Evaluating Metformin Efficacy in ALS Using Real-World Data: A Causal Inference Approach

The difference in restricted mean survival time has been increasingly used as an alternative measure to the hazard ratio in survival analysis. Although some statistical methods have been developed for estimating the difference in restricted mean survival time adjusted for measured confounders in observational studies, the impact of unmeasured confounding on the estimate has rarely been assessed. We develop a novel sensitivity analysis for the estimate of the difference in restricted mean survival time with respect to unmeasured confounding. After formulating the sensitivity analysis problem as an optimization problem, we explain how to obtain the sensitivity range of the difference in restricted mean survival time efficiently and assess its uncertainty using the percentile bootstrap confidence interval. Analytic results are provided for some important survival settings. Simulation studies show that the proposed methods perform well in various settings. We illustrate the proposed sensitivity analysis method by analyzing data from the German Breast Cancer Study Group study.

Despite prominent potentials of randomized controlled trials (RCTs) with computer-based interventions, log data poses a challenge since it differs in structure and size from the type of data commonly encountered in studies of causal mechanisms. The current study developed a method for RCTs suitable for big data, or complex implementation data. The method is an extension of principal stratification (PS), a causal framework used for studying how treatment effects vary as a function of post-treatment or intermediate variables. To exploit the complex structure of the log data, the proposed method can incorporate latent variables or measurement models into PS, substantially extending the scope of PS modeling into scenarios with multivariate and complex implementation data. With the method development, we did a simulation study to evaluate if our proposed FLPS model worked properly under various conditions, including sample sizes, number of items, effect sizes, and response rates, with different IRT models. FLPS models will allow researchers to gain deeper and more nuanced insights into the relationship between the effectiveness of the interventions under study, and how they are used. This ability will, in turn, deepen our understanding of our rapidly-evolving and growing interaction with technology, guiding the development of more effective interventions and guiding the implementation decisions of users.

The concept of principal surrogate developed in the causal inference framework (Frangakis and Rubin (Biometrics 58:21–29, 2002); Gilbert and Hudgens (Biometrics 64:1146–1154, 2008)) has drawn much attention in the field of biomarker research. Principal surrogates are defined based on the causal treatment effects in principal strata, which are constructed based on the joint distribution of the potential surrogate markers when a patient receives either the placebo or the treatment. The challenge of evaluating principal surrogates lies in the fact that half of these potential surrogate markers cannot be observed in most clinical trials. Therefore assessing the principal surrogacy of biomarkers is essentially a missing data problem. In this article, we propose a multiple imputation approach to evaluate candidate principal surrogate markers. The proposed method employs baseline variables to impute the missing potential surrogate markers. The stratum-specific causal treatment effects on the clinical endpoint are then estimated for each imputed dataset and the inference for surrogacy of a biomarker is based on the combined results over multiple imputations. Simulation studies are performed to evaluate the performance of the proposed method and the implementation of the method is illustrated using a vaccine study.

