Joint modeling quality of life and survival using a terminal decline model in palliative care studies

Zinpentraxin Alfa for Idiopathic Pulmonary Fibrosis: The Randomized Phase III STARSCAPE Trial

Rationale: A phase II trial reported clinical benefit over 28 weeks in patients with idiopathic pulmonary fibrosis (IPF) who received zinpentraxin alfa. Objectives: To investigate the efficacy and safety of zinpentraxin alfa in patients with IPF in a phase III trial. Methods: This 52-week phase III, double-blind, placebo-controlled, pivotal trial was conducted at 275 sites in 29 countries. Patients with IPF were randomized 1:1 to intravenous placebo or zinpentraxin alfa 10 mg/kg every 4 weeks. The primary endpoint was absolute change from baseline to Week 52 in FVC. Secondary endpoints included absolute change from baseline to Week 52 in percent predicted FVC and 6-minute walk distance. Safety was monitored via adverse events. Post hoc analysis of the phase II and phase III data explored changes in FVC and their impact on the efficacy results. Measurements and Main Results: Of 664 randomized patients, 333 were assigned to placebo and 331 to zinpentraxin alfa. Four of the 664 randomized patients were never administered study drug. The trial was terminated early after a prespecified futility analysis that demonstrated no treatment benefit of zinpentraxin alfa over placebo. In the final analysis, absolute change from baseline to Week 52 in FVC was similar between placebo and zinpentraxin alfa (-214.89 ml and -235.72 ml; P = 0.5420); there were no apparent treatment effects on secondary endpoints. Overall, 72.3% and 74.6% of patients receiving placebo and zinpentraxin alfa, respectively, experienced one or more adverse events. Post hoc analysis revealed that extreme FVC decline in two placebo-treated patients resulted in the clinical benefit of zinpentraxin alfa reported by phase II. Conclusions: Zinpentraxin alfa treatment did not benefit patients with IPF over placebo. Learnings from this program may help improve decision making around trials in IPF. Clinical trial registered with www.clinicaltrials.gov (NCT04552899).

Quantifying bias due to missing data in quality of life surveys of advanced-stage cancer patients

PURPOSE

Many studies on cancer patients investigate the impact of treatment on health-related quality of life (QoL). Typically, QoL is measured longitudinally, at baseline and at predefined timepoints thereafter. The question is whether, at a given timepoint, patients who return their questionnaire (available cases, AC) have a different QoL than those who do not return their questionnaire (non-AC).

METHODS

We employed augmented inverse probability weighting (AIPW) to estimate the average QoL of non-AC in two studies on advanced-stage cancer patients. The AIPW estimator assumed data to be missing at random (MAR) and used machine learning (ML)-based methods to estimate answering probabilities of individuals at given timepoints as well as their reported QoL, as a function of auxiliary variables. These auxiliary variables were selected by medical oncologists based on domain expertise. We aggregated results both by timepoint and by time until death and compared AIPW estimates to the AC averages. Additionally, we used a pattern mixture model (PMM) to check sensitivity of our AIPW estimates against violation of the MAR assumption.

RESULTS

Our study included 1927 patients with advanced pancreatic and 797 patients with advanced breast cancer. The AIPW estimate for average QoL of non-AC was below the average QoL of AC when aggregated by timepoint. The difference vanished when aggregated by time until death. PMM estimates were below AIPW estimates.

CONCLUSIONS

Our results indicate that non-AC have a lower average QoL than AC. However, estimates for QoL of non-AC are subject to unverifiable assumptions about the missingness mechanism.

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.

Statistical methods and graphical displays of quality of life with survival outcomes in oncology clinical trials for supporting the estimand framework

Background

Although there are discussions regarding standards of the analysis of patient-reported outcomes and quality of life (QOL) in oncology clinical trials, that of QOL with death events is not within their scope. For example, ignoring death can lead to bias in the QOL analysis for patients with moderate or high mortality rates in the palliative care setting. This is discussed in the estimand framework but is controversial. Information loss by summary measures under the estimand framework may make it challenging for clinicians to interpret the QOL analysis results. This study illustrated the use of graphical displays in the framework. They can be helpful for discussions between clinicians and statisticians and decision-making by stakeholders.

