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Limitations of using the traditional Cox's hazard ratio for summarizing the magnitude of the treatment effect on time-to-event outcomes have been widely discussed, and alternative measures that do not have such limitations are gaining attention. One of the alternative methods recently proposed, in a simple 2-sample comparison setting, uses the average hazard with survival weight (AH), which can be interpreted as the general censoring-free person-time incidence rate on a given time window. In this paper, we propose a new regression analysis approach for the AH with a truncation time τ. We investigate 3 versions of AH regression analysis, assuming (1) independent censoring, (2) group-specific censoring, and (3) covariate-dependent censoring. The proposed AH regression methods are closely related to robust Poisson regression. While the new approach needs to require a truncation time τ explicitly, it can be more robust than Poisson regression in the presence of censoring. With the AH regression approach, one can summarize the between-group treatment difference in both absolute difference and relative terms, adjusting for covariates that are associated with the outcome. This property will increase the likelihood that the treatment effect magnitude is correctly interpreted. The AH regression approach can be a useful alternative to the traditional Cox's hazard ratio approach for estimating and reporting the magnitude of the treatment effect on time-to-event outcomes., (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)

Many clinical trials assess time-to-event endpoints. To describe the difference between groups in terms of time to event, we often employ hazard ratios. However, the hazard ratio is only informative in the case of proportional hazards (PHs) over time. There exist many other effect measures that do not require PHs. One of them is the average hazard ratio (AHR). Its core idea is to utilize a time-dependent weighting function that accounts for time variation. Though propagated in methodological research papers, the AHR is rarely used in practice. To facilitate its application, we unfold approaches for sample size calculation of an AHR test. We assess the reliability of the sample size calculation by extensive simulation studies covering various survival and censoring distributions with proportional as well as nonproportional hazards (N-PHs). The findings suggest that a simulation-based sample size calculation approach can be useful for designing clinical trials with N-PHs. Using the AHR can result in increased statistical power to detect differences between groups with more efficient sample sizes., (© 2024 The Author(s). Biometrical Journal published by Wiley‐VCH GmbH.)

In disease settings where study participants are at risk for death and a serious nonfatal event, composite endpoints defined as the time until the earliest of death or the nonfatal event are often used as the primary endpoint in clinical trials. In practice, if the nonfatal event can only be detected at clinic visits and the death time is known exactly, the resulting composite endpoint exhibits "component-wise censoring." The standard method used to estimate event-free survival in this setting fails to account for component-wise censoring. We apply a kernel smoothing method previously proposed for a marker process in a novel way to produce a nonparametric estimator for event-free survival that accounts for component-wise censoring. The key insight that allows us to apply this kernel method is thinking of nonfatal event status as an intermittently observed binary time-dependent variable rather than thinking of time to the nonfatal event as interval-censored. We also propose estimators for the probability in state and restricted mean time in state for reversible or irreversible illness-death models, under component-wise censoring, and derive their large-sample properties. We perform a simulation study to compare our method to existing multistate survival methods and apply the methods on data from a large randomized trial studying a multifactor intervention for reducing morbidity and mortality among men at above average risk of coronary heart disease., (© 2021 The International Biometric Society.)

The Cox proportional hazards model, commonly used in clinical trials, assumes proportional hazards. However, it does not hold when, for example, there is a delayed onset of the treatment effect. In such a situation, an acute change in the hazard ratio function is expected to exist. This paper considers the Cox model with change-points and derives Akaike information criterion (AIC)-type information criteria for detecting those change-points. The change-point model does not allow for conventional statistical asymptotics due to its irregularity, thus a formal AIC that penalizes twice the number of parameters would not be analytically derived, and using it would clearly give overfitting analysis results. Therefore, we will construct specific asymptotics using the partial likelihood estimation method in the Cox model with change-points, and propose information criteria based on the original derivation method for AIC. If the partial likelihood is used in the estimation, information criteria with penalties much larger than twice the number of parameters could be obtained in an explicit form. Numerical experiments confirm that the proposed criteria are clearly superior in terms of the original purpose of AIC, which are to provide an estimate that is close to the true structure. We also apply the proposed criterion to actual clinical trial data to indicate that it will easily lead to different results from the formal AIC., (© 2023 The Authors. Biometrics published by Wiley Periodicals LLC on behalf of International Biometric Society.)

In a clinical trial with a time-to-event endpoint the treatment effect can be measured in various ways. Under proportional hazards all reasonable measures (such as the hazard ratio and the difference in restricted mean survival time) are consistent in the following sense: Take any control group survival distribution such that the hazard rate remains above zero; if there is no benefit by any measure there is no benefit by all measures, and as the magnitude of treatment benefit increases by any measure it increases by all measures. Under nonproportional hazards, however, survival curves can cross, and the direction of the effect for any pair of measures can be inconsistent. In this paper we critically evaluate a variety of treatment effect measures in common use and identify flaws with them. In particular, we demonstrate that a treatment's benefit has two distinct and independent dimensions which can be measured by the difference in the survival rate at the end of follow-up and the difference in restricted mean survival time, and that commonly used measures do not adequately capture both dimensions. We demonstrate that a generalized hazard difference, which can be estimated by the difference in exposure-adjusted subject incidence rates, captures both dimensions, and that its inverse, the number of patient-years of follow-up that results in one fewer event (the NYNT), is an easily interpretable measure of the magnitude of clinical benefit., (© 2022 John Wiley & Sons Ltd.)

Background: Bone metastases can compromise the integrity of the spinal canal and cause epidural spinal cord compression (ESCC). The Spinal Instability Neoplastic Score (SINS) was developed in order to evaluate spinal instability due to a neoplastic process. The SINS has reached wide acceptance among clinicans but its prognostic value is still controversial. The aim was to investigate the correlation between the SINS and ESCC and the association between SINS and ambulation before and survival after surgery. Methods: Correlations were assessed between SINS and grades of ESCC in patients who underwent spine surgery for spinal metastases. CT and MRI were used to calculate SINS and the grades of ESCC respectively. Correlations were analyzed with the Spearman's correlation test. Postoperative survival was estimated with Kaplan-Meier analysis and survival curves were compared with the log-rank test. The Cox proportional hazard model was used to assess the effect of prognostic variables including age, ambulation before surgery, SINS, and the Karnofsky Performance Status (KPS) as covariates. Results: The study included 256 patients (196 men and 60 women) with a median age of 70 (24–88) years. The mean SINS was 10. One hundred fifty-two patients (59%) had lost ambulation before surgery. One hundred and one patients had grades 0–2 and 155 patients had grade 3 according to the ESCC-scale. SINS correlated with the grades of ESCC (p = 0.001). The SINS score was not associated with ambulation before surgery (p = 0.63). The median postoperative survival was 10 months, and there was no difference in postoperative survival between the SINS categories (p = 0.25). The ability to walk before surgery and a high KPS were associated with longer postoperative survival. Conclusion: SINS correlated with grades of ESCC, which implies that higher SINS may be considered as an indicator of risk for developing ESCC. The SINS was not associated with ambulation before or survival after surgery. [ABSTRACT FROM AUTHOR]

Background: This study aimed to develop a higher performance nomogram based on explainable machine learning methods, and to predict the risk of death of stroke patients within 30 days based on clinical characteristics on the first day of intensive care units (ICU) admission. Methods: Data relating to stroke patients were extracted from the Medical Information Marketplace of the Intensive Care (MIMIC) IV and III database. The LightGBM machine learning approach together with Shapely additive explanations (termed as explain machine learning, EML) was used to select clinical features and define cut-off points for the selected features. These selected features and cut-off points were then evaluated using the Cox proportional hazards regression model and Kaplan-Meier survival curves. Finally, logistic regression-based nomograms for predicting 30-day mortality of stroke patients were constructed using original variables and variables dichotomized by cut-off points, respectively. The performance of two nomograms were evaluated in overall and individual dimension. Results: A total of 2982 stroke patients and 64 clinical features were included, and the 30-day mortality rate was 23.6% in the MIMIC-IV datasets. 10 variables ("sofa (sepsis-related organ failure assessment)", "minimum glucose", "maximum sodium", "age", "mean spo2 (blood oxygen saturation)", "maximum temperature", "maximum heart rate", "minimum bun (blood urea nitrogen)", "minimum wbc (white blood cells)" and "charlson comorbidity index") and respective cut-off points were defined from the EML. In the Cox proportional hazards regression model (Cox regression) and Kaplan-Meier survival curves, after grouping stroke patients according to the cut-off point of each variable, patients belonging to the high-risk subgroup were associated with higher 30-day mortality than those in the low-risk subgroup. The evaluation of nomograms found that the EML-based nomogram not only outperformed the conventional nomogram in NIR (net reclassification index), brier score and clinical net benefits in overall dimension, but also significant improved in individual dimension especially for low "maximum temperature" patients. Conclusions: The 10 selected first-day ICU admission clinical features require greater attention for stroke patients. And the nomogram based on explainable machine learning will have greater clinical application. [ABSTRACT FROM AUTHOR]

