Predicting the restricted mean event time with the subject's baseline covariates in survival analysis

Time- vs Step-Based Physical Activity Metrics for Health

Importance

Current US physical activity (PA) guidelines prescribe moderate to vigorous PA (MVPA) time of at least 150 minutes per week for health. An analogous step-based recommendation has not been issued due to insufficient evidence.

Objective

To examine the associations of MVPA time and step counts with all-cause mortality and cardiovascular disease (CVD).

Design, Setting, and Participants

This cohort study analyzed data from an ongoing follow-up study of surviving participants of the Women's Health Study, a randomized clinical trial conducted from 1992 to 2004 in the US to evaluate use of low-dose aspirin and vitamin E for preventing cancer and CVD. Participants were 62 years or older who were free from CVD and cancer, completed annual questionnaires, and agreed to measure their PA with an accelerometer as part of a 2011-2015 ancillary study. Participants were followed up through December 31, 2022.

Exposures

Time spent in MVPA and step counts, measured with an accelerometer for 7 consecutive days.

Main Outcomes and Measures

The associations of MVPA time and step counts with all-cause mortality and CVD (composite of myocardial infarction, stroke, and CVD mortality) adjusted for confounders. Cox proportional hazards regression models, restricted mean survival time differences, and area under the receiver operating characteristic curve (AUC) were used to evaluate the associations.

Results

A total of 14 399 women (mean [SD] age, 71.8 [5.6] years) were included. The median (IQR) MVPA time and step counts were 62 (20-149) minutes per week and 5183 (3691-7001) steps per day, respectively. During a median (IQR) follow-up of 9.0 (8.0-9.9) years, the hazard ratios (HR) per SD for all-cause mortality were 0.82 (95% CI, 0.75-0.90) for MVPA time and 0.74 (95% CI, 0.69-0.80) for step counts. Greater MVPA time and step counts (top 3 quartiles vs bottom quartile) were associated with a longer period free from death: 2.22 (95% CI, 1.58-2.85) months and 2.36 (95% CI, 1.73-2.99) months at 9 years follow-up, respectively. The AUCs for all-cause mortality from MVPA time and step counts were similar: 0.55 (95% CI, 0.52-0.57) for both metrics. Similar associations of these 2 metrics with CVD were observed.

Conclusion and Relevance

Results of this study suggest that among females 62 years or older, MVPA time and step counts were qualitatively similar in their associations with all-cause mortality and CVD. Step count-based goals should be considered for future guidelines along with time-based goals, allowing for the accommodation of personal preferences.

Scaling and interpreting treatment effects in clinical trials using restricted mean survival time

BACKGROUND

Restricted mean survival time is the expected duration of survival up to a chosen time of restriction τ . For comparison studies, the difference in restricted mean survival times between two groups provides a summary measure of the treatment effect that is free of assumptions regarding the relative shape of the two survival curves, such as proportional hazards. However, it can be difficult to judge the magnitude of the effect from a comparison of restricted means due to the truncation of observation at time τ .

METHODS

In this article, we describe additional ways of expressing the treatment effect based on restricted means that can be helpful in this regard. These include the ratio of restricted means, the ratio of life-years (or time) lost, and the average integrated difference between the survival curves, equal to the difference in restricted means divided by τ . These alternative metrics are straightforward to calculate and provide a means for scaling the effect size as an aid to interpretation. Examples from two randomized, multicenter clinical trials in prostate cancer, NRG/RTOG 0521 and NRG/RTOG 0534, with primary endpoints of overall survival and biochemical/radiological progression-free survival, respectively, are presented to illustrate the ideas.

RESULTS

The four effect measures (restricted mean survival time difference, restricted mean survival time ratio, time lost ratio, and average survival rate difference) were 0.45 years, 1.05, 0.81, and 0.038 for RTOG 0521 and 1.36 years, 1.17, 0.56, and 0.12 for RTOG 0534 with τ  = 12 and 11 years, respectively. Thus, for example, the 0.45-year difference in the first trial translates into a 19% reduction in time lost and a 3.8% average absolute difference between the survival curves over the 12-year horizon, a modest effect size, whereas the 1.36-year difference in the second trial corresponds to a 44% reduction in time lost and a 12% absolute survival difference, a rather large effect.

CONCLUSIONS

In addition to the difference in restricted mean survival times, these alternative measures can be helpful in determining whether the magnitude of the treatment effect is clinically meaningful.

Rationale and design of the Early valve replacement in severe ASYmptomatic Aortic Stenosis Trial

BACKGROUND

Aortic valve replacement in asymptomatic severe aortic stenosis is controversial. The Early valve replacement in severe ASYmptomatic Aortic Stenosis (EASY-AS) trial aims to determine whether early aortic valve replacement improves clinical outcomes, quality of life and cost-effectiveness compared to a guideline recommended strategy of 'watchful waiting'.

METHODS

In a pragmatic international, open parallel group randomized controlled trial (NCT04204915), 2844 patients with severe aortic stenosis will be randomized 1:1 to either a strategy of early (surgical or transcatheter) aortic valve replacement or aortic valve replacement only if symptoms or impaired left ventricular function develop. Exclusion criteria include other severe valvular disease, planned cardiac surgery, ejection fraction <50%, previous aortic valve replacement or life expectancy <2 years. The primary outcome is a composite of cardiovascular mortality or heart failure hospitalization. The primary analysis will be undertaken when 663 primary events have accrued, providing 90% power to detect a reduction in the primary endpoint from 27.7% to 21.6% (hazard ratio 0.75). Secondary endpoints include disability-free survival, days alive and out of hospital, major adverse cardiovascular events and quality of life.

RESULTS

Recruitment commenced in March 2020 and is open in the UK, Australia, New Zealand and Serbia. Feasibility requirements were met in July 2022, and the main phase opened in October 2022, with additional international centers in set-up.

CONCLUSIONS

The EASY-AS trial will establish whether a strategy of early aortic valve replacement in asymptomatic patients with severe aortic stenosis reduces cardiovascular mortality or heart failure hospitalization and improves other important outcomes.

Do machine learning methods lead to similar individualized treatment rules? A comparison study on real data

Identifying patients who benefit from a treatment is a key aspect of personalized medicine, which allows the development of individualized treatment rules (ITRs). Many machine learning methods have been proposed to create such rules. However, to what extent the methods lead to similar ITRs, that is, recommending the same treatment for the same individuals is unclear. In this work, we compared 22 of the most common approaches in two randomized control trials. Two classes of methods can be distinguished. The first class of methods relies on predicting individualized treatment effects from which an ITR is derived by recommending the treatment evaluated to the individuals with a predicted benefit. In the second class, methods directly estimate the ITR without estimating individualized treatment effects. For each trial, the performance of ITRs was assessed by various metrics, and the pairwise agreement between all ITRs was also calculated. Results showed that the ITRs obtained via the different methods generally had considerable disagreements regarding the patients to be treated. A better concordance was found among akin methods. Overall, when evaluating the performance of ITRs in a validation sample, all methods produced ITRs with limited performance, suggesting a high potential for optimism. For non-parametric methods, this optimism was likely due to overfitting. The different methods do not lead to similar ITRs and are therefore not interchangeable. The choice of the method strongly influences for which patients a certain treatment is recommended, drawing some concerns about their practical use.

