Estimating Treatment Effect With Clinical Interpretation From a Comparative Clinical Trial With an End Point Subject to Competing Risks

The recombinant shingles vaccine is associated with lower risk of dementia

There is emerging evidence that the live herpes zoster (shingles) vaccine might protect against dementia. However, the existing data are limited and refer only to the live vaccine, which is now discontinued in the United States and many other countries in favor of a recombinant vaccine. Whether the recombinant shingles vaccine protects against dementia remains unknown. Here we used a natural experiment opportunity created by the rapid transition from the use of live to the use of recombinant vaccines to compare the risk of dementia between vaccine types. We show that the recombinant vaccine is associated with a significantly lower risk of dementia in the 6 years post-vaccination. Specifically, receiving the recombinant vaccine is associated with a 17% increase in diagnosis-free time, translating into 164 additional days lived without a diagnosis of dementia in those subsequently affected. The recombinant shingles vaccine was also associated with lower risks of dementia than were two other vaccines commonly used in older people: influenza and tetanus-diphtheria-pertussis vaccines. The effect was robust across multiple secondary analyses, and was present in both men and women but was greater in women. These findings should stimulate studies investigating the mechanisms underpinning the protection and could facilitate the design of a large-scale randomized control trial to confirm the possible additional benefit of the recombinant shingles vaccine.

Association between intensive blood pressure lowering and stroke-free survival among patients with and without Diabetes

This study pooled data from SPRINT (Systolic Blood Pressure Intervention Trial) and ACCORD-BP (Action to Control Cardiovascular Risk in Diabetes Blood Pressure) trial to estimate the treatment effect of intensive BP on stroke prevention, and investigate whether stroke risk score impacted treatment effect. Of all the potential manifestations of the hypertension, the most severe outcomes were stroke or death. A composite endpoint of time to death or stroke (stroke-free survival [SFS]), whichever occurred first, was defined as the outcome of interest. Participants without prevalent stroke were stratified into stroke risk tertiles based on the predicted revised Framingham Stroke Risk Score. The stratified Cox model was used to calculate the hazard ratio (HR) for the intensive BP treatment. 834 (5.92%) patients had SFS events over a median follow-up of 3.68 years. A reduction in the risk for SFS was observed among the intensive BP group as compared with the standard BP group (HR: 0.76, 95% CI: 0.65, 0.89; risk difference: 0.98([0.20, 1.76]). Further analyses demonstrated the significant benefit of intensive BP treatment on SFS only among participants having a high stroke risk (risk tertile 1: 0.76 [0.52, 1.11], number needed to treat [NNT] = 861; risk tertile 2: 0.87[0.65, 1.16], NNT = 91; risk tertile 3: 0.69[0.56, 0.86], NNT = 50). Intensive BP treatment lowered the risk of SFS, particularly for those at high risk of stroke.

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

BACKGROUND

According to long-term follow-up data of malignant tumor patients, assessing treatment effects requires careful consideration of competing risks. The commonly used cause-specific hazard ratio (CHR) and sub-distribution hazard ratio (SHR) are relative indicators and may present challenges in terms of proportional hazards assumption and clinical interpretation. Recently, the restricted mean time lost (RMTL) has been recommended as a supplementary measure for better clinical interpretation. Moreover, for observational study data in epidemiological and clinical settings, due to the influence of confounding factors, covariate adjustment is crucial for determining the causal effect of treatment.

METHODS

We construct an RMTL estimator after adjusting for covariates based on the inverse probability weighting method, and derive the variance to construct interval estimates based on the large sample properties. We use simulation studies to study the statistical performance of this estimator in various scenarios. In addition, we further consider the changes in treatment effects over time, constructing a dynamic RMTL difference curve and corresponding confidence bands for the curve.

RESULTS

The simulation results demonstrate that the adjusted RMTL estimator exhibits smaller biases compared with unadjusted RMTL and provides robust interval estimates in all scenarios. This method was applied to a real-world cervical cancer patient data, revealing improvements in the prognosis of patients with small cell carcinoma of the cervix. The results showed that the protective effect of surgery was significant only in the first 20 months, but the long-term effect was not obvious. Radiotherapy significantly improved patient outcomes during the follow-up period from 17 to 57 months, while radiotherapy combined with chemotherapy significantly improved patient outcomes throughout the entire period.

CONCLUSIONS

We propose the approach that is easy to interpret and implement for assessing treatment effects in observational competing risk data.