Methods

We reviewed the time-to-deterioration analysis, prioritized composite outcome approach, semi-competing risk analysis, survivor analysis, linear mixed model for repeated measures, and principal stratification approach. We summarized attributes of estimands and graphs in the statistical analysis and evaluated them in various hypothetical randomized controlled trials.

Results

Graphs for each analysis method provide different information and impressions. In the time-to-deterioration analysis, it was not easy to interpret the difference in the curves as an effect on QOL. The prioritized composite outcome approach provided new insights for QOL considering death by defining better conditions based on the distinction of OS and QOL. The semi-competing risk analysis provided different insights compared with the time-to-deterioration analysis and prioritized composite outcome approach. Due to the missing assumption, graphs by the linear mixed model for repeated measures should be carefully interpreted, even for descriptive purposes. The principal stratification approach provided pure comparison, but the interpretation was difficult because the target population was unknown.

Conclusions

Graphical displays can capture different aspects of treatment effects that should be described in the estimand framework.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01735-1.

Randomized Trial of a Palliative Care Intervention to Improve End-of-Life Care Discussions in Patients With Metastatic Breast Cancer

BACKGROUND

Studies show that early, integrated palliative care (PC) improves quality of life (QoL) and end-of-life (EoL) care for patients with poor-prognosis cancers. However, the optimal strategy for delivering PC for those with advanced cancers who have longer disease trajectories, such as metastatic breast cancer (MBC), remains unknown. We tested the effect of a PC intervention on the documentation of EoL care discussions, patient-reported outcomes, and hospice utilization in this population.

PATIENTS AND METHODS

Patients with MBC and clinical indicators of poor prognosis (n=120) were randomly assigned to receive an outpatient PC intervention (n=61) or usual care (n=59) between May 2, 2016, and December 26, 2018, at an academic cancer center. The intervention entailed 5 structured PC visits focusing on symptom management, coping, prognostic awareness, decision-making, and EoL planning. The primary outcome was documentation of EoL care discussions in the electronic health record (EHR). Secondary outcomes included patient-report of discussions with clinicians about EoL care, QoL, and mood symptoms at 6, 12, 18, and 24 weeks after baseline and hospice utilization.

RESULTS

The rate of EoL care discussions documented in the EHR was higher among intervention patients versus those receiving usual care (67.2% vs 40.7%; P=.006), including a higher completion rate of a Medical Orders for Life-Sustaining Treatment form (39.3% vs 13.6%; P=.002). Intervention patients were also more likely to report discussing their EoL care wishes with their doctor (odds ratio [OR], 3.10; 95% CI, 1.21-7.94; P=.019) and to receive hospice services (OR, 4.03; 95% CI, 1.10-14.73; P=.035) compared with usual care patients. Study groups did not differ in patient-reported QoL or mood symptoms.

CONCLUSIONS

This PC intervention significantly improved rates of discussion and documentation regarding EoL care and delivery of hospice services among patients with MBC, demonstrating that PC can be tailored to address the supportive care needs of patients with longer disease trajectories. ClinicalTrials.gov identifier: NCT02730858.

Protocol for an observational cohort study identifying factors predicting accurately end of life in dementia with Lewy bodies and promoting quality end-of-life experiences: the PACE-DLB study

INTRODUCTION

Dementia with Lewy bodies (DLB) is one of the most common degenerative dementias. Despite the fact that most individuals with DLB die from complications of the disease, little is known regarding what factors predict impending end of life or are associated with a quality end of life.

