A Proportional Hazards Regression Model for the Sub-distribution with Covariates Adjusted Censoring Weight for Competing Risks Data

Significance of Persistent Inflammation in Patients With Chronic Coronary Syndrome: Insights From the REAL-CAD Study

Background

The prognostic implications of persistent low-grade inflammation in patients with chronic coronary syndrome (CCS) are underexplored. The REAL-CAD (Randomized Evaluation of Aggressive or Moderate Lipid Lowering Therapy with Pitavastatin in Coronary Artery Disease) study demonstrated the benefit of higher intensity pitavastatin in Japanese patients with CCS.

Objectives

This prespecified subanalysis of the REAL-CAD study aimed to assess the prognostic effect of the persistent low-grade inflammation represented by high-sensitivity C-reactive protein (hs-CRP) in CCS patients.

Methods

The present analysis involved patients without events until 6 months after randomization and whose hs-CRP levels were available at baseline and 6 months (n = 10,460). The primary endpoint was the composite of cardiovascular mortality, myocardial infarction, stroke, and unstable angina hospitalization. Landmark analyses evaluated the prognostic impact of continuous inflammation in 4 groups based on the median levels of hs-CRP (0.5 mg/L for both) at baseline and 6 months. The 4 groups included patient with persistently low, elevated (increased), reduced, and persistently high hs-CRP.

Results

Adjusted Cox proportional hazard analyses demonstrated an increased risk of the primary endpoint in the group with persistently high hs-CRP when compared to the group with persistently low hs-CRP as a reference (adjusted HR: 1.48, 95% CI: 1.18-1.89; P = 0.001), but with a similar risk in the group with elevated (HR: 1.07, 95% CI: 0.77-1.49, P = 0.68) and reduced (HR: 0.92; 95% CI: 0.66-1.27; P = 0.60) hs-CRP.

Conclusions

The study shows that persistent low-grade inflammation is associated with poor outcomes and underscores the need to address residual inflammatory risk in CCS patients. (Randomized Evaluation of Aggressive or Moderate Lipid Lowering Therapy With Pitavastatin in Coronary Artery Disease [REAL-CAD]; NCT01042730).

Age-specific risk factors of depression among the oldest-old - evidence from the multicenter AgeCoDe-AgeQualiDe study

Purpose

The present study aimed to investigate age-group-specific incidence rates and risk factors for depressive symptoms in the highest age groups.

Methods

Data were derived from a prospective multicenter cohort study conducted in primary care - the AgeCoDe/AgeQualiDe study. In total, 2,436 patients 75 years and older were followed from baseline to ninth follow-up. To assess depressive symptoms, the short version of the Geriatric Depression Scale (GDS-15, cutoff score 6) was used. Age-specific competing risk regressions were performed to analyze risk factors for incident depressive symptoms in different age groups (75 to 79, 80 to 84, 85+ years), taking into account the accumulated mortality.

Results

The age-specific incidence rate of depression was 33 (95% CI 29-38), 46 (95% CI 40-52) and 63 (95% CI 45-87) per 1,000 person years for the initial age groups 75 to 79, 80 to 84 and 85+ years, respectively. In competing risk regression models, female sex, mobility as well as vision impairment, and subjective cognitive decline (SCD) were found to be risk factors for incident depression for age group 75 to 79, female sex, single/separated marital status, mobility as well as hearing impairment, and SCD for age group 80 to 84, and mobility impairment for age group 85+.

Conclusion

Depressive symptoms in latest life are common and the incidence increases with increasing age. Modifiable and differing risk factors across the highest age groups open up the possibility of specifically tailored prevention concepts.

Predictors of loss to follow up among adults on antiretroviral therapy before and after the start of treat-all strategy in public health facilities of Hawassa city, Ethiopia: A Competing risk regression

BACKGROUND

Treat-all strategies improved patient outcomes, despite higher rates of loss to follow-up compared to the pre-treat era. Patients in Ethiopia experienced a higher rate of LTFU during the treat-all strategy period; however, studies did not identify contributing factors in comparison with previous strategies. This study aimed to assess the incidence and predictors of loss to follow-up before and after the start of the treat-all strategy among adults on anti-retroviral therapy in public health facilities in Hawassa City, Ethiopia.

METHODS

An institution-based retrospective follow-up study was conducted among 1190 randomly selected adults on antiretroviral therapy in public health facilities in Hawassa City. Using the Open Data Kit (ODK), data were collected from medical records and exported to Stata version 16 and R 4.2.1 for analysis. A Grays test and cumulative incidence curve were used to compare the cumulative incidence function of loss to follow-up. Bivariable and multivariable competing risk regression were fitted to identify predictors of LTFU and variables with a p-value <0.05 were considered significant.

