Adjusted restricted mean survival times in observational studies

Sentinel lymph node biopsy versus observation in high-risk cutaneous squamous cell carcinoma in immunosuppressed and immunocompetent patients: An inverse probability of treatment weighting study

BACKGROUND

The survival benefit of sentinel lymph node biopsy (SLNB) in immunocompetent and immunosuppressed patients with high-risk cutaneous squamous cell carcinoma (cSCC) has not been established.

OBJECTIVE

To determine whether SLNB improves disease-specific survival (DSS) in high-risk cSCC. Secondary objectives were to analyse disease-free survival, nodal recurrence-free survival and overall survival (OS).

METHODS

Multicentre, retrospective, observational cohort study comparing survival outcomes in immunosuppressed and immunocompetent patients treated with SLNB or watchful waiting. Inverse probability of treatment weighting was used to adjust for possible confounding effects.

RESULTS

We studied 638 tumours in immunocompetent patients (SLNB n = 42, observation n = 596) and 173 tumours in immunosuppressed patients (SLNB n = 28, observation n = 145). Overall, SLNB was positive in 15.7% of tumours. SLNB was associated with a reduced risk of nodal recurrence (NR) (hazard ratio [HR], 0.05 [95% CI, 0.01-0.43]; p = 0.006), disease specific mortality (HR, 0.17 [95% CI, 0.04-0.72]; p = 0.016) and all-cause mortality (HR, 0.33 [95% CI, 0.15-0.71]; p = 0.004) only in immunocompetent patients.

CONCLUSIONS

SLNB was associated with improvements in NR, DSS and OS in immunocompetent but not in immunosuppressed patients with high-risk cSCC.

Evaluation of the proportional hazards assumption and covariate adjustment methods in comparative surgical observational studies with time-to-event endpoints

INTRODUCTION

Comparative studies on surgical treatments with time-to-event endpoints have provided substantial evidence for clinical practice, but the accurate use of survival data analysis and the control of confounding bias remain big challenges.

METHODS

This was a survey of surgical studies with survival outcomes published in four general medical journals and five general surgical journals in 2021. The two most concerned statistical issues were evaluated, including confounding control by propensity score analysis (PSA) or multivariable analysis and testing of proportional hazards (PH) assumption in Cox model.

RESULTS

A total of 74 studies were included, comprising 63 observational studies and 11 randomized controlled trials. Among the observational studies, the proportion of studies utilizing PSA in surgical oncology and non-oncology studies was similar (40.9 % versus 36.8 %, P = 0.762). However, the former reported a significantly lower proportion of PH assumption assessments compared to the latter (13.6 % versus 42.1 %, P = 0.020). Twenty-five observational studies (25/63) used PSA methods, but two-thirds of them (17/25) showed unclear balance of baseline data after PSA. And the proportion of PH assumption testing after PSA was slightly lower than that before PSA, but the difference was not statistically significant (24.0 % versus 28.0 %, P = 0.317). Comprehensive suggestions were given on confounding control in survival analysis and alternative resolutions for non-compliance with PH assumption.

CONCLUSION

This study highlights suboptimal reporting of PH assumption evaluation in observational surgical studies both before and after PSA. Efforts and consensus are needed with respect to the underlying assumptions of statistical methods.

Incidence of type 2 diabetes, cardiovascular disease and chronic kidney disease in patients with multiple sclerosis initiating disease-modifying therapies: Retrospective cohort study using a frequentist model averaging statistical framework

Researchers are increasingly using insights derived from large-scale, electronic healthcare data to inform drug development and provide human validation of novel treatment pathways and aid in drug repurposing/repositioning. The objective of this study was to determine whether treatment of patients with multiple sclerosis with dimethyl fumarate, an activator of the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway, results in a change in incidence of type 2 diabetes and its complications. This retrospective cohort study used administrative claims data to derive four cohorts of adults with multiple sclerosis initiating dimethyl fumarate, teriflunomide, glatiramer acetate or fingolimod between January 2013 and December 2018. A causal inference frequentist model averaging framework based on machine learning was used to compare the time to first occurrence of a composite endpoint of type 2 diabetes, cardiovascular disease or chronic kidney disease, as well as each individual outcome, across the four treatment cohorts. There was a statistically significantly lower risk of incidence for dimethyl fumarate versus teriflunomide for the composite endpoint (restricted hazard ratio [95% confidence interval] 0.70 [0.55, 0.90]) and type 2 diabetes (0.65 [0.49, 0.98]), myocardial infarction (0.59 [0.35, 0.97]) and chronic kidney disease (0.52 [0.28, 0.86]). No differences for other individual outcomes or for dimethyl fumarate versus the other two cohorts were observed. This study effectively demonstrated the use of an innovative statistical methodology to test a clinical hypothesis using real-world data to perform early target validation for drug discovery. Although there was a trend among patients treated with dimethyl fumarate towards a decreased incidence of type 2 diabetes, cardiovascular disease and chronic kidney disease relative to other disease-modifying therapies-which was statistically significant for the comparison with teriflunomide-this study did not definitively support the hypothesis that Nrf2 activation provided additional metabolic disease benefit in patients with multiple sclerosis.

Screening Implications for Distribution of Colorectal Cancer Subsite by Age and Role of Flexible Sigmoidoscopy