Visualizing hypothesis tests in survival analysis under anticipated delayed effects

What can be considered an appropriate statistical method for the primary analysis of a randomized clinical trial (RCT) with a time-to-event endpoint when we anticipate non-proportional hazards owing to a delayed effect? This question has been the subject of much recent debate. The standard approach is a log-rank test and/or a Cox proportional hazards model. Alternative methods have been explored in the statistical literature, such as weighted log-rank tests and tests based on the Restricted Mean Survival Time (RMST). While weighted log-rank tests can achieve high power compared to the standard log-rank test, some choices of weights may lead to type-I error inflation under particular conditions. In addition, they are not linked to a mathematically unambiguous summary measure. Test statistics based on the RMST, on the other hand, allow one to investigate the average difference between two survival curves up to a pre-specified time pointτ $$ \tau $$ -a mathematically unambiguous summary measure. However, by emphasizing differences prior toτ $$ \tau $$ , such test statistics may not fully capture the benefit of a new treatment in terms of long-term survival. In this article, we introduce a graphical approach for direct comparison of weighted log-rank tests and tests based on the RMST. This new perspective allows a more informed choice of the analysis method, going beyond power and type I error comparison.

Where you live matters: Area deprivation predicts poor survival and liver transplant waitlisting

Social determinants of health (SDOH) are important predictors of poor clinical outcomes in chronic diseases, but their associations among the general cirrhosis population and liver transplantation (LT) are limited. We conducted a retrospective, multiinstitutional analysis of adult (≥18-years-old) patients with cirrhosis in metropolitan Chicago to determine the associations of poor neighborhood-level SDOH on decompensation complications, mortality, and LT waitlisting. Area deprivation index and covariates extracted from the American Census Survey were aspects of SDOH that were investigated. Among 15 101 patients with cirrhosis, the mean age was 57.2 years; 6414 (42.5%) were women, 6589 (43.6%) were non-Hispanic White, 3652 (24.2%) were non-Hispanic Black, and 2662 (17.6%) were Hispanic. Each quintile increase in area deprivation was associated with poor outcomes in decompensation (sHR [subdistribution hazard ratio] 1.07; 95% CI 1.05-1.10; P < .001), waitlisting (sHR 0.72; 95% CI 0.67-0.76; P < .001), and all-cause mortality (sHR 1.09; 95% CI 1.06-1.12; P < .001). Domains of SDOH associated with a lower likelihood of waitlisting and survival included low income, low education, poor household conditions, and social support (P < .001). Overall, patients with cirrhosis residing in poor neighborhood-level SDOH had higher decompensation, and mortality, and were less likely to be waitlisted for LT. Further exploration of structural barriers toward LT or optimizing health outcomes is warranted.

Association of plant-based diets with total and cause-specific mortality across socioeconomic deprivation level: a large prospective cohort

PURPOSE

Current evidence on the association between plant-based diet indices (PDIs) and mortality is inconsistent. We aimed to investigate the association of PDIs with all-cause and cause-specific mortality and to examine whether such associations were modified by socioeconomic deprivation level.

METHODS

A total of 189,003 UK Biobank participants with at least one 24-h dietary assessment were included. All food items were categorised into three groups, including healthy plant foods, less healthy plant foods, and animal foods. Three PDIs, including the overall PDI (positive scores for all plant-based food intake and inverse scores for animal-based foods), the healthful PDI (hPDI) (positive scores only for healthy plant food intake and inverse scores for others), and the unhealthful PDI (uPDI) (positive scores only for less healthy plant food intake and inverse scores for others), were calculated according to the quantities of each food subgroup in three categories. The Townsend deprivation index was used as the indicator of socioeconomic deprivation level. Cox proportional hazard models were used to estimate the hazard ratios (HRs) of PDIs for all-cause and cause-specific mortality. The modification effects of socioeconomic deprivation levels on these associations were evaluated.

RESULTS

During a median follow-up of 9.6 years, 9335 deaths were documented. Compared with the lowest quintile, the highest quintile of overall PDI was associated with adjusted HRs of 0.87 (95% CI 0.81-0.93) for all-cause mortality and 0.77 (0.66-0.91) for cardiovascular mortality. Compared with the lowest quintile, the highest quintile of hPDI was associated with lower risks of all-cause mortality (0.92, 0.86-0.98), and death caused by respiratory disease (0.63, 0.47-0.86), neurological disease (0.65, 0.48-0.88), and cancer (0.90, 0.82-0.99). Compared with the lowest quintile, the highest quintile of uPDI was associated with an HR of 1.29 (1.20-1.38) for all-cause mortality, 1.95 (1.40-2.73) for neurological mortality, 1.54 (1.13-2.09) for respiratory mortality, and 1.16 (1.06-1.27) for cancer mortality. The magnitudes of associations of hPDI and uPDI with mortality were larger in the most socioeconomically deprived participants (the highest tertile) than in the less deprived ones (p-values for interaction were 0.039 and 0.001, respectively).

CONCLUSIONS

This study showed that having a high overall PDI and hPDI were related to a reduced risk of death, while the uPDI was linked to a higher risk of death. Sticking to a healthy plant-based diet may help decrease mortality risks across socioeconomic deprivation levels, especially for those who are the most socioeconomically deprived.

Regression models for average hazard

Limitations of using the traditional Cox's hazard ratio for summarizing the magnitude of the treatment effect on time-to-event outcomes have been widely discussed, and alternative measures that do not have such limitations are gaining attention. One of the alternative methods recently proposed, in a simple 2-sample comparison setting, uses the average hazard with survival weight (AH), which can be interpreted as the general censoring-free person-time incidence rate on a given time window. In this paper, we propose a new regression analysis approach for the AH with a truncation time τ. We investigate 3 versions of AH regression analysis, assuming (1) independent censoring, (2) group-specific censoring, and (3) covariate-dependent censoring. The proposed AH regression methods are closely related to robust Poisson regression. While the new approach needs to require a truncation time τ explicitly, it can be more robust than Poisson regression in the presence of censoring. With the AH regression approach, one can summarize the between-group treatment difference in both absolute difference and relative terms, adjusting for covariates that are associated with the outcome. This property will increase the likelihood that the treatment effect magnitude is correctly interpreted. The AH regression approach can be a useful alternative to the traditional Cox's hazard ratio approach for estimating and reporting the magnitude of the treatment effect on time-to-event outcomes.

Comparison of baseline covariate adjustment methods for restricted mean survival time

The restricted mean survival time provides a straightforward clinical measure that dispenses with the need for proportional hazards assumptions. We focus on two strategies to directly model the survival time and adjust covariates. Firstly, pseudo-survival time is calculated for each subject using a leave-one-out approach, followed by a model analysis that adjusts for covariates using all pseudo-values. This method is used to reflect information of censored subjects in the model analysis. The second approach adjusts for covariates for those subjects with observed time-to-event while incorporating censored subjects using inverse probability of censoring weighting (IPCW). This paper evaluates these methods' power to detect group differences through computer simulations. We find the interpretation of pseudo-values challenging with the pseudo-survival time method and confirm that pseudo-survival times deviate from actual data in a primary biliary cholangitis clinical trial, mainly due to extensive censoring. Simulations reveal that the IPCW method is more robust, unaffected by the balance of censors, whereas pseudo-survival time is influenced by this balance. The IPCW method retains a nominal significance level for the type-1 error rate, even amidst group differences concerning censor incidence rates and covariates. Our study concludes that IPCW and pseudo-survival time methods differ significantly in handling censored data, impacting parameter estimations. Our findings suggest that the IPCW method provides more robust results than pseudo-survival time and is recommended, even when censor probabilities vary between treatment groups. However, pseudo-survival time remains a suitable choice when censoring probabilities are balanced.