Time-varying effect in older patients with early-stage breast cancer: a model considering the competing risks based on a time scale

Background

Patients with early-stage breast cancer may have a higher risk of dying from other diseases, making a competing risks model more appropriate. Considering subdistribution hazard ratio, which is used often, limited to model assumptions and clinical interpretation, we aimed to quantify the effects of prognostic factors by an absolute indicator, the difference in restricted mean time lost (RMTL), which is more intuitive. Additionally, prognostic factors of breast cancer may have dynamic effects (time-varying effects) in long-term follow-up. However, existing competing risks regression models only provide a static view of covariate effects, leading to a distorted assessment of the prognostic factor.

Methods

To address this issue, we proposed a dynamic effect RMTL regression that can explore the between-group cumulative difference in mean life lost over a period of time and obtain the real-time effect by the speed of accumulation, as well as personalized predictions on a time scale.

Results

A simulation validated the accuracy of the coefficient estimates in the proposed regression. Applying this model to an older early-stage breast cancer cohort, it was found that 1) the protective effects of positive estrogen receptor and chemotherapy decreased over time; 2) the protective effect of breast-conserving surgery increased over time; and 3) the deleterious effects of stage T2, stage N2, and histologic grade II cancer increased over time. Moreover, from the view of prediction, the mean C-index in external validation reached 0.78.

Conclusion

Dynamic effect RMTL regression can analyze both dynamic cumulative effects and real-time effects of covariates, providing a more comprehensive prognosis and better prediction when competing risks exist.

Communicating and understanding statistical measures when quantifying the between-group difference in competing risks

Competing risks issues are common in clinical trials and epidemiological studies for patients in follow-up who may experience a variety of possible outcomes. Under such competing risks, two hazard-based statistical methods, cause-specific hazard (CSH) and subdistribution hazard (SDH), are frequently used to assess treatment effects among groups. However, the outcomes of the CSH-based and SDH-based methods have a close connection with the proportional hazards (CSH or SDH) assumption and may have an non-intuitive interpretation. Recently, restricted mean time lost (RMTL) has been used as an alternative summary measure for analysing competing risks, due to its clinical interpretability and robustness to the proportional hazards assumption. Considering the above approaches, we summarize the differences between hazard-based and RMTL-based methods from the aspects of practical interpretation, proportional hazards model assumption and the selection of restricted time points, and propose corresponding suggestions for the analysis of between-group differences under competing risks. Moreover, an R package 'cRMTL' and corresponding step-by-step guidance are available to help users for applying these approaches.

A cohort study of differences in trauma outcomes between females and males at four Indian Urban Trauma Centers

Background Studies from high income countries suggest improved survival for females as compared to males following trauma. However, data regarding differences in trauma outcomes between females and males is severely lacking from low- and middle-income countries. The objective of this study was to determine the association between sex and clinical outcomes amongst Indian trauma patients using the Australia-India Trauma Systems Collaboration database. Methods A prospective multicentre cohort study was performed across four urban public hospitals in India April 2016 through February 2018. Bivariate analyses compared admission physiological parameters and mechanism of injury. Logistic regression assessed association of sex with the primary outcomes of 30-day and 24-hour in-hospital mortality. Secondary outcomes included ICU admission, ICU length of stay, ventilator requirement, and time on a ventilator. Results Of 8,605 patients, 1,574 (18.3%) were females. The most common mechanism of injury was falls for females (52.0%) and road traffic injury for males (49.5%). On unadjusted analysis, there was no difference in 30-day in-hospital mortality between females (11.6%) and males (12.6%, p = 0.323). However, females demonstrated a lower mortality at 24-hours (1.1% vs males 2.1%, p = 0.011) on unadjusted analysis. Females were also less likely to require a ventilator (17.3% vs 21.0% males, p = 0.001) or ICU admission (34.4% vs 37.5%, p = 0.028). Stratification by age or by ISS demonstrated no difference in 30-day in-hospital mortality for males vs females across age and ISS categories. On multivariable regression analysis, sex was not associated significantly with 30-day or 24-hour in-hospital mortality. Conclusion This study did not demonstrate a significant difference in the 30-day trauma mortality or 24-hour trauma mortality between female and male trauma patients in India on adjusted analyses. A more granular data is needed to understand the interplay of injury severity, immediate post-traumatic hormonal and immunological alterations, and the impact of gender-based disparities in acute care settings.

