Nonparametric analysis of recurrent events and death

Early Midterm Results After Valve Replacement With Contemporary Pericardial Prostheses for Severe Aortic Stenosis

BACKGROUND

Clinical studies have demonstrated improved gradients after aortic valve replacement with the Trifecta (TR) valve (Abbott Cardiovascular, St Paul, MN) as compared with the Carpentier-Edwards Magna Ease (ME) valve (Edwards Lifesciences, Irvine, CA). Clinical benefits of this strategy have not been demonstrated.

METHODS

Patients undergoing aortic valve replacement for severe aortic stenosis with either valve were included. Patients were excluded if they underwent concomitant procedures other than coronary artery bypass grafting. Inverse proportion treatment weighting was used in the analysis. The primary outcome was a composite of cardiac mortality, need for reintervention, and freedom from first congestive heart failure (CHF). Secondary outcomes were all-cause mortality, the composite components, and cumulative CHF admission. Follow-up echocardiograms were assessed in a cohort of patients to assess structural valve degeneration.

RESULTS

There were 331 patients in the TR group and 360 patients in the ME group. The TR group had more women (48% vs 32%, P < .001) with smaller roots (left ventricular outflow tract diameter: TR, 2.11 cm; ME, 2.17 cm; P < .001). After weighting there was no significant difference in the composite measure between groups (P > .05). There was no difference in all-cause mortality (hazard ratio, 0.82; 95% confidence interval, 0.42-1.59; P = .56), and 5-year survival was 91.9% in the ME group and 93.4% in the TR group. There was no difference in cardiac death, reintervention, or first onset of CHF or incidence of structural valve degeneration between groups. There was no difference in the rate of admissions for CHF per 100 patients between the 2 valve types (P = .19).

CONCLUSIONS

Early hemodynamic benefits have not translated into differences in medium-term clinical outcomes between these 2 valves. Long-term follow-up is necessary.

Risk of late health effects after soft-tissue sarcomas in childhood - a population-based cohort study within the Adult Life after Childhood Cancer in Scandinavia research programme

BACKGROUND

In the 1960s only 1/3 of children with soft-tissue sarcomas survived, however with improved treatments survival today has reached 70%. Given the previous poor survival and the rarity of soft-tissue sarcomas, the risk of somatic late effects in a large cohort of Nordic soft-tissue sarcoma survivors has not yet been assessed.

METHODS

In this population-based cohort study we identified 985 five-year soft-tissue sarcoma survivors in Nordic nationwide cancer registries and late effects in national hospital registries covering the period 1964-2012. Information on tumour site and radiotherapy was available for Danish and Finnish survivors (N = 531). Using disease-specific rates of first-time hospital contacts for somatic diseases in survivors and in 4,830 matched comparisons we calculated relative rates (RR) and rate differences (RD).

RESULTS

Survivors had a RR of 1.5 (95% CI 1.4-1.7) and an absolute RD of 23.5 (17.7-29.2) for a first hospital contact per 1,000 person-years. The highest risks in both relative and absolute terms were of endocrine disorders (RR = 2.5; RD = 7.6), and diseases of the nervous system (RR = 1.9; RD = 6.6), digestive organs (RR = 1.7; RD = 5.4) and urinary system (RR = 1.7; RD = 5.6). By tumour site, excess risk was lower after extremity tumours. Irradiated survivors had a 2.6 (1.2-5.9) times higher risk than non-irradiated.

CONCLUSIONS

Soft-tissue sarcoma survivors have an increased risk of somatic late effects in 5 out of 10 main diagnostic groups of diseases, and the risk remains increased up to 40 years after cancer diagnosis. Risks were slightly lower for those treated for tumours in the extremities, and radiotherapy increased the risk by more than two-fold.

A semiparametric additive rates model for the weighted composite endpoint of recurrent and terminal events

Recurrent event data with a terminal event commonly arise in longitudinal follow-up studies. We use a weighted composite endpoint of all recurrent and terminal events to assess the overall effects of covariates on the two types of events. A semiparametric additive rates model is proposed to analyze the weighted composite event process and the dependence structure among recurrent and terminal events is left unspecified. An estimating equation approach is developed for inference, and the asymptotic properties of the resulting estimators are established. The finite-sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a bladder cancer study is illustrated.

Association of Sustained Blood Pressure Control with Multimorbidity Progression Among Older Adults

BACKGROUND/OBJECTIVES

Due to the high costs and excess mortality associated with multimorbidity, there is a need to develop approaches for delaying its progression. High blood pressure (BP) is a common chronic condition and a risk factor for many additional chronic conditions, making it an ideal target for intervention. The purpose of this analysis was to determine the association between the level of sustained BP control and the progression of multimorbidity.

DESIGN

Retrospective cohort study.

SETTING

Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT) linked to Medicare claims.

PARTICIPANTS

A total of 6,591 ALLHAT participants with Medicare who had systolic BP (SBP) measurements at eight or more study visits.

MEASUREMENTS

SBP control was categorized as lower than 140 mm Hg at less than 50%, 50% to less than 75%, 75% to less than 100%, and 100% of visits. Multimorbidity progression was defined by the number of incident chronic conditions, including arthritis, asthma, atrial fibrillation, cancer, chronic kidney disease, chronic obstructive pulmonary disease, coronary heart disease, dementia, depression, diabetes mellitus, heart failure, hyperlipidemia, osteoporosis, and stroke. Recurrent event survival analysis was used to calculate rate ratios (RRs) for the association of sustained SBP control with progression of multimorbidity.

