On the restricted mean survival time curve in survival analysis

Comparing the effectiveness and cost-effectiveness of sulfonylureas and newer diabetes drugs as second-line therapy for patients with type 2 diabetes

INTRODUCTION

We aimed to compare the effectiveness and cost-effectiveness profiles of glucagon-like peptide-1 receptor agonist (GLP-1-RA), sodium-glucose cotransporter 2 inhibitor (SGLT2i), and dipeptidyl peptidase-4 inhibitor (DPP-4i) compared with sulfonylureas and glinides (SU).

RESEARCH DESIGN AND METHODS

Population-based retrospective cohort study based on linked regional healthcare utilization databases. The cohort included all residents in Lombardy aged ≥40 years, treated with metformin in 2014, who started a second-line treatment between 2015 and 2018 with SU, GLP-1-RA, SGLT2i, or DPP-4i. For each cohort member who started SU, one patient who began other second-line treatments was randomly selected and matched for sex, age, Multisource Comorbidity Score, and previous duration of metformin treatment. Cohort members were followed up until December 31, 2022. The association between second-line treatment and clinical outcomes was assessed using Cox proportional hazards models. The incremental cost-effectiveness ratios (ICERs) were calculated and compared between newer diabetes drugs and SU.

RESULTS

Overall, 22 867 patients with diabetes were included in the cohort, among which 10 577, 8125, 2893 and 1272 started a second-line treatment with SU, DPP-4i, SGLT2i and GLP-1-RA, respectively. Among these, 1208 patients for each group were included in the matched cohort. As compared with SU, those treated with DPP-4i, SGLT2i and GLP-1-RA were associated to a risk reduction for hospitalization for major adverse cardiovascular events (MACE) of 22% (95% CI 3% to 37%), 29% (95% CI 12% to 44%) and 41% (95% CI 26% to 53%), respectively. The ICER values indicated an average gain of €96.2 and €75.7 each month free from MACE for patients on DPP-4i and SGLT2i, respectively.

CONCLUSIONS

Newer diabetes drugs are more effective and cost-effective second-line options for the treatment of type 2 diabetes than SUs.

Adherence to GLP1-RA and SGLT2-I affects clinical outcomes and costs in patients with type 2 diabetes

AIMS

To evaluate the impact of adherence to glucagon like peptide-1 receptor agonists (GLP1-RA) and sodium-glucose transporter two inhibitors (SGLT2-I) on clinical outcomes and costs in patients with type 2 diabetes mellitus (T2DM).

MATERIALS AND METHODS

The 121,115 residents of the Lombardy Region (Italy) aged ≥40 years newly treated with metformin during 2007-2015 were followed to identify those who started therapy with GLP1-RA or SGLT2-I. Adherence to drug therapy over the first year was defined as the proportion of days covered >80%. Within each drug class, for each adherent patient, one non-adherent patient was matched for age, sex, duration, adherence to metformin treatment and propensity score. The primary clinical outcome was a composite of insulin initiation, hospitalisation for micro- and macrovascular complications and all-cause mortality after the first year of drug treatment. Costs were evaluated based on reimbursements from the national healthcare system.

RESULTS

After matching, 1182 pairs of adherent and non-adherent GLP1-RA users and 1126 pairs of adherent and non-adherent SGLT2-I users were included. In both groups, adherent patients experienced a significantly lower incidence of the primary outcome (HR: 0.85, 95% CI 0.72-0.98 for GLP1-RA and HR: 0.69, 95% CI 0.55-0.87 for SGLT2-I). A significant reduction in hospitalizations was found for adherent patients in the GLP1-RA group but not for the SGLT2-I group. Results were consistent when analyses were stratified by age and sex. While higher drug-related costs in the adherent group were counterbalanced by decreased hospitalisation costs in SGLT2-I treated patients, this was not the case for GLP1-RA.

CONCLUSIONS

Higher adherence to drug treatment with GLP1-RA and SGLT2-I during the first year of the drug intake is associated with a lower incidence of adverse clinical outcomes in a real-world setting.

Simultaneous inference procedures for the comparison of multiple characteristics of two survival functions

Survival time is the primary endpoint of many randomized controlled trials, and a treatment effect is typically quantified by the hazard ratio under the assumption of proportional hazards. Awareness is increasing that in many settings this assumption is a priori violated, for example, due to delayed onset of drug effect. In these cases, interpretation of the hazard ratio estimate is ambiguous and statistical inference for alternative parameters to quantify a treatment effect is warranted. We consider differences or ratios of milestone survival probabilities or quantiles, differences in restricted mean survival times, and an average hazard ratio to be of interest. Typically, more than one such parameter needs to be reported to assess possible treatment benefits, and in confirmatory trials, the according inferential procedures need to be adjusted for multiplicity. A simple Bonferroni adjustment may be too conservative because the different parameters of interest typically show considerable correlation. Hence simultaneous inference procedures that take into account the correlation are warranted. By using the counting process representation of the mentioned parameters, we show that their estimates are asymptotically multivariate normal and we provide an estimate for their covariance matrix. We propose according to the parametric multiple testing procedures and simultaneous confidence intervals. Also, the logrank test may be included in the framework. Finite sample type I error rate and power are studied by simulation. The methods are illustrated with an example from oncology. A software implementation is provided in the R package nph.

Transvaginal Polytetrafluoroethylene Mesh Surgery for Pelvic Organ Prolapse: One-Year Safety and Efficacy Results

OBJECTIVE

To evaluate the efficacy and safety of the polytetrafluoroethylene (PTFE) mesh by comparing conventionally used polypropylene (PP) mesh in tension-free vaginal mesh (TVM) surgery for pelvic organ prolapse (POP).

METHODS

We conducted an observational cohort study of patients who underwent TVM using a PTFE or PP mesh. PTFE was used from June 2019 to May 2021, and PP mesh from January 2018 to May 2019. Outcomes included POP recurrence, perioperative complications, and patient satisfaction. Restricted mean survival time was used to analyze POP recurrence, comparing the time to recurrence between the two groups at 1year after TVM.

RESULTS

Of 171 patients, 104 underwent PP mesh placement (PP group) and 67 underwent PTFE mesh placement (PTFE group). POP recurrence was observed in 10 and nine patients in the PP and PTFE groups, respectively. The mean time until the recurrence in the PTFE group was significantly shorter than that in the PP group (restricted mean survival time difference: -20.3days; 95% CI, -40.1 to -0.5; P = .044). Subgroup analysis revealed the meantime until recurrence was significantly shorter in the PTFE group for postoperative periods 3months or less, ages >70years, and POP stage ≥3. There were no intervention cases in either group and no significant differences in the perioperative complications. Patient satisfaction was greater in the PTFE group after 3months postoperatively.

CONCLUSION

TVM surgery with a PTFE mesh is more prone to recurrence than that with a PP mesh, but with higher patient satisfaction. Within 3months of surgery, elderly patients and those with advanced-stage POP require care to prevent recurrence.