Objectives: The effectiveness of colonoscopy to reduce colorectal cancer (CRC) mortality is extrapolated from cohort studies in the absence of randomized controlled trial (RCT) data, whereas flexible sigmoidoscopy is supported by RCT data and may be easier to implement in practice. We characterized the anatomic distribution of CRC to determine the proportion that is visible with sigmoidoscopy. Methods: Patients with a primary diagnosis of colorectal adenocarcinoma were identified in the Surveillance, Epidemiology, and End Results program (2000-2020). Tumors from the rectum to the descending colon were categorized as visible by sigmoidoscopy, whereas more proximal tumors required colonoscopy. Differential prognosis between tumor locations, stratified by age groups and stage, was assessed using the overall restricted mean survival time (RMST) at 2, 5, and 10 years. Results: Among 309,466 patients, 58% had tumors visible by sigmoidoscopy, including 73% of those under age 50 (OR 2.10, 95% CI 2.03-2.16 age < 45, OR 2.20, 95% CI 2.13-2.27 age 45-49 versus age ≥ 50). Male sex (OR 1.54, 95% CI 1.51-1.56) and Asian or Pacific Islander race (OR 1.60, 95% CI 1.56-1.64) were also positively associated with tumors visualizable by sigmoidoscopy. Across age groups, for local disease, RMST was comparable for tumors visible versus not visible on sigmoidoscopy. For regional and metastatic cancer, patients with tumors visible by sigmoidoscopy had improved RMST versus those with more proximal tumors. Conclusions: 58% of CRC arises in locations visible by flexible sigmoidoscopy. Flexible sigmoidoscopy should be considered as a viable option for CRC screening, particularly in younger patients unwilling or unable to undergo colonoscopy.

Elranatamab efficacy in MagnetisMM-3 compared with real-world control arms in triple-class refractory multiple myeloma

Elranatamab efficacy in the single-arm, registrational MagnetisMM-3 trial (NCT04649359) was compared with that of physician's choice of treatment (PCT) for triple-class refractory multiple myeloma. MagnestisMM-3 eligibility criteria were applied to two USA-based oncology electronic health record databases, COTA and Flatiron Health (FH), to identify cohorts for this study (NCT05932290). Applied statistical techniques accounted for cohort imbalances. MagnetisMM-3 (BCMA-naive; n = 123) outcomes were compared with those from COTA (n = 239) and FH (n = 152). Elranatamab was associated with a significantly higher objective response rate (risk ratios, 1.88-2.25), significantly longer progression-free survival (hazard ratios [HRs], 0.37-0.57), and, across most analyses, significantly longer overall survival (HRs, 0.46-0.66) versus PCT. BCMA-naive patients who were treated with elranatamab exhibited significantly better outcomes than patients treated in real-world clinical practice.

Clonorchis sinensis on the prognosis of patients with spontaneous rupture of Hepatocellular Carcinoma: An inverse probability of treatment weighting analysis

BACKGROUND

We examined the impact of the Clonorchis sinensis (C. sinensis) infection on the survival outcomes of spontaneous rupture Hepatocellular Carcinoma (srHCC) patients undergoing hepatectomy.

METHODS

Between May 2013 and December 2021, 157 consecutive srHCC patients who underwent hepatectomy were divided into an no C. sinensis group (n = 126) and C. sinensis group (n = 31). To adjust for differences in preoperative characteristics an inverse probability of treatment weighting (IPTW) analysis was done, using propensity scores. Overall survival (OS) and recurrence-free survival (RFS) were compared before and after IPTW. Multivariate Cox regression analysis was performed to determine whether the C. sinensis infection was an independent prognostic factor after IPTW.

RESULTS

In original cohort, the no C. sinensis group did not show a survival advantage over the C. sinensis group. After IPTW adjustment, the median OS for the C. sinensis group was 9 months, compared to 29 months for the no C. sinensis group. C. sinensis group have worse OS than no C. sinensis group (p = 0.024), while it did not differ in RFS(p = 0.065). The multivariate Cox regression analysis showed that C. sinensis infection and lower age were associated with worse OS.

CONCLUSIONS

The C. sinensis infection has an adverse impact on os in srHCC patients who underwent hepatectomy.

Analysis of survival data with nonproportional hazards: A comparison of propensity-score-weighted methods

One of the most common ways researchers compare cancer survival outcomes across treatments from observational data is using Cox regression. This model depends on its underlying assumption of proportional hazards, but in some real-world cases, such as when comparing different classes of cancer therapies, substantial violations may occur. In this situation, researchers have several alternative methods to choose from, including Cox models with time-varying hazard ratios; parametric accelerated failure time models; Kaplan-Meier curves; and pseudo-observations. It is unclear which of these models are likely to perform best in practice. To fill this gap in the literature, we perform a neutral comparison study of candidate approaches. We examine clinically meaningful outcome measures that can be computed and directly compared across each method, namely, survival probability at time T, median survival, and restricted mean survival. To adjust for differences between treatment groups, we use inverse probability of treatment weighting based on the propensity score. We conduct simulation studies under a range of scenarios, and determine the biases, coverages, and standard errors of the average treatment effects for each method. We then demonstrate the use of these approaches using two published observational studies of survival after cancer treatment. The first examines chemotherapy in sarcoma, which has a late treatment effect (i.e., similar survival initially, but after 2 years the chemotherapy group shows a benefit). The other study is a comparison of surgical techniques for kidney cancer, where survival differences are attenuated over time.

Estimating causal effects in observational studies for survival data with a cure fraction using propensity score adjustment

In observational studies, covariates are often confounding factors for treatment assignment. Such covariates need to be adjusted to estimate the causal treatment effect. For observational studies with survival outcomes, it is usually more challenging to adjust for the confounding covariates for causal effect estimation because of censoring. The challenge becomes even thornier when there exists a nonignorable cure fraction in the population. In this paper, we propose a causal effect estimation approach in observational studies for survival data with a cure fraction. We extend the absolute treatment effects on survival outcomes-including the restricted average causal effect and SPCE-to survival outcomes with cure fractions, and construct the corresponding causal effect estimators based on propensity score stratification. We prove the asymptotic properties of the proposed estimators and conduct simulation studies to evaluate their performances. As an illustration, the method is applied to a stomach cancer study.