Survival benefit of living donor kidney transplantation in patients on hemodialysis

BACKGROUND

Donors bravely donate their kidneys because they expect that living donor kidney transplantation (LKT) confers benefits to recipients. However, the magnitude of the survival benefit of LKT is uncertain.

METHODS

This prospective cohort study used two Japanese nationwide databases for dialysis and kidney transplantation and included 862 LKT recipients and 285,242 hemodialysis (HD) patients in the main model and 5299 LKT recipients and 151,074 HD patients in the supplementary model. We employed time-dependent model in the main model and assessed the hazard ratio and the difference in the restricted mean survival time (RMST) between LKT recipients and HD patients. In the main analysis of the main model (LKT, N = 675; HD, N = 675), we matched LKT recipients with HD patients by age, sex, dialysis vintage, and cause of renal failure and excluded HD patients with dementia or performance status grades 2, 3, or 4.

RESULTS

The median observational period was 8.00 (IQR 3.58-8.00) years. LKT was significantly associated with a lower risk of mortality (hazard ratios (95% confidence interval (CI)), 0.50 (0.35-0.72)) and an increase in life expectancy (7-year RMST differences (95% CI), 0.48 (0.35-0.60) years) compared with HD. In subgroup analysis, the survival benefit of LKT was greater in female patients than in male patients in the Cox model; whereas older patients gained longer life expectancy compared with younger patients.

CONCLUSIONS

LKT was associated with better survival benefits than HD, and the estimated increase in life expectancy was 0.48 years for 7 years.

Sample size calculation for multi-arm parallel design with restricted mean survival time

With the recent advances in oncology treatment, restricted mean survival time (RMST) is increasingly being used to replace the routine approach based on hazard ratios in randomized controlled trials for time-to-event outcomes. While RMST has been widely applied in single-arm and two-arm designs, challenges still exist in comparing RMST in multi-arm trials with three or more groups. In particular, it is unclear in the literature how to compare more than one intervention simultaneously or perform multiple testing based on RMST, and sample size determination is a major obstacle to its penetration to practice. In this paper, we propose a novel method of designing multi-arm clinical trials with right-censored survival endpoint based on RMST that can be applied in both phase II/III settings using a global χ 2 test as well as a modeling-based multiple comparison procedure. The framework provides a closed-form sample size formula built upon a multi-arm global test and a sample size determination procedure based on multiple-comparison in the phase II dose-finding study. The proposed method enjoys strong robustness and flexibility as it requires less a priori set-up than conventional work, and obtains a smaller sample size while achieving the target power. In the assessment of sample size, we also incorporate practical considerations, including the presence of non-proportional hazards and staggered patient entry. We evaluate the validity of our method through simulation studies under various scenarios. Finally, we demonstrate the accuracy and stability of our method by implementing it in the design of two real clinical trial examples.

Semiparametric estimation of restricted mean survival time as a function of restriction time

The restricted mean survival time (RMST) is an appealing measurement in clinical or epidemiological studies with censored survival outcome and receives a lot of attention in the past decades. It provides a useful alternative to the Cox model for evaluating the covariate effect on survival time. The covariate effect on RMST usually varies with the restriction time. However, existing methods cannot address this problem properly. In this article, we propose a semiparametric framework that directly models RMST as a function of the restriction time. Our proposed model adopts a widely-used proportional form, enabling the estimation of RMST predictions across an interval using a unified model. Furthermore, the covariate effect for multiple restriction time points can be derived simultaneously. We develop estimators based on estimating equations theories and establish the asymptotic properties of the proposed estimators. The finite sample properties of the estimators are evaluated through extensive simulation studies. We further illustrate the application of our proposed method through the analysis of two real data examples. Supplementary Material are available online.

Analysis of dynamic restricted mean survival time based on pseudo-observations

In clinical follow-up studies with a time-to-event end point, the difference in the restricted mean survival time (RMST) is a suitable substitute for the hazard ratio (HR). However, the RMST only measures the survival of patients over a period of time from the baseline and cannot reflect changes in life expectancy over time. Based on the RMST, we study the conditional restricted mean survival time (cRMST) by estimating life expectancy in the future according to the time that patients have survived, reflecting the dynamic survival status of patients during follow-up. In this paper, we introduce the estimation method of cRMST based on pseudo-observations, the statistical inference concerning the difference between two cRMSTs (cRMSTd), and the establishment of the robust dynamic prediction model using the landmark method. Simulation studies are conducted to evaluate the statistical properties of these methods. The results indicate that the estimation of the cRMST is accurate, and the dynamic RMST model has high accuracy in coefficient estimation and good predictive performance. In addition, an example of patients with chronic kidney disease who received renal transplantations is employed to illustrate that the dynamic RMST model can predict patients' expected survival times from any prediction time, considering the time-dependent covariates and time-varying effects of covariates.

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.

Covariate-adjusted value-guided subgroup identification via boosting

It is widely recognized that treatment effects could differ across subgroups of patients. Subgroup analysis, which assesses such heterogeneity, provides valuable information in developing personalized therapies. There has been extensive research developing novel statistical methods for subgroup identification. The recent contribution is a value-guided subgroup identification method that directly maximizes treatment benefit at the subgroup level for survival outcome, rather than relying on individual treatment effect estimation. In this paper, we first completed this framework by illustrating its application to continuous and binary outcomes. More importantly, we extended the original framework to account for the prognostic effects and named this new method Covariate-Adjusted Value-guided subgroup identification via boosting (CAVboost). The original method directly used the outcome to formulate the value function for subgroup identification. Since the outcome can further be decomposed as prognostic effects and treatment effects, specifying the prognostic effects as the covariates of a model for the outcome can single out the treatment effects and improve the power to detect them across subgroups. Our proposed CAVboost was based on this key idea. It used a covariate-adjusted treatment effect estimator, instead of the outcome itself, to formulate the value function for subgroup identification. CAVboost estimates the treatment effect by using covariates to account for the prognostic effects, which mimics the idea of using covariates in an ANCOVA estimator. We showed that CAVboost could effectively improve the subgroup identification capability for both continuous and binary outcomes.