RESULTS

Rates of incident conditions per 10 person-years (95% CIs) were 5.2 (5.1-5.4), 4.7 (4.5-4.8), 4.4 (4.2-4.5), and 4.0 (3.8-4.2) for participants with SBP control at less than 50%, 50% to less than 75%, 75% to less than 100%, and 100% of visits, respectively, over a median follow-up of 9.0 years. Compared with participants with SBP control at less than 50% of visits, adjusted RRs (95% CIs) for multimorbidity progression were 0.90 (0.86-0.95), 0.85 (0.81-0.89), and 0.77 (0.72-0.82) for those with SBP control at 50% to less than 75%, 75% to less than 100%, and 100% of visits, respectively.

CONCLUSIONS

Sustaining BP control may be an effective approach to slow multimorbidity progression and may reduce the population burden of multimorbidity.

Modernizing adverse events analysis in oncology clinical trials using alternative approaches: rationale and design of the MOTIVATE trial

In oncology clinical research, the analysis and reporting of adverse events is of major interest. A consistent depiction of the safety profile of a new treatment is as crucial in establishing how to use it as its antitumor activity. The advent of new therapeutics has led to major changes in the management of patients and targeted therapies or immune checkpoint inhibitors are administered continuously for months or even years. However, the classical methods of adverse events analysis are no longer adequate to properly assess their safety profile. Indeed, the worst grade method and time-to-event analysis cannot capture the duration or the evolution of adverse events induced by extended treatment durations. Many authors have highlighted this issue and argue that the analysis of safety data from clinical trials should be modernized by considering the dimension of time and the recurrent nature of adverse events. This paper aims to illustrate the limitations of current methods and discusses the value of alternative approaches such as the prevalence function, Q-TWiST, the ToxT and the recurrent event approaches. The rationale and design of the MOTIVATE trial, which aims to model the evolution of toxicities over time using the prevalence function in patients treated by immunotherapy, is also presented ( ClinicalTrials.gov Identifier: NCT03447483; Date of registration: 27 February 2018).

Neurologic disorders in long-term survivors of neuroblastoma - a population-based cohort study within the Adult Life after Childhood Cancer in Scandinavia (ALiCCS) research program

Background: Neuroblastoma is the commonest extracranial solid tumor of childhood, yet rare, and with poor survival before 1990, especially for high-risk disease; thus, information on late effects is sparse. With great advances in cancer treatment, survival has reached 80% in the Nordic countries. The aim of the study was to investigate the risk of developing neurologic disorders after neuroblastoma.Material and methods: Through population-based cancer registries of four Nordic countries we identified 654 5-year survivors of neuroblastoma (diagnosed 1959-2008) and 133,668 matched population comparisons. We grouped neurologic diagnoses from national hospital registries into 11 main diagnostic categories and 56 disease-specific sub-categories and calculated relative risks (RRs), absolute excess risks (AERs), cumulative incidence and mean cumulative count (MCC). Information on cancer treatment was available for 49% of survivors.Results: A hospital contact for a neurologic disorder was observed in 181 survivors 5 years or more from cancer diagnosis with 59 expected, yielding a RR of 3.1 (95% CI 2.7-3.6) and an AER of 16 per 1,000 person-years (95% CI 12-19). The most frequent disorders included epilepsy, paralytic syndromes, diseases of the eyes and ears and hearing loss. The cumulative incidence of any neurologic disorder was 31% in survivors 20 years after cancer diagnosis with a MCC of 0.5 unique diagnoses. All risks were highest in survivors of high-risk neuroblastoma.Conclusion: Neuroblastoma survivors represent a population with a high risk of developing neurologic disorders. Our results should contribute to improving health care planning and underscores the need for systematic follow-up care of this vulnerable group of survivors.

Nonparametric group sequential methods for recurrent and terminal events from multiple follow-up windows

Few methods are currently available for group sequential analysis of recurrent events data subject to a terminal event in the clinical trial setting. This research helps fill this gap by developing a completely nonparametric group sequential monitoring procedure for use with the two-sample Tayob and Murray statistic. Advantages of the Tayob and Murray statistic include high power to detect treatment differences when there is correlation between recurrent event times or between recurrent and terminal events in an individual. This statistic does not suffer bias from dependent censoring, regardless of the correlation between event times in an individual. This manuscript briefly reviews the Tayob and Murray statistic, develops and describes how to use methods for its group sequential analysis, and through simulation, compares its operating characteristics with those of Cook and Lawless, which is currently in use as the only available nonparametric method for group sequential analysis of recurrent event data. The merits of our proposed approach are most clearly demonstrated when gap times between recurrent events are correlated; when gap times between events are independent, the Cook and Lawless method is difficult to beat. Simulations demonstrate that as correlation between recurrent event times grows, the reduction in power using the Cook and Lawless approach is substantial when compared to our method. Finally, we use our method to analyze recurrent acute exacerbation outcomes from the azithromycin in chronic obstructive pulmonary disease trial.

Modeling marginal features in studies of recurrent events in the presence of a terminal event

We study models for recurrent events with special emphasis on the situation where a terminal event acts as a competing risk for the recurrent events process and where there may be gaps between periods during which subjects are at risk for the recurrent event. We focus on marginal analysis of the expected number of events and show that an Aalen-Johansen type estimator proposed by Cook and Lawless is applicable in this situation. A motivating example deals with psychiatric hospital admissions where we supplement with analyses of the marginal distribution of time to the competing event and the marginal distribution of the time spent in hospital. Pseudo-observations are used for the latter purpose.

CABG Improves Outcomes in Patients With Ischemic Cardiomyopathy: 10-Year Follow-Up of the STICH Trial

OBJECTIVES

The authors investigated the impact of coronary artery bypass grafting (CABG) on first and recurrent hospitalization in this population.