METHODS AND ANALYSIS

This is a multisite longitudinal cohort study. Participants are being recruited from five academic centres providing subspecialty DLB care and volunteers through the Lewy Body Dementia Association (not receiving specialty care). Dyads must be US residents, include individuals with a clinical diagnosis of DLB and at least moderate-to-severe dementia and include the primary caregiver, who must pass a brief cognitive screen. The first dyad was enrolled 25 February 2021; recruitment is ongoing. Dyads will attend study visits every 6 months through the end of life or 3 years. Study visits will occur in-person or virtually. Measures include demographics, DLB characteristics, caregiver considerations, quality of life and satisfaction with end-of-life experiences. For dyads where the individual with DLB dies, the caregiver will complete a final study visit 3 months after the death to assess grief, recovery and quality of the end-of-life experience. Terminal trend models will be employed to identify significant predictors of approaching end of life (death in the next 6 months). Similar models will assess caregiver factors (eg, grief, satisfaction with end-of-life experience) after the death of the individual with DLB. A qualitative descriptive analysis approach will evaluate interview transcripts regarding end-of-life experiences.

ETHICS AND DISSEMINATION

This study was approved by the University of Florida institutional review board (IRB202001438) and is listed on clinicaltrials.gov (NCT04829656). Data sharing follows National Institutes of Health policies. Study results will be disseminated via traditional scientific strategies (conferences, publications) and through collaborating with the Lewy Body Dementia Association, National Institute on Aging and other partnerships.

Early Integrated Telehealth versus In-Person Palliative Care for Patients with Advanced Lung Cancer: A Study Protocol

Introduction: Early palliative care (PC) integrated with oncology care improves quality of life (QOL), depression symptoms, illness understanding, and end-of-life (EOL) care for patients with advanced lung cancer. The aims of this trial are to compare the effect of delivering early integrated PC through telehealth versus in-person on patient and caregiver outcomes. We hypothesize that both modalities for delivering early PC would be equivalent for improving patient QOL, communication about EOL care preferences with their oncologist, and length of stay in hospice. Methods: For this comparative effectiveness trial, we will enroll and randomize 1250 adult patients with advanced nonsmall cell lung cancer (NSCLC), who are not being treated with curative intent, to receive either early integrated telehealth or in-person PC at 20 cancer centers throughout the United States. Patients may also invite a family caregiver to participate in the study. Patients and their caregivers in both study groups meet at least every four weeks with a PC clinician from within 12 weeks of patient diagnosis of advanced NSCLC until death. Participants complete measures of QOL, mood, and quality of communication with oncologists at baseline before randomization and at 12, 24, 36, and 48 weeks. Information on health care utilization, including length of stay in hospice, will be collected from patients' health records. To test equivalence in outcomes between study groups, we will compute analysis of covariance and mixed linear models, controlling for baseline scores and study site. Study Implementation and Stakeholder Engagement: To ensure that this comparative effectiveness trial and findings are as patient centered and meaningful as possible, we have incorporated a robust patient and stakeholder engagement plan. Our stakeholder partners include (1) patients/families, (2) PC clinicians, (3) telehealth experts and clinician users, (4) representatives from health care systems and medical insurance providers, and (5) health care policy makers and advocates. These stakeholders will inform and provide feedback about every phase of study implementation.

Multisite, Randomized Trial of Early Integrated Palliative and Oncology Care in Patients with Advanced Lung and Gastrointestinal Cancer: Alliance A221303

Background: We conducted a multicenter, randomized trial of early integrated palliative and oncology care in patients with advanced cancer to confirm the benefits of early palliative care (PC) seen in prior single-center studies. Methods: We randomly assigned patients with newly diagnosed incurable cancer to early integrated palliative and oncology care (n = 195) or usual oncology care (n = 196) at sites through the Alliance for Clinical Trials in Oncology. Patients assigned to the intervention were expected to meet with a PC clinician at least monthly until death, whereas usual care patients consulted PC on request. The primary endpoint was the change in quality of life from baseline to week 12 per the Functional Assessment of Cancer Therapy-General (FACT-G). Secondary outcomes included anxiety, depression, and communication about prognosis and end-of-life care. Results: Due to significant morbidity and a high proportion of measures that were not completed within the protocol window or for unknown reasons, the rate of missing data was high. We anticipated that 70% of patients (n = 280) would complete the FACT-G at baseline and week 12, but only 49.3% (n = 193/391) completed the measure. Delivery of the intervention was also suboptimal, as 14.9% (n = 29/195) of intervention patients had no PC visits by week 12. Intervention patients reported a mean 3.35 (standard deviation [SD] = 14.7) increase in FACT-G scores from baseline to week 12 compared with usual care patients who reported a 0.12 (SD = 12.7) increase from baseline (p = 0.10). Conclusion: This study highlights the difficulties of conducting multicenter trials of supportive care interventions in patients with advanced cancer. Clinical Trials Registration: NCT02349412.