Recognizing the pivotal role of circadian rhythm in the human aging process and its scalability through wearables, we introduce CosinorAge, a digital biomarker of aging developed from wearable-derived circadian rhythmicity from 80,000 midlife and older adults in the UK and US. A one-year increase in CosinorAge corresponded to 8–12% higher all-cause and cause-specific mortality risks and 3–14% increased prospective incidences of age-related diseases. CosinorAge also captured a non-linear decline in resilience and physical functioning, evidenced by an 8–33% reduction in self-rated health and a 3–23% decline in health-related quality of life score, adjusting for covariates and multiple testing. The associations were robust in sensitivity analyses and external validation using an independent cohort from a disparate geographical region using a different wearable device. Moreover, we illustrated a heterogeneous impact of circadian parameters associated with biological aging, with young (<45 years) and fast agers experiencing a substantially delayed acrophase with a 25-minute difference in peak timing compared to slow agers, diminishing to a 7-minute difference in older adults (>65 years). We demonstrated a significant enhancement in the predictive performance when integrating circadian rhythmicity in the estimation of biological aging over physical activity. Our findings underscore CosinorAge's potential as a scalable, economic, and digital solution for promoting healthy longevity, elucidating the critical and multifaceted circadian rhythmicity in aging processes. Consequently, our research contributes to advancing preventive measures in digital medicine. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Despite the availability of direct-acting antivirals that cure individuals infected with the hepatitis C virus (HCV), developing a vaccine is critically needed in achieving HCV elimination. HCV vaccine trials have been performed in populations with high incidence of new HCV infection such as people who inject drugs (PWID). Developing strategies of optimal recruitment of PWID for HCV vaccine trials could reduce sample size, follow-up costs and disparities in enrollment. We investigate trial recruitment informed by machine learning and evaluate a strategy for HCV vaccine trials termed PREDICTEE—Predictive Recruitment and Enrichment method balancing Demographics and Incidence for Clinical Trial Equity and Efficiency. PREDICTEE utilizes a survival analysis model applied to trial candidates, considering their demographic and injection characteristics to predict the candidate's probability of HCV infection during the trial. The decision to recruit considers both the candidate's predicted incidence and demographic characteristics such as age, sex, and race. We evaluated PREDICTEE using in silico methods, in which we first generated a synthetic candidate pool and their respective HCV infection events using HepCEP, a validated agent-based simulation model of HCV transmission among PWID in metropolitan Chicago. We then compared PREDICTEE to conventional recruitment of high-risk PWID who share drugs or injection equipment in terms of sample size and recruitment equity, with the latter measured by participation-to-prevalence ratio (PPR) across age, sex, and race. Comparing conventional recruitment to PREDICTEE found a reduction in sample size from 802 (95%: 642–1010) to 278 (95%: 264–294) with PREDICTEE, while also reducing screening requirements by 30%. Simultaneously, PPR increased from 0.475 (95%: 0.356–0.568) to 0.754 (95%: 0.685–0.834). Even when targeting a dissimilar maximally balanced population in which achieving recruitment equity would be more difficult, PREDICTEE is able to reduce sample size from 802 (95%: 642–1010) to 304 (95%: 288–322) while improving PPR to 0.807 (95%: 0.792–0.821). PREDICTEE presents a promising strategy for HCV clinical trial recruitment, achieving sample size reduction while improving recruitment equity. [ABSTRACT FROM AUTHOR]

Background/Aim: Sarcopenia accompanied by systemic inflammation is associated with poor prognosis in patients with cancer. This study evaluated the prognostic significance of sarcopenia (myopenia and myosteatosis) and systemic inflammatory markers in older patients (aged =80 years) with resected biliary tract cancer. Patients and Methods: Patients who underwent resection for biliary tract cancer between July 2010 and January 2023 at the NHO Fukuyama Medical Center were retrospectively reviewed. Preoperative computed tomography measured myopenia and myosteatosis, using the psoas muscle index and modified intramuscular adipose tissue content. Associations between clinicopathological characteristics, inflammation-based prognostic scores, and overall survival were analyzed using Cox proportional hazards models. Results: Univariate analysis revealed low C-reactive protein-to-albumin ratio (<0.125), low prognostic nutritional index (<42), low modified intramuscular adipose tissue content, higher T-stage (T3-4), lymph node metastasis, and postoperative complications associated with worse overall survival in older patients (aged = 80 years) with resected biliary tract cancer (n=48). Multivariate analysis identified low prognostic nutritional index (<42) (p=0.007), low modified intramuscular adipose tissue content (p=0.015), higher Tstage (T3-4) (p<0.001), lymph node metastasis (p=0.001), and postoperative complications (p=0.017) as independent predictors of overall survival. Conclusion: Preoperative myosteatosis and low prognostic nutritional index are independent prognostic factors for overall survival in older patients (aged =80 years) with resected biliary tract cancer. These factors may be useful for risk stratification and clinical decision-making. Early interventions, such as nutritional support and physical exercise, may improve outcomes after resection of biliary tract cancer. [ABSTRACT FROM AUTHOR]

Simple Summary: Colorectal cancer (CRC) is the second leading cause of cancer-related death globally. Developing liquid biopsies that aid in the detection and monitoring of recurrent CRC may improve clinical management and patient outcomes. In this study, we investigated a serially sampled blood assay of methylated DNA markers (MDMs) in combination with carcinoembryonic antigen as a clinical tool to surveil and monitor recurrent cancer in a prospective cohort of patients who completed curative-intent therapy for CRC. We demonstrated that the MDMs detected recurrent CRC before the clinical or radiographic detection of recurrence. In a small number of patients, we further showed that the MDMs may correlate with the tumor burden. These data highlight the importance of incorporating epigenetic signatures into liquid biopsies and support further research to validate the correlations of circulating tumor DNA with the tumor burden. Background: Radiographic surveillance of colorectal cancer (CRC) after curative-intent therapy is costly and unreliable. Methylated DNA markers (MDMs) detected primary CRC and metastatic recurrence with high sensitivity and specificity in cross-sectional studies. This study evaluated using serial MDMs to detect recurrence and monitor the treatment response to anti-cancer therapies. Methods: A nested case-control study was drawn from a prospective cohort of patients with CRC who completed curative-intent therapy for CRC of all stages. Plasma MDMs were assayed vis target enrichment long-probe quantitative-amplified signal assays, normalized to B3GALT6, and analyzed in combination with serum carcinoembryonic antigen to yield an MDM score. Clinical information, including treatment and radiographic measurements of the tumor burden, were longitudinally collected. Results: Of the 35 patients, 18 had recurrence and 17 had no evidence of disease during the study period. The MDM score was positive in 16 out of 18 patients who recurred and only 2 of the 17 patients without recurrence. The MDM score detected recurrence in 12 patients preceding clinical or radiographic detection of recurrent CRC by a median of 106 days (range 90–232 days). Conclusions: Plasma MDMs can detect recurrent CRC prior to radiographic detection; this tumor-agnostic liquid biopsy approach may assist cancer surveillance and monitoring. [ABSTRACT FROM AUTHOR]

Background: In the era of anti-HER2 targeted therapy, the potential clinical feasibility of considering HER2-overexpressing breast cancer cases presenting with 1-3 positive axillary lymph nodes as low-risk, and thereby contemplating postoperative radiotherapy reduction, remains an important subject for in-depth examination. The aim of this retrospective study was to evaluate the effectiveness of de-escalated radiotherapy in T1-2N1M0 HER2-overexpressing breast cancer patients receiving anti-HER2 targeted therapy. Specifically, omitting regional lymph node irradiation (RNI) after breast-conserving surgery and only performing whole-breast irradiation or omitting postmastectomy radiation therapy. Methods: A retrospective analysis was conducted on 429 patients with stage T1-2N1M0 primary invasive HER2-overexpressing breast cancer from our center between 2004 and 2018. Patients who received anti-HER2 targeted therapy were divided into an RNI group and a no RNI group to assess the role of RNI. The prognostic role of RNI was investigated via the Kaplan-Meier method and Cox proportional hazards modeling. Results: The median follow-up time was 46.8 months (range 7.1-225.8 months). In the anti-HER2 targeted therapy group RNI yielded no significant improvements in invasive disease-free survival (IDFS) (p = 0.940), localregional recurrence-free survival (p = 0.380), distant metastases-free survival (p = 0.698), or overall survival (p = 0.403). Estrogen receptor (ER) status (hazard ratio [HR] 0.105, 95% confidence interval [CI] 0.023-0.749, p = 0.004) and lymph vascular invasion status (LVI) (HR 5.721, 95% CI 1.586-20.633, p = 0.008) were identified as independent prognostic factors for IDFS, and ER-positive and LVInegative patients exhibited better prognoses. Conclusion: Omitting RNI may be a safe option in T1-2N1 HER2-overexpressing breast cancer patients receiving standardized anti-HER2 targeted therapy; particularly in ER-positive or LVI-negative subgroups. [ABSTRACT FROM AUTHOR]

Contemporary works in change-point survival models mainly focus on an unknown universal change-point shared by the whole study population. However, in some situations, the change-point is plausibly individual-specific, such as when it corresponds to the telomere length or menopausal age. Also, maximum-likelihood-based inference for the fixed change-point parameter is notoriously complicated. The asymptotic distribution of the maximum-likelihood estimator is non-standard, and computationally intensive bootstrap techniques are commonly used to retrieve its sampling distribution. This article is motivated by a breast cancer study, where the disease-free survival time of the patients is postulated to be regulated by the menopausal age, which is unobserved. As menopausal age varies across patients, a fixed change-point survival model may be inadequate. Therefore, we propose a novel proportional hazards model with a random change-point. We develop a nonparametric maximum-likelihood estimation approach and devise a stable expectation–maximization algorithm to compute the estimators. Because the model is regular, we employ conventional likelihood theory for inference based on the asymptotic normality of the Euclidean parameter estimators, and the variance of the asymptotic distribution can be consistently estimated by a profile-likelihood approach. A simulation study demonstrates the satisfactory finite-sample performance of the proposed methods, which yield small bias and proper coverage probabilities. The methods are applied to the motivating breast cancer study. [ABSTRACT FROM AUTHOR]

