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The timing of a time-dependent treatment—for example, when to perform a kidney transplantation—is an important factor for evaluating treatment efficacy. A naïve comparison between the treated and untreated groups, while ignoring the timing of treatment, typically yields biased results that might favour the treated group because only patients who survive long enough will get treated. On the other hand, studying the effect of a time-dependent treatment is often complex, as it involves modelling treatment history and accounting for the possible time-varying nature of the treatment effect. We propose a varying-coefficient Cox model that investigates the efficacy of a time-dependent treatment by utilizing a global partial likelihood, which renders appealing statistical properties, including consistency, asymptotic normality and semiparametric efficiency. Extensive simulations verify the finite sample performance, and we apply the proposed method to study the efficacy of kidney transplantation for end-stage renal disease patients in the US Scientific Registry of Transplant Recipients.

Background Longitudinal matching can mitigate confounding in observational, real-world studies of time-dependent treatments. To date, these methods have required iterative, manual re-specifications to achieve covariate balance. We propose a longitudinal extension of genetic matching, a machine learning approach that automates balancing of covariate histories. We examine performance by comparing the proposed extension against baseline propensity score matching and time-dependent propensity score matching. Methods To evaluate comparative performance, we developed a Monte Carlo simulation framework that reflects a static treatment assigned at multiple time points. Data generation considers a treatment assignment model, a continuous outcome model, and underlying covariates. In simulation, we generated 1,000 datasets, each consisting of 1,000 subjects, and applied: (1) nearest neighbour matching on time-invariant, baseline propensity scores; (2) sequential risk set matching on time-dependent propensity scores; and (3) longitudinal genetic matching on time-dependent covariates. To measure comparative performance, we estimated covariate balance, efficiency, bias, and root mean squared error (RMSE) of treatment effect estimates. In scenario analysis, we varied underlying assumptions for assumed covariate distributions, correlations, treatment assignment models, and outcome models. Results In all scenarios, baseline propensity score matching resulted in biased effect estimation in the presence of time-dependent confounding, with mean bias ranging from 29.7% to 37.2%. In contrast, time-dependent propensity score matching and longitudinal genetic matching achieved stronger covariate balance and yielded less biased estimation, with mean bias ranging from 0.7% to 13.7%. Across scenarios, longitudinal genetic matching achieved similar or better performance than time-dependent propensity score matching without requiring manual re-specifications or normality of covariates. Conclusions While the most appropriate longitudinal method will depend on research questions and underlying data patterns, our study can help guide these decisions. Simulation results demonstrate the validity of our longitudinal genetic matching approach for supporting future real-world assessments of treatments accessible at multiple time points.

Marginal structural models (MSMs) are becoming increasingly popular as a tool for causal inference from longitudinal data. Unlike standard regression models, MSMs can adjust for time-dependent observed confounders while avoiding the bias due to the direct adjustment for covariates affected by the treatment. Despite their theoretical appeal, a main practical difficulty of MSMs is the required estimation of inverse probability weights. Previous studies have found that MSMs can be highly sensitive to misspecification of treatment assignment model even when the number of time periods is moderate. To address this problem, we generalize the covariate balancing propensity score (CBPS) methodology of Imai and Ratkovic to longitudinal analysis settings. The CBPS estimates the inverse probability weights such that the resulting covariate balance is improved. Unlike the standard approach, the proposed methodology incorporates all covariate balancing conditions across multiple time periods. Since the number of these conditions grows exponentially as the number of time period increases, we also propose a low-rank approximation to ease the computational burden. Our simulation and empirical studies suggest that the CBPS significantly improves the empirical performance of MSMs by making the treatment assignment model more robust to misspecification. Open-source software is available for implementing the proposed methods.

Stroke, the second highest leading cause of death, is caused by an abrupt interruption of blood to the brain. Supply of blood needs to be promptly restored to salvage brain tissues from irreversible neuronal death. Existing assessment of stroke patients is based largely on detailed clinical evaluation that is complemented by neuroimaging methods. However, emerging data point to the potential use of blood-derived biomarkers in aiding clinical decision-making especially in the diagnosis of ischemic stroke, triaging patients for acute reperfusion therapies, and in informing stroke mechanisms and prognosis. The demand for newer techniques to deliver individualized information on-site for incorporation into a time-sensitive work-flow has become greater. In this review, we examine the roles of a portable and easy to use point-of-care-test (POCT) in shortening the time-to-treatment, classifying stroke subtypes and improving patient’s outcome. We first examine the conventional stroke management workflow, then highlight situations where a bedside biomarker assessment might aid clinical decision-making. A novel stroke POCT approach is presented, which combines the use of quantitative and multiplex POCT platforms for the detection of specific stroke biomarkers, as well as data-mining tools to drive analytical processes. Further work is needed in the development of POCTs to fulfill an unmet need in acute stroke management.