Survival models and health sequences

Survival studies often generate not only a survival time for each patient but also a sequence of health measurements at annual or semi-annual check-ups while the patient remains alive. Such a sequence of random length accompanied by a survival time is called a survival process. Robust health is ordinarily associated with longer survival, so the two parts of a survival process cannot be assumed independent. This paper is concerned with a general technique-reverse alignment-for constructing statistical models for survival processes, here termed revival models. A revival model is a regression model in the sense that it incorporates covariate and treatment effects into both the distribution of survival times and the joint distribution of health outcomes. The revival model also determines a conditional survival distribution given the observed history, which describes how the subsequent survival distribution is determined by the observed progression of health outcomes.

Defining the Elements of Early Palliative Care That Are Associated With Patient-Reported Outcomes and the Delivery of End-of-Life Care

Purpose We describe the key elements of early palliative care (PC) across the illness trajectory and examine whether visit content was associated with patient-reported outcomes and end-of-life care. Methods We performed a secondary analysis of patients with newly diagnosed advanced lung or noncolorectal GI cancer (N = 171) who were randomly assigned to receive early PC. Participants attended at least monthly visits with board-certified PC physicians and advanced practice nurses at Massachusetts General Hospital. PC clinicians completed surveys documenting visit content after each encounter. Patients reported quality of life (Functional Assessment of Cancer Therapy-General) and mood (Hospital Anxiety and Depression Scale and Patient Health Questionnaire-9) at baseline and 24 weeks. End-of-life care data were abstracted from the electronic health record. We summarized visit content over time and used linear and logistic regression to identify whether the proportion of visits addressing a content area was associated with patient-reported outcomes and end-of-life care. Results We analyzed data from 2,921 PC visits, most of which addressed coping (64.2%) and symptom management (74.5%). By 24 weeks, patients who had a higher proportion of visits that addressed coping experienced improved quality of life ( P = .02) and depression symptoms (Depression subscale of the Hospital Anxiety and Depression Scale, P = .002; Patient Health Questionnaire-9, P = .004). Patients who had a higher proportion of visits address treatment decisions were less likely to initiate chemotherapy ( P = .02) or be hospitalized ( P = .005) in the 60 days before death. Patients who had a higher proportion of visits addressing advance care planning were more likely to use hospice ( P = .03). Conclusion PC clinicians' focus on coping, treatment decisions, and advance care planning is associated with improved patient outcomes. These data define the key elements of early PC to enable dissemination of the integrated care model.

Effects of Early Integrated Palliative Care on Caregivers of Patients with Lung and Gastrointestinal Cancer: A Randomized Clinical Trial

BACKGROUND

The family and friends (caregivers) of patients with advanced cancer often experience tremendous distress. Although early integrated palliative care (PC) has been shown to improve patient-reported quality of life (QOL) and mood, its effects on caregivers' outcomes is currently unknown.

MATERIALS AND METHODS

We conducted a randomized trial of early PC integrated with oncology care versus oncology care alone for patients who were newly diagnosed with incurable lung and noncolorectal gastrointestinal cancers and their caregivers. The early PC intervention focused on addressing the needs of both patients and their caregivers. Eligible caregivers were family or friends who would likely accompany patients to clinic visits. The intervention entailed at least monthly patient visits with PC from the time of diagnosis. Caregivers were encouraged, but not required, to attend the palliative care visits. We used the Hospital Anxiety and Depression Scale (HADS) and Medical Health Outcomes Survey Short-Form to assess caregiver mood and QOL.