We examined the role of receptor profiles and other prognostic factors in survival outcomes after stereotactic radiosurgery (SRS) for brain metastases in breast cancer patients, to help improve selection of candidates for SRS. We included 149 consecutive patients who received SRS between 2012 and 2019 at the University Hospital of Copenhagen, Rigshospitalet, Denmark. Overall survival (OS) following SRS was determined through the Kaplan–Meier method, while CNS progression-free survival (CNS-PFS) was determined through competing risk analysis. Prognostic factors for both OS and CNS-PFS were evaluated through uni- and multivariate Cox regression and Fine-Gray models, respectively. The proportional hazards assumptions were tested through Schoenfeld residuals, and non-proportionality was accounted for by the inclusion of time-dependent variables. Median OS was 14.8 months for the entire cohort and was as follows for the four receptor profiles: 33.3 months for ER+/HER2+ (ER: estrogen receptor, HER2: human epidermal growth factor receptor 2), 11.0 months for ER+/HER2−, 17.7 months for ER−/HER2+, and 5.3 months for ER−/HER2−. In the multivariate model, the ER−/HER2− receptor profile (hazard ratio (HR): 2.00, 95% confidence interval (CI): 1.09–3.67) and the presence of extracranial visceral metastases (HR: 2.90, 95% CI: 1.53–5.50) were associated with worse OS. The ER+/HER2+ receptor profile (HR: 0.43, 95% CI: 0.19–0.96) and 5+ lines of treatment (HR: 0.40, 95% CI: 0.20–0.82) were both associated with improved OS. For CNS-PFS, 5+ lines of treatment (sub-distributional hazard ratio (SHR): 2.88, 95% CI: 1.06–7.81) was associated with worse CNS-PFS, while extracranial visceral metastases (SHR: 0.54, 95% CI: 0.30–0.97) was associated with reduced risk of CNS progression – which is primarily due to patients with extracranial metastases dying before developing new CNS progression. Extracranial visceral disease and the ER−/HER2− receptor profile were associated with poor survival outcomes following SRS. [ABSTRACT FROM AUTHOR]

Purpose: Next-generation sequencing (NGS) technology detects specific mutations that can provide treatment opportunities for colorectal cancer (CRC) patients. Patients and Methods: We analyzed the mutation frequencies of common actionable genes and their association with clinicopathological characteristics and oncologic outcomes using targeted NGS in 107 Saudi Arabian patients without a family history of CRC. Results: Approximately 98% of patients had genetic alterations. Frequent mutations were observed in BRCA2 (79%), CHEK1 (78%), ATM (76%), PMS2 (76%), ATR (74%), and MYCL (73%). The APC gene was not included in the panel. Statistical analysis using the Cox proportional hazards model revealed an unusual positive association between poorly differentiated tumors and survival rates (p = 0.025). Although no significant univariate associations between specific mutations or overall mutation rate and overall survival were found, our preliminary analysis of the molecular markers for CRC in a predominantly Arab population can provide insights into the molecular pathways that play a significant role in the underlying disease progression. Conclusions: These results may help optimize personalized therapy when drugs specific to a patient's mutation profile have already been developed. [ABSTRACT FROM AUTHOR]

Background: Many studies analyze sexual and reproductive event data using descriptive life tables. Survival analysis has better power to estimate factors associated with age at first sex (AFS), but proportional hazards models may not be right model to use. This study used accelerated failure time (AFT) models, restricted Mean Survival time model (RMST) models, with semi and non-parametric methods to assess age at first sex (AFS), factors associated with AFS, and verify underlying assumptions for each analysis. Methods: Self-reported sexual debut data was used from respondents 15–24 years in eight cross-sectional surveys between 1994–2016, and from adolescents' survey in an observational community study (2019–2020) in northwest Tanzania. Median AFS was estimated in each survey using non-parametric and parametric models. Cox regression, AFT parametric models (exponential, gamma, generalized gamma, Gompertz, Weibull, log-normal and log-logistic), and RMST were used to estimate and identify factors associated with AFS. The models were compared using Akaike information criterion (AIC) and Bayesian information criterion (BIC), where lower values represent a better model fit. Results: The results showed that in every survey, the Cox regression model had higher AIC and BIC compared to the other models. Overall, AFT had the best fit in every survey round. The estimated median AFS using the parametric and non-parametric methods were close. In the adolescent survey, log-logistic AFT showed that females and those attending secondary and higher education level had a longer time to first sex (Time ratio (TR) = 1.03; 95% CI: 1.01–1.06, TR = 1.05; 95% CI: 1.02–1.08, respectively) compared to males and those who reported not being in school. Cell phone ownership (TR = 0.94, 95% CI: 0.91–0.96), alcohol consumption (TR = 0.88; 95% CI: 0.84–0.93), and employed adolescents (TR = 0.95, 95% CI: 0.92–0.98) shortened time to first sex. Conclusion: The AFT model is better than Cox PH model in estimating AFS among the young population. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to survey the statistical inference for covariate-specific time-dependent receiver operating characteristic (ROC) curves with nonignorable missing continuous biomarker values. To construct time-dependent ROC curves, we consider a joint model which assumes that the failure time depends on the continuous biomarker and the covariates through a Cox proportional hazards model and that the continuous biomarker depends on the covariates through a semiparametric location model. Assuming a purely parametric model on the propensity score, we utilize instrumental variables to deal with the identifiable issue and estimate the unknown parameters of the propensity score by a simple and efficient method. In addition, when the propensity score is estimated, we develop HT and AIPW approaches to estimate our interested quantities. In the presence of nonignorable missing biomarker, our AIPW estimators of the interested quantities are still doubly robust when the true propensity score is a special parametric logistic model. At last, simulation studies are conducted to assess the performance of our proposed approaches, and a real data analysis is also carried out to illustrate its application. [ABSTRACT FROM AUTHOR]

Background: Administration of vancomycin as a continuous infusion has been associated with reduced nephrotoxicity. Given limited published experience with continuous infusion vancomycin in outpatient parenteral antimicrobial therapy (OPAT) programs, we reviewed outcomes from our center. Methods: This was a retrospective, single‐center study of adult patients receiving vancomycin OPAT as continuous or intermittent infusion for an intended treatment duration of at least 7 days. The primary outcome was time to nephrotoxicity with continuous versus intermittent infusion vancomycin while on OPAT; additional outcomes included time to any vancomycin‐associated adverse event, time to 60‐day death or readmission, and time to 60‐day emergency department encounter. Proportional hazards modeling was used to identify variables independently associated with outcomes, as well as assess the strength of association of continuous infusion with each outcome. Results: Four‐hundred ninety‐two patients were included: 118 treated with continuous and 374 with intermittent vancomycin infusion. Continuous infusion was not associated with lower rates of nephrotoxicity compared to intermittent infusion (adjusted hazard ratio (aHR) 0.72, 95% CI: 0.35–1.50). There were no advantages of continuous over intermittent infusion in the rates of any adverse event (aHR 0.93, 95% CI: 0.56–1.53), 60‐day death or readmission (aHR 1.04, 95% CI: 0.68–1.61), or 60‐day emergency department encounter (aHR 1.17, 95% CI: 0.68–1.99). Vancomycin area under the concentration‐time curve (AUC) at discharge was the only modifiable factor identified that was independently associated with patient safety outcomes. Conclusion: There was no appreciable benefit of continuous infusion vancomycin on outpatient safety outcomes. AUC‐centered dosing approaches warrant further investigation as strategies to improve vancomycin safety in OPAT programs. [ABSTRACT FROM AUTHOR]

Aims To determine whether fully automated artificial intelligence-based global circumferential strain (GCS) assessed during vasodilator stress cardiovascular (CV) magnetic resonance (CMR) can provide incremental prognostic value. Methods and results Between 2016 and 2018, a longitudinal study included all consecutive patients with abnormal stress CMR defined by the presence of inducible ischaemia and/or late gadolinium enhancement. Control subjects with normal stress CMR were selected using a propensity score-matching. Stress-GCS was assessed using a fully automatic machine-learning algorithm based on featured-tracking imaging from short-axis cine images. The primary outcome was the occurrence of major adverse clinical events (MACE) defined as CV mortality or nonfatal myocardial infarction. Cox regressions evaluated the association between stress-GCS and the primary outcome after adjustment for traditional prognosticators. In 2152 patients [66 ± 12 years, 77% men, 1:1 matched patients (1076 with normal and 1076 with abnormal CMR)], stress-GCS was associated with MACE [median follow-up 5.2 (4.8–5.5) years] after adjustment for risk factors in the propensity-matched population [adjusted hazard ratio (HR), 1.12 (95% CI, 1.06–1.18)], and patients with normal CMR [adjusted HR, 1.35 (95% CI, 1.19–1.53), both P < 0.001], but not in patients with abnormal CMR (P = 0.058). In patients with normal CMR, an increased stress-GCS showed the best improvement in model discrimination and reclassification above traditional and stress CMR findings (C-statistic improvement: 0.14; NRI = 0.430; IDI = 0.089, all P < 0.001; LR-test P < 0.001). Conclusion Stress-GCS is not a predictor of MACE in patients with ischaemia, but has an incremental prognostic value in those with a normal CMR although the absolute event rate remains low. [ABSTRACT FROM AUTHOR]