Survival analysis under imperfect record linkage using historic census data

BACKGROUND

Advancements in linking publicly available census records with vital and administrative records have enabled novel investigations in epidemiology and social history. However, in the absence of unique identifiers, the linkage of the records may be uncertain or only be successful for a subset of the census cohort, resulting in missing data. For survival analysis, differential ascertainment of event times can impact inference on risk associations and median survival.

METHODS

We modify some existing approaches that are commonly used to handle missing survival times to accommodate this imperfect linkage situation including complete case analysis, censoring, weighting, and several multiple imputation methods. We then conduct simulation studies to compare the performance of the proposed approaches in estimating the associations of a risk factor or exposure in terms of hazard ratio (HR) and median survival times in the presence of missing survival times. The effects of different missing data mechanisms and exposure-survival associations on their performance are also explored. The approaches are applied to a historic cohort of residents in Ambler, PA, established using the 1930 US census, from which only 2,440 out of 4,514 individuals (54%) had death records retrievable from publicly available data sources and death certificates. Using this cohort, we examine the effects of occupational and paraoccupational asbestos exposure on survival and disparities in mortality by race and gender.

RESULTS

We show that imputation based on conditional survival results in less bias and greater efficiency relative to a complete case analysis when estimating log-hazard ratios and median survival times. When the approaches are applied to the Ambler cohort, we find a significant association between occupational exposure and mortality, particularly among black individuals and males, but not between paraoccupational exposure and mortality.

DISCUSSION

This investigation illustrates the strengths and weaknesses of different imputation methods for missing survival times due to imperfect linkage of the administrative or registry data. The performance of the methods may depend on the missingness process as well as the parameter being estimated and models of interest, and such factors should be considered when choosing the methods to address the missing event times.

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.

Private Continuous Survival Analysis with Distributed Multi-Site Data

Effective disease surveillance systems require large-scale epidemiological data to improve health outcomes and quality of care for the general population. As data may be limited within a single site, multi-site data (e.g., from a number of local/regional health systems) need to be considered. Leveraging distributed data across multiple sites for epidemiological analysis poses significant challenges. Due to the sensitive nature of epidemiological data, it is imperative to design distributed solutions that provide strong privacy protections. Current privacy solutions often assume a central site, which is responsible for aggregating the distributed data and applying privacy protection before sharing the results (e.g., aggregation via secure primitives and differential privacy for sharing aggregate results). However, identifying such a central site may be difficult in practice and relying on a central site may introduce potential vulnerabilities (e.g., single point of failure). Furthermore, to support clinical interventions and inform policy decisions in a timely manner, epidemiological analysis need to reflect dynamic changes in the data. Yet, existing distributed privacy-protecting approaches were largely designed for static data (e.g., one-time data sharing) and cannot fulfill dynamic data requirements. In this work, we propose a privacy-protecting approach that supports the sharing of dynamic epidemiological analysis and provides strong privacy protection in a decentralized manner. We apply our solution in continuous survival analysis using the Kaplan-Meier estimation model while providing differential privacy protection. Our evaluations on a real dataset containing COVID-19 cases show that our method provides highly usable results.

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.

Germline mechanisms of immunotherapy toxicities in the era of genome-wide association studies

Cancer immunotherapy has revolutionized the treatment of advanced cancers and is quickly becoming an option for early-stage disease. By reactivating the host immune system, immunotherapy harnesses patients' innate defenses to eradicate the tumor. By putatively similar mechanisms, immunotherapy can also substantially increase the risk of toxicities or immune-related adverse events (irAEs). Severe irAEs can lead to hospitalization, treatment discontinuation, lifelong immune complications, or even death. Many irAEs present with similar symptoms to heritable autoimmune diseases, suggesting that germline genetics may contribute to their onset. Recently, genome-wide association studies (GWAS) of irAEs have identified common germline associations and putative mechanisms, lending support to this hypothesis. A wide range of well-established GWAS methods can potentially be harnessed to understand the etiology of irAEs specifically and immunotherapy outcomes broadly. This review summarizes current findings regarding germline effects on immunotherapy outcomes and discusses opportunities and challenges for leveraging germline genetics to understand, predict, and treat irAEs.

Selection of maintenance therapy during first-line treatment of advanced ovarian cancer based on pharmacologic characteristics

INTRODUCTION

Maintenance therapy with bevacizumab and the poly (ADP-ribose) polymerase (PARP) inhibitors olaparib and niraparib after first-line treatment of advanced ovarian cancer has been approved. However, it is not clear which one should be used for which patients.

AREAS COVERED

This paper presents a detailed analysis of data from phase 3 trials in ovarian cancer evaluating bevacizumab (ICON7, GOG-0218), olaparib (SOLO1, PAOLA-1), and niraparib (PRIMA, PRIME). We will discuss how the results of these trials relate to the 'rebound effect,' in which the risk of progression increases after discontinuation of bevacizumab in patients receiving bevacizumab, and to the significant difference in tissue permeability between olaparib and niraparib.

EXPERT OPINION

In patients with homologous recombination deficiency and no macroscopic residual disease (R0) after primary debulking surgery (PDS), the combination of bevacizumab plus olaparib seems to be the best regimen. Olaparib monotherapy is suitable for patients with BRCA mutations other than PDS R0. Bevacizumab is most useful in cases with a short duration of the rebound effect, i.e. short survival. Niraparib is useful in others but may be more useful in Asians.

Using information criteria to select smoothing parameters when analyzing survival data with time-varying coefficient hazard models

Analyzing the large-scale survival data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program may help guide the management of cancer. Detecting and characterizing the time-varying effects of factors collected at the time of diagnosis could reveal important and useful patterns. However, fitting a time-varying effect model by maximizing the partial likelihood with such large-scale survival data is not feasible with most existing software. Moreover, estimating time-varying coefficients using spline based approaches requires a moderate number of knots, which may lead to unstable estimation and over-fitting issues. To resolve these issues, adding a penalty term greatly aids estimation. The selection of penalty smoothing parameters is difficult in this time-varying setting, as traditional ways like using Akaike information criterion do not work, while cross-validation methods have a heavy computational burden, leading to unstable selections. We propose modified information criteria to determine the smoothing parameter and a parallelized Newton-based algorithm for estimation. We conduct simulations to evaluate the performance of the proposed method. We find that penalization with the smoothing parameter chosen by a modified information criteria is effective at reducing the mean squared error of the estimated time-varying coefficients. Compared to a number of alternatives, we find that the estimates of the variance derived from Bayesian considerations have the best coverage rates of confidence intervals. We apply the method to SEER head-and-neck, colon, prostate, and pancreatic cancer data and detect the time-varying nature of various risk factors.

Nonparametric inference of general while-alive estimands for recurrent events

Measuring the treatment effect on recurrent events like hospitalization in the presence of death has long challenged statisticians and clinicians alike. Traditional inference on the cumulative frequency unjustly penalizes survivorship as longer survivors also tend to experience more adverse events. Expanding a recently suggested idea of the "while-alive" event rate, we consider a general class of such estimands that adjust for the length of survival without losing causal interpretation. Given a user-specified loss function that allows for arbitrary weighting, we define as estimand the average loss experienced per unit time alive within a target period and use the ratio of this loss rate to measure the effect size. Scaling the loss rate by the width of the corresponding time window gives us an alternative, and sometimes more photogenic, way of showing the data. To make inferences, we construct a nonparametric estimator for the loss rate through the cumulative loss and the restricted mean survival time and derive its influence function in closed form for variance estimation and testing. As simulations and analysis of real data from a heart failure trial both show, the while-alive approach corrects for the false attenuation of treatment effect due to patients living longer under treatment, with increased statistical power as a result. The proposed methods are implemented in the R-package WA, which is publicly available from the Comprehensive R Archive Network (CRAN).