RESULTS

Two hundred seventy-five caregivers (intervention n = 137; control n = 138) of the 350 patients participated. The intervention led to improvement in caregivers' total distress (HADS-total adjusted mean difference = -1.45, 95% confidence interval [CI] -2.76 to -0.15, p = .029), depression subscale (HADS-depression adjusted mean difference = -0.71, 95% CI -1.38 to -0.05, p = .036), but not anxiety subscale or QOL at week 12. There were no differences in caregivers' outcomes at week 24. A terminal decline analysis showed significant intervention effects on caregivers' total distress (HADS-total), with effects on both the anxiety and depression subscales at 3 and 6 months before patient death.

CONCLUSION

Early involvement of PC for patients with newly diagnosed lung and gastrointestinal cancers leads to improvement in caregivers' psychological symptoms. This work demonstrates that the benefits of early, integrated PC models in oncology care extend beyond patient outcomes and positively impact the experience of caregivers.

IMPLICATIONS FOR PRACTICE

Early involvement of palliative care for patients with newly diagnosed lung and gastrointestinal cancers leads to improvement in caregivers' psychological symptoms. The findings of this trial demonstrate that the benefits of the early, integrated palliative care model in oncology care extend beyond patient outcomes and positively impact the experience of caregivers. These findings contribute novel data to the growing evidence base supporting the benefits of integrating palliative care earlier in the course of disease for patients with advanced cancer and their caregivers.

Conditional modeling of longitudinal data with terminal event

We consider a random effects model for longitudinal data with the occurrence of an informative terminal event that is subject to right censoring. Existing methods for analyzing such data include the joint modeling approach using latent frailty and the marginal estimating equation approach using inverse probability weighting; in both cases the effect of the terminal event on the response variable is not explicit and thus not easily interpreted. In contrast, we treat the terminal event time as a covariate in a conditional model for the longitudinal data, which provides a straight-forward interpretation while keeping the usual relationship of interest between the longitudinally measured response variable and covariates for times that are far from the terminal event. A two-stage semiparametric likelihood-based approach is proposed for estimating the regression parameters; first, the conditional distribution of the right-censored terminal event time given other covariates is estimated and then the likelihood function for the longitudinal event given the terminal event and other regression parameters is maximized. The method is illustrated by numerical simulations and by analyzing medical cost data for patients with end-stage renal disease. Desirable asymptotic properties are provided.

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.

Effects of Early Integrated Palliative Care in Patients With Lung and GI Cancer: A Randomized Clinical Trial

Purpose We evaluated the impact of early integrated palliative care (PC) in patients with newly diagnosed lung and GI cancer. Patients and Methods We randomly assigned patients with newly diagnosed incurable lung or noncolorectal GI cancer to receive either early integrated PC and oncology care (n = 175) or usual care (n = 175) between May 2011 and July 2015. Patients who were assigned to the intervention met with a PC clinician at least once per month until death, whereas those who received usual care consulted a PC clinician upon request. The primary end point was change in quality of life (QOL) from baseline to week 12, per scoring by the Functional Assessment of Cancer Therapy-General scale. Secondary end points included change in QOL from baseline to week 24, change in depression per the Patient Health Questionnaire-9, and differences in end-of-life communication. Results Intervention patients ( v usual care) reported greater improvement in QOL from baseline to week 24 (1.59 v -3.40; P = .010) but not week 12 (0.39 v -1.13; P = .339). Intervention patients also reported lower depression at week 24, controlling for baseline scores (adjusted mean difference, -1.17; 95% CI, -2.33 to -0.01; P = .048). Intervention effects varied by cancer type, such that intervention patients with lung cancer reported improvements in QOL and depression at 12 and 24 weeks, whereas usual care patients with lung cancer reported deterioration. Patients with GI cancers in both study groups reported improvements in QOL and mood by week 12. Intervention patients versus usual care patients were more likely to discuss their wishes with their oncologist if they were dying (30.2% v 14.5%; P = .004). Conclusion For patients with newly diagnosed incurable cancers, early integrated PC improved QOL and other salient outcomes, with differential effects by cancer type. Early integrated PC may be most effective if targeted to the specific needs of each patient population.