Variable selection using inverse probability of censoring weighting

In this article, we propose two variable selection methods for adjusting the censoring information for survival times, such as the restricted mean survival time. To adjust for the influence of censoring, we consider an inverse probability of censoring weighted for subjects with events. We derive a least absolute shrinkage and selection operator (lasso)-type variable selection method, which considers an inverse weighting for of the squared losses, and an information criterion-type variable selection method, which applies an inverse weighting of the survival probability to the power of each density function in the likelihood function. We prove the consistency of the inverse probability of censoring weighted lasso estimator and the maximum inverse probability of censoring weighted likelihood estimator. The performance of the inverse probability of censoring weighted lasso and inverse probability of censoring weighted information criterion are evaluated via a simulation study with six scenarios, and then their variable selection ability is demonstrated using data from two clinical studies. The results confirm that inverse probability of censoring weighted lasso and the inverse probability of censoring weighted likelihood function produce good estimation accuracy and consistent variable selection. We conclude that our two proposed methods are useful variable selection tools for adjusting the censoring information for survival time analyses.

Restricted mean survival time to estimate an intervention effect in a cluster randomized trial

For time-to-event outcomes, the difference in restricted mean survival time is a measure of the intervention effect, an alternative to the hazard ratio, corresponding to the expected survival duration gain due to the intervention up to a predefined time t*. We extended two existing approaches of restricted mean survival time estimation for independent data to clustered data in the framework of cluster randomized trials: one based on the direct integration of Kaplan-Meier curves and the other based on pseudo-values regression. Then, we conducted a simulation study to assess and compare the statistical performance of the proposed methods, varying the number and size of clusters, the degree of clustering, and the magnitude of the intervention effect under proportional and non-proportional hazards assumption. We found that the extended methods well estimated the variance and controlled the type I error if there was a sufficient number of clusters (≥ 50) under both proportional and non-proportional hazards assumption. For cluster randomized trials with a limited number of clusters (< 50), a permutation test for pseudo-values regression was implemented and corrected the type I error. We also provided a procedure to estimate permutation-based confidence intervals which produced adequate coverage. All the extended methods performed similarly, but the pseudo-values regression offered the possibility to adjust for covariates. Finally, we illustrated each considered method with a cluster randomized trial evaluating the effectiveness of an asthma-control education program.

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.

Impact of Second-Line Combination Treatment for Type 2 Diabetes Mellitus on Disease Control: A Population-Based Cohort Study

BACKGROUND

Type 2 diabetes mellitus is a chronic disease affecting millions of people worldwide. Achieving and maintaining glycemic control is essential to prevent or delay complications and different strategies are available as second-line treatment options for patients with type 2 diabetes who do not achieve glycemic control with metformin monotherapy.

OBJECTIVE

The aim of this work is to describe the impact of initiating a combination treatment to reduce glycated hemoglobin in patients with type 2 diabetes with insufficient glycemic control.

METHODS

We included patients with a type 2 diabetes diagnosis between 2015 and 2020 at the Information System for Research in Primary Care (SIDIAP) database in Catalonia, Spain. The primary outcome was the time to glycated hemoglobin control (≤ 7%) during the first 720 days, expressed as the restricted mean survival time. Adjusted differences of the restricted mean survival time were compared to analyze the performance of each treatment versus the combination with a sulfonylurea. Adherence was calculated as the medication possession ratio using an algorithm to model treatment exposure.

RESULTS

A total of 28,425 patients were analyzed. The most frequent combinations were those with sulfonylureas and dipeptidyl peptidase-4 inhibitors. All treatments reduced glycated hemoglobin and the restricted mean survival time for the sulfonylurea treatment was 455 (451-459) days although combinations with glucagon-like peptide-1 and insulin reached glycemic control earlier, - 126 days (- 152 to - 100, p < 0.001) and - 69 days (- 88 to - 50, p < 0.001), respectively. Adherence was high in all groups apart from the insulin combination and had a significant effect in reducing glycated hemoglobin except in sodium-glucose cotransporter type 2 inhibitors and insulin. Glucagon-like peptide-1 and sodium-glucose cotransporter type 2 inhibitors showed significant reductions in weight.

CONCLUSIONS

Patients achieved the glycated hemoglobin goal with second-line treatments. Glucagon-like peptide-1 and insulin combinations achieved the goal earlier than sulfonylurea combinations. Adherence significantly reduced the time to glycated hemoglobin control except for the combination with sodium-glucose cotransporter type 2 inhibitors.

Post-COVID symptom profiles and duration in a global convalescent COVID-19 observational cohort: Correlations with demographics, medical history, acute COVID-19 severity and global region

Background

Post-COVID conditions are characterised by persistent symptoms that negatively impact quality of life after SARS-CoV-2 diagnosis. While post-COVID risk factors and symptoms have been extensively described in localised regions, especially in the global north, post-COVID conditions remain poorly understood globally. The global, observational cohort study HVTN 405/HPTN 1901 characterises the convalescent course of SARS-CoV-2 infection among adults in North and South America and Africa.

Methods

We categorised the cohort by infection severity (asymptomatic, symptomatic, no oxygen requirement (NOR), non-invasive oxygen requirement (NIOR), invasive oxygen requirement (IOR)). We applied a regression model to assess correlations of demographics, co-morbidities, disease severity, and concomitant medications with COVID-19 symptom persistence and duration across global regions.

Results

We enrolled 759 participants from Botswana, Malawi, South Africa, Zambia, Zimbabwe, Peru, and the USA a median of 51 (interquartile range (IQR) = 35-66) days post-diagnosis, from May 2020 to March 2021. 53.8% were female, 69.8% were 18-55 years old (median (md) = 44 years old, IQR = 33-58). Comorbidities included obesity (42.8%), hypertension (24%), diabetes (14%), human immunodeficiency virus (HIV) infection (11.6%) and lung disease (7.5%). 76.2% were symptomatic (NOR = 47.4%; NIOR = 22.9%; IOR = 5.8%). Median COVID-19 duration among symptomatic participants was 20 days (IQR = 11-35); 43.4% reported symptoms after COVID-19 resolution, 33.6% reported symptoms ≥30 days, 9.9% reported symptoms ≥60 days. Symptom duration correlated with disease severity (P < 0.001, NIOR vs NOR; P = 0.003, IOR vs NOR), lung disease (P = 0.001), race (P < 0.05, non-Hispanic Black vs White), and global region (P < 0.001). Prolonged viral shedding correlated with persistent abdominal pain (odds ratio (OR) = 5.51, P < 0.05) and persistent diarrhoea (OR = 6.64, P < 0.01).

Conclusions

Post-COVID duration varied with infection severity, race, lung disease, and region. Better understanding post-COVID conditions, including regionally-diverse symptom profiles, may improve clinical assessment and management globally.

Registration

Clinicaltrials.gov (#NCT04403880).

Direct Oral Anticoagulants vs Low-Molecular-Weight Heparin and Recurrent VTE in Patients With Cancer: A Randomized Clinical Trial

Importance

In patients with cancer who have venous thromboembolism (VTE) events, long-term anticoagulation with low-molecular-weight heparin (LMWH) is recommended to prevent recurrent VTE. The effectiveness of a direct oral anticoagulant (DOAC) compared with LMWH for preventing recurrent VTE in patients with cancer is uncertain.

Objective

To evaluate DOACs, compared with LMWH, for preventing recurrent VTE and for rates of bleeding in patients with cancer following an initial VTE event.