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Lifetime risk of cardiovascular disease stratified by traditional risk factors: Findings from the cohort of Tehran lipid and glucose study

BACKGROUND

We aimed to estimate the lifetime risk (LTR) of cardiovascular disease (CVD) in the Iranian population, stratified by sex and traditional risk factors including high body mass index (BMI), hypertension, diabetes, smoking, and hypercholesterolemia.

METHODS

We included 10222 (4430 men) participants aged ≥20 years without CVD at baseline. LTRs at index ages 20 and 40 years and number of years lived without CVD was estimated. We further assessed the effect of traditional risk factors on the LTR of CVD and the number of years lived without CVD, stratified by sex and index ages.

RESULTS

During a median follow-up of 18 years, 1326 participants (774 men) developed CVD and 430 (238 men) died from non-cardiovascular causes. At age 20, the remaining LTR for CVD was 66.7% (95% CI 62.9-70.4) in men and 52.0% (47.6-56.8) in women, with similar LTRs at age 40 for both men and women. The LTRs at both index ages for those with ≥3 risk factors were about 30% and 55% higher in men and women, respectively, than those without any of the five risk factors. At the age of 20, men with ≥3 risk factors lived 24.1 fewer years without CVD compared with men with no risk factors; the corresponding value was 8 years in their female counterparts.

CONCLUSIONS

Our findings suggest that both sexes may benefit from effective prevention strategies early in the life course, despite the observed differences between men and women in LTR for CVD and number of years lived without CVD.

Perioperative Pembrolizumab for Early-Stage Non-Small-Cell Lung Cancer

BACKGROUND

Among patients with resectable early-stage non-small-cell lung cancer (NSCLC), a perioperative approach that includes both neoadjuvant and adjuvant immune checkpoint inhibition may provide benefit beyond either approach alone.

METHODS

We conducted a randomized, double-blind, phase 3 trial to evaluate perioperative pembrolizumab in patients with early-stage NSCLC. Participants with resectable stage II, IIIA, or IIIB (N2 stage) NSCLC were assigned in a 1:1 ratio to receive neoadjuvant pembrolizumab (200 mg) or placebo once every 3 weeks, each of which was given with cisplatin-based chemotherapy for 4 cycles, followed by surgery and adjuvant pembrolizumab (200 mg) or placebo once every 3 weeks for up to 13 cycles. The dual primary end points were event-free survival (the time from randomization to the first occurrence of local progression that precluded the planned surgery, unresectable tumor, progression or recurrence, or death) and overall survival. Secondary end points included major pathological response, pathological complete response, and safety.

RESULTS

A total of 397 participants were assigned to the pembrolizumab group, and 400 to the placebo group. At the prespecified first interim analysis, the median follow-up was 25.2 months. Event-free survival at 24 months was 62.4% in the pembrolizumab group and 40.6% in the placebo group (hazard ratio for progression, recurrence, or death, 0.58; 95% confidence interval [CI], 0.46 to 0.72; P<0.001). The estimated 24-month overall survival was 80.9% in the pembrolizumab group and 77.6% in the placebo group (P = 0.02, which did not meet the significance criterion). A major pathological response occurred in 30.2% of the participants in the pembrolizumab group and in 11.0% of those in the placebo group (difference, 19.2 percentage points; 95% CI, 13.9 to 24.7; P<0.0001; threshold, P = 0.0001), and a pathological complete response occurred in 18.1% and 4.0%, respectively (difference, 14.2 percentage points; 95% CI, 10.1 to 18.7; P<0.0001; threshold, P = 0.0001). Across all treatment phases, 44.9% of the participants in the pembrolizumab group and 37.3% of those in the placebo group had treatment-related adverse events of grade 3 or higher, including 1.0% and 0.8%, respectively, who had grade 5 events.

CONCLUSIONS

Among patients with resectable, early-stage NSCLC, neoadjuvant pembrolizumab plus chemotherapy followed by resection and adjuvant pembrolizumab significantly improved event-free survival, major pathological response, and pathological complete response as compared with neoadjuvant chemotherapy alone followed by surgery. Overall survival did not differ significantly between the groups in this analysis. (Funded by Merck Sharp and Dohme; KEYNOTE-671 ClinicalTrials.gov number, NCT03425643.).

Omnibus test for restricted mean survival time based on influence function

The restricted mean survival time (RMST), which evaluates the expected survival time up to a pre-specified time point τ , has been widely used to summarize the survival distribution due to its robustness and straightforward interpretation. In comparative studies with time-to-event data, the RMST-based test has been utilized as an alternative to the classic log-rank test because the power of the log-rank test deteriorates when the proportional hazards assumption is violated. To overcome the challenge of selecting an appropriate time point τ , we develop an RMST-based omnibus Wald test to detect the survival difference between two groups throughout the study follow-up period. Treating a vector of RMSTs at multiple quantile-based time points as a statistical functional, we construct a Wald χ 2 test statistic and derive its asymptotic distribution using the influence function. We further propose a new procedure based on the influence function to estimate the asymptotic covariance matrix in contrast to the usual bootstrap method. Simulations under different scenarios validate the size of our RMST-based omnibus test and demonstrate its advantage over the existing tests in power, especially when the true survival functions cross within the study follow-up period. For illustration, the proposed test is applied to two real datasets, which demonstrate its power and applicability in various situations.

Pragmatic evaluation of events and benefits of lipid lowering in older adults (PREVENTABLE): Trial design and rationale

Whether initiation of statins could increase survival free of dementia and disability in adults aged ≥75 years is unknown. PREVENTABLE, a double-blind, placebo-controlled randomized pragmatic clinical trial, will compare high-intensity statin therapy (atorvastatin 40 mg) with placebo in 20,000 community-dwelling adults aged ≥75 years without cardiovascular disease, disability, or dementia at baseline. Exclusion criteria include statin use in the prior year or for >5 years and inability to take a statin. Potential participants are identified using computable phenotypes derived from the electronic health record and local referrals from the community. Participants will undergo baseline cognitive testing, with physical testing and a blinded lipid panel if feasible. Cognitive testing and disability screening will be conducted annually. Multiple data sources will be queried for cardiovascular events, dementia, and disability; survival is site-reported and supplemented by a National Death Index search. The primary outcome is survival free of new dementia or persisting disability. Co-secondary outcomes are a composite of cardiovascular death, hospitalization for unstable angina or myocardial infarction, heart failure, stroke, or coronary revascularization; and a composite of mild cognitive impairment or dementia. Ancillary studies will offer mechanistic insights into the effects of statins on key outcomes. Biorepository samples are obtained and stored for future study. These results will inform the benefit of statins for increasing survival free of dementia and disability among older adults. This is a pioneering pragmatic study testing important questions with low participant burden to align with the needs of the growing population of older adults.