Multiply robust causal inference of the restricted mean survival time difference

The hazard ratio (HR) remains the most frequently employed metric in assessing treatment effects on survival times. However, the difference in restricted mean survival time (RMST) has become a popular alternative to the HR when the proportional hazards assumption is considered untenable. Moreover, independent of the proportional hazards assumption, many comparative effectiveness studies aim to base contrasts on survival probability rather than on the hazard function. Causal effects based on RMST are often estimated via inverse probability of treatment weighting (IPTW). However, this approach generally results in biased results when the assumed propensity score model is misspecified. Motivated by the need for more robust techniques, we propose an empirical likelihood-based weighting approach that allows for specifying a set of propensity score models. The resulting estimator is consistent when the postulated model set contains a correct model; this property has been termed multiple robustness. In this report, we derive and evaluate a multiply robust estimator of the causal between-treatment difference in RMST. Simulation results confirm its robustness. Compared with the IPTW estimator from a correct model, the proposed estimator tends to be less biased and more efficient in finite samples. Additional simulations reveal biased results from a direct application of machine learning estimation of propensity scores. Finally, we apply the proposed method to evaluate the impact of intrapartum group B streptococcus antibiotic prophylaxis on the risk of childhood allergic disorders using data derived from electronic medical records from the Children's Hospital of Philadelphia and census data from the American Community Survey.

Propensity score weighting methods for causal subgroup analysis with time-to-event outcomes

Evaluating causal effects of an intervention in pre-specified subgroups is a standard goal in comparative effectiveness research. Despite recent advancements in causal subgroup analysis, research on time-to-event outcomes has been lacking. This article investigates the propensity score weighting method for causal subgroup survival analysis. We introduce two causal estimands, the subgroup marginal hazard ratio and subgroup restricted average causal effect, and provide corresponding propensity score weighting estimators. We analytically established that the bias of subgroup-restricted average causal effect is determined by subgroup covariate balance. Using extensive simulations, we compare the performance of various combinations of propensity score models (logistic regression, random forests, least absolute shrinkage and selection operator, and generalized boosted models) and weighting schemes (inverse probability weighting, and overlap weighting) for estimating the causal estimands. We find that the logistic model with subgroup-covariate interactions selected by least absolute shrinkage and selection operator consistently outperforms other propensity score models. Also, overlap weighting generally outperforms inverse probability weighting in terms of balance, bias and variance, and the advantage is particularly pronounced in small subgroups and/or in the presence of poor overlap. We applied the methods to the observational Comparing Options for Management: PAtient-centered REsults for Uterine Fibroids study to evaluate the causal effects of myomectomy versus hysterectomy on the time to disease recurrence in a number of pre-specified subgroups of patients with uterine fibroids.

A Dynamic Prediction Model Supporting Individual Life Expectancy Prediction Based on Longitudinal Time-Dependent Covariates

In the field of clinical chronic diseases, common prediction results (such as survival rate) and effect size hazard ratio (HR) are relative indicators, resulting in more abstract information. However, clinicians and patients are more interested in simple and intuitive concepts of (survival) time, such as how long a patient may live or how much longer a patient in a treatment group will live. In addition, due to the long follow-up time, resulting in generation of longitudinal time-dependent covariate information, patients are interested in how long they will survive at each follow-up visit. In this study, based on a time scale indicator-restricted mean survival time (RMST)-we proposed a dynamic RMST prediction model by considering longitudinal time-dependent covariates and utilizing joint model techniques. The model can describe the change trajectory of longitudinal time-dependent covariates and predict the average survival times of patients at different time points (such as follow-up visits). Simulation studies through Monte Carlo cross-validation showed that the dynamic RMST prediction model was superior to the static RMST model. In addition, the dynamic RMST prediction model was applied to a primary biliary cirrhosis (PBC) population to dynamically predict the average survival times of the patients, and the average C-index of the internal validation of the model reached 0.81, which was better than that of the static RMST regression. Therefore, the proposed dynamic RMST prediction model has better performance in prediction and can provide a scientific basis for clinicians and patients to make clinical decisions.

Smoking status and the association between patient-level factors and survival among lung cancer patients

BACKGROUND

Declines in the prevalence of cigarette smoking, advances in targeted therapies, and implementation of lung cancer screening have changed the clinical landscape for lung cancer. The proportion of lung cancer deaths is increasing in those who have never smoked cigarettes. To better understand contemporary patterns in survival among patients with lung cancer, a comprehensive evaluation of factors associated with survival, including differential associations by smoking status, is needed.

METHODS

Patients diagnosed with lung cancer between January 1, 2010, and September 30, 2019, were identified. We estimated all-cause and lung cancer-specific median, 5-year, and multivariable restricted mean survival time (RMST) to identify demographic, socioeconomic, and clinical factors associated with survival, overall and stratified by smoking status (never, former, and current).

RESULTS

Analyses included 6813 patients with lung cancer: 13.9% never smoked, 54.2% formerly smoked, and 31.9% currently smoked. All-cause RMST through 5 years for those who never, formerly, and currently smoked was 32.1, 25.9, and 23.3 months, respectively. Lung cancer-specific RMST was 36.3 months, 30.3 months, and 26.0 months, respectively. Across most models, female sex, younger age, higher socioeconomic measures, first-course surgery, histology, and body mass index were positively associated, and higher stage was inversely associated with survival. Relative to White patients, Black patients had increased survival among those who formerly smoked.

CONCLUSIONS

We identify actionable factors associated with survival between those who never, formerly, and currently smoked cigarettes. These findings illuminate opportunities to address underlying mechanisms driving lung cancer progression, including use of first-course treatment, and enhanced implementation of tailored smoking cessation interventions for individuals diagnosed with cancer.

Screening Implications for Distribution of Colorectal Cancer Subsite by Age

Objectives

The effectiveness of colonoscopy to reduce colorectal cancer (CRC) mortality is extrapolated from cohort studies in the absence of randomized controlled trial (RCT) data, whereas flexible sigmoidoscopy is supported by RCT data and may be easier to implement in practice. We characterized the anatomic distribution of CRC to determine the proportion that is visible with sigmoidoscopy.

Methods

Patients with a primary diagnosis of colorectal adenocarcinoma were identified in the Surveillance, Epidemiology, and End Results program (2000-2020). Tumors from the rectum to descending colon were categorized as visible by sigmoidoscopy, whereas more proximal tumors as requiring colonoscopy. Differential prognosis between tumor locations, stratified by age groups and stage, were assessed using overall restricted mean survival time (RMST) at 2, 5, and 10 years.