Design, Setting, and Participants

Unblinded, comparative effectiveness, noninferiority randomized clinical trial conducted at 67 oncology practices in the US that enrolled 671 patients with cancer (any invasive solid tumor, lymphoma, multiple myeloma, or chronic lymphocytic leukemia) who had a new clinical or radiological diagnosis of VTE. Enrollment occurred from December 2016 to April 2020. Final follow-up was in November 2020.

Intervention

Participants were randomized in a 1:1 ratio to either a DOAC (n = 335) or LMWH (n = 336) and were followed up for 6 months or until death. Physicians and patients selected any DOAC or any LMWH (or fondaparinux) and physicians selected drug doses.

Main Outcomes and Measures

The primary outcome was the recurrent VTE rate at 6 months. Noninferiority of anticoagulation with a DOAC vs LMWH was defined by the upper limit of the 1-sided 95% CI for the difference of a DOAC relative to LMWH of less than 3% in the randomized cohort that received at least 1 dose of assigned treatment. The 6 prespecified secondary outcomes included major bleeding, which was assessed using a 2.5% noninferiority margin.

Results

Between December 2016 and April 2020, 671 participants were randomized and 638 (95%) completed the trial (median age, 64 years; 353 women [55%]). Among those randomized to a DOAC, 330 received at least 1 dose. Among those randomized to LMWH, 308 received at least 1 dose. Rates of recurrent VTE were 6.1% in the DOAC group and 8.8% in the LMWH group (difference, -2.7%; 1-sided 95% CI, -100% to 0.7%) consistent with the prespecified noninferiority criterion. Of 6 prespecified secondary outcomes, none were statistically significant. Major bleeding occurred in 5.2% of participants in the DOAC group and 5.6% in the LMWH group (difference, -0.4%; 1-sided 95% CI, -100% to 2.5%) and did not meet the noninferiority criterion. Severe adverse events occurred in 33.8% of participants in the DOAC group and 35.1% in the LMWH group. The most common serious adverse events were anemia and death.

Conclusions and Relevance

Among adults with cancer and VTE, DOACs were noninferior to LMWH for preventing recurrent VTE over 6-month follow-up. These findings support use of a DOAC to prevent recurrent VTE in patients with cancer.

Trial Registration

ClinicalTrials.gov Identifier: NCT02744092.

On resource allocation in health care: The case of concierge medicine

Resource allocation generally involves a tension between efficiency and equity, particularly in health care. The growth in exclusive physician arrangements using non-linear prices is leading to consumer segmentation with theoretically ambiguous welfare implications. We study concierge medicine, in which physicians only provide care to patients paying a retainer fee. We find limited evidence of selection based on health and stronger evidence of selection based on income. Using a matching strategy that leverages the staggered adoption of concierge medicine, we find large spending increases and no average mortality effects for patients impacted by the switch to concierge medicine.

Differences of survival benefits brought by various treatments in ovarian cancer patients with different tumor stages

PURPOSE

The current study aimed to explore the prognosis of ovarian cancer patients in different subgroup using three prognostic research indexes. The current study aimed to build a prognostic model for ovarian cancer patients.

METHODS

The study dataset was downloaded from Surveillance Epidemiology and End Results database. Accelerated Failure Time algorithm was used to construct a prognostic model for ovary cancer.

RESULTS

The mortality rate in the model group was 51.6% (9,314/18,056), while the mortality rate in the validation group was 52.1% (6,358/12,199). The current study constructed a prognostic model for ovarian cancer patients. The C indexes were 0.741 (95% confidence interval: 0.731-0.751) in model dataset and 0.738 (95% confidence interval: 0.726-0.750) in validation dataset. Brier score was 0.179 for model dataset and validation dataset. The C indexes were 0.741 (95% confidence interval: 0.733-0.749) in bootstrap internal validation dataset. Brier score was 0.178 for bootstrap internal validation dataset.

CONCLUSION

The current research indicated that there were significant differences in the survival benefits of treatments among ovarian cancer patients with different stages. The current research developed an individual mortality risk predictive system that could provide valuable predictive information for ovarian cancer patients.

Quantifying and Interpreting the Prediction Accuracy of Models for the Time of a Cardiovascular Event-Moving Beyond C Statistic: A Review

Importance

For personalized or stratified medicine, it is critical to establish a reliable and efficient prediction model for a clinical outcome of interest. The goal is to develop a parsimonious model with fewer predictors for broad future application without compromising predictability. A general approach is to construct various empirical models via individual patients' specific baseline characteristics/biomarkers and then evaluate their relative merits. When the outcome of interest is the timing of a cardiovascular event, a commonly used metric to assess the adequacy of the fitted models is based on C statistics. These measures quantify a model's ability to separate those who develop events earlier from those who develop them later or not at all (discrimination), but they do not measure how closely model estimates match observed outcomes (prediction accuracy). Metrics that provide clinically interpretable measures to quantify prediction accuracy are needed.

Observations

C statistics measure the concordance between the risk scores derived from the model and the observed event time observations. However, C statistics do not quantify the model prediction accuracy. The integrated Brier Score, which calculates the mean squared distance between the empirical cumulative event-free curve and its individual patient's counterparts, estimates the prediction accuracy, but it is not clinically intuitive. A simple alternative measure is the average distance between the observed and predicted event times over the entire study population. This metric directly quantifies the model prediction accuracy and has often been used to evaluate the goodness of fit of the assumed models in settings other than survival data. This time-scale measure is easier to interpret than the C statistics or the Brier score.

Conclusions and Relevance

This article enhances our understanding of the model selection/evaluation process with respect to prediction accuracy. A simple, intuitive measure for quantifying such accuracy beyond C statistics can improve the reliability and efficiency of the selected model for personalized and stratified medicine.

Standard Lymphadenectomy for Esophageal and Lung Cancer: Variability in the Number of Examined Lymph Nodes Among Pathologists and Its Survival Implication

AIM

We compared variability in number of examined lymph nodes between pathologists and analyzed survival implications in lung and esophageal cancer after standardized lymphadenectomy.

METHODS

Outcomes of 294 N2 dissected lung cancer patients and 132 2-field dissected esophageal cancer patients were retrospectively examined. The primary outcome was difference in reported lymph node count among pathologists. Secondary outcomes were overall and disease-specific survival related to this count and survival related to the 50% probability cut-off value of detecting metastasis based on the number of examined lymph nodes.

RESULTS

The median number of examined lymph nodes in lung cancer was 13 (IQR 9-17) and in esophageal cancer it was 22 (18-29). The pathologist with the highest median number of examined nodes had > 50% higher lymph node yield compared with the pathologist with the lowest median number of nodes in lung (15 vs. 9.5, p = 0.003), and esophageal cancer (28 vs. 17, p = 0.003). Survival in patients stratified by median reported lymph node count in both lung (adjusted RMST ratio < 14 vs. ≥ 14 lymph nodes 0.99, 95% CI 0.88-1.10; p = 0.810) and esophageal cancer (adjusted RMST ratio < 25 vs. ≥ 25 lymph nodes 0.95, 95% CI 0.79-1.15, p = 0.612) was similar. The cut-off value for 50% probability of detecting metastasis by number of examined lymph nodes in lung cancer was 15.7 and in esophageal cancer 21.8. When stratified by this cut-off, no survival differences were seen.