BACKGROUND

In the STICH (Surgical Treatment for Ischemic Heart Failure) trial, CABG reduced all-cause death and hospitalization in patients with and ischemic cardiomyopathy and left ventricular ejection fraction <35%.

METHODS

A total of 1,212 patients were randomized (610 to CABG + optimal medical therapy [CABG] and 602 to optimal medical therapy alone [MED] alone) and followed for a median of 9.8 years. All-cause and cause-specific hospitalizations were analyzed as time-to-first-event and as recurrent event analysis.

RESULTS

Of the 1,212 patients, 757 died (62.4%) and 732 (60.4%) were hospitalized at least once, for a total of 2,549 total all-cause hospitalizations. Most hospitalizations (66.2%) were for cardiovascular causes, of which approximately one-half (907 or 52.9%) were for heart failure. More than 70% of all hospitalizations (1,817 or 71.3%) were recurrent events. The CABG group experienced fewer all-cause hospitalizations in the time-to-first-event (349 CABG vs. 383 MED, adjusted hazard ratio [HR]: 0.85; 95% confidence interval [CI]: 0.74 to 0.98; p = 0.03) and in recurrent event analyses (1,199 CABG vs. 1,350 MED, HR: 0.78, 95% CI: 0.65 to 0.94; p < 0.001). This was driven by fewer total cardiovascular (CV) hospitalizations (744 vs. 968; p < 0.001, adjusted HR: 0.66, 95% CI: 0.55 to 0.81; p = 0.001), the majority of which were due to HF (395 vs. 512; p < 0.001, adjusted HR: 0.68, 95% CI: 0.52-0.89; p = 0.005). We did not observe a difference in non-CV events.

CONCLUSIONS

CABG reduces all-cause, CV, and HF hospitalizations in time-to-first-event and recurrent event analyses. (Comparison of Surgical and Medical Treatment for Congestive Heart Failure and Coronary Artery Disease [STICH]; NCT00023595).

Design and analysis of nested case-control studies for recurrent events subject to a terminal event

The process by which patients experience a series of recurrent events, such as hospitalizations, may be subject to death. In cohort studies, one strategy for analyzing such data is to fit a joint frailty model for the intensities of the recurrent event and death, which estimates covariate effects on the two event types while accounting for their dependence. When certain covariates are difficult to obtain, however, researchers may only have the resources to subsample patients on whom to collect complete data: one way is using the nested case-control (NCC) design, in which risk set sampling is performed based on a single outcome. We develop a general framework for the design of NCC studies in the presence of recurrent and terminal events and propose estimation and inference for a joint frailty model for recurrence and death using data arising from such studies. We propose a maximum weighted penalized likelihood approach using flexible spline models for the baseline intensity functions. Two standard error estimators are proposed: a sandwich estimator and a perturbation resampling procedure. We investigate operating characteristics of our estimators as well as design considerations via a simulation study and illustrate our methods using two studies: one on recurrent cardiac hospitalizations in patients with heart failure and the other on local recurrence and metastasis in patients with breast cancer.

Semiparametric frailty models for zero-inflated event count data in the presence of informative dropout

Recurrent events data are commonly encountered in medical studies. In many applications, only the number of events during the follow-up period rather than the recurrent event times is available. Two important challenges arise in such studies: (a) a substantial portion of subjects may not experience the event, and (b) we may not observe the event count for the entire study period due to informative dropout. To address the first challenge, we assume that underlying population consists of two subpopulations: a subpopulation nonsusceptible to the event of interest and a subpopulation susceptible to the event of interest. In the susceptible subpopulation, the event count is assumed to follow a Poisson distribution given the follow-up time and the subject-specific characteristics. We then introduce a frailty to account for informative dropout. The proposed semiparametric frailty models consist of three submodels: (a) a logistic regression model for the probability such that a subject belongs to the nonsusceptible subpopulation; (b) a nonhomogeneous Poisson process model with an unspecified baseline rate function; and (c) a Cox model for the informative dropout time. We develop likelihood-based estimation and inference procedures. The maximum likelihood estimators are shown to be consistent. Additionally, the proposed estimators of the finite-dimensional parameters are asymptotically normal and the covariance matrix attains the semiparametric efficiency bound. Simulation studies demonstrate that the proposed methodologies perform well in practical situations. We apply the proposed methods to a clinical trial on patients with myelodysplastic syndromes.

Treatment with renin-angiotensin system inhibitors and prognosis of heart failure with preserved ejection fraction: A propensity-matched study in the community

AIMS

There is currently no consensus on the effect of treatment with angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs), on the prognosis of patients with heart failure and preserved ejection fraction (HFpEF). Therefore, we have analysed the relationship of commencing treatment with ACEIs or ARBs and the prognosis of patients with incident HFpEF.

METHODS

Retrospective study over 15 years on 3864 patients with HFpEF (GAMIC cohort). Main outcomes were mortality (all-cause and cardiovascular) and hospitalisations for HF. The independent relationship between CT-RASIs and the prognosis, stratifying patients for cardiovascular comorbidity after propensity score-matching was analysed.

RESULTS

During a median follow-up of 7.94 years, 2960 died (76.6%) and 3138 were hospitalised (81.2%). Therapy with RASIs was associated with a lower mortality, all-cause (RR [95% CI] for ACEIs: 0.76 [0.66-0.86], and RR for ARBs: 0.88 [0.80-0.96]; P < 0.001 in both cases), and cardiovascular (RR for ACEIs: 0.72 [0.66-0.78], and RR for ARBs: 0.87 [0.80-0.94]; P < 0.001), a lower hospitalisation rate (RR for ACEIs: 0.82 [0.74-0.90], and RR for ARBs: 0.90 [0.82-0.98]; P < 0.001), and a lower 30-day readmission rate (RR for ACEIs: 0.66 [0.60-0.73], and RR for ARBs: 0.86 [0.75-0.97]; P < 0.001), after adjustment for the propensity to take RASIs or other medications, comorbidities and other potential confounders. Results on the effect of ARBs are compromised by the small number of patients. Analyses of recurrent hospitalisations gave larger treatment benefits than time-to-first-event analyses.