Background Pancreatic cancer is currently the world's seventh most common cancer with it's incidence being equivalent to it's mortality. It is the fourth most common cause of cancer-related mortality and is set to be the second most common by 2030. 5-year overall survival of pancreatic cancer is set at just below 10%, with only approximately 15% - 20% of diagnosed patients being eligible for resection. Patients eligible for resection have a 5-year overall survival improvement of up to 30% over recent years following the combination of resection and adjuvant therapy. The prognostic effects of chemo-preventive agents such as metformin and aspirin have been investigated previously in pancreatic cancer (PC) with variable results. ESPAC-4 was a phase-3 randomised controlled trial which recruited patients with resectable pancreatic cancer and examined the survival benefit between adjuvant monotherapy gemcitabine therapy and adjuvant gemcitabine-capecitabine (GEMCAP) combination therapy. This study aims to evaluate the prognostic impact of aspirin and metformin on survival among PC patients in the ESPAC 4 randomised trial. Methods Concomitant medication data was identified from ESPAC-4 trial database. Primary and secondaryoutcome measures were overall survival (OS) and disease-free survival (DFS) respectively for users and non-users of aspirin and metformin. Survival data was analysed using multivariate and univariate Cox-proportional hazard models and log-rank testing.The primary and secondary outcome was to investigate overall survival (OS) and Relapse-Free Survival (RFS) time respectively, between users and non- users of aspirin and metformin in either treatment arm. Results We identified 86 patients using aspirin and 55 patients using metformin out of 730 randomised trial patients at baseline . Aspirin usage led to a median OS of 30 months versus 26 months with non-users and a hazard ratio of 0.91 (95% CI:0.68–1.2; p=0.499). Median DFS with and without aspirin usage was 17.9 months versus 13.1 months respectively with a hazard ratio of 0.76 (95% CI: 0.58–0.99, p=0.044). Gemcitabine treatment amongst aspirin users demonstrated a median OS of 42.4 months compared to 24.7 months in non-aspirin users and a hazard ratio of 1.69 with non-users (95% CI:1.06–2.67; p=0.026). Aspirin users randomised to gemcitabine had a median DFS of 37.2 months, compared to non-aspirin users with 11.9 months and a hazard ratio of 2.01 (95% CI:1.31–3.08; p=0.001). Metformin usage resulted in a median OS of 23.8 months versus 27.7 months in non-users with a hazard ratio of 0.95 (95% CI:0.65–1.4; p=0.819). Median DFS with and without metformin usage was 14.7 months versus 12.5 months respectively, with a hazard ratio of 0.88 (95% CI:0.61–1.27; p=0.498). Metformin usage in either treatment arm did not demonstrate a significant survival difference Conclusions Though numbers are small, gemcitabine therapy and aspirin usage demonstrated a prognostic benefit in OS and DFS in this study. Further in vivo studies exploring the mechanism of this interaction would be beneficial. There was no survival benefit observed with metformin usage. Standardisation of dosages in chemopreventive agents such as metformin and aspirin in future trials could yield further standardised results.

Metformin was associated with lower all-cause mortality in type 2 diabetes with acute coronary syndrome: A Nationwide registry with propensity score-matched analysis

If a vaccine does not protect individuals completely against infection, it could still reduce infectiousness of infected vaccinated individuals to others. Typically, vaccine efficacy for infectiousness is estimated based on contrasts between the transmission risk to susceptible individuals from infected vaccinated individuals compared with that from infected unvaccinated individuals. Such estimates are problematic, however, because they are subject to selection bias and do not have a causal interpretation. Here, we develop causal estimands for vaccine efficacy for infectiousness for four different scenarios of populations of transmission units of size two. These causal estimands incorporate both principal stratification, based on the joint potential infection outcomes under vaccine and control, and interference between individuals within transmission units. In the most general scenario, both individuals can be exposed to infection outside the transmission unit and both can be assigned either vaccine or control. The three other scenarios are special cases of the general scenario where only one individual is exposed outside the transmission unit or can be assigned vaccine. The causal estimands for vaccine efficacy for infectiousness are well defined only within certain principal strata and, in general, are identifiable only with strong unverifiable assumptions. Nonetheless, the observed data do provide some information, and we derive large sample bounds on the causal vaccine efficacy for infectiousness estimands. An example of the type of data observed in a study to estimate vaccine efficacy for infectiousness is analyzed in the causal inference framework we developed.

BackgroundRestricted mean survival time (RMST) is a measure of average survival time up to a specified time point. There has been an increased interest in using RMST to compare treatment arms in randomized clinical trials (RCTs) because such comparisons do not rely on proportional hazards or other assumptions about the nature of the relationship between survival curves.MethodsThis paper addresses the question of whether covariate adjustment in randomized clinical trials that compare RMSTs improves precision of the estimated treatment effect (difference in RMSTs between treatment arms). Although precision generally increases in linear models when prognostic covariates are added, this is not necessarily the case in non-linear models. For example, in logistic and Cox regression, the standard error of the estimated treatment effect does not decrease when prognostic covariates are added, although the situation is complicated in those settings because the estimand changes as well. Because estimation of RMST in the manner described in this paper is also based on a model that is nonlinear in the covariates, we investigate whether the comparison of RMSTs with adjustment for covariates leads to a reduction in the standard error of the estimated treatment effect relative to the unadjusted estimator or whether covariate adjustment provides no improvement in precision. Chen and Tsiatis (2001) suggest that precision will increase if covariates are chosen judiciously. We present results of simulation studies that compare unadjusted vs. adjusted comparisons of RMST between treatment arms in randomized clinical trials.ResultsWe find that for comparison of restricted means in an RCT, adjusting for covariates that are associated with survival increases precision and therefore statistical power, relative to the unadjusted estimator. Omitting important covariates results in less precision but estimates remain unbiased.ConclusionsWhen comparing restricted means in an RCT, adjusting for prognostic covariates can improve precision and increase power.

