An application of restricted mean survival time in a competing risks setting: comparing time to ART initiation by injection drug use

Evaluation of MADIT-II Risk Stratification Score Among Nationwide Registry of Heart Failure Patients With Primary Prevention Implantable Cardiac Defibrillators or Resynchronization Therapy Devices

The current guidelines advocate prophylactic implantable cardioverter-defibrillator (ICD) for all patients with symptomatic heart failure (HF) with low left ventricular ejection fraction. Because many patients will never use their device, a score delineating subgroups with differential ICD benefit is crucial. We aimed to evaluate the MADIT-II-based Risk Stratification Score (MRSS) feasibility to delineate the ICD survival benefit in a nationwide registry of patients with HF with prophylactic ICDs. Accordingly, all Israeli patients with HF with prophylactic ICD/cardiac resynchronization therapy defibrillators were categorized into MRSS-based risk subgroups. The study end points included overall mortality, sustained ventricular arrhythmia (VA), and a competing risk of VA (potential preventable arrhythmic death, where ICD could benefit survival) versus nonarrhythmic death. Potential ICD survival benefit was estimated by the area between these cumulative incidence curves. In 2,177 patients with HF implanted prophylactic device, 189 patients (8.7%) had VA and 316 (14.5%) died during a median follow-up of 2.9 years. The MRSS risk subgroups were significantly associated with overall mortality (p <0.001) and weakly with VA (p = 0.3). The competing risk analysis of VA versus nonarrhythmic death revealed a significantly shorter duration (p <0.001) and smaller magnitude of ICD survival benefit with increased risk subgroups, yielding an estimated 76, 60, 38, and 0 life days gained from prophylactic ICD implant during a 5-year follow-up for the MRSS low-, intermediate-, high-, and very high-risk subgroups, respectively (p for trend <0.05). In conclusion, MRSS use in a nationwide registry of patients with ischemic and nonischemic cardiomyopathy, revealed subgroups with differing ICD survival benefit, suggesting it could help evaluate prophylactic ICD survival benefit.

Inhaled Amikacin to Prevent Ventilator-Associated Pneumonia

BACKGROUND

Whether preventive inhaled antibiotics may reduce the incidence of ventilator-associated pneumonia is unclear.

METHODS

In this investigator-initiated, multicenter, double-blind, randomized, controlled, superiority trial, we assigned critically ill adults who had been undergoing invasive mechanical ventilation for at least 72 hours to receive inhaled amikacin at a dose of 20 mg per kilogram of ideal body weight once daily or to receive placebo for 3 days. The primary outcome was a first episode of ventilator-associated pneumonia during 28 days of follow-up. Safety was assessed.

RESULTS

A total of 850 patients underwent randomization, and 847 were included in the analyses (417 assigned to the amikacin group and 430 to the placebo group). All three daily nebulizations were received by 337 patients (81%) in the amikacin group and 355 patients (83%) in the placebo group. At 28 days, ventilator-associated pneumonia had developed in 62 patients (15%) in the amikacin group and in 95 patients (22%) in the placebo group (difference in restricted mean survival time to ventilator-associated pneumonia, 1.5 days; 95% confidence interval [CI], 0.6 to 2.5; P = 0.004). An infection-related ventilator-associated complication occurred in 74 patients (18%) in the amikacin group and in 111 patients (26%) in the placebo group (hazard ratio, 0.66; 95% CI, 0.50 to 0.89). Trial-related serious adverse effects were seen in 7 patients (1.7%) in the amikacin group and in 4 patients (0.9%) in the placebo group.

CONCLUSIONS

Among patients who had undergone mechanical ventilation for at least 3 days, a subsequent 3-day course of inhaled amikacin reduced the burden of ventilator-associated pneumonia during 28 days of follow-up. (Funded by the French Ministry of Health; AMIKINHAL ClinicalTrials.gov number, NCT03149640; EUDRA Clinical Trials number, 2016-001054-17.).

Alcohol use and the longitudinal HIV care continuum for people with HIV who enrolled in care between 2011 and 2019

PURPOSE

We described the impact of alcohol use on longitudinal engagement in HIV care including loss to follow-up, durability of viral suppression, and death.