To describe the use of risk-difference curves for communicating time-dependent absolute treatment effects. Three examples based on individual patient data meta-analyses for adjuvant treatments for early-stage breast cancer are presented. Unit record datasets were re-created from the published Kaplan–Meier curves and numbers at risk or person-years at risk. Risk-difference curves, with corresponding 95% confidence bands, are presented and discussed. Risk-difference curves are useful for communicating the results from trials of adjuvant treatments for early-stage cancer when standard measures of the absolute treatment effect for survival data (ie, difference-in-mean and difference-in-median survival) can be difficult to estimate. They also avoid the problem of “evolving selection bias”, which can affect interval-specific hazard ratio (HR)s in trials with long follow-up and where the participants are heterogeneous with respect to prognosis. Clinical epidemiologists should consider reporting risk-difference curves in addition to Kaplan–Meier curves and the HR.

BackgroundControlling Nutritional Status (CONUT), as calculated from serum albumin, total cholesterol concentration, and total lymphocyte count, was previously shown to be useful for nutritional assessment. The current study investigated the potential use of CONUT as a prognostic marker in gastric cancer patients after curative resection.MethodsPreoperative CONUT was retrospectively calculated in 416 gastric cancer patients who underwent curative resection at Kumamoto University Hospital from 2005 to 2014. The patients were divided into two groups: CONUT-high (≥4) and CONUT-low (≤3), according to time-dependent receiver operating characteristic (ROC) analysis. The associations of CONUT with clinicopathological factors and survival were evaluated.ResultsCONUT-high patients were significantly older (p < 0.001) and had a lower body mass index (p = 0.019), deeper invasion (p < 0.001), higher serum carcinoembryonic antigen (p = 0.037), and higher serum carbohydrate antigen 19-9 (p = 0.007) compared with CONUT-low patients. CONUT-high patients had significantly poorer overall survival (OS) compared with CONUT-low patients according to univariate and multivariate analyses (hazard ratio: 5.09, 95% confidence interval 3.12–8.30, p < 0.001). In time-dependent ROC analysis, CONUT had a higher area under the ROC curve (AUC) for the prediction of 5-year OS than the neutrophil lymphocyte ratio, the Modified Glasgow Prognostic Score, or pStage. When the time-dependent AUC curve was used to predict OS, CONUT tended to maintain its predictive accuracy for long-term survival at a significantly higher level for an extended period after surgery when compared with the other markers tested.ConclusionsCONUT is useful for not only estimating nutritional status but also for predicting long-term OS in gastric cancer patients after curative resection.

Background It is known that measuring the triglyceride glucose (TyG) index and TyG-related parameters [triglyceride glucose-body mass index (TyG-BMI), triglyceride glucose-waist circumference (TyG-WC), and triglyceride glucose-waist to height ratio (TyG-WHtR)] can predict diabetes; this study aimed to compare the predictive value of the baseline TyG index and TyG-related parameters for the onset of diabetes at different future periods. Methods We conducted a longitudinal cohort study involving 15,464 Japanese people who had undergone health physical examinations. The subject’s TyG index and TyG-related parameters were measured at the first physical examination, and diabetes was defined according to the American Diabetes Association criteria. Multivariate Cox regression models and time-dependent receiver operating characteristic (ROC) curves were constructed to examine and compare the risk assessment/predictive value of the TyG index and TyG-related parameters for the onset of diabetes in different future periods. Results The mean follow-up period of the current study cohort was 6.13 years, with a maximum of 13 years, and the incidence density of diabetes was 39.88/10,000 person-years. In multivariate Cox regression models with standardized hazard ratios (HRs), we found that both the TyG index and TyG-related parameters were significantly and positively associated with diabetes risk and that the TyG-related parameters were stronger in assessing diabetes risk than the TyG index, with TyG-WC being the best parameter (HR per SD increase: 1.70, 95% CI 1.46, 1.97). In addition, TyG-WC also showed the highest predictive accuracy in time-dependent ROC analysis for diabetes occurring in the short-term (2–6 years), while TyG-WHtR had the highest predictive accuracy and the most stable predictive threshold for predicting the onset of diabetes in the medium- to long-term (6–12 years). Conclusions These results suggest that the TyG index combined with BMI, WC, and WHtR can further improve its ability to assess/predict the risk of diabetes in different future periods, where TyG-WC was not only the best parameter for assessing diabetes risk but also the best risk marker for predicting future diabetes in the short-term, while TyG-WHtR may be more suitable for predicting future diabetes in the medium- to long-term.