CONCLUSION

The quality of lymphadenectomy based on lymph node yield is susceptible to error due to detected variability between pathologists in the number of examined lymph nodes. This variability in yield did not have any survival effect after standardized lymphadenectomy.

Progression-free Survival After Second Line of Therapy for Metastatic Clear Cell Renal Cell Carcinoma in Patients Treated with First-line Immunotherapy Combinations

Immunotherapy (IO)-based combinations used to treat metastatic clear cell renal cell carcinoma (ccRCC) include dual immune checkpoint inhibition with ipilimumab and nivolumab (IO/IO) and several combinations of vascular endothelial growth factor receptor-targeting tyrosine kinase inhibitors (TKIs) with an immune checkpoint inhibitor (TKI/IO). IO/IO and TKI/IO approaches have not been compared directly, and it is unknown whether patients who do not respond to first-line IO/IO can salvage long-term survival by receiving a second-line TKI. Progression-free survival after second-line therapy (PFS-2) evaluates the ability to be salvaged by second-line therapy. We retrospectively evaluated 173 patients treated with first-line IO/IO or TKI/IO for metastatic ccRCC at Memorial Sloan Kettering Cancer Center and report PFS-2, overall survival, and response to second line of therapy (ORR2nd) for groups defined by first-line category. Although ORR2nd was significantly higher with IO/IO than with TKI/IO (47% vs 13%, p < 0.001), there was no significant difference in median PFS-2 for TKI/IO versus IO/IO (44 vs 23 mo, log-rank p = 0.1) or restricted mean survival time (RMST) for PFS-2 when adjusted for propensity score (33 vs 30 mo; difference 2.6 mo [95% confidence interval {CI}: -2.6, 7.9]; p = 0.3). There was also no significant difference in RMST for overall survival when adjusted for propensity score (38 vs 37 mo; group difference 1.0 mo [95% CI: -3.4, 5.5]; p = 0.7). These findings do not support a change in current utilization practices for IO/IO and TKI/IO treatment strategies for ccRCC. PATIENT SUMMARY: In cases of metastatic clear cell renal cell carcinoma, no significant difference was observed in progression-free survival after second line of therapy between patients receiving ipilimumab plus nivolumab and those receiving a combination of a tyrosine kinase inhibitor and an immune checkpoint inhibitor.

Selection of the optimal personalized treatment from multiple treatments with right-censored multivariate outcome measures

We propose a novel personalized concept for the optimal treatment selection for a situation where the response is a multivariate vector that could contain right-censored variables such as survival time. The proposed method can be applied with any number of treatments and outcome variables, under a broad set of models. Following a working semiparametric Single Index Model that relates covariates and responses, we first define a patient-specific composite score, constructed from individual covariates. We then estimate conditional means of each response, given the patient score, correspond to each treatment, using a nonparametric smooth estimator. Next, a rank aggregation technique is applied to estimate an ordering of treatments based on ranked lists of treatment performance measures given by conditional means. We handle the right-censored data by incorporating the inverse probability of censoring weighting to the corresponding estimators. An empirical study illustrates the performance of the proposed method in finite sample problems. To show the applicability of the proposed procedure for real data, we also present a data analysis using HIV clinical trial data, that contained a right-censored survival event as one of the endpoints.

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

Objective

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

Methods

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

Results

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

Conclusions

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

Intensive Versus Standard Blood Pressure Lowering and Days Free of Cardiovascular Events and Serious Adverse Events: a Post Hoc Analysis of Systolic Blood Pressure Intervention Trial

BACKGROUND

Communication of the benefits and harms of blood pressure lowering strategy is crucial for shared decision-making.

OBJECTIVES

To quantify the effect of intensive versus standard systolic blood pressure lowering in terms of the number of event-free days DESIGN: Post hoc analysis of the Systolic Blood Pressure Intervention Trial PARTICIPANTS: A total of 9361 adults 50 years or older without diabetes or stroke who had a systolic blood pressure of 130-180 mmHg and elevated cardiovascular risk INTERVENTIONS: Intensive (systolic blood pressure goal <120 mmHg) versus standard blood pressure lowering (<140 mmHg) MAIN MEASURES: Days free of major adverse cardiovascular events (MACE), serious adverse events (SAE), and monitored adverse events (hypotension, syncope, bradycardia, electrolyte abnormalities, injurious falls, or acute kidney injury) over a median follow-up of 3.33 years KEY RESULTS: The intensive treatment group gained 14.7 more MACE-free days over 4 years (difference, 14.7 [95% confidence interval: 5.1, 24.4] days) than the standard treatment group. The benefit of the intensive treatment varied by cognitive function (normal: difference, 40.7 [13.0, 68.4] days; moderate-to-severe impairment: difference, -15.0 [-56.5, 26.4] days; p-for-interaction=0.009) and self-rated health (excellent: difference, -22.7 [-51.5, 6.1] days; poor: difference, 156.1 [31.1, 281.2] days; p-for-interaction=0.001). The mean overall SAE-free days were not significantly different between the treatments (difference, -14.8 [-35.3, 5.7] days). However, the intensive treatment group had 28.5 fewer monitored adverse event-free days than the standard treatment group (difference, -28.5 [-40.3, -16.7] days), with significant variations by frailty status (non-frail: difference, 38.8 [8.4, 69.2] days; frail: difference, -15.5 [-46.6, 15.7] days) and self-rated health (excellent: difference, -12.9 [-45.5, 19.7] days; poor: difference, 180.6 [72.9, 288.4] days; p-for-interaction <0.001).

CONCLUSIONS

Over 4 years, intensive systolic blood pressure lowering provides, on average, 14.7 more MACE-free days than standard treatment, without any difference in SAE-free days. Whether this time-based effect summary improves shared decision-making remains to be elucidated.

TRIAL REGISTRATION

ClinicalTrials.gov Registration: NCT01206062.

Influence of high altitude on the expression of HIF-1 and on the prognosis of Ecuadorian patients with gastric adenocarcinoma

Since the incidence of gastric adenocarcinoma (GA) is high in populations living at high altitudes, we evaluated the influence of altitude on the expression of HIF-1 and survival of Ecuadorian GA patients. Method: 155 GA cases were studied: 56 from coastal (GAC) and 99 from mountainous regions (GAM), and 74 non-GA controls (25 coast and 49 mountain). The expression of HIF-1/HER2 was analyzed by immunohistochemistry. Analyses were performed using Fisher's exact and Breslow-Day tests for homogeneity and Kaplan-Meier curves and restricted median survival time ΔRMST. Results: HIF-1 was overexpressed in normal/inflamed gastric mucosa, especially in mountainous non-GA patients (p = 0.001). There was no difference between GAC and GAM in terms of age/gender, HIF-1/HER2 expression, stage/tumor location. Median survival at 120 months was significantly higher among GAC, with a difference (ΔRMST) of 43.7 months (95% CI 29.5, 57.8) (p < 0.001) and those with positive HIF-1 expression: ΔRMST 26.6 months (95% CI 11.0, 42.1) (p < 0.001). Positive HIF-1 expression was associated with better GAM survival, with ΔRMST 33.6 months (95% CI 14.2, 52.9) (p < 0.001). Conclusion: Despite the limitations of this retrospective study, GA patients in the coastal region and those who expressed HIF-1 exhibited a better prognosis, but this factor was associated with better survival only in the mountain region.