METHODS

We followed a cohort of 1781 people with HIV from enrolled in care at one of seven US clinics, 2011-2019 through 102 months. We used a multistate, time-varying Markov process and restricted mean time to summarize engagement in HIV care over follow-up according to baseline self-reported alcohol use (none, moderate, or unhealthy).

RESULTS

Our sample (86% male, 54% White) had median age of 35 years. Over 102 months, people with no, moderate, and unhealthy alcohol use averaged 62.3, 61.1, and 59.5 months virally suppressed, respectively. People who reported unhealthy or moderate alcohol use spent 5.1 (95% confidence intervals (CI): 0.8, 9.3) and 7.6 (95%CI: 3.1, 11.7) more months lost to care than nondrinkers. Compared to no use, unhealthy alcohol use was associated with 3.4 (95%CI: -5.6, -1.6) fewer months in care, not virally suppressed. There were no statistically significant differences after adjustment for demographic and clinical characteristics.

CONCLUSIONS

Moderate or unhealthy drinking at enrollment in HIV care was associated with poor retention in care. Alcohol use was not associated with time spent virally suppressed.

A Randomized Clinical Trial Testing Hydroxychloroquine for Reduction of SARS-CoV-2 Viral Shedding and Hospitalization in Early Outpatient COVID-19 Infection

Early in the COVID-19 pandemic, no effective treatment existed to prevent clinical worsening of COVID-19 among recently diagnosed outpatients. At the University of Utah, Salt Lake City, Utah, we conducted a phase 2 prospective parallel group randomized placebo-controlled trial (NCT04342169) to determine whether hydroxychloroquine given early in disease reduces the duration of SARS-CoV-2 shedding. We enrolled nonhospitalized adults (≥18 years of age) with a recent positive diagnostic test for SARS-CoV-2 (within 72 h of enrollment) and adult household contacts. Participants received either 400 mg hydroxychloroquine by mouth twice daily on day 1 followed by 200 mg by mouth twice daily on days 2 to 5 or oral placebo with the same schedule. We performed SARS-CoV-2 nucleic acid amplification testing (NAAT) on oropharyngeal swabs on days 1 to 14 and 28 and monitored clinical symptomatology, rates of hospitalization, and viral acquisition by adult household contacts. We identified no overall differences in the duration of oropharyngeal carriage of SARS-CoV-2 (hazard ratio of viral shedding time comparing hydroxychloroquine to placebo, 1.21; 95% confidence interval [CI], 0.91, 1.62). Overall, 28-day hospitalization incidence was similar between treatments (4.6% hydroxychloroquine versus 2.7% placebo). No differences were seen in symptom duration, severity, or viral acquisition in household contacts between treatment groups. The study did not reach the prespecified enrollment target, which was likely influenced by a steep decline in COVID-19 incidence corresponding to the initial vaccine rollout in the spring of 2021. Oropharyngeal swabs were self-collected, which may introduce variability in these results. Placebo treatments were not identical to hydroxychloroquine treatments (capsules versus tablets) which may have led to inadvertent participant unblinding. In this group of community adults early in the COVID-19 pandemic, hydroxychloroquine did not significantly alter the natural history of early COVID-19 disease. (This study has been registered at ClinicalTrials.gov under registration no. NCT04342169). IMPORTANCE Early in the COVID-19 pandemic, no effective treatment existed to prevent clinical worsening of COVID-19 among recently diagnosed outpatients. Hydroxychloroquine received attention as a possible early treatment; however, quality prospective studies were lacking. We conducted a clinical trial to test the ability of hydroxychloroquine to prevent clinical worsening of COVID-19.

Performance of Restricted Mean Survival Time Based Methods and Traditional Survival Methods: An Application in an Oncological Data

Objective

To compare restricted mean survival time- (RMST-) based methods with traditional survival methods when multiple covariates are of interest.