Outcomes of Ultrasound Guided Peripheral Intravenous Catheters Placed in the Emergency Department and Factors Associated with Survival

Background

Patients with difficult peripheral intravenous (IV) access are common in emergency departments (EDs). Ultrasound-guided peripheral intravenous catheters (USIVs) are frequently used in this population; however, information regarding the effect of patient and IV characteristics on the dwell time (DT) and survival probability (SP) of USIVs is limited.

Objective

Our study aimed to evaluate for associations between patient or IV characteristics and the DT and SP of USIVs.

Methods

Retrospective analysis was performed on a database from an ED nurse (RN) USIV training program at an urban, academic hospital. Patients over 18 years with an USIV placed during the study period were included. Subject demographics, history, IV characteristics, insertion, and removal times were collected. Data were analyzed using descriptive statistics and univariable and multivariable Cox regression. USIV survival times for variates of interest were estimated using Kaplan-Meier curves for three censoring points.

Results

The final analysis cohort was 388 patients. Mean age was 56.6 years, 66.5% were female, mean BMI was 29.9 kg/m², and 42.5% were obese (BMI ≥30). Median DT was 40.3 hours in admitted patients (N=340). SP for USIVs at 96 hours was 87.8%. A total of 21 of 340 (6.2%) USIVs failed. USIV location conferred a difference on DT in obese patients when dichotomized into upper arm versus antecubital fossa and forearm together (38.6 hours vs 44.6 hours, p=0.03). No factors were associated with a difference in USIV SP.

Conclusion

Median USIV DT of 40.3 hours for admitted patients was higher than in previous studies. Only 7% of USIVs in our study failed. Overall, catheters survived longer than expected.

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.

On assessing survival benefit of immunotherapy using long-term restricted mean survival time

The pattern of the difference between two survival curves we often observe in randomized clinical trials for evaluating immunotherapy is not proportional hazards; the treatment effect typically appears several months after the initiation of the treatment (ie, delayed difference pattern). The commonly used logrank test and hazard ratio estimation approach will be suboptimal concerning testing and estimation for those trials. The long-term restricted mean survival time (LT-RMST) approach is a promising alternative for detecting the treatment effect that potentially appears later in the study. A challenge in employing the LT-RMST approach is that it must specify a lower end of the time window in addition to a truncation time point that the RMST requires. There are several investigations and suggestions regarding the choice of the truncation time point for the RMST. However, little has been investigated to address the choice of the lower end of the time window. In this paper, we propose a flexible LT-RMST-based test/estimation approach that does not require users to specify a lower end of the time window. Numerical studies demonstrated that the potential power loss by adopting this flexibility was minimal, compared to the standard LT-RMST approach using a prespecified lower end of the time window. The proposed method is flexible and can offer higher power than the RMST-based approach when the delayed treatment effect is expected. Also, it provides a robust estimate of the magnitude of the treatment effect and its confidence interval that corresponds to the test result.

On restricted mean time in favor of treatment

The restricted mean time in favor (RMT-IF) of treatment is a nonparametric effect size for complex life history data. It is defined as the net average time the treated spend in a more favorable state than the untreated over a prespecified time window. It generalizes the familiar restricted mean survival time (RMST) from the two-state life-death model to account for intermediate stages in disease progression. The overall estimand can be additively decomposed into stage-wise effects, with the standard RMST as a component. Alternate expressions of the overall and stage-wise estimands as integrals of the marginal survival functions for a sequence of landmark transitioning events allow them to be easily estimated by plug-in Kaplan-Meier estimators. The dynamic profile of the estimated treatment effects as a function of follow-up time can be visualized using a multilayer, cone-shaped "bouquet plot." Simulation studies under realistic settings show that the RMT-IF meaningfully and accurately quantifies the treatment effect and outperforms traditional tests on time to the first event in statistical efficiency thanks to its fuller utilization of patient data. The new methods are illustrated on a colon cancer trial with relapse and death as outcomes and a cardiovascular trial with recurrent hospitalizations and death as outcomes. The R-package rmt implements the proposed methodology and is publicly available from the Comprehensive R Archive Network (CRAN).

Upfront Advanced Radiotherapy and New Drugs for NSCLC Patients with Synchronous Brain Metastases: Is the Juice Worth the Squeeze? A Real-World Analysis from Lombardy, Italy

AIM

Healthcare administrative databases represent a valuable source for real-life data analysis. The primary aim of this study is to compare effectiveness and cost profile in non-small-cell lung cancer (NSCLC) patients harboring synchronous brain metastases (BMs) who received non-chemo first-line systemic therapy with or without advanced radiotherapy (aRT).

METHODS

Diagnostic ICD-9-CM codes were used for identifying all patients with a new diagnosis of lung cancer between 2012 and 2019. Among these, patients who had started a first-line systemic treatment with either TKIs or pembrolizumab, alone or in combination with intensity-modulated or stereotactic RT, were selected. Clinical outcomes investigated included overall survival (OS), progression-free survival (PFS), and time-to-treatment failure (TTF). The cost outcome was defined as the average per capita cumulative healthcare direct costs of the treatment, including all inpatient and outpatient costs.

RESULTS

The final cohort included 177 patients, of whom 58 were treated with systemic treatment plus aRT (STRT) and 119 with systemic treatment alone. The addition of aRT to systemic treatment was associated with a significantly better OS (p = 0.020) and PFS (p = 0.041) than systemic therapy alone. The ICER (incremental cost-effectiveness ratio) value indicated an average cost of €3792 for each month of survival after STRT treatment and confirmed clinical effectiveness but higher healthcare costs.

CONCLUSIONS

This real-world study suggests that upfront aRT for NCLSC patients with synchronous BMs represents a valid treatment strategy, boosting the efficacy of novel and emerging drug classes with sustainable costs for the health service.

TRANSLATIONAL RELEVANCE

The present real-world study reports that the use of upfront advanced radiotherapyaRT and new-generation systemic agents, such as TKIs and pembrolizumab, may have higher oncological control and an improved cost-effectiveness profile than the use of new-generation systemic agents alone in NCLSC patients with synchronous brain metastases. Acquired evidence can also be used to inform policymakers that adding advanced radiotherapy results is a sustainable cost for the health service. Since approximately 50% of patients do not meet RCT inclusion criteria, a significant proportion of them is receiving treatment that is not evidence-informed; therefore, these results warrant further studies to identify the best radiotherapy timing and possible dose escalation approaches to improving treatment efficacy in patient subgroups not typically represented in randomized controlled trials.

Stratified modestly weighted log-rank tests in settings with an anticipated delayed separation of survival curves

Delayed separation of survival curves is a common occurrence in confirmatory studies in immuno-oncology. Many novel statistical methods that aim to efficiently capture potential long-term survival improvements have been proposed in recent years. However, the vast majority do not consider stratification, which is a major limitation considering that most large confirmatory studies currently employ a stratified primary analysis. In this article, we combine recently proposed weighted log-rank tests that have been designed to work well under a delayed separation of survival curves, with stratification by a baseline variable. The aim is to increase the efficiency of the test when the stratifying variable is highly prognostic for survival. As there are many potential ways to combine the two techniques, we compare several possibilities in an extensive simulation study. We also apply the techniques retrospectively to two recent randomized clinical trials.