RESULTS

The cumulative incidence of lost-to-follow-up was 4.92(3.84,6.3) and 8.67(7.26,10.3) per 100 person-years (PY) in pre-treat all and treat all cohorts, respectively. The cumulative incidence of mortality was 5.86(4.67,7.35) and 3(2.26,4.12) per 100 PY in pre-treat and treat all cohorts, respectively. Fair/poor adherence (aSHR:5.17; (95% CI 1.97, 13.51), underweight (aSHR:2.13; 95% CI: 1.15-3.93) and WHO stage III/IV (aSHR:2.69; 95% CI: 1.27, 5.71) were predictors of loss up in pre-treat all, whereas fair/poor adherence (aSHR = 2.07; 95% CI: 1.18, 3.68), underweight (aSHR:1.71; 95% CI: 1.13, 2.56), and CD4 cell >350 cell/m3 (aSHR: 1.67; 95% CI: 1.05, 2.65) predicts of loss up in treat all cohorts.

CONCLUSION

This study demonstrated that the incidence of loss to follow-up was considerably higher in the treat-all period as compared to the pre-treat-all era. Poor medication compliance, underweight, and a CD4 level >350 cells/m3 contributed to the higher rate of LTFU in the treat-all strategy. Targeted interventions, such as nutritional support and strengthening medication adherence counseling, should be implemented to maintain treatment retention and reduce antiretroviral therapy dropout rates.

Accelerated and Interpretable Oblique Random Survival Forests

The oblique random survival forest (RSF) is an ensemble supervised learning method for right-censored outcomes. Trees in the oblique RSF are grown using linear combinations of predictors, whereas in the standard RSF, a single predictor is used. Oblique RSF ensembles have high prediction accuracy, but assessing many linear combinations of predictors induces high computational overhead. In addition, few methods have been developed for estimation of variable importance (VI) with oblique RSFs. We introduce a method to increase computational efficiency of the oblique RSF and a method to estimate VI with the oblique RSF. Our computational approach uses Newton-Raphson scoring in each non-leaf node, We estimate VI by negating each coefficient used for a given predictor in linear combinations, and then computing the reduction in out-of-bag accuracy. In benchmarking experiments, we find our implementation of the oblique RSF is hundreds of times faster, with equivalent prediction accuracy, compared to existing software for oblique RSFs. We find in simulation studies that "negation VI" discriminates between relevant and irrelevant numeric predictors more accurately than permutation VI, Shapley VI, and a technique to measure VI using analysis of variance. All oblique RSF methods in the current study are available in the aorsf R package, and additional supplemental materials are available online.

Software Application Profile: dynamicLM-a tool for performing dynamic risk prediction using a landmark supermodel for survival data under competing risks

MOTIVATION

Providing a dynamic assessment of prognosis is essential for improved personalized medicine. The landmark model for survival data provides a potentially powerful solution to the dynamic prediction of disease progression. However, a general framework and a flexible implementation of the model that incorporates various outcomes, such as competing events, have been lacking. We present an R package, dynamicLM, a user-friendly tool for the landmark model for the dynamic prediction of survival data under competing risks, which includes various functions for data preparation, model development, prediction and evaluation of predictive performance.

IMPLEMENTATION

dynamicLM as an R package.

GENERAL FEATURES

The package includes options for incorporating time-varying covariates, capturing time-dependent effects of predictors and fitting a cause-specific landmark model for time-to-event data with or without competing risks. Tools for evaluating the prediction performance include time-dependent area under the ROC curve, Brier Score and calibration.

AVAILABILITY

Available on GitHub [https://github.com/thehanlab/dynamicLM].

Additive subdistribution hazards regression for competing risks data in case-cohort studies

In survival data analysis, a competing risk is an event whose occurrence precludes or alters the chance of the occurrence of the primary event of interest. In large cohort studies with long-term follow-up, there are often competing risks. Further, if the event of interest is rare in such large studies, the case-cohort study design is widely used to reduce the cost and achieve the same efficiency as a cohort study. The conventional additive hazards modeling for competing risks data in case-cohort studies involves the cause-specific hazard function, under which direct assessment of covariate effects on the cumulative incidence function, or the subdistribution, is not possible. In this paper, we consider an additive hazard model for the subdistribution of a competing risk in case-cohort studies. We propose estimating equations based on inverse probability weighting methods for the estimation of the model parameters. Consistency and asymptotic normality of the proposed estimators are established. The performance of the proposed methods in finite samples is examined through simulation studies and the proposed approach is applied to a case-cohort dataset from the Sister Study.

Fifty years of BMT: risk stratification, donor matching, and stem cell collection for transplantation

In this review, we discuss recipient risk assessment for allo-HCT regarding comorbidities present at baseline to predict non relapse mortality. We further reviewed the incorporation of remission status and cytogenetic risk prior to allograft transplantation to predict relapse rates for hematologic malignancies. HCT-CI and DRI are tools available to physicians to assess the risk-benefit of allo-HCT in patients referred for transplantation. Next, we discuss our algorithm for donor selection and criteria for donor selection in case matched donors are not available. Finally, we discuss our approach for stem cell mobilization, especially in donors failing G-CSF, and our approach for the use of plerixafor and data supporting its use.