CONCLUSION

In this propensity-matched study, commencing treatment with ACEIs is associated with an improved prognosis of patients newly diagnosed with incident HFpEF.

Semiparametric temporal process regression of survival-out-of-hospital

The recurrent/terminal event data structure has undergone considerable methodological development in the last 10-15 years. An example of the data structure that has arisen with increasing frequency involves the recurrent event being hospitalization and the terminal event being death. We consider the response Survival-Out-of-Hospital, defined as a temporal process (indicator function) taking the value 1 when the subject is currently alive and not hospitalized, and 0 otherwise. Survival-Out-of-Hospital is a useful alternative strategy for the analysis of hospitalization/survival in the chronic disease setting, with the response variate representing a refinement to survival time through the incorporation of an objective quality-of-life component. The semiparametric model we consider assumes multiplicative covariate effects and leaves unspecified the baseline probability of being alive-and-out-of-hospital. Using zero-mean estimating equations, the proposed regression parameter estimator can be computed without estimating the unspecified baseline probability process, although baseline probabilities can subsequently be estimated for any time point within the support of the censoring distribution. We demonstrate that the regression parameter estimator is asymptotically normal, and that the baseline probability function estimator converges to a Gaussian process. Simulation studies are performed to show that our estimating procedures have satisfactory finite sample performances. The proposed methods are applied to the Dialysis Outcomes and Practice Patterns Study (DOPPS), an international end-stage renal disease study.

Bayesian Semiparametric Joint Regression Analysis of Recurrent Adverse Events and Survival in Esophageal Cancer Patients

We propose a Bayesian semiparametric joint regression model for a recurrent event process and survival time. Assuming independent latent subject frailties, we define marginal models for the recurrent event process intensity and survival distribution as functions of the subject's frailty and baseline covariates. A robust Bayesian model, called Joint-DP, is obtained by assuming a Dirichlet process for the frailty distribution. We present a simulation study that compares posterior estimates under the Joint-DP model to a Bayesian joint model with lognormal frailties, a frequentist joint model, and marginal models for either the recurrent event process or survival time. The simulations show that the Joint-DP model does a good job of correcting for treatment assignment bias, and has favorable estimation reliability and accuracy compared with the alternative models. The Joint-DP model is applied to analyze an observational dataset from esophageal cancer patients treated with chemo-radiation, including the times of recurrent effusions of fluid to the heart or lungs, survival time, prognostic covariates, and radiation therapy modality.

Sentinel lymph node excision with or without preoperative hybrid single-photon emission computed tomography/computed tomography (SPECT/CT) in melanoma: study protocol for a multicentric randomized controlled trial

Background

Melanoma has become a growing interdisciplinary problem in public health worldwide. According to the World Health Organization, the incidence of melanoma is increasing faster than any other cancer in the world. Because melanoma metastasizes early into the regional lymph nodes, sentinel lymph node excision (SLNE) is included in the current American Joint Committee of Cancer guidelines. However SLNE of melanoma has a high false-negative rate of up to 44%.

Methods

The gold standard for detection and extirpation of the sentinel lymph node is preoperative lymphoscintigraphy. SPECT/CT provides complementary information: the advantages include accurate anatomical localization, identification of false positives, reduction in the number of false negatives, and alteration of the surgical approach. Therefore, sentinel lymph node-SPECT/CT provides valuable information before sentinel lymph node excision and advocates its use in melanoma. We present a multicenter, unblinded superiority randomized controlled trial to compare SPECT/CT-aided SLNE versus standard SLNE in melanoma patients.

Discussion

The primary efficacy endpoint is distant metastasis-free survival. Secondary endpoints comprise overall survival, disease-free survival, rate of local relapses within the follow-up period (false-negative rate of sentinel lymph node), number of positive sentinel lymph nodes (sensitivity, false-positive rate), complication rate, quality of life, quality-adjusted life years, inpatient days, and overall costs during hospital stays.

Trial registration

ClinicalTrials.gov, NCT03683550. Registered on 20 September 2018.

Electronic supplementary material

The online version of this article (10.1186/s13063-019-3197-7) contains supplementary material, which is available to authorized users.

Intravenous Iron in Patients Undergoing Maintenance Hemodialysis

BACKGROUND

Intravenous iron is a standard treatment for patients undergoing hemodialysis, but comparative data regarding clinically effective regimens are limited.

METHODS

In a multicenter, open-label trial with blinded end-point evaluation, we randomly assigned adults undergoing maintenance hemodialysis to receive either high-dose iron sucrose, administered intravenously in a proactive fashion (400 mg monthly, unless the ferritin concentration was >700 μg per liter or the transferrin saturation was ≥40%), or low-dose iron sucrose, administered intravenously in a reactive fashion (0 to 400 mg monthly, with a ferritin concentration of <200 μg per liter or a transferrin saturation of <20% being a trigger for iron administration). The primary end point was the composite of nonfatal myocardial infarction, nonfatal stroke, hospitalization for heart failure, or death, assessed in a time-to-first-event analysis. These end points were also analyzed as recurrent events. Other secondary end points included death, infection rate, and dose of an erythropoiesis-stimulating agent. Noninferiority of the high-dose group to the low-dose group would be established if the upper boundary of the 95% confidence interval for the hazard ratio for the primary end point did not cross 1.25.