Methods

4405 osteosarcomas were captured from Surveillance, Epidemiology, and End Results Program Database. RMST-based methods included group comparison using Kaplan-Meier (KM) method, pseudovalue (PV) regression, and inverse probability of censoring probability (IPCW) regressions with group-specific and individual weights. Log-rank test, Wilcoxon test, Cox regression, and its extension with time-dependent variables were selected as traditional methods. Proportional hazard (PH) assumption and homogeneity of censoring mechanism assumption were assessed. We estimated hazard ratio (HR) and difference in RMST and explored their relationships.

Results

When covariate violated PH assumption, time-varying HR was inconvenient to report as a single value but PH assumption-free RMST allowed to report a single value of difference in RMST. In univariable analyses, using the difference in RMST calculated by KM method as reference, PV regressions (slope = 1.02 and R ² = 0.98) and IPCW regressions with group-specific weights (slope = 0.98 and R ² = 0.99) gave more consistent estimation than IPCW with individual weights (slope = 0.31 and R ² = 0.06), moreover, PV regressions presented more robust statistical power than IPCW regressions with group-specific weights. In multivariable analyses, IPCW regression with group-specific weights was limited when multiple covariates violated homogeneity of censoring mechanism assumption. For covariates met PH assumption, well-fitted logarithmic relationships between HR and difference in RMST estimated by PV regression were observed in both univariable and multivariable analyses (R ² = 0.97 and R ² = 0.94, respectively), which supported the robustness of PV regression and possible conversion between the two effect measures.

Conclusions

Difference in RMST is more interpretable than time-varying HR. The performance supports KM method and PV regression to be the preferred ones in RMST-based methods. IPCW regression can be an alternative sensitivity analysis. We encourage adoption of both traditional methods and RMST-based methods to present effects of covariates comprehensively.

Estimating causal effects in the presence of competing events using regression standardisation with the Stata command standsurv

BACKGROUND

When interested in a time-to-event outcome, competing events that prevent the occurrence of the event of interest may be present. In the presence of competing events, various estimands have been suggested for defining the causal effect of treatment on the event of interest. Depending on the estimand, the competing events are either accommodated or eliminated, resulting in causal effects with different interpretations. The former approach captures the total effect of treatment on the event of interest while the latter approach captures the direct effect of treatment on the event of interest that is not mediated by the competing event. Separable effects have also been defined for settings where the treatment can be partitioned into two components that affect the event of interest and the competing event through different causal pathways.

METHODS

We outline various causal effects that may be of interest in the presence of competing events, including total, direct and separable effects, and describe how to obtain estimates using regression standardisation with the Stata command standsurv. Regression standardisation is applied by obtaining the average of individual estimates across all individuals in a study population after fitting a survival model.

RESULTS

With standsurv several contrasts of interest can be calculated including differences, ratios and other user-defined functions. Confidence intervals can also be obtained using the delta method. Throughout we use an example analysing a publicly available dataset on prostate cancer to allow the reader to replicate the analysis and further explore the different effects of interest.

CONCLUSIONS

Several causal effects can be defined in the presence of competing events and, under assumptions, estimates of those can be obtained using regression standardisation with the Stata command standsurv. The choice of which causal effect to define should be given careful consideration based on the research question and the audience to which the findings will be communicated.

Population-based estimates of overtreatment with adjuvant systemic therapy in early breast cancer patients with data from the Netherlands and the USA

Purpose

Although adjuvant systemic therapy (AST) helps increase breast cancer-specific survival (BCSS), there is a growing concern for overtreatment. By estimating the expected BCSS of AST using PREDICT, this study aims to quantify the number of patients treated with AST without benefit to provide estimates of overtreatment.

Methods

Data of all non-metastatic unilateral breast cancer patients diagnosed in 2015 were retrieved from cancer registries from The Netherlands and the USA. The PREDICT tool was used to estimate AST survival benefit. Overtreatment was defined as the proportion of patients that would have survived regardless of or died despite AST within 10 years. Three scenarios were evaluated: actual treatment, and recommendations by the Dutch or USA guidelines.

Results

59.5% of Dutch patients were treated with AST. 6.4% (interquartile interval [IQI] = 2.5, 8.2%) was expected to survive at least 10 years due to AST, leaving 93.6% (IQI = 91.8, 97.5%) without AST benefit (overtreatment). The lowest expected amount of overtreatment was in the targeted and chemotherapy subgroup, with 86.5% (IQI = 83.4, 89.6%) overtreatment, and highest in the only endocrine treatment subgroup, with 96.7% (IQI = 96.0, 98.1%) overtreatment. Similar results were obtained using data from the USA, and guideline recommendations.

