Advanced hepatocellular carcinoma (HCC) has limited treatment options and poor survival. Early detection of HCC improves survival, but current ultrasound screening is inconsistent and poorly adhered to. Biomarker-based blood tests offer a more accurate, accessible, and reliable alternative. Recent developments in methods for early detection of HCC have focused on the trajectories of multiple biomarkers, assuming that biomarkers follow a joint hierarchical mixture model with random changepoints. We extend this model, allowing covariates to influence three aspects of the model: biomarker trajectories, diagnosis times, and subject-specific prior risks. We evaluated whether covariates could capture unexplained heterogeneity in biomarker levels to improve early detection. A Markov chain Monte Carlo algorithm was implemented to derive posterior distributions used in risk calculations among future patients and to determine whether a patient has a positive screen. The screening algorithm was evaluated through simulation studies under a range of possible covariate extensions. The Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) trial was used to further study the proposed biomarker screening algorithms for HCC using cross-validation.
A joint modeling technique for longitudinal data and time-to-event data is advanced statistical methodology to analyze repeated measurements and time-to-event outcomes. In the present article, a shared random effects approach is considered to account for both longitudinal data and survival outcomes. The longitudinal data is submodeled by a linear mixed-effects model that captures the individual response over time. Whereas the time-to-event sub-model uses the generalized exponential distribution as the baseline distribution for the proportional hazard model to incorporate survival times as well as time-independent covariates. To estimate model parameters, the Expectation-Maximization approach has been used, which is effective with latent random effects since random effects are unobservable in nature. In the E-step, the expected log-likelihood is derived based on current parameter estimates, and in the M-step, we maximize this likelihood to update the parameters. To show the efficacy of the proposed approach, an extensive simulation study has been conducted. Furthermore, the model is tested on a real-world medical dataset, heart valve data, to demonstrate its efficacy in analyzing the link between longitudinal responses and survival outcomes.
Community-based public health interventions often use three-level cluster randomised trials (CRTs), for example, individuals are nested within households which are again nested within communities. In these trials, outcome missingness commonly depends on baseline covariates measured at multiple levels. Although two-level CRTs are well studied, the effects of subcluster-level attrition and hierarchical covariate correlations on bias and variance are not yet fully explored. We develop an analytical framework for three-level CRTs under multi-level covariate-dependent missingness (CDM), explicitly incorporating within- and between-subcluster covariate correlations. We examine the performance of unadjusted and covariate-adjusted cluster-level treatment effect estimators using Monte Carlo simulations and a plasmode simulation based on the 2021 Madagascar Demographic and Health Survey. Our study reveals that unadjusted cluster-level analyses produce biased estimates under multi-level CDM. Linear mixed models are unbiased when correctly specified under missing-at-random assumptions but may be computationally unstable in small samples. The covariate-adjusted cluster-level estimator remains unbiased when covariate effects and missingness mechanisms are homogeneous across treatment arms. Subcluster-level attrition can lead to greater variance inflation than individual-level attrition. For CRTs with fewer than 30 clusters per arm and homogeneous treatment effects, covariate-adjusted cluster-level analysis may serve as a practical alternative to mixed models. A decision flowchart is provided to guide method selection.
In clinical trials, the comparison between treatment arms of a measurement is often complicated by an event such as death or progression that can make this measurement irrelevant or difficult to perform. In this article, an approach is proposed to compare treatment arms using the net treatment benefit or the win ratio. A more favorable outcome (win) for the experimental arm is defined as the event of (1) a longer time to event for a patient in the experimental arm compared to a patient from the control arm until a prespecified restriction time ϵ or (2) a more favorable outcome with respect to the measurement during follow-up for a patient from the experimental arm and the time to event greater than ϵ for both patients. An estimator is proposed. A simulation study showed approximately no bias. The methodology is illustrated using a leukemia trial.
Studies comparing two survival curves to assess treatment differences are commonplace. The widely used log-rank test is designed to operate best under the proportional hazards (PH) assumption. Alternative methods, such as the Fleming-Harrington weighted log-rank tests, rely on prior knowledge of survival patterns, which is often difficult to specify before a study. The recently developed MaxCombo test does not require such prior information; however, it leaves substantial room for power improvement, particularly when survival curves cross. To address these issues, we propose new nonparametric and semiparametric methods for comparing two survival curves based on the receiver operating characteristic (ROC) curve length and the overlap coefficient, along with a joint inference approach combining both measures. Our methods are applicable under non-PH settings and can accommodate right, left, and double censoring. We evaluate our methods through extensive simulation studies under different non-PH configurations. Our results show improved performance of the proposed methods compared to existing nonparametric methods, particularly under early and crossing effects. Real-world examples involving various non-PH scenarios are provided, illustrating the superiority of our methods in detecting survival differences. We further provide an R package named ‘‘ROCsurvcomp” that implements all our proposed approaches and is available on CRAN.