RESULTS

A total of 2141 patients underwent randomization (1093 patients to the high-dose group and 1048 to the low-dose group). The median follow-up was 2.1 years. Patients in the high-dose group received a median monthly iron dose of 264 mg (interquartile range [25th to 75th percentile], 200 to 336), as compared with 145 mg (interquartile range, 100 to 190) in the low-dose group. The median monthly dose of an erythropoiesis-stimulating agent was 29,757 IU in the high-dose group and 38,805 IU in the low-dose group (median difference, -7539 IU; 95% confidence interval [CI], -9485 to -5582). A total of 320 patients (29.3%) in the high-dose group had a primary end-point event, as compared with 338 (32.3%) in the low-dose group (hazard ratio, 0.85; 95% CI, 0.73 to 1.00; P<0.001 for noninferiority; P=0.04 for superiority). In an analysis that used a recurrent-events approach, there were 429 events in the high-dose group and 507 in the low-dose group (rate ratio, 0.77; 95% CI, 0.66 to 0.92). The infection rate was the same in the two groups.

CONCLUSIONS

Among patients undergoing hemodialysis, a high-dose intravenous iron regimen administered proactively was superior to a low-dose regimen administered reactively and resulted in lower doses of erythropoiesis-stimulating agent being administered. (Funded by Kidney Research UK; PIVOTAL EudraCT number, 2013-002267-25 .).

Randomized Trial Comparing Proactive, High-Dose versus Reactive, Low-Dose Intravenous Iron Supplementation in Hemodialysis (PIVOTAL): Study Design and Baseline Data

BACKGROUND

Intravenous (IV) iron supplementation is a standard maintenance treatment for hemodialysis (HD) patients, but the optimum dosing regimen is unknown.

METHODS

PIVOTAL (Proactive IV irOn Therapy in hemodiALysis patients) is a multicenter, open-label, blinded endpoint, randomized controlled (PROBE) trial. Incident HD adults with a serum ferritin < 400 µg/L and transferrin saturation (TSAT) levels < 30% receiving erythropoiesis-stimulating agents (ESA) were eligible. Enrolled patients were randomized to a proactive, high-dose IV iron arm (iron sucrose 400 mg/month unless ferritin > 700 µg/L and/or TSAT ≥40%) or a reactive, low-dose IV iron arm (iron sucrose administered if ferritin <200 µg/L or TSAT < 20%). We hypothesized that proactive, high-dose IV iron would be noninferior to reactive, low-dose IV iron for the primary outcome of first occurrence of nonfatal myocardial infarction (MI), nonfatal stroke, hospitalization for heart failure or death from any cause. If noninferiority is confirmed with a noninferiority limit of 1.25 for the hazard ratio of the proactive strategy relative to the reactive strategy, a test for superiority will be carried out. Secondary outcomes include infection-related endpoints, ESA dose requirements, and quality-of-life measures. As an event-driven trial, the study will continue until at least 631 primary outcome events have accrued, but the expected duration of follow-up is 2-4 years.

RESULTS

Of the 2,589 patients screened across 50 UK sites, 2,141 (83%) were randomized. At baseline, 65.3% were male, the median age was 65 years, and 79% were white. According to eligibility criteria, all patients were on ESA at screening. Prior stroke and MI were present in 8 and 9% of the cohort, respectively, and 44% of patients had diabetes at baseline. Baseline data for the randomized cohort were generally concordant with recent data from the UK Renal Registry.

CONCLUSIONS

PIVOTAL will provide important information about the optimum dosing of IV iron in HD patients representative of usual clinical practice.

TRIAL REGISTRATION

EudraCT number: 2013-002267-25.

High-dose influenza vaccine to reduce clinical outcomes in high-risk cardiovascular patients: Rationale and design of the INVESTED trial

BACKGROUND

Influenza leads to significant cardiopulmonary morbidity and mortality-particularly in patients with cardiovascular disease-that may be prevented with a standard influenza vaccine. However, patients with cardiovascular conditions have a reduced immune response to influenza vaccine, potentially resulting in reduced effectiveness for preventing clinical events. High-dose vaccine augments immune response in cardiac patients, suggesting that a high-dose influenza vaccination strategy may further reduce morbidity and mortality. Alternatively, broader coverage with an influenza vaccine containing an increased number of viral strains is an alternative strategy without direct evaluation.

RESEARCH DESIGN AND METHODS

INfluenza Vaccine to Effectively Stop Cardio Thoracic Events and Decompensated heart failure (INVESTED) is a pragmatic, randomized, double-blind, parallel-group, active-controlled trial comparing the effectiveness of an annual vaccination strategy of high-dose trivalent versus standard-dose quadrivalent influenza vaccine in patients with a history of recent heart failure or myocardial infarction hospitalization. The trial will enroll approximately 9,300 patients over 4 influenza seasons. The primary hypothesis is that high-dose influenza vaccine will reduce the composite outcome of all-cause mortality and hospitalization from a cardiovascular or pulmonary cause compared with standard-dose influenza vaccine within each enrolling season. Approximately 1,300 primary outcome events will provide >90% power to detect an 18% relative risk reduction at a 2-sided α level of .05.

CONCLUSION

INVESTED is the largest and longest study to assess whether high-dose influenza vaccine is superior to standard-dose influenza vaccine in reducing cardiopulmonary events in a high-risk cardiovascular population (ClinicalTrials.gov Identifier: NCT02787044).

