Semiparametric modeling and estimation of the terminal behavior of recurrent marker processes before failure events

Joint modeling in presence of informative censoring on the retrospective time scale with application to palliative care research

Joint modeling of longitudinal data such as quality of life data and survival data is important for palliative care researchers to draw efficient inferences because it can account for the associations between those two types of data. Modeling quality of life on a retrospective from death time scale is useful for investigators to interpret the analysis results of palliative care studies which have relatively short life expectancies. However, informative censoring remains a complex challenge for modeling quality of life on the retrospective time scale although it has been addressed for joint models on the prospective time scale. To fill this gap, we develop a novel joint modeling approach that can address the challenge by allowing informative censoring events to be dependent on patients' quality of life and survival through a random effect. There are two sub-models in our approach: a linear mixed effect model for the longitudinal quality of life and a competing-risk model for the death time and dropout time that share the same random effect as the longitudinal model. Our approach can provide unbiased estimates for parameters of interest by appropriately modeling the informative censoring time. Model performance is assessed with a simulation study and compared with existing approaches. A real-world study is presented to illustrate the application of the new approach.

Premature termination of the unified protocol for the transdiagnostic treatment of emotional disorders: The role of ambivalence towards change

Ambivalence towards change is an expected, recurrent process in psychological change. However, the prolonged experience of ambivalence in psychotherapy contributes to client disengagement, which could result in treatment dropout. Considering the negative effects of premature termination of therapy and the convenience of the identification of clients who are at risk of dropping out before achieving good-outcome, the current study explored the predictive power of ambivalence for premature therapy termination using a multilevel time-backwards model (i.e., considering the session of the dropout as session zero and then modelling what occurred from the dropout until session 1). Participants included a total of 96 psychotherapy clients (38 dropouts) treated in a university-based clinic following the Unified Protocol for Transdiagnostic Treatment of Emotional Disorders. Multilevel modelling using a time-backwards model to analyse dropout data provided evidence of the predictive power of ambivalence evolution throughout treatment on the decision to prematurely discontinue treatment (p < .0001; R² _adj  = .29). Specifically, good-outcome dropouts presented a decreasing ambivalence trend throughout treatment, whereas poor-outcome dropouts tended to experience the same levels of ambivalence before deciding to drop out (time × dropout; β₁₁  = .64, p = .014). Additionally, poor-outcome dropouts presented higher levels of ambivalence (β₀₁  = 9.92, p < .0001) in the last session. The results suggest that the pattern of client ambivalence towards change is a predictor of premature termination of therapy. Implications for clinical and research contexts are discussed.

A joint modeling approach for analyzing marker data in the presence of a terminal event

In many medical studies, markers are contingent on recurrent events and the cumulative markers are usually of interest. However, the recurrent event process is often interrupted by a dependent terminal event, such as death. In this article, we propose a joint modeling approach for analyzing marker data with informative recurrent and terminal events. This approach introduces a shared frailty to specify the explicit dependence structure among the markers, the recurrent, and terminal events. Estimation procedures are developed for the model parameters and the degree of dependence, and a prediction of the covariate-specific cumulative markers is provided. The finite sample performance of the proposed estimators is examined through simulation studies. An application to a medical cost study of chronic heart failure patients from the University of Virginia Health System is illustrated.

A semiparametric joint model for terminal trend of quality of life and survival in palliative care research

Palliative medicine is an interdisciplinary specialty focusing on improving quality of life (QOL) for patients with serious illness and their families. Palliative care programs are available or under development at over 80% of large US hospitals (300+ beds). Palliative care clinical trials present unique analytic challenges relative to evaluating the palliative care treatment efficacy which is to improve patients' diminishing QOL as disease progresses towards end of life (EOL). A unique feature of palliative care clinical trials is that patients will experience decreasing QOL during the trial despite potentially beneficial treatment. Often longitudinal QOL and survival data are highly correlated which, in the face of censoring, makes it challenging to properly analyze and interpret terminal QOL trend. To address these issues, we propose a novel semiparametric statistical approach to jointly model the terminal trend of QOL and survival data. There are two sub-models in our approach: a semiparametric mixed effects model for longitudinal QOL and a Cox model for survival. We use regression splines method to estimate the nonparametric curves and AIC to select knots. We assess the model performance through simulation to establish a novel modeling approach that could be used in future palliative care research trials. Application of our approach in a recently completed palliative care clinical trial is also presented.