Results

Among 309,466 patients, 58% had tumors visible by sigmoidoscopy, including 73% of those under age 50 (OR 2.10, 95%CI 2.03-2.16 age <45, OR 2.20, 95%CI 2.13-2.27 age 45-49 versus age > 50). Male sex (OR 1.54, 95%CI 1.51-1.56) and Asian or Pacific Islander race (OR 1.60, 95%CI 1.56-1.64) were also positively associated with tumors visualizable by sigmoidoscopy. Across age groups, for local disease, RMST was comparable for tumors visible versus not visible on sigmoidoscopy. For regional and metastatic cancer, patients with tumors visible by sigmoidoscopy had improved RMST versus those with more proximal tumors.

Conclusions

Most CRC arise in locations visible by flexible sigmoidoscopy. Flexible sigmoidoscopy should be considered as a viable option for CRC screening, particularly in younger patients unwilling or unable to undergo colonoscopy.

Ratio and difference of average hazard with survival weight: New measures to quantify survival benefit of new therapy

The hazard ratio (HR) has been the most popular measure to quantify the magnitude of treatment effect on time-to-event outcomes in clinical research. However, the traditional Cox's HR approach has several drawbacks. One major issue is that there is no clear interpretation when the proportional hazards (PH) assumption does not hold, because the estimated HR is affected by study-specific censoring time distribution in non-PH cases. Another major issue is that the lack of a group-specific absolute hazard value in each group obscures the clinical significance of the magnitude of the treatment effect. Given these, we propose average hazard with survival weight (AH-SW) as a summary metric of event time distribution and will use difference in AH-SW (DAH-SW) or ratio of AH-SW (RAH-SW) to quantify the treatment effect magnitude. The AH-SW is interpreted as a person-time incidence rate that does not depend on random censoring. It is defined as the ratio of cumulative incidence probability and restricted mean survival time (RMST), which can be estimated non-parametrically. Numerical studies demonstrate that DAH-SW and RAH-SW offer almost identical power to Cox's HR-based tests under PH scenarios and can be more powerful for delayed-difference patterns often seen in immunotherapy trials. Like median and RMST differences, the proposed approach is a good model-free alternative to the HR-based approach for evaluating the treatment effect magnitude. Such a model-free measure will increase the likelihood that results from clinical studies are correctly interpreted and generalized to future populations.

The impact of age and comorbidity on localized pancreatic cancer outcomes: A US retrospective cohort analysis with implications for surgical centralization

Introduction

Age and comorbidity are independently associated with worse outcomes for pancreatic adenocarcinoma (PDAC). However, the effect of combined age and comorbidity on PDAC outcomes has rarely been studied. This study assessed the impact of age and comorbidity (CACI) and surgical center volume on PDAC 90-day and overall survival (OS).

Methods

This retrospective cohort study used the National Cancer Database from 2004 to 2016 to evaluate resected stage I/II PDAC patients. The predictor variable, CACI, combined the Charlson/Deyo comorbidity score with additional points for each decade lived ≥50 years. The outcomes were 90-day mortality and OS.

Results

The cohort included 29,571 patients. Ninety-day mortality ranged from 2 % for CACI 0 to 13 % for CACI 6+ patients. There was a negligible difference (1 %) in 90-day mortality between high- and low-volume hospitals for CACI 0-2 patients; however, there was greater difference for CACI 3-5 (5 % vs. 9 %) and CACI 6+ (8 % vs. 15 %). The overall survival for CACI 0-2, 3-5, and 6+ cohorts was 24.1, 19.8, and 16.2 months, respectively. Adjusted overall survival showed a 2.7 and 3.1 month survival benefit for care at high-volume vs. low-volume hospitals for CACI 0-2 and 3-5, respectively. However, there was no OS volume benefit for CACI 6+ patients.

Conclusions

Combined age and comorbidity are associated with short- and long-term survival for resected PDAC patients. A protective effect of higher-volume care was more impactful for 90-day mortality for patients with a CACI above 3. A centralization policy based on volume may have greater benefit for older, sicker patients.

Key message

Combined comorbidity and age are strongly associated with 90-day mortality and overall survival for resected pancreatic cancer patients. When assessing the impact of age and comorbidity on resected pancreatic adenocarcinoma outcomes, 90-day mortality was 7 % higher (8 % vs. 15 %) for older, sicker patients treated at high-volume vs. low-volume centers but only 1 % (3 % vs. 4 %) for younger, healthier patients.

Common analgesics and ovarian cancer survival: the Ovarian cancer Prognosis And Lifestyle (OPAL) Study

BACKGROUND

Most women with ovarian cancer (OC) are diagnosed with advanced disease. They often experience recurrence after primary treatment, and their subsequent prognosis is poor. Our goal was to evaluate the association between use of nonsteroidal antiinflammatory drugs (NSAIDs), including regular and low-dose aspirin, and 5-year cancer-specific survival after an OC diagnosis.

METHODS

The Ovarian cancer Prognosis And Lifestyle study is a prospective population-based cohort of 958 Australian women with OC. Information was gathered through self-completed questionnaires. We classified NSAID use during the year prediagnosis and postdiagnosis as none or occasional (<1 d/wk), infrequent (1-3 d/wk), and frequent (≥4 d/wk) use. We measured survival from the start of primary treatment: surgery or neoadjuvant chemotherapy for analyses of prediagnosis use, or 12 months after starting treatment (postdiagnosis use) until the earliest of date of death from OC (other deaths were censored) or last follow-up to 5 years. We used Cox proportional hazards regression to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) and applied inverse-probability of treatment weighting to minimize confounding. We also calculated restricted mean survival times.

RESULTS

Compared with nonusers and infrequent users, we observed better survival associated with frequent NSAID use prediagnosis (HR = 0.73, 95% CI = 0.55 to 0.97) or postdiagnosis (HR = 0.65, 95% CI = 0.45 to 0.94). Estimates were similar for aspirin and nonaspirin NSAIDs, new and continuous users and in weighted models. These differences would translate to a 2.5-month increase in mean survival by 5 years postdiagnosis. There was no association with acetaminophen.