A Review of Causal Inference for External Comparator Arm Studies

Randomized controlled trials (RCTs) are the gold standard design to establish the efficacy of new drugs and to support regulatory decision making. However, a marked increase in the submission of single-arm trials (SATs) has been observed in recent years, especially in the field of oncology due to the trend towards precision medicine contributing to the rise of new therapeutic interventions for rare diseases. SATs lack results for control patients, and information from external sources can be compiled to provide context for better interpretability of study results. External comparator arm (ECA) studies are defined as a clinical trial (most commonly a SAT) and an ECA of a comparable cohort of patients-commonly derived from real-world settings including registries, natural history studies, or medical records of routine care. This publication aims to provide a methodological overview, to sketch emergent best practice recommendations and to identify future methodological research topics. Specifically, existing scientific and regulatory guidance for ECA studies is reviewed and appropriate causal inference methods are discussed. Further topics include sample size considerations, use of estimands, handling of different data sources regarding differential baseline covariate definitions, differential endpoint measurements and timings. In addition, unique features of ECA studies are highlighted, specifically the opportunity to address bias caused by unmeasured ECA covariates, which are available in the SAT.

Weighted Log-Rank Test for Clinical Trials with Delayed Treatment Effect Based on a Novel Hazard Function Family

In clinical trials with delayed treatment effect, the standard log-rank method in testing the difference between survival functions may have problems, including low power and poor robustness, so the method of weighted log-rank test (WLRT) is developed to improve the test performance. In this paper, a hyperbolic-cosine-shaped (CH) hazard function family model is proposed to simulate delayed treatment effect scenarios. Then, based on Fleming and Harrington’s method, this paper derives the corresponding weight function and its regular corrections, which are powerful in test, theoretically. Alternative methods of parameters selection based on potential information are also developed. Further, the simulation study is conducted to compare the power performance between CH WLRT, classical WLRT, modest weighted log-rank test and WLRT with logistic-type weight function under different hazard scenarios and simulation settings. The results indicate that the CH statistics are powerful and robust in testing the late difference, so the CH test is useful and meaningful in practice.

Regression modeling of restricted mean survival time for left-truncated right-censored data

The restricted mean survival time (RMST) is a clinically meaningful summary measure in studies with survival outcomes. Statistical methods have been developed for regression analysis of RMST to investigate impacts of covariates on RMST, which is a useful alternative to the Cox regression analysis. However, existing methods for regression modeling of RMST are not applicable to left-truncated right-censored data that arise frequently in prevalent cohort studies, for which the sampling bias due to left truncation and informative censoring induced by the prevalent sampling scheme must be properly addressed. The pseudo-observation (PO) approach has been used in regression modeling of RMST for right-censored data and competing-risks data. For left-truncated right-censored data, we propose to directly model RMST as a function of baseline covariates based on POs under general censoring mechanisms. We adjust for the potential covariate-dependent censoring or dependent censoring by the inverse probability of censoring weighting method. We establish large sample properties of the proposed estimators and assess their finite sample performances by simulation studies under various scenarios. We apply the proposed methods to a prevalent cohort of women diagnosed with stage IV breast cancer identified from surveillance, epidemiology, and end results-medicare linked database.

Racial and Ethnic Differences in All-Cause and Cardiovascular Disease Mortality: The MESA Study

BACKGROUND

Despite improvements in population health, marked racial and ethnic disparities in longevity and cardiovascular disease (CVD) mortality persist. This study aimed to describe risks for all-cause and CVD mortality by race and ethnicity, before and after accounting for socioeconomic status (SES) and other factors, in the MESA study (Multi-Ethnic Study of Atherosclerosis).

METHODS

MESA recruited 6814 US adults, 45 to 84 years of age, free of clinical CVD at baseline, including Black, White, Hispanic, and Chinese individuals (2000-2002). Using Cox proportional hazards modeling with time-updated covariates, we evaluated the association of self-reported race and ethnicity with all-cause and adjudicated CVD mortality, with progressive adjustments for age and sex, SES (neighborhood SES, income, education, and health insurance), lifestyle and psychosocial risk factors, clinical risk factors, and immigration history.

RESULTS

During a median of 15.8 years of follow-up, 22.8% of participants (n=1552) died, of which 5.3% (n=364) died of CVD. After adjusting for age and sex, Black participants had a 34% higher mortality hazard (hazard ratio [HR], 1.34 [95% CI, 1.19-1.51]), Chinese participants had a 21% lower mortality hazard (HR, 0.79 [95% CI, 0.66-0.95]), and there was no mortality difference in Hispanic participants (HR, 0.99 [95% CI, 0.86-1.14]) compared with White participants. After adjusting for SES, the mortality HR for Black participants compared with White participants was reduced (HR, 1.16 [95% CI, 1.01-1.34]) but still statistically significant. With adjustment for SES, the mortality hazards for Chinese and Hispanic participants also decreased in comparison with White participants. After further adjustment for additional risk factors and immigration history, Hispanic participants (HR, 0.77 [95% CI, 0.63-0.94]) had a lower mortality risk than White participants, and hazard ratios for Black participants (HR, 1.08 [95% CI, 0.92-1.26]) and Chinese participants (HR, 0.81 [95% CI, 0.60-1.08]) were not significantly different from those of White participants. Similar trends were seen for CVD mortality, although the age- and sex-adjusted HR for CVD mortality for Black participants compared with White participants was greater than all-cause mortality (HR, 1.72 [95% CI, 1.34-2.21] compared with HR, 1.34 [95% CI, 1.19-1.51]).

CONCLUSIONS

These results highlight persistent racial and ethnic differences in overall and CVD mortality, largely attributable to social determinants of health, and support the need to identify and act on systemic factors that shape differences in health across racial and ethnic groups.

Comparison of the efficacies of 1.0 and 1.5 mm silicone tubes for the treatment of nasolacrimal duct obstruction

This retrospective observational study analyzed the postoperative outcomes of bicanalicular intubation using different diameters of tube stents for treating postsaccal nasolacrimal duct obstruction. A total of 130 patients diagnosed with postsaccal obstruction who underwent endoscopic-assisted silicone tube intubation were included in the study. Patients intubated with a 1.5-mm large-diameter tube were designated as the LD group, and those with a 1.0-mm normal-diameter tube were designated as the ND group. The patency rates of the two groups at 1 year after tube removal were compared using the Kaplan–Meier curve and restricted mean survival time (RMST) method with τ = 365 days. Results demonstrated that the recurrence rate after tube removal was significantly lower in the LD group as compared with the ND group (p = 0.001). The patency rates at 1 year after removal in the LD and ND group were 85.7% (95% confidence interval [CI]: 75.4, 91.9) and 73.9% (95% CI: 61.7, 82.8), respectively. When comparing the patency rates by the RMST method at τ = 365 days, the RMST difference, RMST ratio, and RMTL ratio were higher in the LD group at p = 0.045, 0.052, and 0.046, respectively.

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.