Using the Electronic Health Record to Develop a Gastric Cancer Risk Prediction Model

BackgroundSpontaneous rupture of hepatocellular carcinoma (rHCC) poses a life-threatening complication with a mortality rate of 25–75%. Treatment aims at achieving hemostasis and includes options such as trans-arterial embolization, perihepatic packing, and hepatic resection. The optimal treatment remains a subject of debate. Our retrospective review evaluates these treatments and investigates imaging’s role in prognosis for rHCC patients.PurposeWe aimed to compare survival outcomes among rHCC patients who received transarterial embolization (TAE), surgery (perihepatic packing, hepatectomy), or best supportive care (BSC), while also identifying predictive imaging factors in these patients.Materials and methodsAll patients diagnosed with rHCC and admitted to Maharaj Nakorn Chiangmai Hospital between January 2012 and December 2021 were included. We reviewed clinical features, imaging results, treatment modalities, and outcomes. In order to balance pretreatment confounders, inverse probability treatment weighting (IPTW) was employed. Flexible parametric survival regression was utilized to compare survival outcomes and identify imaging factors predicting the survival of rHCC patients. Hazard ratios (HR) and the difference in restricted mean survival time (RMST) were reported.ResultAmong the 186 rHCC patients included, we observed 90-day and 1-year mortality rates of 64% and 84%, respectively. Both the TAE and surgery groups exhibited significantly lower 1-year mortality rates compared to BSC. The HR were 0.56 (95% CI 0.33–0.96) for TAE and 0.52 (95% CI 0.28–0.95) for surgery compared to BSC. Both the TAE and surgery also significantly extended the 1-yeaar life expectancy post-initial treatment when compared to BSC, with an RMST difference of + 55.40 days (95% CI 30.18–80.63) for TAE vs. BSC and + 68.43 days (95% CI 38.77–98.09) for surgery vs. BSC. The presence of active contrast extravasation and bleeding in both lobes were independent prognostic factors for 1-year survival.ConclusionsTAE and surgical treatments provide comparable survival benefits for rHCC patients, extending survival time by approximately 2 months compared to best supportive care. We strongly recommend active management for all rHCC patients whenever possible.

In randomized clinical trials with a time-to-event outcome, the intervention effect could be quantified by a difference in restricted mean survival time (ΔRMST) between the intervention and control groups, defined as the expected survival duration gain due to the intervention over a fixed follow-up period. In cluster randomized trials (CRTs), social units are randomized to intervention or control groups; the correlation between survival times of the individuals within the same cluster must be taken into account in the statistical analysis. In a previous work, we proposed the use of pseudo-values regression, based on generalized estimating equations (GEEs), for estimating ΔRMST in CRTs. We showed that this method correctly estimated the ΔRMST and controlled the type I error rate in CRTs with at least 50 clusters. Here, we propose methods for CRTs with a small number of clusters (<50). We evaluated the performance of four bias-corrections of the GEE sandwich variance estimator of the intervention effect. We also considered the use of a Student t distribution as an alternative to the normal distribution of the GEE Wald test statistic for testing the intervention effect and constructing the confidence interval. With a simulation study, assuming proportional or non-proportional hazards, we showed that the Student t distribution outperformed the normal distribution in terms of type I error rate, and the Fay and Graubard bias-corrected variance led to an appropriate type I error rate whatever the number of clusters. Therefore, we recommend the use of the Fay and Graubard variance estimator combined with a Student t distribution for the pseudo-values regression to correctly estimate the variance of the intervention effect. Finally, we provide an illustrative analysis of the DEMETER trial evaluating the use of a specific endotracheal tube for subglottic secretion drainage to prevent ventilator-associated pneumonia, by comparing each of the methods considered.