Intravascular Imaging-Guided or Angiography-Guided Complex PCI

BACKGROUND

Data regarding clinical outcomes after intravascular imaging-guided percutaneous coronary intervention (PCI) for complex coronary-artery lesions, as compared with outcomes after angiography-guided PCI, are limited.

METHODS

In this prospective, multicenter, open-label trial in South Korea, we randomly assigned patients with complex coronary-artery lesions in a 2:1 ratio to undergo either intravascular imaging-guided PCI or angiography-guided PCI. In the intravascular imaging group, the choice between intravascular ultrasonography and optical coherence tomography was at the operators' discretion. The primary end point was a composite of death from cardiac causes, target-vessel-related myocardial infarction, or clinically driven target-vessel revascularization. Safety was also assessed.

RESULTS

A total of 1639 patients underwent randomization, with 1092 assigned to undergo intravascular imaging-guided PCI and 547 assigned to undergo angiography-guided PCI. At a median follow-up of 2.1 years (interquartile range, 1.4 to 3.0), a primary end-point event had occurred in 76 patients (cumulative incidence, 7.7%) in the intravascular imaging group and in 60 patients (cumulative incidence, 12.3%) in the angiography group (hazard ratio, 0.64; 95% confidence interval, 0.45 to 0.89; P = 0.008). Death from cardiac causes occurred in 16 patients (cumulative incidence, 1.7%) in the intravascular imaging group and in 17 patients (cumulative incidence, 3.8%) in the angiography group; target-vessel-related myocardial infarction occurred in 38 (cumulative incidence, 3.7%) and 30 (cumulative incidence, 5.6%), respectively; and clinically driven target-vessel revascularization in 32 (cumulative incidence, 3.4%) and 25 (cumulative incidence, 5.5%), respectively. There were no apparent between-group differences in the incidence of procedure-related safety events.

CONCLUSIONS

Among patients with complex coronary-artery lesions, intravascular imaging-guided PCI led to a lower risk of a composite of death from cardiac causes, target-vessel-related myocardial infarction, or clinically driven target-vessel revascularization than angiography-guided PCI. (Supported by Abbott Vascular and Boston Scientific; RENOVATE-COMPLEX-PCI ClinicalTrials.gov number, NCT03381872).

Risk factors for HCC in contemporary cohorts of patients with cirrhosis

BACKGROUND AND AIMS

Etiological risk factors for cirrhosis have changed in the last decade. It remains unclear to what extent these trends in cirrhosis risk factors have changed HCC risk.

APPROACH AND RESULTS

We used data from two contemporary, prospective multiethnic cohorts of patients with cirrhosis: the Texas Hepatocellular Carcinoma Consortium Cohort and the Houston Veterans Administration Cirrhosis Surveillance Cohort. Patients with cirrhosis were enrolled from seven US centers and followed until HCC diagnosis, transplant, death, or June 30, 2021. We calculated the annual incidence rates for HCC and examined the effects of etiology, demographic, clinical, and lifestyle factors on the risk of HCC. We included 2733 patients with cirrhosis (mean age 60.1 years, 31.3% women). At enrollment, 19.0% had active HCV, 23.3% had cured HCV, 16.1% had alcoholic liver disease, and 30.1% had NAFLD. During 7406 person-years of follow-up, 135 patients developed HCC at an annual incidence rate of 1.82% (95% CI, 1.51-2.13). The annual HCC incidence rate was 1.71% in patients with cured HCV, 1.32% in patients with alcoholic liver disease, and 1.24% in patients with NAFLD cirrhosis. Compared to patients with NAFLD, the risk of progression to HCC was 2-fold higher in patients with cured HCV (HR, 2.04; 95% CI, 1.24-3.35). Current smoking (HR, 1.63; 95% CI, 1.01-2.63) and overweight/obesity (HR, 1.79; 95% CI, 1.08-2.95) were also associated with HCC risk.

CONCLUSIONS

HCC incidence among patients with cirrhosis was lower than previously reported. HCC risk was variable across etiologies, with higher risk in patients with HCV cirrhosis and lower risk in those with NAFLD cirrhosis. Current smoking and overweight/obesity increased HCC risk across etiologies.

Marginal semiparametric transformation models for clustered multivariate competing risks data

Multivariate survival models are often used in studying multiple outcomes for right-censored data. However, the outcomes of interest often have competing risks, where standard multivariate survival models may lead to invalid inferences. For example, patients who had stem cell transplantation may experience multiple types of infections after transplant while reconstituting their immune system, where death without experiencing infections is a competing risk for infections. Such competing risks data often suffer from cluster effects due to a matched pair design or correlation within study centers. The cumulative incidence function (CIF) is widely used to summarize competing risks outcomes. Thus, it is often of interest to study direct covariate effects on the CIF. Most literature on clustered competing risks data analyses is limited to the univariate proportional subdistribution hazards model with inverse probability censoring weighting which requires correctly specifying the censoring distribution. We propose a marginal semiparametric transformation model for multivariate competing risks outcomes. The proposed model does not require modeling the censoring distribution, accommodates nonproportional subdistribution hazards structure, and provides a platform for joint inference of all causes and outcomes.