Conversion of non-inferiority margin from hazard ratio to restricted mean survival time difference using data from multiple historical trials

The restricted mean survival time measure has gained a lot of interests for designing and analyzing oncology trials with time-to-event endpoints due to its intuitive clinical interpretation and potentially high statistical power. In the non-inferiority trial literature, restricted mean survival time has been used as an alternative measure for reanalyzing a completed trial, which was originally designed and analyzed based on traditional proportional hazard model. However, the reanalysis procedure requires a conversion from the non-inferiority margin measured in hazard ratio to a non-inferiority margin measured by restricted mean survival time difference. An existing conversion method assumes a Weibull distribution for the population survival time of the historical active control group under the proportional hazard assumption using data from a single trial. In this article, we develop a methodology for non-inferiority margin conversion when data from multiple historical active control studies are available, and introduce a Kaplan-Meier estimator-based method for the non-inferiority margin conversion to relax the parametric assumption. We report extensive simulation studies to examine the performances of proposed methods under the Weibull data generative models and a piecewise-exponential data generative model that mimic the tumor recurrence and survival characteristics of advanced colon cancer. This work is motivated to achieve non-inferiority margin conversion, using historical patient-level data from a large colon cancer clinical database, to reanalyze an internationally collaborated non-inferiority study that evaluates 6-month versus 3-month duration of adjuvant chemotherapy in stage III colon cancer patients.

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.

Clustered restricted mean survival time regression

For multicenter randomized trials or multilevel observational studies, the Cox regression model has long been the primary approach to study the effects of covariates on time-to-event outcomes. A critical assumption of the Cox model is the proportionality of the hazard functions for modeled covariates, violations of which can result in ambiguous interpretations of the hazard ratio estimates. To address this issue, the restricted mean survival time (RMST), defined as the mean survival time up to a fixed time in a target population, has been recommended as a model-free target parameter. In this article, we generalize the RMST regression model to clustered data by directly modeling the RMST as a continuous function of restriction times with covariates while properly accounting for within-cluster correlations to achieve valid inference. The proposed method estimates regression coefficients via weighted generalized estimating equations, coupled with a cluster-robust sandwich variance estimator to achieve asymptotically valid inference with a sufficient number of clusters. In small-sample scenarios where a limited number of clusters are available, however, the proposed sandwich variance estimator can exhibit negative bias in capturing the variability of regression coefficient estimates. To overcome this limitation, we further propose and examine bias-corrected sandwich variance estimators to reduce the negative bias of the cluster-robust sandwich variance estimator. We study the finite-sample operating characteristics of proposed methods through simulations and reanalyze two multicenter randomized trials.

Doubly-robust methods for differences in restricted mean lifetimes using pseudo-observations

In clinical studies or trials comparing survival times between two treatment groups, the restricted mean lifetime (RML), defined as the expectation of the survival from time 0 to a prespecified time-point, is often the quantity of interest that is readily interpretable to clinicians without any modeling restrictions. It is well known that if the treatments are not randomized (as in observational studies), covariate adjustment is necessary to account for treatment imbalances due to confounding factors. In this article, we propose a simple doubly-robust pseudo-value approach to effectively estimate the difference in the RML between two groups (akin to a metric for estimating average causal effects), while accounting for confounders. The proposed method combines two general approaches: (a) group-specific regression models for the time-to-event and covariate information, and (b) inverse probability of treatment assignment weights, where the RMLs are replaced by the corresponding pseudo-observations for survival outcomes, thereby mitigating the estimation complexities in presence of censoring. The proposed estimator is double-robust, in the sense that it is consistent if at least one of the two working models remains correct. In addition, we explore the potential of available machine learning algorithms in causal inference to reduce possible bias of the causal estimates in presence of a complex association between the survival outcome and covariates. We conduct extensive simulation studies to assess the finite-sample performance of the pseudo-value causal effect estimators. Furthermore, we illustrate our methodology via application to a dataset from a breast cancer cohort study. The proposed method is implementable using the R package drRML, available in GitHub.

Handling death as an Intercurrent event in time to recovery analysis in COVID-19 treatment clinical trials

In clinical trials with the objective to evaluate the treatment effect on time to recovery, such as investigational trials on therapies for COVID-19 hospitalized patients, the patients may face a mortality risk that competes with the opportunity to recover (e.g., be discharged from the hospital). Therefore, an appropriate analytical strategy to account for death is particularly important due to its potential impact on the estimation of the treatment effect. To address this challenge, we conducted a thorough evaluation and comparison of nine survival analysis methods with different strategies to account for death, including standard survival analysis methods with different censoring strategies and competing risk analysis methods. We report results of a comprehensive simulation study that employed design parameters commonly seen in COVID-19 trials and case studies using reconstructed data from a published COVID-19 clinical trial. Our research results demonstrate that, when there is a moderate to large proportion of patients who died before observing their recovery, competing risk analyses and survival analyses with the strategy to censor death at the maximum follow-up timepoint would be able to better detect a treatment effect on recovery than the standard survival analysis that treat death as a non-informative censoring event. The aim of this research is to raise awareness of the importance of handling death appropriately in the time-to-recovery analysis when planning current and future COVID-19 treatment trials.

Estimation and inference of predictive discrimination for survival outcome risk prediction models

Accurate risk prediction has been the central goal in many studies of survival outcomes. In the presence of multiple risk factors, a censored regression model can be employed to estimate a risk prediction rule. Before the prediction tool can be popularized for practical use, it is crucial to rigorously assess its prediction performance. In our motivating example, researchers are interested in developing and validating a risk prediction tool to identify future lung cancer cases by integrating demographic information, disease characteristics and smoking-related data. Considering the long latency period of cancer, it is desirable for a prediction tool to achieve discriminative performance that does not weaken over time. We propose estimation and inferential procedures to comprehensively assess both the overall predictive discrimination and the temporal pattern of an estimated prediction rule. The proposed methods readily accommodate commonly used censored regression models, including the Cox proportional hazards model and the accelerated failure time model. The estimators are consistent and asymptotically normal, and reliable variance estimators are also developed. The proposed methods offer an informative tool for inferring time-dependent predictive discrimination, as well as for comparing the discrimination performance between candidate models. Applications of the proposed methods demonstrate enduring performance of the risk prediction tool in the PLCO study and detected decaying performance in a study of liver disease.

Analyzing differences between restricted mean survival time curves using pseudo-values

Hazard ratios are ubiquitously used in time to event analysis to quantify treatment effects. Although hazard ratios are invaluable for hypothesis testing, other measures of association, both relative and absolute, may be used to fully elucidate study results. Restricted mean survival time (RMST) differences between groups have been advocated as useful measures of association. Recent work focused on model-free estimates of the difference in restricted mean survival through follow-up times, instead of focusing on a single time horizon. The resulting curve can be used to quantify the association in time units with a simultaneous confidence band. In this work a model-based estimate of the curve is proposed using pseudo-values allowing for possible covariate adjustment. The method is easily implementable with available software and makes possible to compute a simultaneous confidence region for the curve. The pseudo-values regression using multiple restriction times is in good agreement with the estimates obtained by standard direct regression models fixing a single restriction time. Moreover, the proposed method is flexible enough to reproduce the results of the non-parametric approach when no covariates are considered. Examples where it is important to adjust for baseline covariates will be used to illustrate the different methods together with some simulations.