Whether non-alcoholic fatty liver disease (NAFLD) improves risk prediction for subsequent type 2 diabetes mellitus (T2DM) in women with prior gestational diabetes mellitus (pGDM) is uncertain. We examined the combined effects of NAFLD and pGDM on risk prediction for incident T2DM. This retrospective cohort study included 97,347 Korean parous women without diabetes mellitus at baseline whose mean (SD) age was 39.0 (7.8) years. Cox proportional hazards models were used to estimate hazard ratios (HRs) for incident T2DM according to self-reported pGDM and ultrasound-diagnosed NAFLD at baseline. When combined with conventional diabetes risk factors, the incremental predictive ability of NAFLD and pGDM to identify incident T2DM was assessed. During a median follow-up of 3.9 years, 1,515 cases of incident T2DM occurred. Multivariable-adjusted HRs (95% confidence intervals [CIs]) for incident T2DM comparing pGDM alone, NAFLD alone, and both NAFLD and pGDM to the reference (neither NAFLD nor pGDM) were 2.61 (2.06–3.31), 2.26 (1.96–2.59), and 6.45 (5.19-8.00), respectively (relative excess risk of interaction = 2.58 [95% CI, 1.21–3.95]). These associations were maintained after adjusting for insulin resistance, body mass index, and other potential confounders as time-dependent covariates. The combination of NAFLD and pGDM improved risk prediction for incident T2DM (based on Harrell's C−index, also known as the concordance index, and net reclassification improvement) compared to conventional diabetes risk factors. In conclusion, NAFLD synergistically increases the risk of subsequent T2DM in women with pGDM. The combination of NAFLD and pGDM improves risk prediction for T2DM. [ABSTRACT FROM AUTHOR]

The standard treatment for platinum-sensitive relapsed ovarian cancer (PSROC) is platinum-based chemotherapy followed by olaparib monotherapy. A retrospective study was conducted to identify factors affecting the survival of patients with PSROC undergoing olaparib monotherapy in real-world clinical settings. The study enrolled 122 patients who received olaparib monotherapy between April 2018 and December 2020 at three national centers in Japan. The study used the Kaplan–Meier method and univariable and multivariable Cox proportional hazards models to evaluate the associations between factors and progression-free survival (PFS). Patients with BRCA1/2 mutations had a significantly longer median PFS than those without these mutations. Both the BRCA1/2 mutation-positive and mutation-negative groups exhibited a prolonged PFS when the platinum-free interval (PFI) was ≥ 12 months. Cancer antigen 125 (CA-125) level within reference values was significantly linked to prolonged PFS, while a high platelet-to-lymphocyte ratio (≥ 210) was significantly associated with poor PFS in the BRCA1/2 mutation-negative group. The study suggests that a PFI of ≥ 12 months may predict survival after olaparib monotherapy in patients with PSROC, regardless of their BRCA1/2 mutation status. Additionally, a CA-125 level within reference values may be associated with extended survival in patients without BRCA1/2 mutations. A larger prospective study should confirm these findings. [ABSTRACT FROM AUTHOR]

The classical approach to analyze time-to-event data, e.g. in clinical trials, is to fit Kaplan–Meier curves yielding the treatment effect as the hazard ratio between treatment groups. Afterwards, a log-rank test is commonly performed to investigate whether there is a difference in survival or, depending on additional covariates, a Cox proportional hazard model is used. However, in numerous trials these approaches fail due to the presence of non-proportional hazards, resulting in difficulties of interpreting the hazard ratio and a loss of power. When considering equivalence or non-inferiority trials, the commonly performed log-rank based tests are similarly affected by a violation of this assumption. Here we propose a parametric framework to assess equivalence or non-inferiority for survival data. We derive pointwise confidence bands for both, the hazard ratio and the difference of the survival curves. Further we propose a test procedure addressing non-inferiority and equivalence by directly comparing the survival functions at certain time points or over an entire range of time. Once the model's suitability is proven the method provides a noticeable power benefit, irrespectively of the shape of the hazard ratio. On the other hand, model selection should be carried out carefully as misspecification may cause type I error inflation in some situations. We investigate the robustness and demonstrate the advantages and disadvantages of the proposed methods by means of a simulation study. Finally, we demonstrate the validity of the methods by a clinical trial example. [ABSTRACT FROM AUTHOR]

Simple Summary: The current study investigated the potential use of fluorometric cfDNA quantification as a prognostic biomarker in advanced non-small cell lung cancer (NSCLC) patients treated with an Immune Checkpoint Blockade (ICB). Results showed that a cfDNA cut-off of 0.55 ng/µL before the ICB determines the overall survival of patients with a log rank p-value of 3.3 × 10−4. High cfDNA concentrations identify patients with advanced NSCLC who do not benefit from ICB. The determination of cfDNA is a simple test that could select a group of patients in whom new therapeutic strategies are needed. The present study aimed to investigate the potential of basal cell-free fluorometric DNA (cfDNA) quantification as a prognostic biomarker in advanced non-small cell lung cancer (NSCLC) patients treated with an Immune Checkpoint Blockade (ICB). A discovery and validation cohort of 61 and 31 advanced lung cancer patients treated with ICB were included in this study. Quantification of cfDNA concentration was performed before the start of the treatment and patients were followed up for a median of 34 (30–40) months. The prognostic predicted value of cfDNA was evaluated based on ROC, and Cox regression was conducted via univariate and multivariate analyses to estimate the hazard ratio. We observed that a cfDNA cut-off of 0.55 ng/µL before the ICB determines the overall survival of patients with a log rank p-value of 3.3 × 10−4. That represents median survivals of 3.8 vs. 17.5 months. Similar results were obtained in the validation cohort being the log rank p-value 3.8 × 10−2 with median survivals of 5.9 vs. 24.3. The univariate and multivariate analysis revealed that the cut-off of 0.55 ng/µL before ICB treatment was an independent predictive factor and was significantly associated with a better survival outcome. High cfDNA concentrations identify patients with advanced NSCLC who do not benefit from the ICB. The determination of cfDNA is a simple test that could select a group of patients in whom new therapeutic strategies are needed. [ABSTRACT FROM AUTHOR]

Triple‐negative breast cancer (TNBC) is known to have a relatively poor outcome with variable prognoses, raising the need for more informative risk stratification. We investigated a set of digital, artificial intelligence (AI)‐based spatial tumour microenvironment (sTME) features and explored their prognostic value in TNBC. After performing tissue classification on digitised haematoxylin and eosin (H&E) slides of TNBC cases, we employed a deep learning‐based algorithm to segment tissue regions into tumour, stroma, and lymphocytes in order to compute quantitative features concerning the spatial relationship of tumour with lymphocytes and stroma. The prognostic value of the digital features was explored using survival analysis with Cox proportional hazard models in a cross‐validation setting on two independent international multi‐centric TNBC cohorts: The Australian Breast Cancer Tissue Bank (AUBC) cohort (n = 318) and The Cancer Genome Atlas Breast Cancer (TCGA) cohort (n = 111). The proposed digital stromal tumour‐infiltrating lymphocytes (Digi‐sTILs) score and the digital tumour‐associated stroma (Digi‐TAS) score were found to carry strong prognostic value for disease‐specific survival, with the Digi‐sTILs and Digi‐TAS scores giving C‐index values of 0.65 (p = 0.0189) and 0.60 (p = 0.0437), respectively, on the TCGA cohort as a validation set. Combining the Digi‐sTILs feature with the patient's positivity status for axillary lymph nodes yielded a C‐index of 0.76 on unseen validation cohorts. We surmise that the proposed digital features could potentially be used for better risk stratification and management of TNBC patients. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

The standard modeling approach for time-to-event outcomes subject to censoring is based on the hazard function, with hazard ratios capturing the effect of exposures on the risk of outcome. The restricted mean survival time, defined as the expected time to event up to a pre-specified time horizon, provides an alternative useful summary of time-to-event outcomes. Restricted mean survival time can be estimated nonparametrically and can be used to compare groups or interventions when the proportional hazards (PHs) assumption does not hold. Moreover, even when the proportional hazards assumption holds, the restricted mean survival time, an additive measure of risk, provides additional information to the hazard ratio, which is a measure of relative risk that can be difficult to interpret in absence of an estimate of the reference risk. Herein, a generalized fiducial approach is proposed for restricted mean survival time, and its asymptotic properties are investigated. Numerical simulations show the proposed approach provides one- and two-sided confidence intervals with coverage probabilities close to nominal values and controls the type-I error for two-group comparisons even for small sample sizes with a low number of events. Data from a type 1 diabetes study is used for illustration. [ABSTRACT FROM AUTHOR]

Screening mammography is the primary preventive strategy for early detection of breast cancer and an essential input to breast cancer risk prediction and application of prevention/risk management guidelines. Identifying regions of interest within mammogram images that are associated with 5- or 10-year breast cancer risk is therefore clinically meaningful. The problem is complicated by the irregular boundary issue posed by the semi-circular domain of the breast area within mammograms. Accommodating the irregular domain is especially crucial when identifying regions of interest, as the true signal comes only from the semi-circular domain of the breast region, and noise elsewhere. We address these challenges by introducing a proportional hazards model with imaging predictors characterized by bivariate splines over triangulation. The model sparsity is enforced with the group lasso penalty function. We apply the proposed method to the motivating Joanne Knight Breast Health Cohort to illustrate important risk patterns and show that the proposed method is able to achieve higher discriminatory performance. [ABSTRACT FROM AUTHOR]