The kidney allocation system and pediatric transplantation at 5 years

BACKGROUND

A new Kidney Allocation System (KAS) was implemented in the United States in 2014 with the goal of improving equity and utility.

METHODS

In this study, we compare outcomes for kidney-alone candidates less than 18 years of age, at the time of listing, in the 5 years prior to and following policy implementation using Organ Procurement and Transplantation Network data.

RESULTS

While the pediatric deceased donor transplant rate increased under KAS, this increase was due solely to improved access for children aged 11-17 years; there was an 18.9% decrease in the deceased donor transplant rate among children 0-5 years old, from 117.94 to 95.8 transplants per 100 person-years (p = .001). The cumulative incidence of deceased donor transplantation by 1 year after listing decreased from 39.3% in the pre-KAS era to 35.5% in the post-KAS era (p = .004), a decline that was driven entirely by longer wait times for children 0-5 years old (p = .017). Candidates with a calculated panel reactive antibody of 98%-100% experienced a significant increase in transplant rate, but there was no change in transplant rate for Black or Hispanic candidates.

CONCLUSION

Overall, KAS increased transplantation access for teenaged and highly sensitized candidates but resulted in decreased access for the youngest children with no improvement in racial/ethnic equality.

Oral anticoagulant decreases stroke recurrence in patients with atrial fibrillation detected after stroke

Background

Atrial fibrillation detected after stroke (AFDAS) has a lower risk of ischemic stroke recurrence than known atrial fibrillation (KAF). While the benefit of oral anticoagulants (OAC) for preventing ischemic stroke recurrence in KAF is well established, their role in patients with AFDAS is more controversial. This study aimed to evaluate the association between OAC use and the risk of recurrent ischemic stroke in patients with AFDAS in a real-world setting.

Methods

This nationwide retrospective cohort study was conducted using the Taiwan National Health Insurance Research Database. Patients hospitalized with a first-ever ischemic stroke and AFDAS confirmed within 30 days after hospitalization were assigned to OAC and non-OAC cohorts. Inverse probability of treatment weighting was applied to balance the baseline characteristics of the cohorts. The primary outcome was ischemic stroke recurrence. Secondary outcomes were intracranial hemorrhage (ICH), death, and the composite outcome of "ischemic stroke recurrence, ICH, or death." Multivariate Cox proportional hazard models were used to estimate adjusted hazard ratios (aHR) and 95% confidence intervals (CI).

Results

A total of 4,508 hospitalized patients with stroke and AFDAS were identified. Based on OAC use, 2,856 and 1,652 patients were assigned to the OAC and non-OAC groups, respectively. During the follow-up period (median duration, 2.76 years), the OAC cohort exhibited a lower risk of ischemic stroke recurrence (aHR, 0.84; 95% CI, 0.70-0.99), death (aHR, 0.65; 95% CI, 0.58-0.73), and composite outcome (aHR, 0.70; 95% CI, 0.63-0.78) than did the non-OAC cohort. The risk of ICH (aHR, 0.96; 95% CI, 0.62-1.50) was not significantly different between the two cohorts.

Conclusion

OAC use in patients with AFDAS was associated with reduced risk of ischemic stroke recurrence, without an increased risk of ICH. This supports current guidelines recommending OACs for secondary stroke prevention in patients with AF, regardless of the time of diagnosis.

Clinical implication of residual MIBG-positive disease in the follow-up of high-risk neuroblastoma treated with tandem high-dose chemotherapy and autologous stem cell transplantation

BACKGROUND

The implication of residual metaiodobenzylguanidine (MIBG)-positive disease in the era of tandem high-dose chemotherapy (HDCT) with autologous stem cell transplantation (auto-SCT) has not yet been established in neuroblastoma. Moreover, most published studies have not evaluated the long-term prognosis of patients with residual MIBG-positive disease following treatment completion. Therefore, we investigated the prognostic significance of residual MIBG-positive disease at each treatment phase and after treatment completion.

METHODS

We assessed MIBG scans labeled with either iodine-123 (¹²³ I) or ¹³¹ I from 150 patients with MIBG-avid and high-risk neuroblastoma enrolled in the NB-2004, -2009, and -2014 trials at postinduction, posttandem HDCT/auto-SCT, and completion of treatment.

