over the year were also left out of the analyses. Survival functions were analyzed using the Kaplan-Meyer (KM) and Cox models, while the Hazard function was analyzed using the Cox model. For the Cox model, contemporary group (concatenation of flock, year, and season of ewe’s birth) was fitted as a random effect; the ewe’s BR class (combined effect of birth/rearing types: 1/1, 2/1, 2/2, 3/2, 3/3, with digit-3 including counts ≥ 3), ewe’s age class at first lambing (≤ 378d; 379 to 700d; and &amp;gt; 700 d), and the ewe’s dam’s age class (A: &amp;lt; 713 d; B: 713 to 1,486 d; C: &amp;gt; 1486 d) were fitted as fixed effects. All survival analyses were performed using the Survival R-package. All factors included in the Cox model were highly significant (P &amp;lt; 0.001), except dam age class (P &amp;gt; 0.05). The proportional hazard assumption was checked using a graphical diagnostic based on the scaled Schoenfeld residuals, and there was no failure in the assumption. Moreover, there was no evidence of influential or outlier records in the graphical analysis of the residuals. The Cox survival curve overlapped with the KM survival curve (Figure 1), indicating a good survival prediction with the Cox model. The median PL was 1,036 d (95%CI: 1001 to 1,056 d) with the KM model and 1,056 d (95%CI: 984 to 1,078 d) with the Cox model. The hazard ratio indicated BR levels 2/2 and 3/3 showed less hazards than group 1/1 (Figure 2), and thereby longer PL; no significant difference was observed between 1/1 and the other two levels (2/1 and 2/3). Ewes that first lambed before 378 d had less hazard than those with the first lambing after this age (Figure 2). There was no significant difference between the dam age classes. In conclusion, over 50% of Katahdin sheep have less than 3 yr PL. Moreover, U.S. Katahdin ewes born from larger litters and having the first lambing before 378 d are likelier to have a greater PL.

Association between Timing of Vascular Access Creation and Mortality in Patients Initiating Hemodialysis: A Nationwide Cohort Study in Japan

Introduction: The optimal time for vascular access (VA) creation remains controversial. Methods: We conducted a cohort study using data from the Japanese Society for Dialysis Therapy Renal Data Registry. Adult patients who started receiving hemodialysis in 2007 and had a permanent VA created were included. The exposure of interest was the timing of VA creation, categorized into three groups: early VA creation (defined as creation at least 4 months before hemodialysis initiation), just prior VA creation (creation between 1 and 3 months before hemodialysis initiation), and late VA creation (creation within 1 month of or after hemodialysis initiation). Cox regression analyses were used to compare 1-year all-cause mortality, with late VA creation as the reference group. Owing to the violations of the proportional hazards assumptions, the follow-up period was divided into “early” (1–4 months follow-up) and “late” (5–12 months follow-up) periods. Results: Overall, 1,280 (15.4%) of 8,322 patients died. Both early creation and just prior creation were associated with lower all-cause mortality in the early period compared with late creation. In the late period, the hazard ratios (HRs) for all-cause mortality decreased with earlier VA creation (adjusted HRs [95% confidence intervals]: 0.49 [0.35–0.67] for the early creation group and 0.63 [0.51–0.79] for the just prior creation group). Conclusion: Our study suggests that VA creation at least 1 month before hemodialysis initiation is associated with lower all-cause mortality in the early period, with earlier VA creation resulting in further mortality reduction in the late period.

Incidence and predictors of mortality among neonates with congenital heart disease in Ethiopia: a retrospective cohort study

BackgroundNeonatal mortality poses a significant public health challenge in sub-Saharan Africa, with congenital heart disease emerging as the leading cause of morbidity and mortality among neonates, especially in countries like Ethiopia. Despite efforts to reduce neonatal mortality rates, Ethiopia continues to experience an increased mortality rate, particularly among neonates with congenital heart disease. This study aims to investigate the incidence and predictors of mortality in this vulnerable population within Ethiopia.MethodA retrospective cohort study was conducted at an institution, involving 583 randomly selected neonates diagnosed with congenital heart disease. In the current study, the dependent variable was survival status. Data entry utilized EpiData data version 4.6, and analysis was performed using STATA version 16. Probability of death was compared using the log-rank test and Kaplan-Meier failure curve. Significant predictors were identified using bivariable and multivariate Cox regression. Model fitness and proportional hazard assumptions were evaluated using the Cox-Snell graph and Global test, respectively. Associations were assessed by adjusted hazard ratios with 95% confidence intervals.ResultsThe study participants were followed for 4844 days. The mortality rate was 9.9%. The incidence density was 11.9 per 1000 person-days of observation. Neonatal sepsis (AHR: 2.24; 95% CI [1.18–4.23]), cyanotic congenital heart disease (AHR: 3.49; 95% CI [1.93–6.28]), home delivery (AHR: 1.9; 95% CI [1.06–3.6]), maternal history of gestational diabetes mellitus (AHR: 1.94; 95% CI [1.04–3.61]), and having additional congenital malformations (AHR: 2.49; 95% CI [1.33–4.67]) were significant predictors for neonatal mortality.Conclusion and recommendationThe incidence density of mortality was high compared to studies conducted in developed countries. Neonatal sepsis, type of congenital heart disease, place of delivery, maternal history of gestational diabetes mellitus, and having an additional congenital malformation were significant predictors of mortality among neonates with congenital heart disease. Therefore, healthcare providers should pay special attention to patients with identified predictors. Furthermore, the Federal Ministry of Health, stakeholders, and policymakers should collaborate to address this issue.