Background: The association of aspartate aminotransferase to alanine aminotransferase ratio (De Ritis ratio) with clinical outcomes in patients with chronic coronary syndromes (CCS) remains unclear. This study aims to assess the association of De Ritis ratio with adverse cardiovascular events in patients with CCS. Materials and Methods: The study included 5020 patients with CCS undergoing percutaneous coronary intervention. Patients were categorized into groups according to tertiles of the De Ritis ratio: tertile 1 (De Ritis ratio: <.75; n = 1688 patients), tertile 2 (De Ritis ratio:.75–1.08; n = 1666 patients) and tertile 3 (De Ritis ratio: >1.08; n = 1666 patients). The primary endpoint was 3‐year mortality. Results: At 3 years, there were 384 deaths, 176 myocardial infarctions and 61 strokes. In groups with De Ritis in the 1st, 2nd and 3rd tertiles, deaths occurred in 5.0%, 7.5% and 14.5% of the patients, respectively (adjusted hazard ratio = 1.09, 95% confidence interval [1.06–1.12], p <.001); myocardial infarctions occurred in 2.6%, 3.5% and 5.1% of the patients, respectively (p <.001); strokes occurred in 1.0%, 1.2% and 1.9% of the patients, respectively (p =.030); bleeding at 30 days (n = 112) occurred in 1.4%, 1.6% and 3.7% of the patients, respectively (p <.001). The C‐statistic of the Cox proportional hazards model for all‐cause mortality with baseline data without the De Ritis ratio was.815 [.794–.836] and.818 [.797–.838] after the inclusion of the De Ritis ratio (delta C‐statistic =.003; p =.005). Conclusions: In patients with CCS undergoing percutaneous coronary intervention, an elevated De Ritis ratio was associated with long‐term major adverse cardiovascular events. [ABSTRACT FROM AUTHOR]

Simple Summary: Hepatocellular carcinoma (HCC) is the most common primary liver cancer, and despite best efforts to stratify patients recurrence remains a major issue. Our study attempted to identify what variables are involved in recurrence of HCC after resection and if they be used to stratify an individual patient's risk of recurrence. We developed a simple-to-implement RESected Tumor Outcome and Recurrence (RESTORE) index comprising three commonly assessed variables: alpha-fetoprotein level, vascular invasion, and tumor burden. The RESTORE index was highly predictive of HCC recurrence risk after resection. The RESTORE index will help identify patients who would potentially benefit from more intensive post-resection surveillance or adjuvant therapeutics. Although many variables have been associated with increased risk of hepatocellular carcinoma (HCC) recurrence after resection, no simple-to-implement risk score has been developed to determine this post-resection risk. Objective: We aimed to identify risk factors for HCC recurrence and develop a risk score for predicting recurrence of HCC in patients who undergo resection with curative intent. Design: Single-center retrospective analysis Setting: Single-center tertiary care referral hospital (University of San Francisco, California). Participants: Patients who underwent resection with curative intent for HCC between January 2005 and May 2019 with complete pathologic findings and recorded follow up. Main Outcomes and Measures: Univariate and multivariate Cox regression analysis were used to identify independent risk factors for HCC recurrence. A multivariable Cox proportional-hazard regression model with listwise deletion was used to create a risk score. Results: A total of 179 patients were included in the study; 129 (72.9%) were men, and the median (IQR) age was 63 (57–67) years. Median alpha-fetoprotein (AFP) was 12.3 ng/mL at time of resection. Most patients (82%) had a single tumor nodule, and the mean aggregate nodule size was 6.75 cm; 28.4% had evidence of vascular invasion. On multivariable Cox proportional-hazards regression, AFP ≥1000 ng/mL, multinodularity, and vascular invasion were independently associated with HCC recurrence. The RESTORE index was created using stratified pre-operative AFP, vascular invasion, and the presence of a single lesion within or beyond Milan Criteria versus multiple lesions. The RESTORE index ranged from 0–9 (highest patient score was 8) and was highly predictive of HCC recurrence (C statistic 0.70). RESTORE could stratify 5-year post-resection HCC recurrence risk, ranging from less than 25% with a score of 0 to more than 80% with a score of 5–8. Conclusions and Relevance: The RESTORE index that we developed and validated is a simple-to-implement and novel risk score for patients undergoing resection for HCC and may help identify those who would benefit most from intensive surveillance strategies or adjuvant therapies. [ABSTRACT FROM AUTHOR]

Background & Aims: In this retrospective study, we aimed to elucidate how the initial recurrence site influences the post-recurrence survival (PRS) after the curative resection of colorectal cancer. Patients and methods: We collected samples from patients with stage I-III colorectal adenocarcinoma who were admitted to Yunnan Cancer Hospital from January 2008 to December 2019. Four hundred and six patients who developed recurrence after radical resection were included. The cases were classified according to the original site of recurrence as follows: liver metastases (n = 98), lung metastases (n = 127), peritoneum (n = 32), other individual organ (n = 69), two or more organs or sites (n = 49), and local recurrence (n = 31). Kaplan–Meier survival curves were used to compare the PRS of patients with different initial sites of recurrence. The influence of the initial recurrence site on PRS was analyzed using the Cox proportional hazards model. Results: The 3-year PRS of simple liver metastasis was 54.04% (95% CI, 45.46%-64.24%), and the 3-year PRS of simple lung metastasis was 50.05% (95% CI, 42.50%-58.95%). No significant difference was observed between simple liver metastasis or simple lung metastasis and local recurrence with a 3-year PRS of 66.99% (95% CI, 53.23%-84.32%). The 3-year PRS for peritoneal metastases was 25.43% (95% CI, 14.76%-43.82%), and the 3-year PRS for two or more organ sites was 34.84% (95% CI, 24.16%-50.24%). The peritoneal (hazard ratio [HR], 1.75; 95% CI, 1.10–2.79; P = 0.0189) and metastasis to two or more organs or sites (HR, 1.59; 95% CI, 1.05–2.43; P = 0.0304) were PRS-independent adverse prognostic factors. Conclusion: The prognosis of patients with peritoneum and multiple organs or sites recurred was poor. This study suggests early monitoring of peritoneal and multiple organ or site recurrence after surgery. This part of patients should receive comprehensive treatment as early as possible to improve their prognosis. [ABSTRACT FROM AUTHOR]

Simple Summary: Severe clinical mastitis is an inflammation of the mammary gland with systemic clinical signs. It is a frequent and highly fatal disease of dairy cattle worldwide. Nomograms are practical tools that help predict a specific issue based on statistical models. Here, we developed nomograms for prediction of death or culling related with cases of severe clinical mastitis that were treated in dairy farm settings following a veterinary clinical examination. Such a tool will help veterinarians in prognosis prediction. A reliable prognosis is important to make euthanasia decisions in poor prognosis cases. Severe clinical mastitis is a frequent disease of dairy cattle. An effective mean of predicting survival despite treatment would be helpful for making euthanasia decisions in poor prognosis cases. The objective was to develop a nomogram for prediction of death or culling in the 60 days following a severe mastitis episode in dairy cows at first veterinary visit in farm settings. A total of 224 dairy cows presenting severe clinical mastitis and examined for the first time by a veterinarian were included in a prospective study. Clinical and laboratory (complete blood cell count, L-lactate, cardiac troponin I, milk culture) variables were recorded. Animals were followed for 60 days. A nomogram was built with an adaptive elastic-net Cox proportional hazards model. Performances and relevance were evaluated by area under the receiver operating characteristic curve (AUC), Harrell's concordance index (C-index), calibration curve, decision curve analysis (DCA) and misclassification cost term (MCT). The nomogram included: lactation number, recumbency, depression intensity, capillary refilling time, ruminal motility rate, dehydration level, lactates concentration, hematocrit, band neutrophils count, monocyte count, and milk bacteriology. The AUC and C-index showed a good calibration and ability to discriminate. The DCA suggested that the nomogram was clinically relevant. Euthanizing animals having less than 25% probability of survival is economically optimal. It could be used for early euthanasia decisions in animals that would not survive despite treatment. To facilitate the use of this nomogram by veterinarians, a web-based app was developed. [ABSTRACT FROM AUTHOR]

Commonly used prediction models have been primarily constructed without taking physical activity into account. Using the Kailuan physical activity cohorts from Asymptomatic Polyvascular Abnormalities in Community (APAC) study, we developed a 9‐year cardiovascular or cerebrovascular disease (CVD) risk prediction equation. Participants in this study were included from APAC cohort, which included 5440 participants from the Kailuan cohort in China. Cox proportional hazard regression model was applied to construct sex‐specific risk prediction equations for the physical activity cohort (PA equation). Proposed equations were compared with the 10‐year risk prediction model, which is developed for atherosclerotic cardiovascular disease risk in Chinese cohorts (China‐PAR equation). C statistics of PA equations were 0.755 (95% confidence interval, 0.750–0.758) for men and 0.801 (95% confidence interval, 0.790–0.813) for women. The estimated area under the receiver operating characteristic curves in the validation set shows that the PA equations perform as good as the China‐PAR. From calibration among four categories of predicted risks, the predicted risk rates by PA equations were almost identical to the Kaplan–Meier observed rates. Therefore, our developed sex‐specific PA equations have effective performance for predicting CVD for physically active cohorts in the physical activity cohort in Kailuan. [ABSTRACT FROM AUTHOR]

Purpose: Little is known about the prognostic factors for survival after endoscopic submucosal dissection (ESD) in elderly patients with early gastric cancer (EGC). The aim of this study is to determine prognostic factors and a prediction model of 3-year survival after ESD for EGC in patients aged ≥ 85 years. Methods: We retrospectively evaluated the clinical outcomes of 740 patients with EGC aged ≥ 85 years, who were treated by ESD at 30 institutions in Japan. Overall survival (OS) and disease-specific survival (DSS) were calculated with the Kaplan–Meier method. Prediction models for 3-year OS after ESD were estimated using the Cox proportional hazards model based on Uno's C-statistics. Results: During the follow-up period, 309 patients died of any cause and 10 patients died of gastric cancer. OS and DSS after 3 years were 82.7% and 99.2%, respectively. No significant differences in OS were found among curability categories. The Cox proportional hazards model revealed the geriatric nutritional risk index (GNRI) and the Charlson comorbidity index (CCI) to be predictors of 3-year survival. We established a final model (EGC-2 model) expressed by GNRI − (2.2×CCI) with a cutoff value of 96. The overall survival rate was significantly lower in the model value < 96 group than in the model value ≥ 96 group (P < 0.001). Conclusions: The prediction model using GNRI and CCI will be useful to support decision-making for the treatment of EGC in elderly patients aged ≥ 85 years. [ABSTRACT FROM AUTHOR]