Survival analysis following oncological treatments require specific analysis techniques to account for data considerations, such as failure to observe the time of event, patient withdrawal, loss to follow-up, and differential follow up. These techniques can include Kaplan-Meier and Cox proportional hazard analyses. However, studies do not always report overall survival (OS), disease-free survival (DFS), or cancer recurrence using hazard ratios, making the synthesis of such oncologic outcomes difficult. We propose a hierarchical utilization of methods to extract or estimate the hazard ratio to standardize time-to-event outcomes so that study inclusion into meta-analyses can be maximized. We also provide proof-of concept results from a statistical analysis that compares OS, DFS, and cancer recurrence for robotic surgery to open and non-robotic minimally invasive surgery. In our example, use of the proposed methodology would allow for the increase in data inclusion from 108 hazard ratios reported to 240 hazard ratios reported or estimated, resulting in an increase of 122%. While there are publications summarizing the motivation for these analyses, and comprehensive papers describing strategies to obtain estimates from published time-dependent analyses, we are not aware of a manuscript that describes a prospective framework for an analysis of this scale focusing on the inclusion of a maximum number of publications reporting on long-term oncologic outcomes incorporating various presentations of statistical data.

The efficiency of coal mining is seriously affected by roadway stability, as large time‐dependent deformation of roadway frequently occurs and needs to be maintained several times during its service life. Such rheological deformation was common in soft coal mass at Huaibei coalfield in China. To address this issue, in this study, the time‐dependent deformation of the soft coal roadway was analyzed and a new Jet Grouting (JG) technique was presented for controlling deformation. The time‐dependent deformation of the soft coal roadway was numerically simulated and validated. Based on the field test results and the verified model, a JG support model was established to examine its effect on roadway deformation. The JG support system can reduce the horizontal and vertical displacement of the roadway effectively and constrain the time‐dependent deformation of coal mass. The deformation rate and stabilization time of roadway decreased significantly by comparison with conventional support. This work presented a promising JG support scheme for controlling the time‐dependent deformation in the roadway in deep underground mine, which can greatly promote the JG design and application. We first revealed the time‐dependent behavior of roadway in the deep underground coal mine. A high‐efficiency jet grouting technique on controlling rheological deformation was first evaluated by a time‐dependent 3D numerical model. The results are encouraging and the pioneering work can improve the safety of the coal roadway and the efficiency of coal mining.

The objectives of this study are to illustrate the effects of immortal time bias (ITB) using an oncology outcomes database and quantify through simulations the magnitude and direction of ITB when different analytical techniques are used. A cohort of 11 626 women who received neoadjuvant chemotherapy and underwent mastectomy with pathologically positive lymph nodes were accrued from the National Cancer Database (2004-2008). Standard Cox regression, time-dependent (TD), and landmark models were used to compare overall survival in patients who did or did not receive postmastectomy radiation therapy (PMRT). Simulation studies showing ways to reduce the effect of ITB indicate that TD exposures should be included as variables in hazard-based analyses. Standard Cox regression models comparing overall survival in patients who did and did not receive PMRT showed a significant treatment effect (hazard ratio [HR]: 0.93, 95% confidence interval [CI]: 0.88-0.99). Time-dependent and landmark methods estimated no treatment effect with HR: 0.97, 95% CI: 0.92 to 1.03 and HR: 0.98, 95% CI, 0.92 to 1.04, respectively. In our simulation studies, the standard Cox regression model significantly overestimated treatment effects when no effect was present. Estimates of TD models were closest to the true treatment effect. Landmark model results were highly dependent on landmark timing. Appropriate statistical approaches that account for ITB are critical to minimize bias when examining relationships between receipt of PMRT and survival.