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Longitudinal monitoring of biomarkers is often helpful for predicting disease or a poor clinical outcome. We consider the prediction of both large and small for gestational age births by using longitudinal ultrasound measurements, and we attempt to identify subgroups of women for whom prediction is more (or less) accurate, should they exist. We propose a tree-based approach to identifying such subgroups, and a pruning algorithm which explicitly incorporates a desired type I error rate, allowing us to control the risk of false discovery of subgroups. The methods proposed are applied to data from the Scandinavian Fetal Growth Study and are evaluated via simulations.

Background Ventilator-free days, which combine mortality and duration of mechanical ventilation into a single measure, are often considered as a primary endpoint in clinical trials involving critically ill patients. Despite the composite nature, ventilator-free days are commonly analyzed as continuous or count data with no distinction between a zero score from a patient who dies and a zero score from a patient who is alive but still on ventilator. In this study, we propose a two-part statistical model to compare the effects of two airway management strategies on mortality and duration of mechanical ventilation among patients with out-of-hospital cardiopulmonary arrest in a cluster randomized cross-over clinical trial. Methods In the proposed two-part model, failure to achieve return of spontaneous circulation (ROSC), death after ROSC, and survival are modeled in the first part; the number of ventilator-free days conditional on survival is modeled in the second part. To account for the cluster randomized cross-over design, each part also includes a random cluster effect that is assumed to be either shared or independent across the two parts. We conducted a simulation study to evaluate type I error rates and power of the two-part shared random cluster effect model and the mis-specified two-part model with independent random cluster effects in detecting an overall intervention effect. Results We found that parameter estimates were similar whether the random cluster effects were assumed to be shared or independent across the two parts whereas the shared random cluster effect approach showed higher log-likelihood, but lower Akaike information criterion (AIC) and Bayesian information criterion (BIC). Initial laryngeal tube insertion reduced odds of failing to achieve ROSC and marginally decreased odds of death after ROSC compared with initial endotracheal intubation in Part 1, whereas initial laryngeal tube insertion was not associated with duration of mechanical ventilation among patients alive in Part 2. The shared random cluster effect approach showed higher odds of death associated with lower odds of being ventilator-free. This confirms the expectation that a patient who is less likely to achieve ROSC and survive is more likely to require prolonged mechanical ventilation if the patient indeed survives during hospitalization. Our simulation studies found that the two-part model with a shared random cluster effect yielded type I error rates close to the nominal level. The two-part shared random cluster effect model has better power to detect an overall intervention effect when intervention effects are present in both parts rather than in only one of the two part. Conclusions The proposed two-part model provides a more comprehensive assessment of intervention effects on ventilator-free days in critical care trials. Researchers and clinicians can obtain greater insights with this approach about the direction and magnitude of the intervention effects on mortality, ROSC, and duration of mechanical ventilation.

Missing data are common in longitudinal studies. Likelihood-based methods ignoring the missingness mechanism are unbiased provided missingness is at random (MAR); under not-at-random missingness (MNAR), joint modeling is commonly used, often as part of sensitivity analyses. In our motivating example of modeling CD4 count trajectories during untreated HIV infection, CD4 counts are mainly censored due to treatment initiation, with the nature of this mechanism remaining debatable. Here, we evaluate the bias in the disease progression marker's change over time (slope) of a specific class of joint models, termed shared-random-effects-models (SREMs), under MAR drop-out and propose an alternative SREM model. Our proposed model relates drop-out to both the observed marker's data and the corresponding random effects, in contrast to most SREMs, which assume that the marker and the drop-out processes are independent given the random effects. We analytically calculate the asymptotic bias in two SREMs under specific MAR drop-out mechanisms, showing that the bias in marker's slope increases as the drop-out probability increases. The performance of the proposed model, and other commonly used SREMs, is evaluated under specific MAR and MNAR scenarios through simulation studies. Under MAR, the proposed model yields nearly unbiased slope estimates, whereas the other SREMs yield seriously biased estimates. Under MNAR, the proposed model estimates are approximately unbiased, whereas those from the other SREMs are moderately to heavily biased, depending on the parameterization used. The examined models are also fitted to real data and results are compared/discussed in the light of our analytical and simulation-based findings.