Background and aims: Summarised in polygenic risk scores (PRS), the effect of common, low penetrant genetic variants associated with colorectal cancer (CRC), can be used for risk stratification. Methods: To assess the combined impact of the PRS and other main factors on CRC risk, 163,516 individuals from the UK Biobank were stratified as follows: 1. carriers status for germline pathogenic variants (PV) in CRC susceptibility genes (APC, MLH1, MSH2, MSH6, PMS2), 2. low (< 20%), intermediate (20–80%), or high PRS (> 80%), and 3. family history (FH) of CRC. Multivariable logistic regression and Cox proportional hazards models were applied to compare odds ratios and to compute the lifetime incidence, respectively. Results: Depending on the PRS, the CRC lifetime incidence for non-carriers ranges between 6 and 22%, compared to 40% and 74% for carriers. A suspicious FH is associated with a further increase of the cumulative incidence reaching 26% for non-carriers and 98% for carriers. In non-carriers without FH, but high PRS, the CRC risk is doubled, whereas a low PRS even in the context of a FH results in a decreased risk. The full model including PRS, carrier status, and FH improved the area under the curve in risk prediction (0.704). Conclusion: The findings demonstrate that CRC risks are strongly influenced by the PRS for both a sporadic and monogenic background. FH, PV, and common variants complementary contribute to CRC risk. The implementation of PRS in routine care will likely improve personalized risk stratification, which will in turn guide tailored preventive surveillance strategies in high, intermediate, and low risk groups. [ABSTRACT FROM AUTHOR]

Objectives: The B cell activating factor (BAFF) is a B cell survival factor involved in atherosclerosis and ischemia-reperfusion (IR) injury. This study sought to investigate whether BAFF is a potential predictor of poor outcomes in patients with ST-segment elevation myocardial infarction (STEMI). Methods: We prospectively enrolled 299 patients with STEMI, and serum levels of BAFF were measured. All subjects were followed for three years. The primary endpoint was major adverse cardiovascular events (MACEs), including cardiovascular death, nonfatal reinfarction, hospitalization for heart failure (HF), and stroke. Multivariable Cox proportional hazards models were constructed to analyze the predictive value of BAFF for MACEs. Results: In multivariate analysis, BAFF was independently associated with risk of MACEs (adjusted HR 1.525, 95% CI 1.085–2.145; p = 0.015) and cardiovascular death (adjusted hazard ratio [HR] 3.632, 95% confidence interval [CI] 1.132–11.650, p = 0.030) after adjustment for traditional risk factors. Kaplan-Meier survival curves demonstrated that patients with BAFF levels above the cut-off value (1.46 ng/mL) were more likely to have MACEs (log-rank p < 0.0001) and cardiovascular death (log-rank p < 0.0001). In subgroup analysis, the impact of high BAFF on MACEs development was stronger in patients without dyslipidemia. Furthermore, the C-statistic and Integrated Discrimination Improvement (IDI) values for MACEs were improved with BAFF as an independent risk factor or when combined with cardiac troponin I. Conclusions: This study suggests that higher BAFF levels in the acute phase are an independent predictor of the incidence of MACEs in patients with STEMI. [ABSTRACT FROM AUTHOR]

Background and objectives: In light of the growing burden of chronic kidney disease (CKD), it is of particular importance to create disease prediction models that can assist healthcare providers in identifying cases of CKD individual risk and integrate risk-based care for disease progress management. The objective of this study was to develop and validate a new pragmatic end-stage kidney disease (ESKD) risk prediction utilizing the Cox proportional hazards model (Cox) and machine learning (ML). Design, setting, participants, and measurements: The Chinese Cohort Study of Chronic Kidney Disease (C-STRIDE), a multicenter CKD cohort in China, was employed as the model's training and testing datasets, with a split ratio of 7:3. A cohort from Peking University First Hospital (PKUFH cohort) served as the external validation dataset. The participants' laboratory tests in those cohorts were conducted at PKUFH. We included individuals with CKD stages 1~4 at baseline. The incidence of kidney replacement therapy (KRT) was defined as the outcome. We constructed the Peking University-CKD (PKU-CKD) risk prediction model employing the Cox and ML methods, which include extreme gradient boosting (XGBoost) and survival support vector machine (SSVM). These models discriminate metrics by applying Harrell's concordance index (Harrell's C-index) and Uno's concordance (Uno's C). The calibration performance was measured by the Brier score and plots. Results: Of the 3216 C-STRIDE and 342 PKUFH participants, 411 (12.8%) and 25 (7.3%) experienced KRT with mean follow-up periods of 4.45 and 3.37 years, respectively. The features included in the PKU-CKD model were age, gender, estimated glomerular filtration rate (eGFR), urinary albumin–creatinine ratio (UACR), albumin, hemoglobin, medical history of type 2 diabetes mellitus (T2DM), and hypertension. In the test dataset, the values of the Cox model for Harrell's C-index, Uno's C-index, and Brier score were 0.834, 0.833, and 0.065, respectively. The XGBoost algorithm values for these metrics were 0.826, 0.825, and 0.066, respectively. The SSVM model yielded values of 0.748, 0.747, and 0.070, respectively, for the above parameters. The comparative analysis revealed no significant difference between XGBoost and Cox, in terms of Harrell's C, Uno's C, and the Brier score (p = 0.186, 0.213, and 0.41, respectively) in the test dataset. The SSVM model was significantly inferior to the previous two models (p < 0.001), in terms of discrimination and calibration. The validation dataset showed that XGBoost was superior to Cox, regarding Harrell's C, Uno's C, and the Brier score (p = 0.003, 0.027, and 0.032, respectively), while Cox and SSVM were almost identical concerning these three parameters (p = 0.102, 0.092, and 0.048, respectively). Conclusions: We developed and validated a new ESKD risk prediction model for patients with CKD, employing commonly measured indicators in clinical practice, and its overall performance was satisfactory. The conventional Cox regression and certain ML models exhibited equal accuracy in predicting the course of CKD. [ABSTRACT FROM AUTHOR]

Background Timely diagnosis of heart failure (HF) is essential to optimize treatment opportunities that improve symptoms, quality of life, and survival. While most patients consult their general practitioner (GP) prior to HF, the early stages of HF may be difficult to identify. An integrated clinical support tool may aid in identifying patients at high risk of HF. We therefore constructed a prediction model using routine health care data. Methods Our study involved a dynamic cohort of patients (≥35 years) who consulted their GP with either dyspnoea and/or peripheral oedema within the Amsterdam metropolitan area from 2011 to 2020. The outcome of interest was incident HF, verified by an expert panel. We developed a regularized, cause-specific multivariable proportional hazards model (TARGET-HF). The model was evaluated with bootstrapping on an isolated validation set and compared to an existing model developed with hospital insurance data as well as patient age as a sole predictor. Results Data from 31,905 patients were included (40% male, median age 60 years) of whom 1,301 (4.1%) were diagnosed with HF over 124,676 person-years of follow-up. Data were allocated to a development (n  = 25,524) and validation (n  = 6,381) set. TARGET-HF attained a C-statistic of 0.853 (95% CI, 0.834 to 0.872) on the validation set, which proved to provide a better discrimination than C = 0.822 for age alone (95% CI, 0.801 to 0.842, P  < 0.001) and C = 0.824 for the hospital-based model (95% CI, 0.802 to 0.843, P  < 0.001). Conclusion The TARGET-HF model illustrates that routine consultation codes can be used to build a performant model to identify patients at risk for HF at the time of GP consultation. [ABSTRACT FROM AUTHOR]

Background: In the older general population, neurodegenerative diseases (NDs) are associated with increased disability, decreased physical and cognitive function. Detecting risk factors can help implement prevention measures. Using deep neural networks (DNNs), a machine-learning algorithm could be an alternative to Cox regression in tabular datasets with many predictive features. We aimed to compare the performance of different types of DNNs with regularized Cox proportional hazards models to predict NDs in the older general population. Methods: We performed a longitudinal analysis with participants of the English Longitudinal Study of Ageing. We included men and women with no NDs at baseline, aged 60 years and older, assessed every 2 years from 2004 to 2005 (wave2) to 2016–2017 (wave 8). The features were a set of 91 epidemiological and clinical baseline variables. The outcome was new events of Parkinson's, Alzheimer or dementia. After applying multiple imputations, we trained three DNN algorithms: Feedforward, TabTransformer, and Dense Convolutional (Densenet). In addition, we trained two algorithms based on Cox models: Elastic Net regularization (CoxEn) and selected features (CoxSf). Results: 5433 participants were included in wave 2. During follow-up, 12.7% participants developed NDs. Although the five models predicted NDs events, the discriminative ability was superior using TabTransformer (Uno's C-statistic (coefficient (95% confidence intervals)) 0.757 (0.702, 0.805). TabTransformer showed superior time-dependent balanced accuracy (0.834 (0.779, 0.889)) and specificity (0.855 (0.0.773, 0.909)) than the other models. With the CoxSf (hazard ratio (95% confidence intervals)), age (10.0 (6.9, 14.7)), poor hearing (1.3 (1.1, 1.5)) and weight loss 1.3 (1.1, 1.6)) were associated with a higher DNN risk. In contrast, executive function (0.3 (0.2, 0.6)), memory (0, 0, 0.1)), increased gait speed (0.2, (0.1, 0.4)), vigorous physical activity (0.7, 0.6, 0.9)) and higher BMI (0.4 (0.2, 0.8)) were associated with a lower DNN risk. Conclusion: TabTransformer is promising for prediction of NDs with heterogeneous tabular datasets with numerous features. Moreover, it can handle censored data. However, Cox models perform well and are easier to interpret than DNNs. Therefore, they are still a good choice for NDs. [ABSTRACT FROM AUTHOR]