Doubly-robust estimator of the difference in restricted mean times lost with competing risks data

In the context of competing risks data, the subdistribution hazard ratio has limited clinical interpretability to measure treatment effects. An alternative is the difference in restricted mean times lost (RMTL), which gives the mean time lost to a specific cause of failure between treatment groups. In non-randomized studies, the average causal effect is conventionally used for decision-making about treatment and public health policies. We show how the difference in RMTL can be estimated by contrasting the integrated cumulative incidence functions from a Fine-Gray model. We also show how the difference in RMTL can be estimated by using inverse probability of treatment weighting and contrasts between weighted non-parametric estimators of the area below the cumulative incidence. We use pseudo-observation approaches to estimate both component models and we integrate them into a doubly-robust estimator. We demonstrate that this estimator is consistent when either component is correctly specified. We conduct simulation studies to assess its finite-sample performance and demonstrate its inherited consistency property from its component models. We also examine the performance of this estimator under varying degrees of covariate overlap and under a model misspecification of nonlinearity. We apply the proposed method to assess biomarker-treatment interaction in subpopulations of the POPLAR and OAK randomized controlled trials of second-line therapy for advanced non-small-cell lung cancer.

Implementation of an Alternative Method for Assessing Competing Risks: Restricted Mean Time Lost

In clinical and epidemiologic studies, hazard ratios are often applied to compare treatment effects between 2 groups for survival data. For competing-risks data, the corresponding quantities of interest are cause-specific hazard ratios and subdistribution hazard ratios. However, they both have some limitations related to model assumptions and clinical interpretation. Therefore, we recommend restricted mean time lost (RMTL) as an alternative measure that is easy to interpret in a competing-risks framework. Based on the difference in RMTL (RMTLd), we propose a new estimator, hypothetical test, and sample-size formula. Simulation results show that estimation of the RMTLd is accurate and that the RMTLd test has robust statistical performance (both type I error and statistical power). The results of 3 example analyses also verify the performance of the RMTLd test. From the perspectives of clinical interpretation, application conditions, and statistical performance, we recommend that the RMTLd be reported along with the hazard ratio in analyses of competing-risks data and that the RMTLd even be regarded as the primary outcome when the proportional hazards assumption fails.

Counterfactual mediation analysis in the multistate model framework for surrogate and clinical time-to-event outcomes in randomized controlled trials

In cancer randomized controlled trials, surrogate endpoints are frequently time-to-event endpoints, subject to the competing risk from the time-to-event clinical outcome. In this context, we introduce a counterfactual-based mediation analysis for a causal assessment of surrogacy. We use a multistate model for risk prediction to account for both direct transitions towards the clinical outcome and indirect transitions through the surrogate outcome. Within the counterfactual framework, we define natural direct and indirect effects with a causal interpretation. Based on these measures, we define the proportion of the treatment effect on the clinical outcome mediated by the surrogate outcome. We estimate the proportion for both the cumulative risk and restricted mean time lost. We illustrate our approach by using 18-year follow-up data from the SPCG-4 randomized trial of radical prostatectomy for prostate cancer. We assess time to metastasis as a surrogate outcome for prostate cancer-specific mortality.

Practical Recommendations on Quantifying and Interpreting Treatment Effects in the Presence of Terminal Competing Risks: A Review

Importance

In a comparative trial, the time to a clinical event is often a key end point. However, the occurrence of a terminal event, such as death or premature study discontinuation, may preclude observation of this outcome. Although various methods for handling competing risks are available, no specific recommendations have been made for scenarios encountered in practice, especially when the terminal event profiles of the study arms are dissimilar. Moreover, appropriate methods for a desirable outcome, such as live hospital discharge, have seldom been discussed.

Observations

Several of the most commonly used methods are reviewed. The first regards the terminal event as censoring and applies standard survival analysis to the event of interest. The between-group difference is usually summarized by the cause-specific hazard ratio. This summary measure is inappropriate when the new therapy markedly prolongs time to the terminal event. Moreover, the corresponding Kaplan-Meier curve for the end point of interest is uninterpretable. The second method is to use the cumulative incidence curve, which is the probability of experiencing the event of interest by each time point, acknowledging that patients who have died will never experience the event. However, the resulting pseudo hazard ratio is difficult to interpret. With a proper alternative summary measure, this approach works well for a desirable outcome but may not for an undesirable outcome. The third method focuses on the event-free survival time by combining information from occurrences of the terminal event and the event of interest simultaneously. This clinically interpretable method naturally accounts for differences in terminal event rates when comparing treatments with respect to the time to an undesirable outcome.