CONCLUSIONS

Our findings confirm a previous study suggesting NSAID use might improve OC survival.

Comparison of Drug-Coated Balloons vs Bare-Metal Stents in Patients With Femoropopliteal Arterial Disease

BACKGROUND

Endovascular treatment of femoropopliteal artery disease has shifted toward drug-coated balloons (DCB). However, limited data are available regarding the safety and efficacy of DCB vs bare-metal stents (BMS).

OBJECTIVES

The purpose of this study was to compare DCB vs BMS outcomes in a propensity-adjusted, pooled analysis of 4 prospective, multicenter trials.

METHODS

Patient-level data were pooled from 4 prospective, multicenter studies: the IN.PACT SFA I/II and IN.PACT SFA Japan randomized controlled DCB trials and the Complete SE and DURABILITY II single-arm BMS studies. Outcomes were compared using inverse probability of treatment weighting (IPTW). Clinical endpoints were 12-month primary patency, freedom from 36-month clinically driven target lesion revascularization, and cumulative 36-month major adverse events (MAE).

RESULTS

The primary analysis included 771 patients (288 DCB, 483 BMS). IPTW-adjusted demographic, baseline lesion, and procedural characteristics were matched between groups. The adjusted mean lesion length was 8.1 ± 4.7 cm DCB and 7.9 ± 4.5 cm BMS. The IPTW-adjusted Kaplan-Meier estimates of 12-month primary patency (90.4% DCB, 80.9% BMS, P = 0.007), freedom from 36-month clinically driven target lesion revascularization (85.6% DCB, 73.7% BMS, P = 0.001), and cumulative incidence of 36-month MAE (25.3% DCB, 38.8% BMS, P < 0.001) favored DCB. There were no statistically significant differences observed in all-cause mortality, target limb major amputation, or thrombosis through 36 months.

CONCLUSIONS

In a patient-level, IPTW-adjusted pooled analysis of prospective, multicenter pivotal studies, DCB demonstrated significantly higher patency, lower revascularization and MAE rates, and no statistically significant differences in mortality, amputation, or thrombosis vs BMS. This analysis supports DCB use vs BMS in moderately complex femoropopliteal lesions amenable to both treatments.

Association Between Aspirin Use and Sepsis Outcomes: A National Cohort Study

BACKGROUND

Aspirin has anti-inflammatory and antiplatelet activities and directly inhibits bacterial growth. These effects of aspirin may improve survival in patients with sepsis. We retrospectively reviewed a large national health database to test the relationship between prehospital aspirin use and sepsis outcomes.

METHODS

We conducted a retrospective population-based cohort study using the National Health Insurance Research Database of Taiwan from 2001 to 2011 to examine the relationship between aspirin use before hospital admission and sepsis outcomes. The association between aspirin use and 90-day mortality in sepsis patients was determined using logistic regression models and weighting patients by the inverse probability of treatment weighting (IPTW) with the propensity score. Kaplan-Meier survival curves for each IPTW cohort were plotted for 90-day mortality. For sensitivity analyses, restricted mean survival times (RMSTs) were calculated based on Kaplan-Meier curves with 3-way IPTW analysis comparing current use, past use, and nonuse.

RESULTS

Of 52,982 patients with sepsis, 12,776 took aspirin before hospital admission (users), while 39,081 did not take any antiplatelet agents including aspirin before hospital admission (nonusers). After IPTW analysis, we found that when compared to nonusers, patients who were taking aspirin within 90 days before sepsis onset had a lower 90-day mortality rate (IPTW odds ratio [OR], 0.90; 95% confidence interval [CI], 0.88-0.93; P < .0001). Based on IPTW RMST analysis, nonusers had an average survival of 71.75 days, while current aspirin users had an average survival of 73.12 days. The difference in mean survival time was 1.37 days (95% CI, 0.50-2.24; P = .002).

CONCLUSIONS

Aspirin therapy before hospital admission is associated with a reduced 90-day mortality in sepsis patients.

Restricted mean survival time regression model with time-dependent covariates

In clinical or epidemiological follow‐up studies, methods based on time scale indicators such as the restricted mean survival time (RMST) have been developed to some extent. Compared with traditional hazard rate indicator system methods, the RMST is easier to interpret and does not require the proportional hazard assumption. To date, regression models based on the RMST are indirect or direct models of the RMST and baseline covariates. However, time‐dependent covariates are becoming increasingly common in follow‐up studies. Based on the inverse probability of censoring weighting (IPCW) method, we developed a regression model of the RMST and time‐dependent covariates. Through Monte Carlo simulation, we verified the estimation performance of the regression parameters of the proposed model. Compared with the time‐dependent Cox model and the fixed (baseline) covariate RMST model, the time‐dependent RMST model has a better prediction ability. Finally, an example of heart transplantation was used to verify the above conclusions.

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.

Prehospital airway management for out-of-hospital cardiac arrest: A nationwide multicenter study from the KoCARC registry

AIM

This study investigated whether prehospital advanced airway management (AAM) is associated with improved survival of out-of-hospital cardiac arrest (OHCA) compared with conventional bag-valve-mask (BVM) ventilation.

METHODS

We investigated the neurologically favorable survival of adult patients with OHCA who underwent BVM or AAM using the Korean Cardiac Arrest Research Consortium (KoCARC), a multicenter OHCA registry of Korea. The differences in clinical characteristics were adjusted by matching or weighting the clinical propensity for use of AAM or by least absolute shrinkage and selection operator (LASSO). The primary outcome was 30-day survival with neurologically favorable status defined by cerebral performance category 1 or 2.

RESULTS

Of the 9,616 patients enrolled (median age = 71 years; 65% male), there were 6,243 AAM and 3,354 BVM patients. In unadjusted analysis, the 30-day neurologically favorable survival was lower in the AAM group compared with the BVM group (5.5% vs. 10.0%; hazard ratio [HR] = 1.21, 95% confidence interval [CI] = 1.16 to 1.27; all p < 0.001). In propensity score matching-adjusted analysis, these differences were not found (9.6% vs. 10.0%; HR = 0.98, 95% CI = 0.93 to 1.03, p > 0.05). Inverse probability of treatment weighting- and LASSO-adjusted analyses replicated these results.