Treatment effect measures under nonproportional hazards

In a clinical trial with a time-to-event endpoint the treatment effect can be measured in various ways. Under proportional hazards all reasonable measures (such as the hazard ratio and the difference in restricted mean survival time) are consistent in the following sense: Take any control group survival distribution such that the hazard rate remains above zero; if there is no benefit by any measure there is no benefit by all measures, and as the magnitude of treatment benefit increases by any measure it increases by all measures. Under nonproportional hazards, however, survival curves can cross, and the direction of the effect for any pair of measures can be inconsistent. In this paper we critically evaluate a variety of treatment effect measures in common use and identify flaws with them. In particular, we demonstrate that a treatment's benefit has two distinct and independent dimensions which can be measured by the difference in the survival rate at the end of follow-up and the difference in restricted mean survival time, and that commonly used measures do not adequately capture both dimensions. We demonstrate that a generalized hazard difference, which can be estimated by the difference in exposure-adjusted subject incidence rates, captures both dimensions, and that its inverse, the number of patient-years of follow-up that results in one fewer event (the NYNT), is an easily interpretable measure of the magnitude of clinical benefit.

Examining evidence of time-dependent treatment effects: an illustration using regression methods

BACKGROUND

For the design and analysis of clinical trials with time-to-event outcomes, the Cox proportional hazards model and the logrank test have been the cornerstone methods for many decades. Increasingly, the key assumption of proportionality-or time-fixed effects-that underpins these methods has been called into question. The availability of novel therapies with new mechanisms of action and clinical trials of longer duration mean that non-proportional hazards are now more frequently encountered.

METHODS

We compared several regression-based methods to model time-dependent treatment effects. For illustration purposes, we used selected endpoints from a large, community-based clinical trial of low dose daily aspirin in older persons. Relative and absolute estimands were defined, and analyses were conducted in all participants. Additional exploratory analyses were undertaken by selected subgroups of interest using interaction terms in the regression models.

DISCUSSION

In the trial with median 4.7 years follow-up, we found evidence for non-proportionality and a time-dependent treatment effect of aspirin on cancer mortality not previously reported in trial findings. We also found some evidence of time-dependence to an aspirin by age interaction for major adverse cardiovascular events. For other endpoints, time-fixed treatment effect estimates were confirmed as appropriate.

CONCLUSIONS

The consideration of treatment effects using both absolute and relative estimands enhanced clinical insights into potential dynamic treatment effects. We recommend these analytical approaches as an adjunct to primary analyses to fully explore findings from clinical trials.

On the targets of inference with multivariate failure time data

There are several different topics that can be addressed with multivariate failure time regression data. Data analysis methods are needed that are suited to each such topic. Specifically, marginal hazard rate models are well suited to the analysis of exposures or treatments in relation to individual failure time outcomes, when failure time dependencies are themselves of little or no interest. On the other hand semiparametric copula models are well suited to analyses where interest focuses primarily on the magnitude of dependencies between failure times. These models overlap with frailty models, that seem best suited to exploring the details of failure time clustering. Recently proposed multivariate marginal hazard methods, on the other hand, are well suited to the exploration of exposures or treatments in relation to single, pairwise, and higher dimensional hazard rates. Here these methods will be briefly described, and the final method will be illustrated using the Women's Health Initiative hormone therapy trial data.

Glycemia Reduction in Type 2 Diabetes - Glycemic Outcomes

BACKGROUND

The comparative effectiveness of glucose-lowering medications for use with metformin to maintain target glycated hemoglobin levels in persons with type 2 diabetes is uncertain.

METHODS

In this trial involving participants with type 2 diabetes of less than 10 years' duration who were receiving metformin and had glycated hemoglobin levels of 6.8 to 8.5%, we compared the effectiveness of four commonly used glucose-lowering medications. We randomly assigned participants to receive insulin glargine U-100 (hereafter, glargine), the sulfonylurea glimepiride, the glucagon-like peptide-1 receptor agonist liraglutide, or sitagliptin, a dipeptidyl peptidase 4 inhibitor. The primary metabolic outcome was a glycated hemoglobin level, measured quarterly, of 7.0% or higher that was subsequently confirmed, and the secondary metabolic outcome was a confirmed glycated hemoglobin level greater than 7.5%.

RESULTS

A total of 5047 participants (19.8% Black and 18.6% Hispanic or Latinx) who had received metformin for type 2 diabetes were followed for a mean of 5.0 years. The cumulative incidence of a glycated hemoglobin level of 7.0% or higher (the primary metabolic outcome) differed significantly among the four groups (P<0.001 for a global test of differences across groups); the rates with glargine (26.5 per 100 participant-years) and liraglutide (26.1) were similar and lower than those with glimepiride (30.4) and sitagliptin (38.1). The differences among the groups with respect to a glycated hemoglobin level greater than 7.5% (the secondary outcome) paralleled those of the primary outcome. There were no material differences with respect to the primary outcome across prespecified subgroups defined according to sex, age, or race or ethnic group; however, among participants with higher baseline glycated hemoglobin levels there appeared to be an even greater benefit with glargine, liraglutide, and glimepiride than with sitagliptin. Severe hypoglycemia was rare but significantly more frequent with glimepiride (in 2.2% of the participants) than with glargine (1.3%), liraglutide (1.0%), or sitagliptin (0.7%). Participants who received liraglutide reported more frequent gastrointestinal side effects and lost more weight than those in the other treatment groups.

CONCLUSIONS

All four medications, when added to metformin, decreased glycated hemoglobin levels. However, glargine and liraglutide were significantly, albeit modestly, more effective in achieving and maintaining target glycated hemoglobin levels. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases and others; GRADE ClinicalTrials.gov number, NCT01794143.).

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.

Effect of family history of diabetes and obesity status on lifetime risk of type 2 diabetes in the Iranian population

Background

Data are scarce for the lifetime risk of diabetes in the Middle East and North Africa region countries. We estimated the lifetime risk of type 2 diabetes among Iranian adults at age 20 and 40 years, and their variation by family history of diabetes and body mass index (BMI).

Methods

The data from 8435 diabetes-free participants from the Tehran Lipid and Glucose study were used in this analysis. We estimated the lifetime risk of diabetes stratified by sex, and quantified the impact of family history of diabetes and BMI status on the lifetime risks, singly and jointly.

Results

At age 20 years, the overall lifetime risk of diabetes was 57.8% (95% CI = 54.0%-61.8%) for men and 61.3% (57.2%-65.4%) for women. Having both family history of diabetes and increased level of BMI, alone, increased the lifetime risk of diabetes in both sexes. Moreover, the simultaneous presence of family history of diabetes and overweigh/obesity increased the lifetime risk of diabetes in both sexes. So that, at age 20 years the lifetime risk in obese men with positive family history of diabetes was about 54% higher, compared to normal weight men without family history of diabetes; the corresponding value for women was 42%. Also, normal weight men without family history of diabetes lived 24 years longer free of diabetes, compared with obese men with family history of diabetes. In women, the corresponding value was 20 years.