Conclusion

Based on PREDICT, AST prevents 10-year breast cancer death in 6.4% of the patients treated with AST. Consequently, AST yields no survival benefit to many treated patients. Especially improved personalization of endocrine therapy is relevant, as this therapy is widely used and is associated with the highest amount of overtreatment.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10549-022-06550-2.

Stratified Restricted Mean Survival Time Model for Marginal Causal Effect in Observational Survival Data

Time to event outcomes is commonly encountered in epidemiologic research. Multiple papers have discussed the inadequacy of using the hazard ratio as a causal effect measure due to its noncollapsibility and the time-varying nature. In this paper, we further clarified that the hazard ratio might be used as a conditional causal effect measure, but it is generally not a valid marginal effect measure, even under randomized design. We proposed to use the restricted mean survival time (RMST) difference as a causal effect measure, since it essentially measures the mean difference over a specified time horizon and has a simple interpretation as the area under survival curves. For observational studies, propensity score adjustment can be implemented with RMST estimation to remove observed confounding bias. We proposed a propensity score stratified RMST estimation strategy, which performs well in our simulation evaluation and is relatively easy to implement for epidemiologists in practice. Our stratified RMST estimation includes two different versions of implementation, depending on whether researchers want to involve regression modeling adjustment, which provides a powerful tool to examine the marginal causal effect with observational survival data.

A Value-of-Information Framework for Personalizing the Timing of Surveillance Testing

BACKGROUND

Patient surveillance using repeated biomarker measurements presents an opportunity to detect and treat disease progression early. Frequent surveillance testing using biomarkers is recommended and routinely conducted in several diseases, including cancer and diabetes. However, frequent testing involves tradeoffs. Although surveillance tests provide information about current disease status, the complications and costs of frequent tests may not be justified for patients who are at low risk of progression. Predictions based on patients' earlier biomarker values may be used to inform decision making; however, predictions are uncertain, leading to decision uncertainty.

METHODS

We propose the Personalized Risk-Adaptive Surveillance (PRAISE) framework, a novel method for embedding predictions into a value-of-information (VOI) framework to account for the cost of uncertainty over time and determine the time point at which collection of biomarker data would be most valuable. The proposed sequential decision-making framework is innovative in that it leverages the patient's longitudinal history, considers individual benefits and harms, and allows for dynamic tailoring of surveillance intervals by considering the uncertainty in current information and estimating the probability that new information may change treatment decisions, as well as the impact of this change on patient outcomes.

RESULTS

When applied to data from cystic fibrosis patients, PRAISE lowers costs by allowing some patients to skip a visit, compared to an "always test" strategy. It does so without compromising expected survival, by recommending less frequent testing among those who are unlikely to be treated at the skipped time point.

CONCLUSIONS

A VOI-based approach to patient monitoring is feasible and could be applied to several diseases to develop more cost-effective and personalized strategies for ongoing patient care.

HIGHLIGHTS

In many patient-monitoring settings, the complications and costs of frequent tests are not justified for patients who are at low risk of disease progression. Predictions based on patient history may be used to individualize the timing of patient visits based on evolving risk.We propose Personalized Risk-Adaptive Surveillance (PRAISE), a novel method for personalizing the timing of surveillance testing, where prediction modeling projects the disease trajectory and a value-of-information (VOI)-based pragmatic decision-theoretic framework quantifies patient- and time-specific benefit-harm tradeoffs.A VOI-based approach to patient monitoring could be applied to several diseases to develop more personalized and cost-effective strategies for ongoing patient care.