CONCLUSIONS

In this nationwide real-world data analysis of OHCA, the 30-day neurologically favorable survival did not differ between prehospital AAM and BVM after adjustment for clinical characteristics.

Life-Years Lost After Newly Diagnosed Atrial Fibrillation in Patients with Heart Failure

Objective

Prior work estimated excess death rates associated with atrial fibrillation (AF) in heart failure (HF) with hazard ratios (HR). The aim was to estimate the life-years lost after newly diagnosed AF in HF patients.

Methods

Among patients diagnosed with HF in 2008–2018 in the nationwide Danish Heart Failure Registry, we compared patients with incident AF to referents matched on age, sex, and time since HF. We estimated the marginal hazard ratio (HR) for death and marginal difference in restricted mean survival times (RMST) between AF cases and referents at 10 years after AF diagnosis. We adjusted for sex, age at AF diagnosis, clinical and lifestyle risk factors, and medications.

Results

Among 4463 AF cases and 17,792 referents (mean age 73.7 years, 29% women), the HR was 1.41 (95% CI 1.38; 1.44) but there was evidence of non-proportional hazards. The difference in RMST was −18.2 months (95% CI −16.8; −19.6) at 10 years after AF diagnosis. There were differences in life-years lost between patients diagnosed with AF >1 year and ≤1 year after HF (−25.7 months, 95% CI −23.7; −27.7 vs −10.4 months, 95% CI −8.2; −12.5, p < 0.001), women and men (−20.3 months, 95% CI −17.7; −21.9 vs −17.2 months, 95% CI −15.5; −19.0, p = 0.05), patients with low, medium, and high CHA₂DS₂-VASc (10.3 months, 95% CI −4.6; −16.1 vs −18.5 months, 95% CI −16.7; −20.4 vs 22.1, 95% CI −18.8; −22.3, p = 0.002).

Conclusion

HF patients with incident AF lost on average 1.5 life-years over 10 years after AF. Life-years lost were larger among patients diagnosed with AF >1 year after HF, women, and patients with higher CHA₂DS₂-VASc.

Continuous renal replacement therapy versus intermittent hemodialysis as first modality for renal replacement therapy in severe acute kidney injury: a secondary analysis of AKIKI and IDEAL-ICU studies

Background

Intermittent hemodialysis (IHD) and continuous renal replacement therapy (CRRT) are the two main RRT modalities in patients with severe acute kidney injury (AKI). Meta-analyses conducted more than 10 years ago did not show survival difference between these two modalities. As the quality of RRT delivery has improved since then, we aimed to reassess whether the choice of IHD or CRRT as first modality affects survival of patients with severe AKI.

Methods

This is a secondary analysis of two multicenter randomized controlled trials (AKIKI and IDEAL-ICU) that compared an early RRT initiation strategy with a delayed one. We included patients allocated to the early strategy in order to emulate a trial where patients would have been randomized to receive either IHD or CRRT within twelve hours after the documentation of severe AKI. We determined each patient’s modality group as the first RRT modality they received. The primary outcome was 60-day overall survival. We used two propensity score methods to balance the differences in baseline characteristics between groups and the primary analysis relied on inverse probability of treatment weighting.

Results

A total of 543 patients were included. Continuous RRT was the first modality in 269 patients and IHD in 274. Patients receiving CRRT had higher cardiovascular and total-SOFA scores. Inverse probability weighting allowed to adequately balance groups on all predefined confounders. The weighted Kaplan–Meier death rate at day 60 was 54·4% in the CRRT group and 46·5% in the IHD group (weighted HR 1·26, 95% CI 1·01–1·60). In a complementary analysis of less severely ill patients (SOFA score: 3–10), receiving IHD was associated with better day 60 survival compared to CRRT (weighted HR 1.82, 95% CI 1·01–3·28; p < 0.01). We found no evidence of a survival difference between the two RRT modalities in more severe patients.

Conclusion

Compared to IHD, CRRT as first modality seemed to convey no benefit in terms of survival or of kidney recovery and might even have been associated with less favorable outcome in patients with lesser severity of disease. A prospective randomized non-inferiority trial should be implemented to solve the persistent conundrum of the optimal RRT technique.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-022-03955-9.

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.

G-computation and doubly robust standardisation for continuous-time data: A comparison with inverse probability weighting

In time-to-event settings, g-computation and doubly robust estimators are based on discrete-time data. However, many biological processes are evolving continuously over time. In this paper, we extend the g-computation and the doubly robust standardisation procedures to a continuous-time context. We compare their performance to the well-known inverse-probability-weighting estimator for the estimation of the hazard ratio and restricted mean survival times difference, using a simulation study. Under a correct model specification, all methods are unbiased, but g-computation and the doubly robust standardisation are more efficient than inverse-probability-weighting. We also analyse two real-world datasets to illustrate the practical implementation of these approaches. We have updated the R package RISCA to facilitate the use of these methods and their dissemination.

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.

Overall Survival of Patients Receiving Cisplatin or Carboplatin for Primary Metastatic Urothelial Carcinoma of the Bladder: A Contemporary Dutch Nationwide Cohort Study

BACKGROUND

Cisplatin is preferred to carboplatin when treating metastatic urothelial carcinoma of the bladder (mUCB), despite its greater toxicity. Randomised studies underpinning this have been performed in noncontemporary populations with limitations in sample sizes and analyses, affecting their validity in current clinical practice.

OBJECTIVE

To estimate overall survival (OS) and assess the benefit of cisplatin-based regimens over carboplatin-based regimens in a contemporary cohort of patients with mUCB.

DESIGN, SETTING, AND PARTICIPANTS

A nationwide retrospective cohort study was conducted in patients diagnosed with de novo mUCB in the Netherlands between 2016 and 2019, who underwent first-line treatment with cisplatin- or carboplatin-based chemotherapy, based on the data from the Netherlands Cancer Registry.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

A propensity model for receiving cisplatin-based chemotherapy based on age, sex, age-adjusted Charlson Comorbidity Index, renal function, performance status, serum haemoglobin, and the presence of visceral and bone metastases was used to produce inverse probability weighting (IPW) per patient. Unadjusted and IPW-adjusted Kaplan-Meier OS curves of both chemotherapy groups were compared by restricted mean survival time (RMST).