RESULTS

The residual MIBG-positive disease at postinduction and posttandem HDCT/auto-SCT evaluation was highly correlated with the risk of progression. However, at treatment completion, there was no significant difference in survival and risk of progression between patients with residual MIBG-positive disease and MIBG-negative patients. Patients with persistent MIBG-positive disease at the end of treatment were more likely to have indolent tumor characteristics, such as favorable histology at diagnosis, lower incidence of MYCN amplification, and slow response to chemotherapy.

CONCLUSION

Residual MIBG-positive disease during treatment predicted unfavorable outcomes for patients with high-risk neuroblastoma, even under tandem HDCT/auto-SCT. However, persistent MIBG uptake at the completion of all treatments may not always indicate an active disease.

Cardiovascular Outcomes in the Patients With Colorectal Cancer: A Multi-Registry-Based Cohort Study of 197,699 Cases in the Real World

Purpose

We aimed to investigate the mortality patterns and quantitatively assess the risks of cardiovascular death (CVD) in patients with colorectal cancer (CRC). We also established a competing-risk model to predict the probability of CVD for patients with CRC.

Patients and Methods

Patients with CRC who diagnosed between 2007 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were included in the present study. The cumulative incidence function (CIF) was used for CVD and other causes of death, and Gray’s test was used to determine the subgroup difference in CIF. The Fine-Gray proportional subdistribution hazards model was used for identifying independent risk factors for CVD. A novel competing-risk model was established to evaluate the probability of CVD for patients with CRC. The performance of the nomogram was measured by concordance index (C-index), calibration curve, decision curve analysis (DCA), and risk stratification.

Results

After a median follow-up of 37.00 months, 79,455 deaths occurred, of whom 56,185 (70.71%) succumbed to CRC and 23,270 (29.29%) patients died due to non-CRC, among which CVD accounted for 9,702 (41.69%), being the major cause of non-cancer deaths. The 1-, 3-, and 5-year cumulative rates for CVD were 12.20, 24.25, and 30.51%, respectively. In multivariate analysis, age, race, marital status, tumor size, tumor stage, advanced stage, surgery, and chemotherapy were independent risk factors of CVD among patients with CRC. The nomogram was well calibrated and had good discriminative ability, with a c-index of 0.719 (95% CI, 0.738–0.742) in the training cohort and 0.719 (95% CI, 0.622–0.668) in the validation cohort. DCA demonstrated that nomogram produced more benefit within wide ranges of threshold probabilities for 1-, 3-, and 5-year CVD, respectively.

Conclusion

This study was the first to analyze the CIF and risk factors for CVD among CRC based on a competing-risk model. We have also built the first 1-, 3-, and 5-year competing nomogram for predicting CVD. This nomogram had excellent performance and could help clinicians to provide individualized management in clinical practice.

A Novel Nomogram Based on a Competing Risk Model Predicting Cardiovascular Death Risk in Patients With Chronic Kidney Disease

Objective

Chronic kidney disease (CKD) patients are more likely to die from cardiovascular disease (CVD) than develop renal failure. This study aimed to develop a new nomogram for predicting the risk of cardiovascular death in CKD patients.

Methods

This study enrolled 1656 CKD patients from NHANES 2003 to 2006 survey. Data sets from 2005 to 2006 survey population were used to build a nomogram for predicting the risk of cardiovascular death, and the nomogram was validated using data from 2003 to 2004 survey population. To identify the main determinants of cardiovascular death, we performed univariate analysis and backward-stepwise regression to select the key factors. The probability of cardiovascular death for each patient in 5, 7, and 9 years was calculated using a nomogram based on the predictors. To assess the nomogram's performance, the area under receiver operating characteristic curve (AUC) and the calibration curve with 1,000 bootstraps resamples were utilized. The prediction model's discrimination was examined using cumulative incidence function (CIF).

Results

Age, homocysteine, potassium levels, CKD stage, and anemia were included in the nomogram after screening risk factors using univariate analysis and backward-stepwise regression. Internal validation revealed that this nomogram possesses high discrimination and calibration (AUC values of 5-, 7-, and 9-years were 0.79, 0.81, and 0.81, respectively). External validation confirmed the same findings (AUC values of 5-, 7- and 9-years were 0.76, 0.73, and 0.73, respectively). According to CIF, the established nomogram effectively differentiates patients at a high risk of cardiovascular death from those at low risk.

Conclusion

This work develops a novel nomogram that integrates age, homocysteine, potassium levels, CKD stage, and anemia and can be used to more easily predict cardiovascular death in CKD patients, highlighting its potential value in clinical application.