Adjusting for covariates in analysis based on restricted mean survival times

We summarize extensions to the analysis of restricted mean survival time (RMST) in the context of time-to-event outcomes. The RMST estimate and its inference are based on the classical Kaplan-Meier curves. When covariate effects are considered, an adjusted RMST (ARMST) estimate can be derived analogously based on adjusted Kaplan-Meier curves. The adjusted Kaplan-Meier Estimator (AKME) was developed to reduce confounding by the method of inverse probability of treatment weighting. We will show how the ARMST method combines the concepts of the RMST and AKME to make inferences. Two regression based methods to adjust for potential covariate effect on the RMST estimates will be compared with the ARMST approach. Simulation studies are performed to compare the different methods with and without covariate adjustments. In addition, we will summarize the extension of RMST and ARMST to the setting with competing risks. The restricted mean time lost (RMTL) and adjusted RMTL (ARMTL) are estimates of interest from cumulative incidence curves. A phase 3 oncology clinical trial example is provided to demonstrate the applications of these methods.

Effectiveness of angiotensin-neprilysin inhibitor treatment versus renin-angiotensin system blockade in older adults with heart failure in clinical care

OBJECTIVE

To evaluate the effectiveness of angiotensin receptor-neprilysin inhibitor (ARNI) versus renin-angiotensin system (RAS) blockade alone in older adults with heart failure with reduced ejection fraction (HFrEF).

METHODS

We conducted a cohort study using US Medicare fee-for-service claims data (2014-2017). Patients with HFrEF ≥65 years were identified in two cohorts: (1) initiators of ARNI or RAS blockade alone (ACE inhibitor, ACEI; or angiotensin receptor blocker, ARB) and (2) switchers from an ACEI to either ARNI or ARB. HR with 95% CI from Cox proportional hazard regression and 1-year restricted mean survival time (RMST) difference with 95% CI were calculated for a composite outcome of time to first worsening heart failure event or all-cause mortality after adjustment for 71 pre-exposure characteristics through propensity score fine-stratification weighting. All analyses of initiator and switcher cohorts were conducted separately and then combined using fixed effects.

RESULTS

51 208 patients with a mean age of 76 years were included, with 16 193 in the ARNI group. Adjusted HRs comparing ARNI with RAS blockade alone were 0.92 (95% CI 0.84 to 1.00) among initiators and 0.79 (95% CI 0.74 to 0.85) among switchers, with a combined estimate of 0.84 (95% CI 0.80 to 0.89). Adjusted 1-year RMST difference (95% CI) was 4 days in the initiator cohort (-1 to 9) and 12 days (8 to 17) in the switcher cohort, resulting in a pooled estimate of 9 days (6 to 12) favouring ARNI.

CONCLUSION

ARNI treatment was associated with lower risk of a composite effectiveness endpoint compared with RAS blockade alone in older adults with HFrEF.

Estimating restricted mean survival time and expected life-years lost in the presence of competing risks within flexible parametric survival models

Background

Royston-Parmar flexible parametric survival models (FPMs) can be fitted on either the cause-specific hazards or cumulative incidence scale in the presence of competing risks. An advantage of modelling within this framework for competing risks data is the ease at which alternative predictions to the (cause-specific or subdistribution) hazard ratio can be obtained. Restricted mean survival time (RMST), or restricted mean failure time (RMFT) on the mortality scale, is one such measure. This has an attractive interpretation, especially when the proportionality assumption is violated. Compared to similar measures, fewer assumptions are required and it does not require extrapolation. Furthermore, one can easily obtain the expected number of life-years lost, or gained, due to a particular cause of death, which is a further useful prognostic measure as introduced by Andersen.

Methods

In the presence of competing risks, prediction of RMFT and the expected life-years lost due to a cause of death are presented using Royston-Parmar FPMs. These can be predicted for a specific covariate pattern to facilitate interpretation in observational studies at the individual level, or at the population-level using standardisation to obtain marginal measures. Predictions are illustrated using English colorectal data and are obtained using the Stata post-estimation command, standsurv.

Results

Reporting such measures facilitate interpretation of a competing risks analysis, particularly when the proportional hazards assumption is not appropriate. Standardisation provides a useful way to obtain marginal estimates to make absolute comparisons between two covariate groups. Predictions can be made at various time-points and presented visually for each cause of death to better understand the overall impact of different covariate groups.