Quantifying the totality of treatment effect with multiple event-time observations in the presence of a terminal event from a comparative clinical study

To evaluate the totality of one treatment's benefit/risk profile relative to an alternative treatment via a longitudinal comparative clinical study, the timing and occurrence of multiple clinical events are typically collected during the patient's follow-up. These multiple observations reflect the patient's disease progression/burden over time. The standard practice is to create a composite endpoint from the multiple outcomes, the timing of the occurrence of the first clinical event, to evaluate the treatment via the standard survival analysis techniques. By ignoring all events after the composite outcome, this type of assessment may not be ideal. Various parametric or semiparametric procedures have been extensively discussed in the literature for the purposes of analyzing multiple event-time data. Many existing methods were developed based on extensive model assumptions. When the model assumptions are not plausible, the resulting inferences for the treatment effect may be misleading. In this article, we propose a simple, nonparametric inference procedure to quantify the treatment effect, which has an intuitive clinically meaningful interpretation. We use the data from a cardiovascular clinical trial for heart failure to illustrate the procedure. A simulation study is also conducted to evaluate the performance of the new proposal.

Estimating the effect of a rare time-dependent treatment on the recurrent event rate

In many observational studies, the objective is to estimate the effect of treatment or state-change on the recurrent event rate. If treatment is assigned after the start of follow-up, traditional methods (eg, adjustment for baseline-only covariates or fully conditional adjustment for time-dependent covariates) may give biased results. We propose a two-stage modeling approach using the method of sequential stratification to accurately estimate the effect of a time-dependent treatment on the recurrent event rate. At the first stage, we estimate the pretreatment recurrent event trajectory using a proportional rates model censored at the time of treatment. Prognostic scores are estimated from the linear predictor of this model and used to match treated patients to as yet untreated controls based on prognostic score at the time of treatment for the index patient. The final model is stratified on matched sets and compares the posttreatment recurrent event rate to the recurrent event rate of the matched controls. We demonstrate through simulation that bias due to dependent censoring is negligible, provided the treatment frequency is low, and we investigate a threshold at which correction for dependent censoring is needed. The method is applied to liver transplant (LT), where we estimate the effect of development of post-LT End Stage Renal Disease (ESRD) on rate of days hospitalized.

Effect of sacubitril/valsartan on recurrent events in the Prospective comparison of ARNI with ACEI to Determine Impact on Global Mortality and morbidity in Heart Failure trial (PARADIGM-HF)

Aims

Recurrent hospitalizations are a major part of the disease burden in heart failure (HF), but conventional analyses consider only the first event. We compared the effect of sacubitril/valsartan vs. enalapril on recurrent events, incorporating all HF hospitalizations and cardiovascular (CV) deaths in PARADIGM‐HF, using a variety of statistical approaches advocated for this type of analysis.

Methods and results

In PARADIGM‐HF, a total of 8399 patients were randomized and followed for a median of 27 months. We applied various recurrent event analyses, including a negative binomial model, the Wei, Lin and Weissfeld (WLW), and Lin, Wei, Ying and Yang (LWYY) methods, and a joint frailty model, all adjusted for treatment and region. Among a total of 3181 primary endpoint events (including 1251 CV deaths) during the trial, only 2031 (63.8%) were first events (836 CV deaths). Among a total of 1195 patients with at least one HF hospitalization, 410 (34%) had at least one further HF hospitalization. Sacubitril/valsartan compared with enalapril reduced the risk of recurrent HF hospitalization using the negative binomial model [rate ratio (RR) 0.77, 95% confidence interval (CI) 0.67–0.89], the WLW method [hazard ratio (HR) 0.79, 95% CI 0.71–0.89], the LWYY method (RR 0.78, 95% CI 0.68–0.90), and the joint frailty model (HR 0.75, 95% CI 0.66–0.86) (all P < 0.001). The effect of sacubitril/valsartan vs. enalapril on recurrent HF hospitalizations/CV death was similar.

Conclusions

In PARADIGM‐HF, approximately one third of patients with a primary endpoint (time‐to‐first) experienced a further event. Compared with enalapril, sacubitril/valsartan reduced both first and recurrent events. The treatment effect size was similar, regardless of the statistical approach applied.

Regression analysis of mixed panel count data with dependent terminal events

Event history studies are commonly conducted in many fields, and a great deal of literature has been established for the analysis of the two types of data commonly arising from these studies: recurrent event data and panel count data. The former arises if all study subjects are followed continuously, while the latter means that each study subject is observed only at discrete time points. In reality, a third type of data, a mixture of the two types of the data earlier, may occur and furthermore, as with the first two types of the data, there may exist a dependent terminal event, which may preclude the occurrences of recurrent events of interest. This paper discusses regression analysis of mixed recurrent event and panel count data in the presence of a terminal event and an estimating equation-based approach is proposed for estimation of regression parameters of interest. In addition, the asymptotic properties of the proposed estimator are established, and a simulation study conducted to assess the finite-sample performance of the proposed method suggests that it works well in practical situations. Finally, the methodology is applied to a childhood cancer study that motivated this study. Copyright © 2017 John Wiley & Sons, Ltd.

Evaluating Utility Measurement from Recurrent Marker Processes in the Presence of Competing Terminal Events

In follow-up studies, utility marker measurements are usually collected upon the occurrence of recurrent events until a terminal event such as death takes place. In this article, we define the recurrent marker process to characterize utility accumulation over time. For example, with medical cost and repeated hospitalizations being treated as marker and recurrent events respectively, the recurrent marker process is the trajectory of cumulative cost, which stops to increase after death. In many applications, competing risks arise as subjects are at risk of more than one mutually exclusive terminal event, such as death from different causes, and modeling the recurrent marker process for each failure type is often of interest. However, censoring creates challenges in the methodological development, because for censored subjects, both failure type and recurrent marker process after censoring are unobserved. To circumvent this problem, we propose a nonparametric framework for recurrent marker process with competing terminal events. In the presence of competing risks, we start with an estimator by using marker information from uncensored subjects. As a result, the estimator can be inefficient under heavy censoring. To improve efficiency, we propose a second estimator by combining the first estimator with auxiliary information from the estimate under non-competing risks model. The large sample properties and optimality of the second estimator is established. Simulation studies and an application to the SEER-Medicare linked data are presented to illustrate the proposed methods. Supplemental materials are available online.