In certain diseases, outcome is the number of morbid events over the course of follow-up. In epilepsy, e.g., daily seizure counts are often used to reflect disease severity. Follow-up of patients in clinical trials of such diseases is often subject to censoring due to patients dying or dropping out. If the sicker patients tend to be censored in such trials, estimates of the treatment effect that do not incorporate the censoring process may be misleading. We extend the shared random effects approach of Wu and Carroll (1988, Biometrics44, 175-188) to the setting of repeated counts of events. Three strategies are developed. The first is a likelihood-based approach for jointly modeling the count and censoring processes. A shared random effect is incorporated to introduce dependence between the two processes. The second is a likelihood-based approach that conditions on the dropout times in adjusting for informative dropout. The third is a generalized estimating equations (GEE) approach, which also conditions on the dropout times but makes fewer assumptions about the distribution of the count process. Estimation procedures for each of the approaches are discussed, and the approaches are applied to data from an epilepsy clinical trial. A simulation study is also conducted to compare the various approaches. Through analyses and simulations, we demonstrate the flexibility of the likelihood-based conditional model for analyzing data from the epilepsy trial.

Background Tacrolimus (TAC) is an immunosuppressant drug given to kidney transplant recipients post-transplant to prevent antibody formation and kidney rejection. The optimal therapeutic dose for TAC is poorly defined and therapy requires frequent monitoring of drug trough levels. Analyzing the association between TAC levels over time and the development of potentially harmful de novo donor specific antibodies (dnDSA) is complex because TAC levels are subject to measurement error and dnDSA is assessed at discrete times, so it is an interval censored time-to-event outcome. Methods Using data from the University of Colorado Transplant Center, we investigated the association between TAC and dnDSA using a shared random effects (intercept and slope) model with longitudinal and interval censored survival sub-models (JM) and compared it with the more traditional interval censored survival model with a time-varying covariate (TVC). We carried out simulations to compare bias, level and power for the association parameter in the TVC and JM under varying conditions of measurement error and interval censoring. In addition, using Markov Chain Monte Carlo (MCMC) methods allowed us to calculate clinically relevant quantities along with credible intervals (CrI). Results The shared random effects model was a better fit and showed both the average TAC and the slope of TAC were associated with risk of dnDSA. The simulation studies demonstrated that, in the presence of heavy interval censoring and high measurement error, the TVC survival model underestimates the association between the survival and longitudinal measurement and has inflated type I error and considerably less power to detect associations. Conclusions To avoid underestimating associations, shared random effects models should be used in analyses of data with interval censoring and measurement error.

Longitudinal studies involve recording observations at scheduled visits or time points for individuals until a predetermined event, like reaching satisfactory tumor shrinkage in cancer studies. Furthermore, dropout in longitudinal studies leads to incomplete data, which significantly increases the risk of bias. An amended joint shared-random effects model (SREM) is proposed for mixed continuous and binary longitudinal measurements and a time-to-event (TTE) outcome, incorporating missing covariates. In the proposed model, a conditional model is applied for the mixed continuous and binary longitudinal outcomes; a mixed effect model is considered for the continuous longitudinal outcome. For the binary longitudinal outcome, given the continuous longitudinal outcome, a logistic mixed effect model is considered. These models share common random effects with the model for the event time outcome. The model formulation is based on Bayesian statistical thinking via Markov Chain Monte Carlo (MCMC). The proposed joint modelling is applied to contribute to the understanding of the progression of prostate cancer (PCa) by considering a generalized linear mixed effects model for time-varying covariates that incorporate ignorable missingness. The association between prostate-specific antigen (PSA) with alkaline phosphatase (ALP) and tumor status has been studied with mixed conclusions.Article HighlightsThe utilization of PSA and ALP biomarkers ensures precision in assessing PCa disease progression after treatment, empowering clinicians with comprehensive and accurate dynamic monitoring.Accounting for missing observations of Platelets and Bilirubin during intermittent follow-up is crucial in improving the accuracy of the analysis, ensuring the generation of valid conclusions regarding PCa insights.Utilizing statistical models that incorporate prior information to update current scenarios is paramount in extracting valuable insights from disease data. The application of Bayesian thinking is instrumental in making this process possible, offering clinicians a powerful tool for informed decision-making.