Conclusions

We describe estimation of RMFT, and expected life-years lost partitioned by each competing cause of death after fitting a single FPM on either the log-cumulative subdistribution, or cause-specific hazards scale. These can be used to facilitate interpretation of a competing risks analysis when the proportionality assumption is in doubt.

Optimal two-stage designs based on restricted mean survival time for a single-arm study

Restricted mean survival time is an alternative measure of treatment effect to hazard ratio in clinical trials with time-to-event outcome. The current methods have been focused on one-stage designs. In this article, we propose optimal two-stage designs for a single-arm study with the smallest expected sample size. We compare the performance of the new optimal two-stage designs with the existing one-stage design with regards to the expected sample size and the expected total study length. The simulation results indicate that the new two-stage designs can save the expected sample size substantially as compared to the one-stage design. We use a non-small cell lung cancer trial to illustrate the application of the proposed designs. The proposed optimal two-stage designs are recommended for use when time for patient accrual is longer than the pre-specified follow-up time.

Sex Differences in Extrahepatic Outcomes After Antiviral Treatment for Hepatitis C

INTRODUCTION

Despite recognized differences in the rates of cardiovascular and renal disease between men and women in the general population, studies of the downstream effects of antiviral treatment for hepatitis C (HCV) have not investigated differences in outcomes based on sex. We analyzed sex differences in risk of acute coronary syndrome (ACS), end-stage renal disease (ESRD), and ischemic stroke by treatment and response in a large US-based multisite cohort of HCV patients.

METHODS

Observation started at the HCV diagnosis date (untreated) or last antiviral treatment start (treated). Treatment selection bias was addressed using an inverse probability-weighting approach. We estimated the effect of treatment on the cumulative incidence of outcomes using the Fine-Gray method (subdistribution hazard ratios [sHR] and 95% confidence intervals [95% CI]). Death was a competing risk.

RESULTS

Roughly 40% of 15,295 HCV patients were women. After controlling for other risk factors, sustained virological response (SVR) (interferon-based [IFN] or direct-acting antiviral [DAA]) significantly reduced risk of all outcomes, particularly among female patients. Female patients who achieved SVR after IFN-based treatment had significantly lower risk of ACS compared with male patients with SVR from either treatment type (sHR 0.45 [95% CI 0.35-0.59] vs 0.81 [95% CI 0.69-0.96, for DAA SVR] and sHR 0.72 [95% 0.62, 0.85, for IFN SVR]). Successful treatment seemed to be most protective against ESRD; female patients who achieved SVR were at 66%-68% lower risk than untreated patients (sHR 0.32 [95% CI 0.17-0.60 for DAA SVR] and 0.34 [95% CI 0.20-0.58 for IFN SVR]), whereas men were at 38%-42% lower risk (sHR 0.62 [95% CI 0.46-0.85 for DAA SVR] and 0.58 [95% CI 0.43-0.76 for IFN SVR]). IFN treatment failure significantly increased risk of all outcomes by 50%-100% among female patients. Compared with no treatment, female patients who experienced IFN treatment failure were at 63% increased risk of ACS (sHR 1.63 [95% CI 1.35-1.96]), almost twice the risk of ESRD (sHR 1.95 [95% CI 1.43-2.66]) and 51% increased risk of stroke (sHR 1.49 [95%CI 1.11-2.00]).

DISCUSSION

SVR reduced the risk of extrahepatic complications, particularly in females. The significantly increased risk associated with IFN TF in women-a subset who represented roughly 10% of that group-underscores the importance of prioritizing these patients for DAA treatment irrespective of the fibrosis stage.

Estimation and modeling of the restricted mean time lost in the presence of competing risks

Survival data with competing or semi-competing risks are common in observational studies. As an alternative to cause-specific and subdistribution hazard ratios, the between-group difference in cause-specific restricted mean times lost (RMTL) gives the mean difference in life expectancy lost to a specific cause of death or in disease-free time lost, in the case of a nonfatal outcome, over a prespecified period. To adjust for covariates, we introduce an inverse probability weighted estimator and its variance for the marginal difference in RMTL. We also introduce an inverse probability of censoring weighted regression model for the RMTL. In simulation studies, we examined the finite sample performance of the proposed methods under proportional and nonproportional subdistribution hazards scenarios. We illustrated both methods with competing risks data from the Framingham Heart Study. We estimated sex differences in atrial fibrillation (AF)-free times lost over 40 years. We also estimated sex differences in mean lifetime lost to cardiovascular disease (CVD) and non-CVD death over 10 years among individuals with AF.