Competing risks regression models with covariates-adjusted censoring weight under the generalized case-cohort design

A generalized case-cohort design has been used when measuring exposures is expensive and events are not rare in the full cohort. This design collects expensive exposure information from a (stratified) randomly selected subset from the full cohort, called the subcohort, and a fraction of cases outside the subcohort. For the full cohort study with competing risks, He et al. (Scand J Stat 43:103-122, 2016) studied the non-stratified proportional subdistribution hazards model with covariate-dependent censoring to directly evaluate covariate effects on the cumulative incidence function. In this paper, we propose a stratified proportional subdistribution hazards model with covariate-adjusted censoring weights for competing risks data under the generalized case-cohort design. We consider a general class of weight functions to account for the generalized case-cohort design. Then, we derive the optimal weight function which minimizes the asymptotic variance of parameter estimates within the general class of weight functions. The proposed estimator is shown to be consistent and asymptotically normally distributed. The simulation studies show (i) the proposed estimator with covariate-adjusted weight is unbiased when the censoring distribution depends on covariates; and (ii) the proposed estimator with the optimal weight function gains parameter estimation efficiency. We apply the proposed method to stem cell transplantation and diabetes data sets.

The relationship between the modified National Institute of Health activity and chronicity scoring system, and the long-term prognosis for lupus nephritis: A retrospective single-center study

BACKGROUND

The revision of International Society of Nephrology/Renal Pathology Society (ISN/RPS) classification guidelines for lupus nephritis (LN) was suggested by a working group, who recommended a modified National Institute of Health (NIH) activity and chronicity scoring system to evaluate active and chronic LN lesions. However, whether this approach was useful for estimating long-term prognosis for LN patients is unclear.

METHODS

We conducted a retrospective cohort study in Japanese subjects with biopsy-proven LN, between 1977 and 2018. Pathologic lesions were evaluated based on ISN/RPS 2003 classifications and the modified NIH scoring system. Patients were grouped by activity index (low, 0-5; moderate, 6-11; high, 12-24), and chronicity index (low, 0-2; moderate, 3-5; high, 6-12). The primary outcome was a composite of end-stage kidney disease (ESKD) or all-cause death, and the secondary outcome was ESKD alone.

RESULTS

Sixty-six subjects with a median age of 31 years were included. During median follow-up (11.5 years), 15 patients reached the primary outcome: 10 had ESKD, four had died, and one had ESKD and died. Kaplan-Meier analysis showed that the cumulative primary outcome incidence increased with a higher chronicity index (log-rank trend p < 0.001). From multivariable survival analysis, moderate (hazard ratio [HR] 6.17, 95% confidence interval [CI] 1.14 to 33.20; p = 0.034) and high chronicity indices (HR 20.20, 95% CI 1.13 to 359.82; p = 0.041) were risk factors for the primary outcome.

CONCLUSION

Moderate and high chronicity indices were associated with an increased ESKD risk for LN.

Comparison of effectiveness and safety between ticagrelor and clopidogrel in patients with acute coronary syndrome and on dialysis in Taiwan

AIMS

This study aimed to evaluate and compare the effectiveness and safety between clopidogrel and ticagrelor in acute coronary syndrome (ACS) with renal dysfunction.

METHODS

We conducted a retrospective cohort study on patients on chronic dialysis and whose admission diagnosis between 1 July 2013 and 31 December 2016 included ACS. The primary effectiveness endpoint was a major adverse cardiovascular event (MACE), and the primary safety endpoint was a major bleeding event. The application of propensity scores through the inverse probability of treatment weighting (IPTW) was applied to the study groups. Cox regression was used to estimate hazard ratios (aHRs) of study endpoints. In addition, the competing risk was adjusted using the Fine and Gray competing risk model.

RESULTS

There were 1915 patients in the clopidogrel group and 270 patients in the ticagrelor group. At 12 months, the ticagrelor group had higher risks for MACE (aHR with IPTW: 1.29; 95% CI 1.16-1.44); death (aHR with IPTW: 1.65; 95% CI 1.47-1.86) and cardiac death (subdistribution HR [SHR] with IPTW: 1.64; 95% CI 1.41-1.91), compared with those in the clopidogrel group. For major bleeding event, the risk was significantly higher with ticagrelor than with clopidogrel (SHR with IPTW: 1.49; 95% CI 1.34-1.65). In terms of the risk for any bleeding event, there was no significant difference between the two groups (SHR with IPTW: 1.05; 95% CI 0.95-1.17).

CONCLUSIONS

Compared with clopidogrel, ticagrelor was associated with higher MACE, death, cardiac death and major bleeding risk within 12 months in patients with ACS and on dialysis.

Optimal treatment regimes for competing risk data using doubly robust outcome weighted learning with bi-level variable selection

The goal of the optimal treatment regime is maximizing treatment benefits via personalized treatment assignments based on the observed patient and treatment characteristics. Parametric regression-based outcome learning approaches require exploring complex interplay between the outcome and treatment assignments adjusting for the patient and treatment covariates, yet correctly specifying such relationships is challenging. Thus, a robust method against misspecified models is desirable in practice. Parsimonious models are also desired to pursue a concise interpretation and to avoid including spurious predictors of the outcome or treatment benefits. These issues have not been comprehensively addressed in the presence of competing risks. Recognizing that competing risks and group variables are frequently present, we propose a doubly robust estimation with adaptive L 1 penalties to select important variables at both group and within-group levels for competing risks data. The proposed method is applied to hematopoietic cell transplantation data to personalize the graft source choice for treatment-related mortality (TRM). While the existing medical literature attempts to find a uniform solution ignoring the heterogeneity of the graft source effects on TRM, the analysis results show the effect of the graft source on TRM could be different depending on the patient-specific characteristics.