Conclusions

Our study shows the alarming lifetime risk of diabetes across the strata of BMI, which emphasizes the need for more effective interventions to reduce incidence, particularly, among individuals with a positive family history of diabetes.

Estimation of Dairy Cow Survival in the First Three Lactations for Different Culling Reasons Using the Kaplan-Meier Method

The aims of the study were: (i) to compare survival curves for cows culled for different reasons over three successive lactations using the Kaplan-Meier estimator; (ii) to determine the effects of breeding documentation parameters on cow survival; (iii) to investigate the similarity between culling categories. The survival times for a subset of 347,939 Holstein-Friesian cows culled between 2017 and 2018 in Poland were expressed in months from calving to culling or the end of lactation. The survival tables were constructed for each culling category and lactation number. The survival curves were also compared. The main culling categories were reproductive disorders-40%, udder diseases-13 to 15%, and locomotor system diseases-above 10%. The survival curves for cows from individual culling categories had similar shapes. A low probability of survival curves for metabolic and digestive system diseases and respiratory diseases was observed in each of the three lactations. The contagious disease category was almost non-existent in the first lactation. The greatest influence on the relative culling risk was exerted by age at first calving, lactation length, calving interval, production subindex, breeding value for longevity, temperament, and average daily milk yield. A more accurate method of determining culling reasons would be required.

A randomised trial comparing weaning from CPAP alone with weaning using heated humidified high flow nasal cannula in very preterm infants: the CHiPS study

OBJECTIVE

To determine whether weaning from nasal continuous positive airway pressure (nCPAP) using heated humidified high flow nasal cannula (nHF) was non-inferior to weaning using nCPAP alone in relation to time on respiratory support.

STUDY DESIGN

Single-centre, non-inferiority, randomised controlled trial.

SETTING

Neonatal Intensive Care Unit, Middlemore Hospital, Auckland, New Zealand.

PATIENTS

120 preterm infants, <30 weeks' gestation at birth, stable on nCPAP for at least 48 hours.

INTERVENTIONS

Infants underwent stratified randomisation to nHF 6 L/min or bubble CPAP 6 cm water. In both groups, stepwise weaning of their respiratory support over 96 hours according to a strict weaning protocol was carried out.

MAIN OUTCOME MEASURES

Time on respiratory support from randomisation to 72 hours off respiratory support or 36 weeks' postmenstrual age. The non-inferiority threshold was set at 15%.

RESULTS

59 infants were randomised to weaning using nHF and 61 using nCPAP. The groups were well balanced in regards to baseline demographics. The restricted mean duration of respiratory support following randomisation for the nCPAP group, using per-protocol analysis was 401 hours (upper boundary, mean plus 0.15, was 461 hours) and 375 hours in the nHF group (upper 95% CI 413 hours). nHF weaning was, therefore, non-inferior to nCPAP weaning at the non-inferiority threshold. There was no significant difference in time to discharge.

CONCLUSION

For infants ready to wean from nCPAP, the CHiPS study found that nHF was non-inferior to discontinuing nCPAP at 5 cm water.

TRIAL REGISTRATION NUMBER

Australia and New Zealand Clinical Trials Registry (ACTRN12615000077561).

Improve individual treatment by comparing treatment benefits: cancer artificial intelligence survival analysis system for cervical carcinoma

Purpose

The current study aimed to construct a novel cancer artificial intelligence survival analysis system for predicting the individual mortality risk curves for cervical carcinoma patients receiving different treatments.

Methods

Study dataset (n = 14,946) was downloaded from Surveillance Epidemiology and End Results database. Accelerated failure time algorithm, multi-task logistic regression algorithm, and Cox proportional hazard regression algorithm were used to develop prognostic models for cancer specific survival of cervical carcinoma patients.

Results

Multivariate Cox regression identified stage, PM, chemotherapy, Age, PT, and radiation_surgery as independent influence factors for cervical carcinoma patients. The concordance indexes of Cox model were 0.860, 0.849, and 0.848 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.881, 0.845, and 0.841 in validation dataset. The concordance indexes of accelerated failure time model were 0.861, 0.852, and 0.851 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.882, 0.847, and 0.846 in validation dataset. The concordance indexes of multi-task logistic regression model were 0.860, 0.863, and 0.861 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.880, 0.860, and 0.861 in validation dataset. Brier score indicated that these three prognostic models have good diagnostic accuracy for cervical carcinoma patients. The current research lacked independent external validation study.

Conclusion

The current study developed a novel cancer artificial intelligence survival analysis system to provide individual mortality risk predictive curves for cervical carcinoma patients based on three different artificial intelligence algorithms. Cancer artificial intelligence survival analysis system could provide mortality percentage at specific time points and explore the actual treatment benefits under different treatments in four stages, which could help patient determine the best individualized treatment. Cancer artificial intelligence survival analysis system was available at: https://zhangzhiqiao15.shinyapps.io/Tumor_Artificial_Intelligence_Survival_Analysis_System/.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03491-8.

COPP-MS: COrticosteroids during the Post-Partum in relapsing Multiple Sclerosis patients

BACKGROUND

No specific treatment has demonstrated its effectiveness to prevent post-partum relapses for multiple sclerosis (MS) women.

OBJECTIVE

To assess the effectiveness of preventive high-dose corticosteroids in the post-partum period by comparing two strategies: (1) no preventive treatment and (2) standardized preventive treatment.

METHODS

We selected five French Multiple Sclerosis centers using the same post-partum strategy for their patients-either high-dose steroids (treating centers TC) or no treatment (non-treating centers NTC). We included relapsing-remitting multiple sclerosis women who delivered between January 2007 and January 2017. Our primary outcomes were the time from delivery to first relapse, EDSS progression and MRI activity between patients of treating centers and non-treating centers, after propensity-score weighting.

RESULTS

350 patients were included (116 from treating centers, 234 from non-treating centers). For both groups, the annualized relapse rate decreased during pregnancy (0.28 in treating centers and 0.34 in non-treating centers during the third trimester) and increased during the first post-partum trimester (0.45 and 0.69, respectively) with 11% and 14% (NS) of patients facing at least one relapse, respectively. Our primary outcomes were not statistically different between both groups.

CONCLUSION

This study provides class III evidence that systematic high-dose corticosteroids are not associated with a reduced inflammatory activity during the post-partum period in multiple sclerosis patients.

A MCP-Mod approach to designing and analyzing survival trials with potential non-proportional hazards

Non-proportional hazards have been observed in many studies especially in immuno-oncology clinical trials. Traditional analysis using the combined approach with log-rank test as the significance test and Cox model for treatment effect estimation becomes questionable as this approach relies heavily on the proportional hazards assumption. Inspired by the MCP-Mod (multiple comparisons and modeling approach) that has been widely used in dose-finding studies, we propose a similar approach to handle non-proportional hazards. Using this approach, efficacy signal is first established by a max-combo test, after which hazard ratios across time will be estimated using a logically nested splines model. Simulations studies and real-data examples are used to illustrate the use of this approach.