The use of restricted mean time lost under competing risks data

Background

Under competing risks, the commonly used sub-distribution hazard ratio (SHR) is not easy to interpret clinically and is valid only under the proportional sub-distribution hazard (SDH) assumption. This paper introduces an alternative statistical measure: the restricted mean time lost (RMTL).

Methods

First, the definition and estimation methods of the measures are introduced. Second, based on the differences in RMTLs, a basic difference test (Diff) and a supremum difference test (sDiff) are constructed. Then, the corresponding sample size estimation method is proposed. The statistical properties of the methods and the estimated sample size are evaluated using Monte Carlo simulations, and these methods are also applied to two real examples.

Results

The simulation results show that sDiff performs well and has relatively high test efficiency in most situations. Regarding sample size calculation, sDiff exhibits good performance in various situations. The methods are illustrated using two examples.

Conclusions

RMTL can meaningfully summarize treatment effects for clinical decision making, which can then be reported with the SDH ratio for competing risks data. The proposed sDiff test and the two calculated sample size formulas have wide applicability and can be considered in real data analysis and trial design.

Limitations of the UNAIDS 90-90-90 metrics: a simulation-based comparison of cross-sectional and longitudinal metrics for the HIV care continuum

The Joint United Nations Programme on HIV/AIDS (UNAIDS) 90–90–90 and other cross-sectional metrics can lead to potentially counterintuitive conclusions when used to evaluate health systems’ performance. This study demonstrates how time and population dynamics impact UNAIDS 90–90–90 metrics in comparison with a longitudinal analogue.

Novel Risk Modeling Approach of Atrial Fibrillation With Restricted Mean Survival Times: Application in the Framingham Heart Study Community-Based Cohort

BACKGROUND

Risk prediction models for atrial fibrillation (AF) do not give information about when AF might develop. Restricted mean survival time (RMST) quantifies risk into the time domain. Our objective was to use RMST to re-express individualized AF risk predictions.

METHODS AND RESULTS

We included AF-free participants from the Framingham Heart Study community-based cohorts. We predicted new-onset AF over 10-year follow-up according to baseline covariates: age, height, weight, systolic blood pressure, diastolic blood pressure, current smoking, antihypertensive treatment, diabetes mellitus, prevalent heart failure, and prevalent myocardial infarction. First, we fitted a Cox regression model and estimated the 10-year predicted risk of AF. Second, we fitted an RMST model and estimated the predicted mean time free of AF and alive over a time horizon of 10 years. We included 7586 AF-free participants contributing to 11 088 examinations (mean age 61±11 years, 44% were men). During 10-year follow-up, 822 participants developed AF. The Cox and RMST models were in agreement regarding the direction, strength, and statistical significance of associations for all covariates. Low (<5%), intermediate (5%-15%), and high (>15%) 10-year predicted risk of AF corresponded to predicted mean time alive and free of AF of 9.9, 9.6, and 8.8 years, respectively. A 60-year-old woman with a body mass index of 25 kg/m2, no use of hypertension treatment and no history of heart failure had a predicted mean time alive and free of AF of 9.9 years, whereas a 70-year-old man with a body mass index of 30 kg/m2, use of hypertension treatment, and with prevalent heart failure had a predicted mean time alive and free of AF of 7.9 years.

CONCLUSIONS

The RMST can be used to develop risk prediction models to express results in a time scale. RMST may offer a complementary risk communication tool for AF in clinical practice.

Individual patient data network meta-analysis using either restricted mean survival time difference or hazard ratios: is there a difference? A case study on locoregionally advanced nasopharyngeal carcinomas

BACKGROUND

This study aimed at applying the restricted mean survival time difference (rmstD) as an absolute outcome measure in a network meta-analysis and comparing the results with those obtained using hazard ratios (HR) from the individual patient data (IPD) network meta-analysis (NMA) on the role of chemotherapy for nasopharyngeal carcinoma (NPC) recently published by the MAC-NPC collaborative group (Meta-Analysis of Chemotherapy [CT] in NPC).