Effect of a Patient-Centered Communication Intervention on Oncologist-Patient Communication, Quality of Life, and Health Care Utilization in Advanced Cancer: The VOICE Randomized Clinical Trial

Importance

Observational studies demonstrate links between patient-centered communication, quality of life (QOL), and aggressive treatments in advanced cancer, yet few randomized clinical trials (RCTs) of communication interventions have been reported.

Objective

To determine whether a combined intervention involving oncologists, patients with advanced cancer, and caregivers would promote patient-centered communication, and to estimate intervention effects on shared understanding, patient-physician relationships, QOL, and aggressive treatments in the last 30 days of life.

Design, Setting, and Participants

Cluster RCT at community- and hospital-based cancer clinics in Western New York and Northern California; 38 medical oncologists (mean age 44.6 years; 11 (29%) female) and 265 community-dwelling adult patients with advanced nonhematologic cancer participated (mean age, 64.4 years, 146 [55.0%] female, 235 [89%] white; enrolled August 2012 to June 2014; followed for 3 years); 194 patients had participating caregivers.

Interventions

Oncologists received individualized communication training using standardized patient instructors while patients received question prompt lists and individualized communication coaching to identify issues to address during an upcoming oncologist visit. Both interventions focused on engaging patients in consultations, responding to emotions, informing patients about prognosis and treatment choices, and balanced framing of information. Control participants received no training.

Main Outcomes and Measures

The prespecified primary outcome was a composite measure of patient-centered communication coded from audio recordings of the first oncologist visit following patient coaching (intervention group) or enrollment (control). Secondary outcomes included the patient-physician relationship, shared understanding of prognosis, QOL, and aggressive treatments and hospice use in the last 30 days of life.

Results

Data from 38 oncologists (19 randomized to intervention) and 265 patients (130 intervention) were analyzed. In fully adjusted models, the intervention resulted in clinically and statistically significant improvements in the primary physician-patient communication end point (adjusted intervention effect, 0.34; 95% CI, 0.06-0.62; P = .02). Differences in secondary outcomes were not statistically significant.

Conclusions and Relevance

A combined intervention that included oncologist communication training and coaching for patients with advanced cancer was effective in improving patient-centered communication but did not affect secondary outcomes.

Trial Registration

clinicaltrials.gov Identifier: NCT01485627.

Analyzing mHealth Engagement: Joint Models for Intensively Collected User Engagement Data

BACKGROUND

Evaluating engagement with an intervention is a key component of understanding its efficacy. With an increasing interest in developing behavioral interventions in the mobile health (mHealth) space, appropriate methods for evaluating engagement in this context are necessary. Data collected to evaluate mHealth interventions are often collected much more frequently than those for clinic-based interventions. Additionally, missing data on engagement is closely linked to level of engagement resulting in the potential for informative missingness. Thus, models that can accommodate intensively collected data and can account for informative missingness are required for unbiased inference when analyzing engagement with an mHealth intervention.

OBJECTIVE

The objectives of this paper are to discuss the utility of the joint modeling approach in the analysis of longitudinal engagement data in mHealth research and to illustrate the application of this approach using data from an mHealth intervention designed to support illness management among people with schizophrenia.

METHODS

Engagement data from an evaluation of an mHealth intervention designed to support illness management among people with schizophrenia is analyzed. A joint model is applied to the longitudinal engagement outcome and time-to-dropout to allow unbiased inference on the engagement outcome. Results are compared to a naïve model that does not account for the relationship between dropout and engagement.