Simple Summary: Short, dysfunctional telomeres represent the genetic biomarkers of cancer. Studies in early-stage non-small-cell lung cancer (NSCLC) have shown that telomere length and telomerase levels are correlated with survival. In patients with advanced NSCLC, telomere status has not yet been investigated, and its clinical significance remains unknown. We studied telomere length and the expression of telomerase and shelterin genes in a cohort of 79 patients with advanced NSCLC, and evaluated these parameters as potential prognostic and predictive factors. Telomere shortening, high levels of telomerase and aberrant expression of shelterin genes TRF2, RAP1 and TIN2 were significantly correlated with shorter survival. Furthermore, a worse response to immunotherapy was observed in patients with shorter telomeres. The determination of telomere parameters in advanced NSCLC could be useful for individualized treatment decisions. Telomere length appears to correlate with survival in early non-small-cell lung cancer (NSCLC), but the prognostic impact of telomere status in advanced NSCLC remains undetermined. Our purpose was to evaluate telomere parameters as prognostic and predictive biomarkers in advanced NSCLC. In 79 biopsies obtained before treatment, we analyzed the telomere length and expression of TERT and shelterin complex genes (TRF1, TRF2, POT1, TPP1, RAP1, and TIN2), using quantitative PCR. Non-responders to first-line chemotherapy were characterized by shorter telomeres and low RAP1 expression (p = 0.0035 and p = 0.0069), and tended to show higher TERT levels (p = 0.058). In multivariate analysis, short telomeres were associated with reduced event-free (EFS, p = 0.0023) and overall survival (OS, p = 0.00041). TERT and TRF2 overexpression correlated with poor EFS (p = 0.0069 and p = 0.00041) and OS (p = 0.0051 and p = 0.007). Low RAP1 and TIN2 expression-levels were linked to reduced EFS (p = 0.00032 and p = 0.0069) and OS (p = 0.000051 and p = 0.02). Short telomeres were also associated with decreased survival after nivolumab therapy (p = 0.097). Evaluation of telomere status in advanced NSCLC emerges as a useful biomarker that allows for the selection of patient groups with different clinical evolutions, to establish personalized treatment. [ABSTRACT FROM AUTHOR]

Background: Hyporesponsiveness to erythropoiesis-stimulating agents (ESAs) is associated with increased risks of all cause and cardiovascular mortality in patients undergoing hemodialysis (HD). However, the impact of the hematopoietic response to ESAs on the development of stroke, including brain hemorrhage and infarction, remains unclear. Methods: In total, 2886 patients undergoing maintenance HD registered in the Q-Cohort Study who were treated with ESAs were prospectively followed up for 4 years. The hematopoietic response to ESAs was evaluated by the erythropoietin resistance index (ERI), calculated by dividing the weekly dose of ESA by post-HD weight and hemoglobin (U/kg/week/g/dL). The primary outcomes were the incidences of brain hemorrhage and infarction. Patients were divided into quartiles based on their ERI at baseline (Q1, ≤ 4.1; Q2, 4.2–7.0; Q3, 7.1–11.2; and Q4, ≥ 11.3). The risks of brain hemorrhage and infarction were estimated using Cox proportional hazards models, adjusting for potential confounders. Results: During the 4 year follow-up period, 71 patients developed brain hemorrhage and 116 developed brain infarction. In the multivariable analysis, the incidence of brain hemorrhage in the highest quartile (Q4) was significantly higher than that in the lowest quartile (Q1) (hazard ratio [95% confidence interval], 2.18 [1.08–4.42]). However, the association between the ERI and the incidence of brain infarction was not significant. Conclusions: A higher ERI was associated with an increased risk of brain hemorrhage, but not brain infarction, in patients undergoing maintenance HD. A high ERI is thus an important risk factor for brain hemorrhage in these patients. [ABSTRACT FROM AUTHOR]

Introduction: Although high-dose erythropoiesis-stimulating agent (ESA) has been shown to increase mortality risk and adverse cardiovascular events in hemodialysis patients, the safety of extremely low-dose ESA is unclear. Methods: We retrospectively analyzed the association between ESA dose and mortality in the monthly dosing range of 0–43,000 U of equivalent epoetin alfa in 304 Taiwan hemodialysis patients by using Cox proportional hazard model and cubic spline model. Results: Compared with mean monthly ESA dose of 15,000–25,000 U (mean ± standard deviation 20,609 ± 2,662 U), monthly ESA dose of less than 15,000 U (mean ± standard deviation 7,413 ± 4,510 U) is associated with increased mortality. Monthly ESA dose of 25,001–43,000 U (mean ± standard deviation 31,160 ± 4,304 U) is not associated with higher mortality risk than monthly ESA dose of 15,000–25,000 U. The results were consistent in Cox proportional hazard models and cubic spline models. Subgroup analyses showed no significant heterogeneities among prespecified subgroups. Conclusions: Extremely low dose of ESA in hemodialysis patients may be associated with increased mortality risk. Future studies are warranted to prove this association. [ABSTRACT FROM AUTHOR]

Background: Precision medicine is an emerging field that involves the selection of treatments based on patients' individual prognostic data. It is formalized through the identification of individualized treatment rules (ITRs) that maximize a clinical outcome. When the type of outcome is time-to-event, the correct handling of censoring is crucial for estimating reliable optimal ITRs. Methods: We propose a jackknife estimator of the value function to allow for right-censored data for a binary treatment. The jackknife estimator or leave-one-out-cross-validation approach can be used to estimate the value function and select optimal ITRs using existing machine learning methods. We address the issue of censoring in survival data by introducing an inverse probability of censoring weighted (IPCW) adjustment in the expression of the jackknife estimator of the value function. In this paper, we estimate the optimal ITR by using random survival forest (RSF) and Cox proportional hazards model (COX). We use a Z-test to compare the optimal ITRs learned by RSF and COX with the zero-order model (or one-size-fits-all). Through simulation studies, we investigate the asymptotic properties and the performance of our proposed estimator under different censoring rates. We illustrate our proposed method on a phase III clinical trial of non-small cell lung cancer data. Results: Our simulations show that COX outperforms RSF for small sample sizes. As sample sizes increase, the performance of RSF improves, in particular when the expected log failure time is not linear in the covariates. The estimator is fairly normally distributed across different combinations of simulation scenarios and censoring rates. When applied to a non-small-cell lung cancer data set, our method determines the zero-order model (ZOM) as the best performing model. This finding highlights the possibility that tailoring may not be needed for this cancer data set. Conclusion: The jackknife approach for estimating the value function in the presence of right-censored data shows satisfactory performance when there is small to moderate censoring. Winsorizing the upper and lower percentiles of the estimated survival weights for computing the IPCWs stabilizes the estimator. [ABSTRACT FROM AUTHOR]

Our study investigates the effects of heterogeneity in image parameters on the reproducibility of prognostic performance of models built using radiomic biomarkers. We compare the prognostic performance of models derived from the heterogeneity-mitigated features with that of models obtained from raw features, to assess whether reproducibility of prognostic scores improves upon application of our methods. We used two datasets: The Breast I-SPY1 dataset—Baseline DCE-MRI scans of 156 women with locally advanced breast cancer, treated with neoadjuvant chemotherapy, publicly available via The Cancer Imaging Archive (TCIA); The NSCLC IO dataset—Baseline CT scans of 107 patients with stage 4 non-small cell lung cancer (NSCLC), treated with pembrolizumab immunotherapy at our institution. Radiomic features (n = 102) are extracted from the tumor ROIs. We use a variety of resampling and harmonization scenarios to mitigate the heterogeneity in image parameters. The patients were divided into groups based on batch variables. For each group, the radiomic phenotypes are combined with the clinical covariates into a prognostic model. The performance of the groups is assessed using the c-statistic, derived from a Cox proportional hazards model fitted on all patients within a group. The heterogeneity-mitigation scenario (radiomic features, derived from images that have been resampled to minimum voxel spacing, are harmonized using the image acquisition parameters as batch variables) gave models with highest prognostic scores (for e.g., IO dataset; batch variable: high kernel resolution—c-score: 0.66). The prognostic performance of patient groups is not comparable in case of models built using non-heterogeneity mitigated features (for e.g., I-SPY1 dataset; batch variable: small pixel spacing—c-score: 0.54, large pixel spacing—c-score: 0.65). The prognostic performance of patient groups is closer in case of heterogeneity-mitigated scenarios (for e.g., scenario—harmonize by voxel spacing parameters: IO dataset; thin slice—c-score: 0.62, thick slice—c-score: 0.60). Our results indicate that accounting for heterogeneity in image parameters is important to obtain more reproducible prognostic scores, irrespective of image site or modality. For non-heterogeneity mitigated models, the prognostic scores are not comparable across patient groups divided based on batch variables. This study can be a step in the direction of constructing reproducible radiomic biomarkers, thus increasing their application in clinical decision making. [ABSTRACT FROM AUTHOR]