PATIENTS AND METHODS

Twenty trials (5144 patients) comparing radiotherapy (RT) with or without CT in non-metastatic NPC were included. Treatments were grouped in seven categories: RT alone (RT), induction CT followed by RT (IC-RT), RT followed by adjuvant CT (RT-AC), IC followed by RT followed by AC (IC-RT-AC), concomitant chemoradiotherapy (CRT), IC followed by CRT (IC-CRT), and CRT followed by AC (CRT-AC). The primary endpoint was overall survival (OS); secondary endpoints were progression-free survival and locoregional control. The rmstD was estimated at t* = 10 years in each trial. Random-effect frequentist NMA models were applied. P score was used to rank treatments. Heterogeneity and inconsistency were evaluated.

RESULTS

The three treatments that had the highest effect on OS with rmstD were CRT-AC, IC-CRT, and CRT (respective P scores of 92%, 72%, and 64%) compared to CRT-AC, CRT, and IC-CRT when using HR (respective P scores of 96%, 71%, and 63%). Of the 32 HR and rmstD analyzed, 5 had a different interpretation, 3 with a direction change (different direction of treatment effect) and 2 with a change in significance (same direction but a change in statistical significance). Results for secondary endpoints were overall in agreement.

CONCLUSION

The use of either HR or rmstD impacts the results of NMA. Given the sensitivity of HR to non-proportional hazards, this finding could have implications in terms of meta-analysis methodology.

Incidence of autoimmune pulmonary alveolar proteinosis estimated using Poisson distribution

The incidence and prevalence of autoimmune pulmonary alveolar proteinosis in Japan were previously estimated to be 0.49 and 6.2 per million, respectively. Thereafter, an increase in serological diagnosis forced a re-estimation of the incidence based on more contemporaneous data using more robust methods.

Sera of 702 patients were positive for granulocyte-macrophage colony-stimulating factor autoantibody during the 2006–2016 period (group A). Of these patients, 43 were actively surveyed in Niigata prefecture (group B) for estimation of the incidence. To estimate the survival period, 103 patients (group C) were investigated retrospectively for the 1999–2017 period using restricted mean survival time.

In group A, the number of patients diagnosed in each prefecture was closely correlated with the corresponding population, indicating no regional integration of onset. In group B, a total of 43 patients were diagnosed, the annual number followed a Poisson distribution and the incidence was thus estimated to be 1.65 per million. In group C, the retrospective cohort study revealed the mean survival period to be 16.1 years. Taken together, the prevalence was estimated to be 26.6 per million, indicating that the previous data for incidence and prevalence was an underestimation.

The latest epidemiologic study of autoimmune pulmonary alveolar proteinosis revealed the incidence and prevalence, estimated using Poisson distribution, to be 1.65 and 26.6 per million, respectivelyhttp://ow.ly/Wyyd30n4IYP

Summarizing and communicating on survival data according to the audience: a tutorial on different measures illustrated with population-based cancer registry data

Survival data analysis results are usually communicated through the overall survival probability. Alternative measures provide additional insights and may help in communicating the results to a wider audience. We describe these alternative measures in two data settings, the overall survival setting and the relative survival setting, the latter corresponding to the particular competing risk setting in which the cause of death is unavailable or unreliable. In the overall survival setting, we describe the overall survival probability, the conditional survival probability and the restricted mean survival time (restricted to a prespecified time window). In the relative survival setting, we describe the net survival probability, the conditional net survival probability, the restricted mean net survival time, the crude probability of death due to each cause and the number of life years lost due to each cause over a prespecified time window. These measures describe survival data either on a probability scale or on a timescale. The clinical or population health purpose of each measure is detailed, and their advantages and drawbacks are discussed. We then illustrate their use analyzing England population-based registry data of men 15-80 years old diagnosed with colon cancer in 2001-2003, aiming to describe the deprivation disparities in survival. We believe that both the provision of a detailed example of the interpretation of each measure and the software implementation will help in generalizing their use.