RESULTS

The joint model shows a strong relationship between engagement and reduced risk of dropout. Using the mHealth app 1 day more per week was associated with a 23% decreased risk of dropout (P<.001). The decline in engagement over time was steeper when the joint model was used in comparison with the naïve model.

CONCLUSIONS

Naïve longitudinal models that do not account for informative missingness in mHealth data may produce biased results. Joint models provide a way to model intensively collected engagement outcomes while simultaneously accounting for the relationship between engagement and missing data in mHealth intervention research.

Joint modeling of dropout and outcome in three pivotal clinical trials of schizophrenia

BACKGROUND

Dropout is a serious challenge to clinical trials in psychiatry, yet standard outcome analyses with mixed models do not account for dropout, while joint modeling uses dropout from a survival model to adjust the outcome from a mixed model, but is untested in clinical trials of schizophrenia.

AIMS

To compare mixed and joint modeling in three acute phase pivotal placebo controlled trials of schizophrenia.

METHOD

Data were reanalyzed on 611 in-patients with acute schizophrenia who participated in three pivotal randomized controlled trials that compared placebo with olanzapine or risperidone (dropout rates placebo: 62.6% and medication: 37.4%). The outcome measures were BPRS or PANSS total change scores. Mixed-effects models for repeated measures and joint models were computed and compared to examine the time-treatment interaction. Effect size comparisons were made.

RESULTS

Antipsychotic treatment was superior to placebo across analyses. Time treatment interactions were significant (p<.05) for the mixed (beta=2.33) and joint models (beta=2.62). Compared with mixed modeling, joint modeling reduced the estimated change score for treatment (21.24 vs 19.74) and placebo (1.64 vs -1.11). The effect size differences between placebo and treatment groups were greater for joint (ES=.89) than mixed modeling (ES=0.83). Sensitivity analysis replicated this trend of results in each of the three trials.

CONCLUSION

Compared to mixed modeling, joint modeling results in a greater separation between treatment and placebo groups. This offers preliminary evidence that joint modeling may be useful in the analysis of antipsychotic placebo controlled RCTs.

Early Versus Delayed Initiation of Concurrent Palliative Oncology Care: Patient Outcomes in the ENABLE III Randomized Controlled Trial

PURPOSE

Randomized controlled trials have supported integrated oncology and palliative care (PC); however, optimal timing has not been evaluated. We investigated the effect of early versus delayed PC on quality of life (QOL), symptom impact, mood, 1-year survival, and resource use.

PATIENTS AND METHODS

Between October 2010 and March 2013, 207 patients with advanced cancer at a National Cancer Institute cancer center, a Veterans Affairs Medical Center, and community outreach clinics were randomly assigned to receive an in-person PC consultation, structured PC telehealth nurse coaching sessions (once per week for six sessions), and monthly follow-up either early after enrollment or 3 months later. Outcomes were QOL, symptom impact, mood, 1-year survival, and resource use (hospital/intensive care unit days, emergency room visits, chemotherapy in last 14 days, and death location).

RESULTS

Overall patient-reported outcomes were not statistically significant after enrollment (QOL, P = .34; symptom impact, P = .09; mood, P = .33) or before death (QOL, P = .73; symptom impact, P = .30; mood, P = .82). Kaplan-Meier 1-year survival rates were 63% in the early group and 48% in the delayed group (difference, 15%; P = .038). Relative rates of early to delayed decedents' resource use were similar for hospital days (0.73; 95% CI, 0.41 to 1.27; P = .26), intensive care unit days (0.68; 95% CI, 0.23 to 2.02; P = .49), emergency room visits (0.73; 95% CI, 0.45 to 1.19; P = .21), chemotherapy in last 14 days (1.57; 95% CI, 0.37 to 6.7; P = .27), and home death (27 [54%] v 28 [47%]; P = .60).

CONCLUSION

Early-entry participants' patient-reported outcomes and resource use were not statistically different; however, their survival 1-year after enrollment was improved compared with those who began 3 months later. Understanding the complex mechanisms whereby PC may improve survival remains an important research priority.