Time to death and its predictors among patients with chronic kidney disease on hemodialysis at dialysis unit in Addis Ababa, Ethiopia: a retrospective cohort study

BackgroundChronic kidney disease is a progressive disease that affects more than 10% of the world's population and is also the leading cause of death in the twenty-first century. Furthermore, it imposes a significant financial burden on people undergoing hemodialysis. However, there is little research, particularly in the study area, on time to death and its predicators among hemodialysis patients in Ethiopia; therefore, knowing time to death and identifying predicators that affect survival time is crucial in order to improve survival time and enhance the prognosis of hemodialysis patients. The aim of this study was to assess time to death and its predictors among patients with chronic kidney disease on hemodialysis at a dialysis unit in Addis Ababa, Ethiopia, in 2023.MethodsAn institution-based retrospective cohort study was carried out among 370 chronic kidney disease patients on hemodialysis from January 1st, 2017 to December 30th, 2022. Data were extracted from April 1st–May 20th, 2023, and each variable was coded and entered into Epi Data version 3.1 and then exported into STATA version 15 software for analysis. Kaplan–Meier and the log-rank test were done. Bivariable Cox-proportional regression was done, and a variable whose p-value was < 0.25 and fulfilled the proportional hazard assumption by using graphical and Shenfield residuals was entered into multivariable Cox-proportional regression. Finally, a variable whose p-value < 0.05 and adjusted hazard ratio with its CI were declared statistically significant predictors.ResultIn this study, the overall median survival time was 47 months (95% CI: 36.7, 56), with an incidence rate of death of 16.8 per 1000 people per month of observation (95% CI: 13.8–20.3). Age 64 and above (Adjusted Hazard Ration: 2.8; 95% CI: 1.67–4.98), catheter vascular access (Adjusted Hazard Ration: 3.47; 95% CI: 2.03–5.93), cardiovascular disease (Adjusted Hazard Ration: 1.88; 95% CI: 1.15–3.07), and blood group B (Adjusted Hazard Ration: 2.07; 95% CI: 1.17–3.69) were significant predictors of time to death among hemodialysis patients.Conclusion and recommendationThe median survival time was 47 months, with an interquartile range of 40. Cardiovascular disease, older adults, central venous catheters, and blood type B were significant predictors of time to death for hemodialysis patients. Therefore, in order to improve the survival of hemodialysis patients, health professionals and concerned bodies should give concern to and work on those predictors.

Assessing treatment effects with adjusted restricted mean time lost in observational competing risks data

BackgroundAccording to long-term follow-up data of malignant tumor patients, assessing treatment effects requires careful consideration of competing risks. The commonly used cause-specific hazard ratio (CHR) and sub-distribution hazard ratio (SHR) are relative indicators and may present challenges in terms of proportional hazards assumption and clinical interpretation. Recently, the restricted mean time lost (RMTL) has been recommended as a supplementary measure for better clinical interpretation. Moreover, for observational study data in epidemiological and clinical settings, due to the influence of confounding factors, covariate adjustment is crucial for determining the causal effect of treatment.MethodsWe construct an RMTL estimator after adjusting for covariates based on the inverse probability weighting method, and derive the variance to construct interval estimates based on the large sample properties. We use simulation studies to study the statistical performance of this estimator in various scenarios. In addition, we further consider the changes in treatment effects over time, constructing a dynamic RMTL difference curve and corresponding confidence bands for the curve.ResultsThe simulation results demonstrate that the adjusted RMTL estimator exhibits smaller biases compared with unadjusted RMTL and provides robust interval estimates in all scenarios. This method was applied to a real-world cervical cancer patient data, revealing improvements in the prognosis of patients with small cell carcinoma of the cervix. The results showed that the protective effect of surgery was significant only in the first 20 months, but the long-term effect was not obvious. Radiotherapy significantly improved patient outcomes during the follow-up period from 17 to 57 months, while radiotherapy combined with chemotherapy significantly improved patient outcomes throughout the entire period.ConclusionsWe propose the approach that is easy to interpret and implement for assessing treatment effects in observational competing risk data.