Conclusions and Relevance

This article enhances our understanding of each method's advantages and shortcomings and assists practitioners in choosing appropriate methods for handling competing risk problems in practice.

Intensive versus standard blood pressure control in type 2 diabetes: a restricted mean survival time analysis of a randomised controlled trial

BACKGROUND

Restricted mean survival time analysis offers an intuitive and robust summary of treatment effect compared with HRs.

OBJECTIVE

To examine the effect of intensive versus standard blood pressure (BP) control on death or cardiovascular events in type 2 diabetes.

DESIGN

Secondary analysis of the Action to Control Cardiovascular Risk in Diabetes Blood Pressure trial.

SETTING

77 sites in the USA and Canada.

PARTICIPANTS

4733 adults with type 2 diabetes at high risk for cardiovascular events.

INTERVENTIONS

Systolic BP target <120 mm Hg (n=2371) versus <140 mm Hg (n=2362).

MEASUREMENTS

Composite endpoint of death, non-fatal myocardial infarction or non-fatal stroke.

RESULTS

The mean event-free survival time over 5 years (1825 days) was similar between intensive and standard BP control (1716 vs 1714 days; mean difference, 1.3 (95% CI -18.1 to 20.7) days). However, intensive BP treatment was more beneficial for those assigned to standard glycaemic control (1725 vs 1697 days; mean difference, 28.1 (95% CI 0.4 to 55.9) days), but not for those assigned to intensive glycaemic control (1706 vs 1731 days; mean difference, -25.2 (95% CI -52.3 to 1.9) days) (p=0.008 for interaction). In subgroup analysis, the mean event-free survival time difference between intensive and standard BP treatment was -76.0 (95% CI -131.8 to -20.3) days for those with cognitive impairment and 21.8 (95% CI -24.0 to 67.5) days for those with normal cognitive function (p=0.008 for interaction). The effect was not different by age, sex and baseline cardiovascular disease status.

CONCLUSIONS

Intensive BP treatment may reduce death and cardiovascular events among patients with type 2 diabetes receiving standard glycaemic treatment and without cognitive impairment.

TRIAL REGISTRATION NUMBER

NCT00000620; Post-results.

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.

Prolonged Intubation in Patients With Prior Cerebrovascular Disease and COVID-19

Objectives: Patients with comorbidities are at increased risk for poor outcomes in COVID-19, yet data on patients with prior neurological disease remains limited. Our objective was to determine the odds of critical illness and duration of mechanical ventilation in patients with prior cerebrovascular disease and COVID-19. Methods: A observational study of 1,128 consecutive adult patients admitted to an academic center in Boston, Massachusetts, and diagnosed with laboratory-confirmed COVID-19. We tested the association between prior cerebrovascular disease and critical illness, defined as mechanical ventilation (MV) or death by day 28, using logistic regression with inverse probability weighting of the propensity score. Among intubated patients, we estimated the cumulative incidence of successful extubation without death over 45 days using competing risk analysis. Results: Of the 1,128 adults with COVID-19, 350 (36%) were critically ill by day 28. The median age of patients was 59 years (SD: 18 years) and 640 (57%) were men. As of June 2nd, 2020, 127 (11%) patients had died. A total of 177 patients (16%) had a prior cerebrovascular disease. Prior cerebrovascular disease was significantly associated with critical illness (OR = 1.54, 95% CI = 1.14-2.07), lower rate of successful extubation (cause-specific HR = 0.57, 95% CI = 0.33-0.98), and increased duration of intubation (restricted mean time difference = 4.02 days, 95% CI = 0.34-10.92) compared to patients without cerebrovascular disease. Interpretation: Prior cerebrovascular disease adversely affects COVID-19 outcomes in hospitalized patients. Further study is required to determine if this subpopulation requires closer monitoring for disease progression during COVID-19.

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.