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.

Disability Progression in Multiple Sclerosis Patients using Early First-line Treatments

BACKGROUND

Therapeutic management of relapsing-remitting multiple sclerosis (RRMS) has evolved towards early treatment. The objective was to assess the impact of early treatment initiation on disability progression among RRMS first-line treated patients.

METHODS

This study included all incident RRMS cases starting interferon or glatiramer acetate for the first time from 1996/01/01 to 2012/31/12 (N=5,279) from ten MS expert OFSEP centers (Observatoire Français de la Sclérose en Plaques). The delay from treatment start to attain an irreversible Expanded Disability Status Scale score of 3.0 were compared between "Early" group (N= 1,882; treated within 12 months following MS clinical onset) and "Later" group using propensity score weighted Kaplan-Meier methods, overall and stratified by age.

RESULTS

Overall, the restricted mean time before reaching EDSS 3.0 (RMST) from treatment start was 11 years and two months for patients treated within the year following MS clinical onset and 10 years and seven months for patients treated later. Thus, early treated patients gained 7 months (95% CI: [4-11] months) in the time to reach EDSS 3.0 compared to patients treated later (treatment start delayed by 28 months). The difference in RMST was respectively six months (95% CI: [1-10] months) and 14 months (95% CI: [4-24] months) in the "≤40 years" age group and in the ">40 years" age group, in favour of early group. .

CONCLUSIONS

Early treatment initiation resulted in a significant reduction of disability progression among patients with RRMS, and also among older patients.

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.

The Proper Use and Reporting of Survival Analysis and Cox Regression

BACKGROUND

Survival analyses are heavily used to analyze data in which the time to event is of interest. The purpose of this paper is to introduce some fundamental concepts for survival analyses in medical studies.

METHODS

We comprehensively review current survival methodologies, such as the nonparametric Kaplan-Meier method used to estimate survival probability, the log-rank test, one of the most popular tests for comparing survival curves, and the Cox proportional hazard model, which is used for building the relationship between survival time and specific risk factors. More advanced methods, such as time-dependent receiver operating characteristic, restricted mean survival time, and time-dependent covariates are also introduced.

RESULTS

This tutorial is aimed toward covering the basics of survival analysis. We used a neurosurgical case series of surgically treated brain metastases from non-small cell lung cancer patients as an example. The survival time was defined from the date of craniotomy to the date of patient death.

CONCLUSIONS

This work is an attempt to encourage more investigators/medical practitioners to use survival analyses appropriately in medical research. We highlight some statistical issues, make recommendations, and provide more advanced survival modeling in this aspect.

Effectiveness and Cost-Effectiveness Profile of Second-Line Treatments with Nivolumab, Pembrolizumab and Atezolizumab in Patients with Advanced Non-Small Cell Lung Cancer

No evidence is available on the head-to-head comparison of clinical outcomes of patients treated with immune checkpoint inhibitors (ICIs) for advanced non-small cell lung cancer (NSCLC) in a real-world setting. We aimed to compare the effectiveness and cost-effectiveness profile of nivolumab, pembrolizumab and atezolizumab. We used a population-based retrospective cohort study based on the healthcare utilization databases of the Lombardy Region, Italy. The study cohort included all patients with a diagnosis of lung cancer, who started a second-line treatment for advanced NSCLC with nivolumab, pembrolizumab or atezolizumab from 2015 to 30 June 2020. Overall survival and average cumulative healthcare costs were measured from the start of second-line treatment until 31 December 2020. The study cohort included 1607 patients who started a second-line treatment with ICIs, of which there were 1193 with nivolumab, 138 with pembrolizumab and 276 with atezolizumab. No differences were observed between treatment arms in terms of sex, age or comorbidities. Median OS was very similar between groups, being 8.9, 9.4 and 8.7 months, respectively, in patients treated with nivolumab, pembrolizumab and atezolizumab (p = 0.898). The adjusted hazard ratio of death of patients treated with pembrolizumab and atezolizumab, as compared to nivolumab, were 1.01 (95% CI: 0.81 to 1.25) and 1.03 (0.88 to 1.21), respectively. Healthcare cumulative costs measured in the first two years of follow-up were EUR 43,764, 46,233 and 34,116, on average, associated with nivolumab, pembrolizumab and atezolizumab, respectively. In our real-world study, atezolizumab was the ICI associated with the most favorable cost-effectiveness profile.

Parametric Survival Extrapolation of Early Survival Data in Economic Analyses: A Comparison of Projected Versus Observed Updated Survival

OBJECTIVES

To establish the value of cancer drugs by cost-effectiveness analysis, lifetime parametric survival extrapolations are often fitted to early data. Recent literature suggests that the benefit of cancer agents in primary publications is often different compared with updated data. This study aimed to examine the projected survival based on parametric extrapolations compared with observed survival based on updated data.

METHODS

US Food and Drug Administration oncology approvals from January 2006 to December 2015 were reviewed to identify randomized controlled trials, with updated overall survival (OS) or progression-free survival (PFS) data within 5 years. Individual patient data were reconstructed using established methods on initial and updated publications. Projected survival was calculated as the best-fit parametric restricted mean survival time (RMST) based on extrapolated initial Kaplan-Meier curves whereas observed survival was calculated as observed RMST based on updated Kaplan-Meier curves. Mean deviations, mean absolute error (MAE), mean absolute percentage error, and linear regressions were conducted to examine the relationship between projected and observed survival.

RESULTS

In total, 32 randomized controlled trials were included. The MAE between the projected RMST and observed RMST was 3.18 months (OS) and 2.84 months (PFS) and absolute percentage error of 100% (OS) and 23% (PFS), suggesting substantial imprecision of the projected RMST in predicting the updated RMST. The linear regression indicated MAE increased as time extrapolated and as the percentage of censored patients increased.

CONCLUSIONS

This study demonstrated substantial difference in projected survival between initial and updated publications. Health technology assessment committees need to be aware of the potential uncertainty of incremental effectiveness and resultant value-for-money assessment when making reimbursement decisions based on initial publications with immature survival data.

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.

A biological approach to adult sex differences in skeletal indicators of childhood stress

OBJECTIVES

In previous work examining the etiology of cribra orbitalia (CO) and porotic hyperostosis (PH) in a contemporary juvenile mortality sample, we noted that males had higher odds of having CO lesions than females. Here, we examine potential reasons for this pattern in greater detail. Four non-mutually exclusive mechanisms could explain the observed sex differences: (1) sex-biased mortality; (2) sexual dimorphism in immune responses; (3) sexual dimorphism in bone turnover; or (4) sexual dimorphism in marrow conversion.

SUBJECTS AND METHODS

The sample consists of postmortem computed tomography scans and autopsy reports, field reports, and limited medical records of 488 individuals from New Mexico (203 females; 285 males) aged between 0.5 and 15 years. We used Kaplan-Meier survival analysis, predicted probabilities, and odds ratios to test each mechanism.