Net survival is used in epidemiological studies to assess excess mortality due to a given disease when causes of death are unreliable. By correcting for the general population mortality, it allows comparisons between regions or periods and thus evaluation of health policies. The Pohar-Perme non-parametric estimator of net survival has been recently proposed, soon followed by an appropriate log-rank-type test. However, log-rank tests are known to be under-optimal in non-proportional settings (e.g. crossing of the hazard functions). In classical survival analysis, one solution is to compare the restricted mean survival times. A difference in restricted mean survival time represents a life benefit or loss over the studied period. In the present article the restricted mean net survival time was used to derive a specific test statistic to compare net survivals in proportional and non-proportional hazards settings. The new test was generalized to more than two groups and to stratified analysis. The test performance was assessed on simulation study, compared to the log-rank-type test, and its use illustrated on a population-based colorectal cancer registry. The new test for net survival comparisons proved robust to non-proportionality and well-performing in proportional hazards situations. Furthermore, it is also suited to the classical survival framework.

Temozolomide (TEM) as a single agent or in combination with capecitabine (CAPTEM) is active in well-differentiated advanced neuroendocrine tumors (NETs) of gastro-entero-pancreatic and thoracic origin. The predictive role of MGMT-promoter methylation in this setting is controversial. We sought to prospectively evaluate the MGMT-promoter methylation status ability to predict outcomes to TEM-based chemotherapy in patients with NET. A single-center, prospective, observational study has been conducted at the ENETS Center-of-Excellence Outpatient Clinic of the IRCCS Policlinico Sant'Orsola-Malpighi in Bologna, Italy. Patients with advanced, gastro-entero-pancreatic or lung well-differentiated NETs candidate to TEM-based chemotherapy and with available tumor samples for MGMT-promoter methylation assessment were included. The MGMT-promoter methylation status was analyzed by using pyrosequencing. The primary endpoint was progression-free survival (PFS) by the MGMT-promoter methylation status. Secondary endpoints included overall survival (OS), objective response rate (ORR), disease control rate (DCR), and safety. Survival outcomes were compared by restricted mean survival time (RMST) difference. Of 26 screened patients, 22 were finally enrolled in the study. The most frequent NET primary sites were the pancreas (64%) and the lung (23%). MGMT promoter was methylated in five tumors (23%). At a median follow-up time of 47.2 months (95%CI 29.3-89.7), the median PFS was 32.8 months (95%CI 17.2-NA), while the median OS was not reached. Patients in the methylated MGMT group, when compared to those in the unmethylated MGMT group, had longer PFS (median not reached [95%CI NA-NA] vs. 30.2 months [95%CI 15.2-NA], respectively; RMST p = 0.005) and OS (median not reached [95%CI NA-NA] vs. not reached [40.1-NA], respectively; RMST p = 0.019). After adjusting for confounding factors, the MGMT-promoter methylation status was independently associated to the PFS. Numerically higher ORR (60% vs. 24%; p = 0.274) and DCR (100% vs. 88%; p = 1.00) were observed in the methylated vs. unmethylated MGMT group. TEM-based chemotherapy was well-tolerated (adverse events grade ≥3 < 10%). In this prospective study, MGMT-promoter methylation predicted better outcomes to TEM-based chemotherapy in patients with NET.