Beta-Adrenergic Antagonists and Cancer Specific Survival in Patients With Advanced Prostate Cancer: A Veterans Administration Cohort Study

OBJECTIVE

To interrogate the National Veterans Health Administration (VA) database to determine if beta-blocker use at time of initiation of androgen therapy deprivation (ADT) would result in improved oncological outcomes in advanced prostate cancer (PCa).

METHODS

All men diagnosed with high risk PCa (PSA >20) from 2000-2008 who were on ADT ≥ 6 months were identified. Patients receiving ADT concurrently with primary radiation therapy were excluded. Pharmacy data was interrogated for all beta-blockers, but then focused on the selective beta-1 blocker metoprolol. Cox proportional hazards ratios were calculated for overall survival (OS), PCa specific survival (CSS) and skeletal related events (SREs).

RESULTS

In 39,198 patients with high risk PCa on ADT, use of any beta-blocker was not associated with improvement in OS, CSS, or SREs. Further analyses focusing on metoprolol found that 10,224 (31.9%) had used metoprolol while 21,834 had no beta-blocker use. Multivariable analysis with Inverse Propensity Score Weighting, adjusted for factors including PSA, Gleason score, and duration ADT, found that utilization of metoprolol was not associated with improvement in OS (hazard ratio [HR] 0.97, P = .19), CSS (HR 0.94, P = .23) or SREs (HR 0.98, P = .79).

CONCLUSION

In this large cohort, metoprolol use in conjunction with ADT in high risk PCa was not associated with improvement in OS, CSS, or risk of SRE. In contrast to a recent smaller clinical study, our data strongly suggests no cancer specific benefit to beta-blocker use in advanced PCa.

Evaluating spillover of HIV knowledge from study participants to their network members in a stepped-wedge behavioural intervention in Tanzania

OBJECTIVES

We aim to describe the social network members of participants of a behavioural intervention, and examine how the effects of the intervention may spillover among network members.

DESIGN

Secondary analysis of a step-wedge randomised controlled trial.

SETTING

Change agents (CAs) were recruited from waiting rooms of HIV treatment facilities in Dar es Salaam, Tanzania, and their network members (NMs) were recruited directly by CAs.

PARTICIPANTS

We enrolled 662 CAs in an HIV behavioural intervention. They, along with 710 of their NMs, completed baseline and follow-up interviews from 2011 to 2013.

PRIMARY AND SECONDARY OUTCOMES

The primary outcome of this study was change in NMs' HIV knowledge, and the secondary outcome was whether the NM was lost to follow-up.

RESULTS

At baseline, many characteristics were different between NMs and CAs. We found a number of NM characteristics significantly associated with follow-up of NMs, particularly female gender (OR=1.64, 95% CI: 1.02 to 2.63) and HIV knowledge (OR=20.0, 95% CI: 3.70 to 125); only one CA variable was significantly associated with NM follow-up: having a private source of water (OR=2.17, 95% CI: 1.33 to 3.57). The 14.2% increase in NMs' HIV knowledge was largely due to CAs feeling empowered to pass on prior knowledge, rather than transmitting new knowledge to their NMs.

CONCLUSIONS

Characteristics of social network members of persons living with HIV persons living with HIV may play a role in study retention. Additionally, the HIV knowledge of these NMs increased largely as a function of CA participation in the intervention, suggesting that intervening among highly-connected individuals may maximise benefits to the potential population for whom spillover can occur.

TRIAL REGISTRATION NUMBER

Clinical Trial: NCT01693458; Post-results.

Excess cumulative incidence estimation for matched cohort survival studies

We suggest a regression approach to estimate the excess cumulative incidence function (CIF) when matched data are available. In a competing risk setting, we define the excess risk as the difference between the CIF in the exposed group and the background CIF observed in the unexposed group. We show that the excess risk can be estimated through an extended binomial regression model that actively uses the matched structure of the data, avoiding further estimation of both the exposed and the unexposed CIFs. The method naturally deals with two time scales, age and time since exposure and simplifies how to deal with the left truncation on the age time-scale. The model makes it easy to predict individual excess risk scenarios and allows for a direct interpretation of the covariate effects on the cumulative incidence scale. After introducing the model and some theory to justify the approach, we show via simulations that our model works well in practice. We conclude by applying the excess risk model to data from the ALiCCS study to investigate the excess risk of late events in childhood cancer survivors.