Background: Klemera-Doubal's method (KDM) is an advanced and widely applied algorithm for estimating biological age (BA), but it has no uniform paradigm for biomarker processing. This article proposed all subsets of biomarkers for estimating BAs and assessed their association with mortality to determine the most predictive subset and BA.Methods: Clinical biomarkers, including those from physical examinations and blood assays, were assessed in the China Health and Nutrition Survey (CHNS) 2009 wave. Those correlated with chronological age (CA) were combined to produce complete subsets, and BA was estimated by KDM from each subset of biomarkers. A Cox proportional hazards regression model was used to examine and compare each BA's effect size and predictive capacity for all-cause mortality. Validation analysis was performed in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and National Health and Nutrition Examination Survey (NHANES). KD-BA and Levine's BA were compared in all cohorts.Results: A total of 130 918 panels of BAs were estimated from complete subsets comprising 3-17 biomarkers, whose Pearson coefficients with CA varied from 0.39 to 1. The most predictive subset consisted of 5 biomarkers, whose estimated KD-BA had the most predictive accuracy for all-cause mortality. Compared with Levine's BA, the accuracy of the best-fitting KD-BA in predicting death varied among specific populations.Conclusion: All-subset analysis could effectively reduce the number of redundant biomarkers and significantly improve the accuracy of KD-BA in predicting all-cause mortality. [ABSTRACT FROM AUTHOR]

Aims Positron emission tomography (PET) myocardial perfusion imaging (MPI) is often combined with coronary artery calcium (CAC) scanning, allowing for a combined anatomic and functional assessment. We evaluated the independent prognostic value of quantitative assessment of myocardial perfusion and CAC scores in patients undergoing PET. Methods and results Consecutive patients who underwent Rb-82 PET with CAC scoring between 2010 and 2018, with follow-up for major adverse cardiovascular events (MACE), were identified. Perfusion was quantified automatically with total perfusion deficit (TPD). Our primary outcome was MACE including all-cause mortality, myocardial infarction (MI), admission for unstable angina, and late revascularization. Associations with MACE were assessed using multivariable Cox models adjusted for age, sex, medical history, and MPI findings including myocardial flow reserve. In total, 2507 patients were included with median age 70. During median follow-up of 3.9 years (interquartile range 2.1–6.1), 594 patients experienced at least one MACE. Increasing CAC and ischaemic TPD were associated with increased MACE, with the highest risk associated with CAC > 1000 [adjusted hazard ratio (HR) 1.67, 95% CI 1.24–2.26] and ischaemic TPD > 10% (adjusted HR 1.80, 95% CI 1.40–2.32). Ischaemic TPD and CAC improved overall patient classification, but ischaemic TPD improved classification of patients who experienced MACE while CAC mostly improved classification of low-risk patients. Conclusions Ischaemic TPD and CAC were independently associated with MACE. Combining extent of atherosclerosis and functional measures improves the prediction of MACE risk, with CAC 0 identifying low-risk patients and regional ischaemia identifying high-risk patients in those with CAC > 0. [ABSTRACT FROM AUTHOR]

Individual participant data (IPD) from oncology clinical trials is invaluable for identifying factors that influence trial success and failure, improving trial design and interpretation, and comparing pre-clinical studies to clinical outcomes. However, the IPD used to generate published survival curves are not generally publicly available. We impute survival IPD from ~500 arms of Phase 3 oncology trials (representing ~220,000 events) and find that they are well fit by a two-parameter Weibull distribution. Use of Weibull functions with overall survival significantly increases the precision of small arms typical of early phase trials: analysis of a 50-patient trial arm using parametric forms is as precise as traditional, non-parametric analysis of a 90-patient arm. We also show that frequent deviations from the Cox proportional hazards assumption, particularly in trials of immune checkpoint inhibitors, arise from time-dependent therapeutic effects. Trial duration therefore has an underappreciated impact on the likelihood of success. Analysis of more than 150 Phase 3 oncology clinical trials supports parametric statistical analysis, significantly increasing the precision of small early-phase trials and relating deviations from the Cox proportional hazards model to trial duration. [ABSTRACT FROM AUTHOR]

Simple Summary: In the past few decades, targeted therapy and immunotherapy have transformed tremendously the chances of survival. Checkpoint inhibitors that block programmed cell death 1 (PD-1) and cytotoxic T-lymphocyte antigen 4 (CTLA-4) pathways hold the most promise. However, more than half of the patients treated with CTLA-4-PD1 inhibition suffered from grade 3 or 4 treatment-related adverse events (TRAEs). Recently, Tawbi et al. reported the initial results of the phase 2–3 RELATIVITY-047 trial which evaluated LAG3-PD1 inhibition of relatlimab plus nivolumab in patients with previously untreated advanced melanoma. Here, we performed an indirect cross-comparison of LAG3-PD1 and CTLA4-PD1 inhibition in patients with previously untreated advanced melanoma by deriving individual patient survival data and safety profiles. We found that the PFS of LAG3-PD1 and CTLA4-PD1 inhibition were similar. Compared with CTLA4-PD1-inhibition, LAG3-PD1 inhibition tended to exhibit earlier survival benefit and lesser TRAEs. Objective: To compare the inhibition of LAG3-PD1 versus the inhibition of CTLA-4-PD1 in patients with previously untreated advanced melanoma. Methods: The individual participant data (IPD) were extracted from the KM plots using a graphical reconstructive algorithm. Log-rank, Cox proportional hazard model, Bayesian hierarchical model with time-varying hazard ratio (HR) effect, and restricted mean survival time (RMST) were performed to estimate survival benefits. Results: The CheckMate-067 (n = 630) and RELATIVITY-047 (n = 714) trials were included for analysis. The graphical reconstructive algorithm showed that IPD had similar HRs and log-rank values as the original plots. The HR of nivolumab plus relatlimab (LAG3 inhibitor) versus nivolumab plus ipilimumab (CTLA4 inhibitor) was 1.19 (95% confidence interval [CI] 0.96 to1.48). The 24-months RMST of nivolumab plus relatlimab versus nivolumab was 2.35 (95% CI 0.77–3.94) months, compared with 1.87 (95% CI, 0.25–3.49) months for nivolumab plus ipilimumab versus nivolumab. The Bayesian hierarchical model showed that patients treated with nivolumab plus relatlimab had earlier PFS benefits than those with nivolumab plus ipilimumab. Grade 3 or 4 treatment-related adverse events occurred in 18.9% of patients using nivolumab plus relatlimab and 55.0% of patients using nivolumab plus ipilimumab. Conclusions: These findings suggest that the PFS of LAG3-PD1 and CTLA4-PD1 inhibition were similar and LAG3-PD1 inhibition exhibited earlier survival benefit and lesser TRAEs. [ABSTRACT FROM AUTHOR]

There are several different topics that can be addressed with multivariate failure time regression data. Data analysis methods are needed that are suited to each such topic. Specifically, marginal hazard rate models are well suited to the analysis of exposures or treatments in relation to individual failure time outcomes, when failure time dependencies are themselves of little or no interest. On the other hand semiparametric copula models are well suited to analyses where interest focuses primarily on the magnitude of dependencies between failure times. These models overlap with frailty models, that seem best suited to exploring the details of failure time clustering. Recently proposed multivariate marginal hazard methods, on the other hand, are well suited to the exploration of exposures or treatments in relation to single, pairwise, and higher dimensional hazard rates. Here these methods will be briefly described, and the final method will be illustrated using the Women's Health Initiative hormone therapy trial data. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to develop a new risk stratification tool to predict freedom from progression (FFP) for newly diagnosed advanced classical Hodgkin lymphoma (cHL). Methods: We collected data from 386 patients with advanced cHL diagnosed between December 8, 2000 and October 29, 2018, and treated with curative intent with ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine) or an ABVD‐equivalent regimen. Cases were randomly divided into training and validation cohorts at a ratio of 7:3. The new model was constructed based on the results of Cox proportional hazards model in the training cohort. Comparisons of discrimination between the new model and other models in the training and validation cohorts for FFP prediction were measured by time‐dependent area under curve (tAUC) and Harrell's C‐index. Calibration plots were constructed to compare the consistency between the predicted and observed estimates of survival probability for the new model in the training and validation cohorts. Results: The new model (IPSPLR) composed of International Prognostic Score (IPS)‐3 and platelet/lymphocyte ratio (PLR) provided four distinct risk groups. The IPSPLR showed better discriminative ability when compared with IPS‐3 and IPS‐7. The AUC of IPSPLR was consistently higher than that of IPS‐3 and IPS‐7 between 12 and 120 months. The C‐index of the IPSPLR was higher than that of IPS‐7 and IPS‐3. The calibration plots showed an excellent agreement between the IPSPLR‐predicted and observed estimates of 5‐year FFP. Conclusion: The IPSPLR is an easily used tool for FFP prediction for newly diagnosed advanced cHL. Validation of this tool in other large datasets is needed. [ABSTRACT FROM AUTHOR]

The restricted mean survival time measure has gained a lot of interests for designing and analyzing oncology trials with time-to-event endpoints due to its intuitive clinical interpretation and potentially high statistical power. In the non-inferiority trial literature, restricted mean survival time has been used as an alternative measure for reanalyzing a completed trial, which was originally designed and analyzed based on traditional proportional hazard model. However, the reanalysis procedure requires a conversion from the non-inferiority margin measured in hazard ratio to a non-inferiority margin measured by restricted mean survival time difference. An existing conversion method assumes a Weibull distribution for the population survival time of the historical active control group under the proportional hazard assumption using data from a single trial. In this article, we develop a methodology for non-inferiority margin conversion when data from multiple historical active control studies are available, and introduce a Kaplan-Meier estimator-based method for the non-inferiority margin conversion to relax the parametric assumption. We report extensive simulation studies to examine the performances of proposed methods under the Weibull data generative models and a piecewise-exponential data generative model that mimic the tumor recurrence and survival characteristics of advanced colon cancer. This work is motivated to achieve non-inferiority margin conversion, using historical patient-level data from a large colon cancer clinical database, to reanalyze an internationally collaborated non-inferiority study that evaluates 6-month versus 3-month duration of adjuvant chemotherapy in stage III colon cancer patients. [ABSTRACT FROM AUTHOR]