Programmed cell death receptor ligand 1 (PD-L1) expression is the most studied biomarker to predict the efficacy of immune checkpoint inhibitors (ICIs), but its clinical significance is controversial. We estimated the distribution of PD-L1 expression scores (ie, tumor proportion score or combined proportion score) and the relationship between PD-L1 levels and ICIs' impact on overall survival (OS). We reconstructed, pooled, and analyzed individual-level data on 7,617 patients with cancer from 14 randomized clinical trials. The effects of ICIs were quantified using differences in 24-month restricted mean survival times (ΔRMSTs; ie, the increase in life expectancy truncated at 2 years associated with ICI therapy). In a simulation study, we compared standard randomized clinical trial designs with a trial design that leverages meta-analytic results like ours. Approximately 93% of patients had a PD-L1 expression ≤ 5% (66% of patients) or > 50% (27% of patients). OS improves with ICIs regardless of PD-L1 expression level, which predicts the benefits' magnitude. For patients with non-small-cell lung cancer (NSCLC), ΔRMSTs ranged from 1.4 months (95% probability interval [PI], 0.7 to 2.2 months) for PD-L1 expression ≤ 1% to 4.1 months (95% PI, 3.2 to 5.2 months) for PD-L1 expression > 80%. For patients with non-NSCLC tumors, ΔRMSTs ranged from 0.8 months (95% PI, -0.1 to 1.7 months) to 2.3 months (95% PI, 1.3 to 4.4 months), again for PD-L1 expression levels of ≤ 1% and > 80%, respectively. Simulations suggested that designs tailored to meta-analytic results can detect the effects of ICIs in PD-L1 subgroups with higher probability (> 15%) than standard designs. The practice of dichotomizing the range of PD-L1 expression scores is inadequate for patient stratification. Meta-analytic estimates of the distribution of PD-L1 scores and subgroup-specific treatment effects can improve the designs of future trials of ICIs.

IntroductionClinical evidence suggests that first-line immune checkpoint inhibitor (ICI) combination therapies can improve survival in patients with advanced non-squamous non-small cell lung cancer (nsq-NSCLC). However, the optimal strategy remains unknown without a systematic comparison of their long-term effects.MethodsWe performed a systematic review and network meta-analysis by retrieving up-to-date literature from PubMed® (National Library of Medicine, Bethesda, MD, USA), Embase® (Elsevier, Amsterdam, Netherlands), MEDLINE® (National Library of Medicine), ClinicalTrials.gov (National Library of Medicine), and major international conference publications. Published studies and abstracts comparing first-line ICI combination therapies with other treatments for patients with advanced nsq-NSCLC were included. Restricted mean survival time (RMST) was measured over 12 months for progression-free survival (PFS) and 18 months for overall survival (OS), and the Royston–Parmar model was used to extrapolate and compare data for the long-term outcomes.ResultsWe included a total of 11 trials involving 12 therapies and 6,130 patients. Pembrolizumab plus chemotherapy exhibited the best overall survival (OS) benefit at both 18 and 60 months [RMST = 2.95, 95% confidence interval (CI) 1.96 to 3.97; life-years gained over a 5-year period = 2.18 years]. Nivolumab plus bevacizumab plus chemotherapy was found to present the best progression-free survival (PFS) benefit at 12 months (RMST 3.02, 95% CI 2.11 to 3.91), whereas atezolizumab plus bevacizumab plus chemotherapy showed the best PFS benefit at 36 months (life-years gained over 3 years = 1.22 years). Subgroup analyses showed that among patients with programmed death-ligand 1 (PD-L1) expression ≥ 50%, atezolizumab plus chemotherapy and nivolumab plus ipilimumab resulted in superior OS benefits at 18 and 60 months, respectively. Among patients with PD-L1 expression< 1%, pembrolizumab plus chemotherapy was associated with OS benefits at both 18 and 60 months. Sintilimab plus chemotherapy was associated with relatively fewer grade ≥ 3 adverse events than other ICI combination therapies.ConclusionOur results show that ICI combination therapies showed better survival benefits than chemotherapy. Pembrolizumab plus chemotherapy could provide the best OS benefits to patients with advanced nsq-NSCLC, whereas atezolizumab plus bevacizumab plus chemotherapy could bring the best PFS benefits. The optimal ICI combination therapy varies depending on PD-L1 expression level.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=325005, identifier CRD42022325005.

Consolidative Radiotherapy for Extensive-Stage Small Cell Lung Cancer: Outcomes Based on Completeness of Consolidation and Oligometastatic State.