Joint scale-change models for recurrent events and failure time

Recurrent event data arise frequently in various fields such as biomedical sciences, public health, engineering, and social sciences. In many instances, the observation of the recurrent event process can be stopped by the occurrence of a correlated failure event, such as treatment failure and death. In this article, we propose a joint scale-change model for the recurrent event process and the failure time, where a shared frailty variable is used to model the association between the two types of outcomes. In contrast to the popular Cox-type joint modeling approaches, the regression parameters in the proposed joint scale-change model have marginal interpretations. The proposed approach is robust in the sense that no parametric assumption is imposed on the distribution of the unobserved frailty and that we do not need the strong Poisson-type assumption for the recurrent event process. We establish consistency and asymptotic normality of the proposed semiparametric estimators under suitable regularity conditions. To estimate the corresponding variances of the estimators, we develop a computationally efficient resampling-based procedure. Simulation studies and an analysis of hospitalization data from the Danish Psychiatric Central Register illustrate the performance of the proposed method.

A Bayesian multivariate joint frailty model for disease recurrences and survival

Motivated by a study for soft tissue sarcoma, this article considers the analysis of diseases recurrence and survival. A multivariate frailty hazard model is established for joint modeling of three correlated time-to-event outcomes: local disease recurrence, distant disease recurrence (metastasis), and death. The goals are to find out (i) the effects of treatments on local and distant disease recurrences, and death, (ii) the effects of local and distant disease recurrences on death, and (iii) the correlation between local and distant recurrences. By our approach, all these three important questions, which are commonly asked in similar medical research studies, can be answered by a single model. We put the proposed joint frailty model in a Bayesian framework and use a hybrid Monte Carlo algorithm for the computation of posterior distributions. This hybrid algorithm relies on the evaluation of the gradient of target log density and a guided walk progress, and it combines these two strategies to suppress random walk behavior. A further distinction is that the hybrid algorithm can update all the components of a multivariate state vector simultaneously. Simulation studies are conducted to assess the proposed joint frailty model and the computation algorithm. The motivating soft tissue sarcoma data set is analyzed for illustration purpose. Copyright © 2016 John Wiley & Sons, Ltd.

Safety data from randomized controlled trials: applying models for recurrent events

Simple descriptive listings and inference statistics based on 2×2 tables are still the most common way of summarizing and reporting adverse events data from randomized controlled trials, although these methods do not account for differences in observation times between treatment groups. Using standard methods from survival analysis such as the Cox model or Kaplan-Meier estimates would overcome this problem but limit the analysis to the first safety-related event of each subject. As an alternative, we discuss two models for recurrent events data-the Andersen-Gill and Prentice-Williams-Peterson model-regarding their applicability to safety data from randomized controlled trials. We argue that these models can be used to estimate two different quantities: a direct treatment effect on the risk of an event (Prentice-Williams-Peterson) and a total treatment effect as sum of the direct effect and the treatment's indirect effect via the event history (Anderson-Gill). Using simulated data, we illustrate the difference between these treatment effects and analyze the performance of both models in different scenarios. Because both models are limited to the analysis of cause-specific hazards if competing risks are present, we suggest to incorporate estimates of the mean frequency of events in the analysis to additionally allow the comparison of treatment effects on absolute event probabilities. We demonstrate the application of both models and the mean frequency function to safety endpoints with an illustrative analysis of data from a randomized phase-III study. Copyright © 2016 John Wiley & Sons, Ltd.

Kernel machine score test for pathway analysis in the presence of semi-competing risks

In cancer studies, patients often experience two different types of events: a non-terminal event such as recurrence or metastasis, and a terminal event such as cancer-specific death. Identifying pathways and networks of genes associated with one or both of these events is an important step in understanding disease development and targeting new biological processes for potential intervention. These correlated outcomes are commonly dealt with by modeling progression-free survival, where the event time is the minimum between the times of recurrence and death. However, identifying pathways only associated with progression-free survival may miss out on pathways that affect time to recurrence but not death, or vice versa. We propose a combined testing procedure for a pathway's association with both the cause-specific hazard of recurrence and the marginal hazard of death. The dependency between the two outcomes is accounted for through perturbation resampling to approximate the test's null distribution, without any further assumption on the nature of the dependency. Even complex non-linear relationships between pathways and disease progression or death can be uncovered thanks to a flexible kernel machine framework. The superior statistical power of our approach is demonstrated in numerical studies and in a gene expression study of breast cancer.

Semiparametric regression for the weighted composite endpoint of recurrent and terminal events

Recurrent event data are commonly encountered in clinical and epidemiological studies. A major complication arises when recurrent events are terminated by death. To assess the overall effects of covariates on the two types of events, we define a weighted composite endpoint as the cumulative number of recurrent and terminal events properly weighted by the relative severity of each event. We propose a semiparametric proportional rates model which specifies that the (possibly time-varying) covariates have multiplicative effects on the rate function of the weighted composite endpoint while leaving the form of the rate function and the dependence among recurrent and terminal events completely unspecified. We construct appropriate estimators for the regression parameters and the cumulative frequency function. We show that the estimators are consistent and asymptotically normal with variances that can be consistently estimated. We also develop graphical and numerical procedures for checking the adequacy of the model. We then demonstrate the usefulness of the proposed methods in simulation studies. Finally, we provide an application to a major cardiovascular clinical trial.