How to Quantify and Interpret Treatment Effects in Comparative Clinical Studies of COVID-19

Clinical trials of treatments for coronavirus disease 2019 (COVID-19) draw intense public attention. More than ever, valid, transparent, and intuitive summaries of the treatment effects, including efficacy and harm, are needed. In recently published and ongoing randomized comparative trials evaluating treatments for COVID-19, time to a positive outcome, such as recovery or improvement, has repeatedly been used as either the primary or key secondary end point. Because patients may die before recovery or improvement, data analysis of this end point faces a competing risk problem. Commonly used survival analysis techniques, such as the Kaplan-Meier method, often are not appropriate for such situations. Moreover, almost all trials have quantified treatment effects by using the hazard ratio, which is difficult to interpret for a positive event, especially in the presence of competing risks. Using 2 recent trials evaluating treatments (remdesivir and convalescent plasma) for COVID-19 as examples, a valid, well-established yet underused procedure is presented for estimating the cumulative recovery or improvement rate curve across the study period. Furthermore, an intuitive and clinically interpretable summary of treatment efficacy based on this curve is also proposed. Clinical investigators are encouraged to consider applying these methods for quantifying treatment effects in future studies of COVID-19.

Selecting appropriate endpoints for assessing treatment effects in comparative clinical studies for COVID-19

To evaluate the efficacy and safety of a new treatment for COVID-19 vs. standard care, certain key endpoints are related to the duration of a specific event, such as hospitalization, ICU stay, or receipt of supplemental oxygen. However, since patients may die in the hospital during study follow-up, using, for example, the duration of hospitalization to assess treatment efficacy can be misleading. If the treatment tends to prolong patients' survival compared with standard care, patients in the new treatment group may spend more time in hospital. This can lead to a "survival bias" issue, where a treatment that is effective for preventing death appears to prolong an undesirable outcome. On the other hand, by using hospital-free survival time as the endpoint, we can circumvent the survival bias issue. In this article, we use reconstructed data from a recent, large clinical trial for COVID-19 to illustrate the advantages of this approach. For the analysis of ICU stay or oxygen usage, where the initiating event is potentially an outcome of treatment, standard survival analysis techniques may not be appropriate. We also discuss issues with analyzing the durations of such events.

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.

Statistical Issues and Lessons Learned From COVID-19 Clinical Trials With Lopinavir-Ritonavir and Remdesivir

Background

Recently, three randomized clinical trials on coronavirus disease (COVID-19) treatments were completed: one for lopinavir-ritonavir and two for remdesivir. One trial reported that remdesivir was superior to placebo in shortening the time to recovery, while the other two showed no benefit of the treatment under investigation.

Objective

The aim of this paper is to, from a statistical perspective, identify several key issues in the design and analysis of three COVID-19 trials and reanalyze the data from the cumulative incidence curves in the three trials using more appropriate statistical methods.

Methods

The lopinavir-ritonavir trial enrolled 39 additional patients due to insignificant results after the sample size reached the planned number, which led to inflation of the type I error rate. The remdesivir trial of Wang et al failed to reach the planned sample size due to a lack of eligible patients, and the bootstrap method was used to predict the quantity of clinical interest conditionally and unconditionally if the trial had continued to reach the originally planned sample size. Moreover, we used a terminal (or cure) rate model and a model-free metric known as the restricted mean survival time or the restricted mean time to improvement (RMTI) to analyze the reconstructed data. The remdesivir trial of Beigel et al reported the median recovery time of the remdesivir and placebo groups, and the rate ratio for recovery, while both quantities depend on a particular time point representing local information. We use the restricted mean time to recovery (RMTR) as a global and robust measure for efficacy.

Results

For the lopinavir-ritonavir trial, with the increase of sample size from 160 to 199, the type I error rate was inflated from 0.05 to 0.071. The difference of RMTIs between the two groups evaluated at day 28 was –1.67 days (95% CI –3.62 to 0.28; P=.09) in favor of lopinavir-ritonavir but not statistically significant. For the remdesivir trial of Wang et al, the difference of RMTIs at day 28 was –0.89 days (95% CI –2.84 to 1.06; P=.37). The planned sample size was 453, yet only 236 patients were enrolled. The conditional prediction shows that the hazard ratio estimates would reach statistical significance if the target sample size had been maintained. For the remdesivir trial of Beigel et al, the difference of RMTRs between the remdesivir and placebo groups at day 30 was –2.7 days (95% CI –4.0 to –1.2; P<.001), confirming the superiority of remdesivir. The difference in the recovery time at the 25th percentile (95% CI –3 to 0; P=.65) was insignificant, while the differences became more statistically significant at larger percentiles.

Conclusions

Based on the statistical issues and lessons learned from the recent three clinical trials on COVID-19 treatments, we suggest more appropriate approaches for the design and analysis of ongoing and future COVID-19 trials.

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.