In-depth mining of clinical data: the construction of clinical prediction model with R

This article is the series of methodology of clinical prediction model construction (total 16 sections of this methodology series). The first section mainly introduces the concept, current application status, construction methods and processes, classification of clinical prediction models, and the necessary conditions for conducting such researches and the problems currently faced. The second episode of these series mainly concentrates on the screening method in multivariate regression analysis. The third section mainly introduces the construction method of prediction models based on Logistic regression and Nomogram drawing. The fourth episode mainly concentrates on Cox proportional hazards regression model and Nomogram drawing. The fifth Section of the series mainly introduces the calculation method of C-Statistics in the logistic regression model. The sixth section mainly introduces two common calculation methods for C-Index in Cox regression based on R. The seventh section focuses on the principle and calculation methods of Net Reclassification Index (NRI) using R. The eighth section focuses on the principle and calculation methods of IDI (Integrated Discrimination Index) using R. The ninth section continues to explore the evaluation method of clinical utility after predictive model construction: Decision Curve Analysis. The tenth section is a supplement to the previous section and mainly introduces the Decision Curve Analysis of survival outcome data. The eleventh section mainly discusses the external validation method of Logistic regression model. The twelfth mainly discusses the in-depth evaluation of Cox regression model based on R, including calculating the concordance index of discrimination (C-index) in the validation data set and drawing the calibration curve. The thirteenth section mainly introduces how to deal with the survival data outcome using competitive risk model with R. The fourteenth section mainly introduces how to draw the nomogram of the competitive risk model with R. The fifteenth section of the series mainly discusses the identification of outliers and the interpolation of missing values. The sixteenth section of the series mainly introduced the advanced variable selection methods in linear model, such as Ridge regression and LASSO regression.

A group sequential test for treatment effect based on the Fine-Gray model

Competing risks endpoints arise when patients can fail therapy from several causes. Analyzing these outcomes allows one to assess directly the benefit of treatment on a primary cause of failure in a clinical trial setting. Regression models can be used in clinical trials to adjust for residual imbalances in patient characteristics, improving the power to detect treatment differences. But, none of the competing risks methods currently available for use in group sequential trials adjust for covariates. We propose a group sequential test for treatment effect that, because it is based on the Fine-Gray model, permits adjustment for covariates. Our derivations show that its sequence of test statistics has an asymptotic distribution with an independent increments structure, which allows standard techniques such as O'Brien-Fleming designs and error spending functions to be employed to meet type I error rate and power specifications. We demonstrate the test in a reanalysis of BMT CTN 0402, a phase III clinical trial that evaluated an experimental treatment for the prevention of adverse outcomes following blood and marrow transplant. Moreover, using a simulation study of randomized group sequential trials, we demonstrate that the proposed method preserves the type I error rate and power at their nominal levels in the presence of influential covariates.

Flexible parametric modelling of the cause-specific cumulative incidence function

Competing risks arise with time-to-event data when individuals are at risk of more than one type of event and the occurrence of one event precludes the occurrence of all other events. A useful measure with competing risks is the cause-specific cumulative incidence function (CIF), which gives the probability of experiencing a particular event as a function of follow-up time, accounting for the fact that some individuals may have a competing event. When modelling the cause-specific CIF, the most common model is a semi-parametric proportional subhazards model. In this paper, we propose the use of flexible parametric survival models to directly model the cause-specific CIF where the effect of follow-up time is modelled using restricted cubic splines. The models provide smooth estimates of the cause-specific CIF with the important advantage that the approach is easily extended to model time-dependent effects. The models can be fitted using standard survival analysis tools by a combination of data expansion and introducing time-dependent weights. Various link functions are available that allow modelling on different scales and have proportional subhazards, proportional odds and relative absolute risks as particular cases. We conduct a simulation study to evaluate how well the spline functions approximate subhazard functions with complex shapes. The methods are illustrated using data from the European Blood and Marrow Transplantation Registry showing excellent agreement between parametric estimates of the cause-specific CIF and those obtained from a semi-parametric model. We also fit models relaxing the proportional subhazards assumption using alternative link functions and/or including time-dependent effects. Copyright © 2016 John Wiley & Sons, Ltd.

Instrumental variable with competing risk model

In this paper, we discuss causal inference on the efficacy of a treatment or medication on a time-to-event outcome with competing risks. Although the treatment group can be randomized, there can be confoundings between the compliance and the outcome. Unmeasured confoundings may exist even after adjustment for measured covariates. Instrumental variable methods are commonly used to yield consistent estimations of causal parameters in the presence of unmeasured confoundings. On the basis of a semiparametric additive hazard model for the subdistribution hazard, we propose an instrumental variable estimator to yield consistent estimation of efficacy in the presence of unmeasured confoundings for competing risk settings. We derived the asymptotic properties for the proposed estimator. The estimator is shown to be well performed under finite sample size according to simulation results. We applied our method to a real transplant data example and showed that the unmeasured confoundings lead to significant bias in the estimation of the effect (about 50% attenuated). Copyright © 2017 John Wiley & Sons, Ltd.