Time-varying coefficients in a multivariate frailty model: Application to breast cancer recurrences of several types and death

During their follow-up, patients with cancer can experience several types of recurrent events and can also die. Over the last decades, several joint models have been proposed to deal with recurrent events with dependent terminal event. Most of them require the proportional hazard assumption. In the case of long follow-up, this assumption could be violated. We propose a joint frailty model for two types of recurrent events and a dependent terminal event to account for potential dependencies between events with potentially time-varying coefficients. For that, regression splines are used to model the time-varying coefficients. Baseline hazard functions (BHF) are estimated with piecewise constant functions or with cubic M-Splines functions. The maximum likelihood estimation method provides parameter estimates. Likelihood ratio tests are performed to test the time dependency and the statistical association of the covariates. This model was driven by breast cancer data where the maximum follow-up was close to 20 years.

Analysis of recurrent events with an associated informative dropout time: Application of the joint frailty model

This paper considers the analysis of a repeat event outcome in clinical trials of chronic diseases in the context of dependent censoring (e.g. mortality). It has particular application in the context of recurrent heart failure hospitalisations in trials of heart failure. Semi‐parametric joint frailty models (JFMs) simultaneously analyse recurrent heart failure hospitalisations and time to cardiovascular death, estimating distinct hazard ratios whilst individual‐specific latent variables induce associations between the two processes. A simulation study was carried out to assess the suitability of the JFM versus marginal analyses of recurrent events and cardiovascular death using standard methods. Hazard ratios were consistently overestimated when marginal models were used, whilst the JFM produced good, well‐estimated results. An application to the Candesartan in Heart failure: Assessment of Reduction in Mortality and morbidity programme was considered. The JFM gave unbiased estimates of treatment effects in the presence of dependent censoring. We advocate the use of the JFM for future trials that consider recurrent events as the primary outcome. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Innovative designs of point-of-care comparative effectiveness trials

One of the provisions of the health care reform legislation in 2010 was for funding pragmatic clinical trials or large observational studies for comparing the effectiveness of different approved medical treatments, involving broadly representative patient populations. After reviewing pragmatic clinical trials and the issues and challenges that have made them just a small fraction of comparative effectiveness research (CER), we focus on a recent development that uses point-of-care (POC) clinical trials to address the issue of "knowledge-action gap" in pragmatic CER trials. We give illustrative examples of POC-CER trials and describe a trial that we are currently planning to compare the effectiveness of newly approved oral anticoagulants. We also develop novel stage-wise designs of information-rich POC-CER trials under competitive budget constraints, by using recent advances in adaptive designs and other statistical methodologies.

Estimating the burden of recurrent events in the presence of competing risks: the method of mean cumulative count

Cumulative incidence has been widely used to estimate the cumulative probability of developing an event of interest by a given time, in the presence of competing risks. When it is of interest to measure the total burden of recurrent events in a population, however, the cumulative incidence method is not appropriate because it considers only the first occurrence of the event of interest for each individual in the analysis: Subsequent occurrences are not included. Here, we discuss a straightforward and intuitive method termed "mean cumulative count," which reflects a summarization of all events that occur in the population by a given time, not just the first event for each subject. We explore the mathematical relationship between mean cumulative count and cumulative incidence. Detailed calculation of mean cumulative count is described by using a simple hypothetical example, and the computation code with an illustrative example is provided. Using follow-up data from January 1975 to August 2009 collected in the Childhood Cancer Survivor Study, we show applications of mean cumulative count and cumulative incidence for the outcome of subsequent neoplasms to demonstrate different but complementary information obtained from the 2 approaches and the specific utility of the former.

Effect of ivabradine on numbers needed to treat for the prevention of recurrent hospitalizations in heart failure patients

BACKGROUND

Ivabradine, a specific heart rate lowering agent, was shown in the SHIFT study to reduce time to first hospitalization for worsening heart failure (HF) in chronic systolic HF patients and also to reduce recurrent/total hospitalizations over the study interval. We assessed the effects of adding ivabradine in patients with systolic HF on the number needed to treat (NNT) to reduce recurrent hospitalizations.

METHODS

The SHIFT trial included 6505 patients with symptomatic HF (NYHA II-IV), left ventricular ejection fraction ≤35% and heart rate ≥70 bpm in sinus rhythm. Patients were randomized to either ivabradine or placebo in addition to guidelines-based drug therapy. The times to first hospitalization were analyzed using a univariate Cox proportional-hazards model; the associated NNT was calculated using Kaplan-Meier estimates of the time-to-event curves at 1 year in each treatment arm. Recurrent hospitalizations were analyzed using a negative binomial and the estimated annual event rates used to calculate the associated patient-time NNTs respectively.

RESULTS

The estimated NNT (number needed to initiate treatment with ivabradine to prevent one first HF hospitalization within 1 year) was 27 (estimated hazard ratio: 0.75, P < 0.0001). For recurrent HF hospitalizations, one event would be prevented on average per 14 patient-years for any year of follow-up over the course of SHIFT (estimated rate ratio: 0.71, P < 0.0001). A key limitation of this analysis is that it did not account for a relationship between recurrent HF hospitalizations and subsequent mortality.

CONCLUSION

In chronic systolic HF the effect of ivabradine on reducing recurrent HF hospitalizations results in a lower NNT compared to the effect on the time for first hospitalization. The effect of ivabradine on recurrent hospitalizations, in addition to first events, may be a more appropriate measure when considering the impact of a treatment with ivabradine on healthcare resource utilization.