RESULTS AND LIMITATIONS

Of the 1041 patients with mUCB, 359 received either cisplatin (n = 170; 47%) or carboplatin (n = 189; 53%) as first line. The cisplatin group was younger, had fewer comorbidities, and had better performance status and renal function. The median OS in the cisplatin and carboplatin groups was 13.1 and 11.5 mo, respectively. After IPW adjustment, prognostic factors were balanced between the two chemotherapy groups (standardised differences <0.1), and differences in RMST were <2.0 mo and not statistically significant up to 24 mo.

CONCLUSIONS

After accounting for all known prognostic factors, we found no significant survival benefit for cisplatin over carboplatin as first-line chemotherapy in mUCB.

PATIENT SUMMARY

In this study, we compared the survival benefits of cisplatin- and carboplatin-based chemotherapy for patients with metastatic bladder cancer.

Is a higher altitude associated with shorter survival among at-risk neonates?

INTRODUCTION

We hypothesize that high altitudes could have an adverse effect on neonatal health outcomes, especially among at-risk neonates. The current study aims to assess the association between higher altitudes on survival time among at-risk neonates.

METHODS

Retrospective survival analysis. Setting: Ecuadorian neonates who died at ≤28 days of life. Patients: We analyzed the nationwide dataset of neonatal deaths from the Surveillance System of Neonatal Mortality of the Ministry of Public Health of Ecuador, registered from 126 public and private health care facilities, between January 2014 to September 2017. Main outcome measures: We retrospectively reviewed 3016 patients. We performed a survival analysis by setting the survival time in days as the primary outcome and fixed and mixed-effects Cox proportional hazards models to estimate hazard ratios (HR) for each altitude stratum of each one of the health care facilities in which those neonates were attended, adjusting by individual variables (i.e., birth weight, gestational age at birth, Apgar scale at 5 minutes, and comorbidities); and contextual variables (i.e., administrative planning areas, type of health care facility, and level of care).

RESULTS

Altitudes of health care facilities ranging from 80 to <2500 m, 2500 to <2750m, and ≥2750 m were associated respectively with 20% (95% CI: 1% to 44%), 32% (95% CI:<1% to 79%) and 37% (95% CI: 8% to 75%) increased HR; compared with altitudes at <80 m.

CONCLUSION

Higher altitudes are independently associated with shorter survival time, as measured by days among at-risk neonates. Altitude should be considered when assessing the risk of having negative health outcomes during neonatal period.

Treatment effect measures for culture conversion endpoints in phase IIb tuberculosis treatment trials

Phase IIb trials of tuberculosis therapy rely on early biomarkers of treatment effect. Despite limited predictive ability for clinical outcomes, culture conversion, the event in which an individual previously culture positive for Mycobacterium tuberculosis yields a negative culture after initiating treatment, is a commonly used endpoint. Lack of consensus on how to define the outcome and corresponding measure of treatment effect complicates interpretation and limits between-trial comparisons. We review common analytic approaches to measuring treatment effect and introduce difference in restricted mean survival times as an alternative to identify faster times to culture conversion and express magnitude of effect on the time scale. Findings from the PanACEA MAMSTB trial are reanalyzed as an illustrative example. In a systematic review we demonstrate variability in analytic approaches, sampling strategies, and outcome definitions in phase IIb tuberculosis trials. Harmonization would allow for larger meta-analyses, and may help expedite advancement of new TB therapeutics.

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.

Does time heal all wounds? Life course associations between child welfare involvement and mortality in prospective cohorts from Sweden and Britain

Child welfare involvement reflects childhood adversity and is associated with increased adult mortality, but it remains unclear how this association changes over the life course. Drawing on the Stockholm Birth Cohort Multigenerational Study (Sweden) and the National Childhood Development Study (Great Britain) this study examines whether inequalities within these cohorts diverge or converge. Involvement with child welfare services (ICWS) is divided into two levels ('child welfare contact' and 'out-of-home care'). For each cohort, we quantify absolute health inequalities as differences in cumulative probabilities of death (18-58 years) and temporary life expectancy; and relative inequalities as hazard ratios in ten-year intervals and ratios of lifetime lost. Persistently, ICWS was associated with premature mortality. The strength of the association varied by age, sex and level of ICWS. Consistently across both countries, the most robust relationship was between out-of-home care and mortality, with statistically significant age-specific hazard ratios ranging between 1.8 and 3.4 for males and 1.8-2.1 for females. Child welfare contact that did not result in out-of-home placement showed less consistent results. Among females the mortality gap developed later compared to males. Estimates attenuate after controlling for family socioeconomic and other background variables but patterns remain intact. Our results show that absolute inequalities widen with increasing age, while relative inequalities might peak in early adulthood and then stabilize in midlife. The relative disadvantage among looked-after children in early adulthood is heightened by overall low rates of mortality at this age. Absolute inequality increases with age, highlighting the weight of the accumulation of disadvantage in mortality over time. The bulk of excess deaths that could be attributed to ICWS occurs from midlife onwards. Mechanisms that uphold the disadvantage after childhood experiences require further exploration. This study highlights that the association between out-of-home care and premature mortality seems to transcend welfare systems.

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.

Treatment effect of radiofrequency ablation versus liver transplantation and surgical resection for hepatocellular carcinoma within Milan criteria: a population-based study

OBJECTIVES

Restricted mean survival time (RMST) has been increasingly used to assess the treatment effect. We aimed to evaluate a treatment effect of radiofrequency ablation (RFA) versus liver transplantation (LT) and surgical resection (SR) for hepatocellular carcinoma (HCC) within Milan criteria by using an adjusted RMST.