RESULTS

Males do not have lower survival probabilities than females, and we find no indications of sex differences in immune response. Overall, males have a higher probability of having CO or PH lesions than females.

CONCLUSIONS

All results indicate that lesion formation in juveniles is influenced by some combination of sex differences in the pace of red-yellow conversion of the bone marrow and bone turnover. The preponderance of males with CO and PH likely speaks to the potential for heightened osteoblastic activity in males. We find no support for the hypotheses that sex biases in mortality or immune responses impacted lesion frequency in this sample. Sex differences in biological processes experienced by children may affect lesion formation and lesion expression in later life.

Ten-year follow-up after mitoxantrone induction for early highly active relapsing-remitting multiple sclerosis: An observational study of 100 consecutive patients

BACKGROUND

Six monthly courses of mitoxantrone were approved in France in 2003 for patients with highly active multiple sclerosis (MS).

OBJECTIVE

To report the 10-year clinical follow-up and safety of mitoxantrone as an induction drug followed by maintenance therapy in patients with early highly active relapsing-remitting MS (RRMS) and an Expanded Disability Status Scale (EDSS) score<4, 12months prior to mitoxantrone initiation.

METHODS

In total, 100 consecutive patients with highly active RRMS from the Rennes EDMUS database received monthly mitoxantrone 20mg combined with methylprednisolone 1g for 3 (n=75) or 6months (n=25) followed by first-line disease-modifying drug (DMD). The 10-year clinical impact was studied through clinical activity, DMD exposure, and adverse events.

RESULTS

Twenty-four percent were relapse-free over 10years and the mean annual number of relapses was 0.2 at 10years. The mean EDSS score remained significantly improved for up to 10years, changing from 3.5 at mitoxantrone initiation to 2.7 at 10years. The probability of disability worsening and improvement from mitoxantrone initiation to 10years were respectively 27% and 58%, and 13% converted to secondary progressive MS. Patients only remained untreated or treated with a first-line maintenance DMD for 6.5years in average. In our cohort, mitoxantrone was generally safe. No leukemia was observed and six patients developed neoplasms, including 4 solid cancers.

CONCLUSION

Monthly mitoxantrone for 3 or 6months, followed by maintenance first-line treatment, may be an attractive therapeutic option for patients with early highly active RRMS, particularly in low-income countries.

Transthyretin V142I Genetic Variant and Cardiac Remodeling, Injury, and Heart Failure Risk in Black Adults

OBJECTIVES

This study evaluated the association of transthyretin (TTR) gene variant, in which isoleucine substitutes for valine at position 122 (V142I), with cardiac structure, function, and heart failure (HF) risk among middle-aged Black adults.

BACKGROUND

The valine-to-isoleucine substitution in the TTR protein is prevalent in Black individuals and causes cardiac amyloidosis.

METHODS

Jackson Heart Study participants without HF at baseline who had available data on the TTR V142I variant were included. The association of the TTR V142I variant with baseline echocardiographic parameters and repeated measures of high-sensitivity cardiac troponin-I (hs-cTnI) was assessed using adjusted linear regression models and linear mixed models, respectively. Adjusted Cox models, restricted mean survival time analysis, and Anderson-Gill models were constructed to determine the association of TTR V142I variant with the risk of incident HF, survival free of HF, and total HF hospitalizations.

RESULTS

A total of 119 of 2,960 participants (4%) were heterozygous carriers of the TTR V142I variant. The TTR V142I variant was not associated with measures of cardiac parameters at baseline but was associated with a greater increase in high-sensitivity troponin I (hs-TnI) levels over time. In adjusted Cox models, TTR V142I variant carriers had significantly higher risk of incident HF (HR: 1.80; 95% CI: 1.07-3.05; P = 0.03), lower survival free of HF (mean difference: 4.0 year; 95% CI: 0.6-6.2 years); P = 0.02), and higher risk of overall HF hospitalizations (HR: 2.12; 95% CI: 1.23-3.63; P = 0.007).

CONCLUSIONS

The TTR V142I variant in middle-aged Black adults is not associated with adverse cardiac remodeling but was associated with a significantly higher burden of chronic myocardial injury, and greater risk of incident HF and overall HF hospitalizations.

Effects of lobectomy in stage II/IIIA second primary lung cancer patients with prior non-small cell lung cancer: a SEER-based study

OBJECTIVE

Our study aimed to reveal the prognostic factors of second primary lung cancer and explore the optimal surgical procedure for Stage II/IIIA second primary lung cancer patients with prior non-small cell lung cancer.

METHODS

Patients with Stage II/IIIA second primary lung cancer were collected from the Surveillance, Epidemiology and End Results database from 2004 to 2016. Lasso regression, along with univariate and multivariate Cox regression, was used to screen prognostic factors. The propensity score matching was used to minimize baseline differences, and restricted mean survival time was used to compare overall survival and cancer-specific survival of different groups.

RESULTS

A total of 579 patients were enrolled in the study. After data was screened by lasso regression and univariate Cox regression, multivariate Cox regression revealed that age, sex, race, tumor size of initial primary lung cancer, tumor size, histological grade, T stage, N stage and surgical procedure of second primary lung cancer were independent prognostic factors. Further analysis showed that surgery, especially lobectomy, provided better survival in Stage II/IIIA second primary lung cancer.

CONCLUSIONS

Our study identified nine independent prognostic factors of Stage II/IIIA second primary lung cancer. Surgery can provide a better prognosis, and lobectomy might be the optimal surgical procedure for these patients.

Impact of postoperative chemotherapy on survival for oesophagogastric adenocarcinoma after preoperative chemotherapy and surgery

BACKGROUND

Perioperative chemotherapy is widely used in the treatment of oesophagogastric adenocarcinoma (OGAC) with a substantial survival benefit over surgery alone. However, the postoperative part of these regimens is given in less than half of patients, reflecting uncertainty among clinicians about its benefit and poor postoperative patient fitness. This study estimated the effect of postoperative chemotherapy after surgery for OGAC using a large population-based data set.

METHODS

Patients with adenocarcinoma of the oesophagus, gastro-oesophageal junction or stomach diagnosed between 2012 and 2018, who underwent preoperative chemotherapy followed by surgery, were identified from a national-level audit in England and Wales. Postoperative therapy was defined as the receipt of systemic chemotherapy within 90 days of surgery. The effectiveness of postoperative chemotherapy compared with observation was estimated using inverse propensity treatment weighting.

RESULTS

Postoperative chemotherapy was given to 1593 of 4139 patients (38.5 per cent) included in the study. Almost all patients received platinum-based triplet regimens (4004 patients, 96.7 per cent), with FLOT used in 3.3 per cent. Patients who received postoperative chemotherapy were younger, with a lower ASA grade, and were less likely to have surgical complications, with similar tumour characteristics. After weighting, the median survival time after postoperative chemotherapy was 62.7 months compared with 50.4 months without chemotherapy (hazard ratio 0.84, 95 per cent c.i. 0.77 to 0.94; P = 0.001).

CONCLUSION

This study has shown that postoperative chemotherapy improves overall survival in patients with OGAC treated with preoperative chemotherapy and surgery.