Background: The present study aimed to estimate the survival of HIV-positive patients and compare the accuracy of two commonly used models, Shared Random-Effect Model (SREM) and Joint Latent Class Model (JLCM) for the analysis of time to death among these patients. Methods: Data on a retrospective survey among HIV-positive patients diagnosed during 1989-2014 who referred to the Behavioral Diseases Consultation Center of Mashhad University of Medical Sciences was used in this study. Participants consisted of HIV-positive high-risk volunteers, referrals of new HIV cases from prisons, blood transfusion organization and hospitals. Subjects were followed from diagnosis until death or the end of study. SREM and JLCM were used to predict the survival of HIV/AIDS patients. In both models age, sex and addiction were included as covariates. To compare the accuracy of these alternative models, dynamic predictions were calculated at specific time points. The receiver operating characteristic (ROC) curve was used to select the more accurate model. Results: Overall, 213 patients were eligible that met entry conditions for the present analysis. Based on BIC criteria, three heterogeneous sub-populations of patients were identified by JLCM and individuals were categorized in these classes (“High Risk”, “Moderate Risk” and “Low Risk”) according to their health status. JLCM had a better predictive accuracy than SREM. The average area under ROC curve for JLCM and SREM was 0.75 and 0.64 respectively. In both models CD4 count decreased with time. Based on the result of JLCM, men had higher hazard rate than women and the CD4 counts levels of patients decreased with increasing age. Conclusion: Predicting risk of death (or survival) is vital for patients care in most medical research. In a heterogeneous population, such as HIV-positive patients fitting JLCM can significantly improve the accuracy of the risk prediction. Therefore, this model is preferred for these populations.

Joint modeling methods have become popular tools to link important features extracted from longitudinal data to a primary event. While most modeling strategies have focused on the association between the longitudinal mean trajectories and risk of an event, we consider joint models that incorporate information from both long-term trends and short-term variability in a longitudinal submodel. We also consider both shared random effect and latent class (LC) approaches in the primary-outcome model to predict a binary outcome of interest. We develop simulation studies to compare and contrast these two modeling strategies; in particular, we study in detail the effects of the primary-outcome model misspecification. Among other findings, we note that when we analyze data from a shared random-effect using a LC model while the information from the longitudinal data is weak, the LC approach is more sensitive to such a model misspecification. Under this setting, the LC model has a superior performance in within-sample prediction that cannot be duplicated when predicting new samples. This is a unique feature of the LC approach that is new as far as we know to the existing literature. Finally, we use the proposed models to study how follicle stimulating hormone (FSH) trajectories are related to the risk of developing severe hot flashes for participating women in the Penn Ovarian Aging Study.

Previous studies have showed some evidences about the relationship between personality traits particularly neuroticism and extroversion, separately, with psychological stress and anxiety. In the current study, we clarified the magnitude of joint interdependence (co-morbidity) of anxiety (continuous) and Psychological stress (dichotomous) as dependent variables of mixed type with five-factor personality traits as independent variables. Data from 3180 participants who attended in the cross-sectional population-based \"study on the epidemiology of psychological, alimentary health and nutrition\" and completed self-administered questionnaires about demographic and life style, gastrointestinal disorders, personality traits, perceived intensity of stress, social support, and psychological outcome was analyzed using shared random effect approach in R Free software. The results indicated high scores of neuroticism increase the chance of high psychological stress (odds ratio [OR] = 5.1; P < 0.001) and anxiety score (B = 1.73; P < 0.001) after adjustment for the probable confounders. In contrast, those who had higher scores of extraversion and conscientiousness experienced lower levels of anxiety score (B = -0.54 and -0.23, respectively, P < 0.001) and psychological stress (OR = 0.36 and 0.65, respectively, P < 0.001). Furthermore, higher score of agreeableness had significant negative relationship with anxiety (B = -0.32, P < 0.001). The present study indicated that the scores of neuroticism, extraversion, agreeableness and conscientiousness strongly predict both anxiety and psychological stress in Iranian adult population. Due to likely mechanism of genetic and environmental factors on the relationships between personality traits and psychological disorders, it is suggested to perform longitudinal studies focusing on both genetic and environmental factors in Iranian population.