METHODS

A total of 7,218 HCC patients (RFA, 3,327; LT, 2,332; SR 1,523) within Milan criteria were eligible for this retrospectively study. The RMST using inverse probability of treatment weighting (IPTW) adjustment were applied to estimate the treatment effect between RFA and LT, RFA, and SR groups.

RESULTS

The 3-, 5-, and 10-year IPTW-adjusted difference in RMST of OS for LT over RFA were + 4.5, + 12.4, and + 36.3 months, respectively. For SR versus RFA group, the survival benefit was + 2.3, + 6.1, and + 15.8 months at 3, 5, and 10 years, respectively. But the incremental survival benefit of SR over RFA was only half than that of LT over RFA. In the subgroup of solitary tumor ≤ 2 cm, the adjusted RMST of RFA versus SR was comparable with no statistical differences. Beyond that, in comparison with RFA, a notably greater efficacy of LT and SR was consistently across all subgroups with solitary HCC > 2.0 cm, AFP positive or negative, and fibrosis score 0-4 or 5-6.

CONCLUSIONS

RMST provides a measure of absolute survival benefit at a specific time point. Using IPTW-adjusted RMST, we showed that the incremental survival benefit of SR over RFA was about half than that of LT over RFA.

KEY POINTS

• The restricted mean survival time offers an intuitive, clinically meaningful interpretation to quantify the treatment effect than the hazard ratio. • Liver transplantation and surgical resection provided better overall survival compared to radiofrequency ablation for HCC patients within Milan criteria, but RFA and SR provide equivalent long-term overall survival for solitary HCC ≤ 2 cm. • The incremental survival benefit of surgical resection over radiofrequency ablation was only half than that of liver transplantation over radiofrequency ablation.

Veridical Causal Inference using Propensity Score Methods for Comparative Effectiveness Research with Medical Claims

Medical insurance claims are becoming increasingly common data sources to answer a variety of questions in biomedical research. Although comprehensive in terms of longitudinal characterization of disease development and progression for a potentially large number of patients, population-based inference using these datasets require thoughtful modifications to sample selection and analytic strategies relative to other types of studies. Along with complex selection bias and missing data issues, claims-based studies are purely observational, which limits effective understanding and characterization of the treatment differences between groups being compared. All these issues contribute to a crisis in reproducibility and replication of comparative findings using medical claims. This paper offers practical guidance to the analytical process, demonstrates methods for estimating causal treatment effects with propensity score methods for several types of outcomes common to such studies, such as binary, count, time to event and longitudinally-varying measures, and also aims to increase transparency and reproducibility of reporting of results from these investigations. We provide an online version of the paper with readily implementable code for the entire analysis pipeline to serve as a guided tutorial for practitioners. The online version can be accessed at https://rydaro.github.io/. The analytic pipeline is illustrated using a sub-cohort of patients with advanced prostate cancer from the large Clinformatics TM Data Mart Database (OptumInsight, Eden Prairie, Minnesota), consisting of 73 million distinct private payer insurees from 2001-2016.

Trends in excess mortality associated with atrial fibrillation over 45 years (Framingham Heart Study): community based cohort study

OBJECTIVE

To assess temporal trends in the association between newly diagnosed atrial fibrillation and death.

DESIGN

Community based cohort study.

SETTING

Framingham Heart Study cohort, in 1972-85, 1986-2000, and 2001-15 (periods 1-3, respectively), in Framingham, MA, USA.

PARTICIPANTS

Participants with no atrial fibrillation, aged 45-95 in each time period, and identified with newly diagnosed atrial fibrillation (or atrial flutter) during each time period.

MAIN OUTCOME MEASURES

The main outcome was all cause mortality. Hazard ratios for the association between time varying atrial fibrillation and all cause mortality were calculated with adjustment for time varying confounding factors. The difference in restricted mean survival times, adjusted for confounders, between participants with atrial fibrillation and matched referents at 10 years after a diagnosis of atrial fibrillation was estimated. Meta-regression was used to test for linear trends in hazard ratios and restricted mean survival times over the different time periods.

RESULTS

5671 participants were selected in time period 1, 6177 in period 2, and 6174 in period 3. Adjusted hazard ratios for all cause mortality between participants with and without atrial fibrillation were 1.9 (95% confidence interval 1.7 to 2.2) in time period 1, 1.4 (1.3 to 1.6) in period 2, and 1.7 (1.5 to 2.0) in period 3 (P_trend=0.70). Ten years after diagnosis of atrial fibrillation, the adjusted difference in restricted mean survival times between participants with atrial fibrillation and matched referents decreased by 31%, from -2.9 years (95% confidence interval -3.2 to -2.5) in period 1, to -2.1 years (-2.4 to -1.8) in period 2, to -2.0 years (-2.3 to -1.7) in period 3 (P_trend=0.03).

CONCLUSIONS

No evidence of a temporal trend in hazard ratios for the association between atrial fibrillation and all cause mortality was found. The mean number of life years lost to atrial fibrillation at 10 years had improved significantly, but a two year gap compared with individuals without atrial fibrillation still remained.

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.

Restricted mean survival time as a summary measure of time-to-event outcome

Many clinical research studies evaluate a time-to-event outcome, illustrate survival functions, and conventionally report estimated hazard ratios to express the magnitude of the treatment effect when comparing between groups. However, it may not be straightforward to interpret the hazard ratio clinically and statistically when the proportional hazards assumption is invalid. In some recent papers published in clinical journals, the use of restricted mean survival time (RMST) or τ-year mean survival time is discussed as one of the alternative summary measures for the time-to-event outcome. The RMST is defined as the expected value of time to event limited to a specific time point corresponding to the area under the survival curve up to the specific time point. This article summarizes the necessary information to conduct statistical analysis using the RMST, including the definition and statistical properties of the RMST, adjusted analysis methods, sample size calculation, information fraction for the RMST difference, and clinical and statistical meaning and interpretation. Additionally, we discuss how to set the specific time point to define the RMST from two main points of view. We also provide developed SAS codes to determine the sample size required to detect an expected RMST difference with appropriate power and reconstruct individual survival data to estimate an RMST reference value from a reported survival curve.