A pattern-mixture model approach for handling missing continuous outcome data in longitudinal cluster randomized trials

Protocol for a pragmatic trial testing a self-directed lifestyle program targeting weight loss among patients with obstructive sleep apnea (POWER Trial)

BACKGROUND

Obesity comprises the single greatest reversible risk factor for obstructive sleep apnea (OSA). Despite the potential of lifestyle-based weight loss services to improve OSA severity and symptoms, these programs have limited reach. POWER is a pragmatic trial of a remote self-directed weight loss care among patients with OSA.

METHODS

POWER randomizes 696 patients with obesity (BMI 30-45 kg/m2) and recent diagnosis or re-confirmation of OSA 1:1 to either a self-directed weight loss intervention or usual care. POWER tests whether such an intervention improves co-primary outcomes of weight and sleep-related quality of life at 12 months. Secondary outcomes include sleep symptoms, global ratings of change, and cardiovascular risk scores. Finally, consistent with a hybrid type 1 approach, the trial embeds an implementation process evaluation. We will use quantitative and qualitative methods including budget impact analyses and qualitative interviews to assess barriers to implementation.

CONCLUSIONS

The results of POWER will inform population health approaches to the delivery of weight loss care. A remote self-directed program has the potential to be disseminated widely with limited health system resources and likely low-cost.

A wide range of missing imputation approaches in longitudinal data: a simulation study and real data analysis

BACKGROUND

Missing data is a pervasive problem in longitudinal data analysis. Several single-imputation (SI) and multiple-imputation (MI) approaches have been proposed to address this issue. In this study, for the first time, the function of the longitudinal regression tree algorithm as a non-parametric method after imputing missing data using SI and MI was investigated using simulated and real data.

METHOD

Using different simulation scenarios derived from a real data set, we compared the performance of cross, trajectory mean, interpolation, copy-mean, and MI methods (27 approaches) to impute missing longitudinal data using parametric and non-parametric longitudinal models and the performance of the methods was assessed in real data. The real data included 3,645 participants older than 18 years within six waves obtained from the longitudinal Tehran cardiometabolic genetic study (TCGS). The data modeling was conducted using systolic and diastolic blood pressure (SBP/DBP) as the outcome variables and included predictor variables such as age, gender, and BMI. The efficiency of imputation approaches was compared using mean squared error (MSE), root-mean-squared error (RMSE), median absolute deviation (MAD), deviance, and Akaike information criteria (AIC).

RESULTS

The longitudinal regression tree algorithm outperformed based on the criteria such as MSE, RMSE, and MAD than the linear mixed-effects model (LMM) for analyzing the TCGS and simulated data using the missing at random (MAR) mechanism. Overall, based on fitting the non-parametric model, the performance of the 27 imputation approaches was nearly similar. However, the SI traj-mean method improved performance compared with other imputation approaches.

CONCLUSION

Both SI and MI approaches performed better using the longitudinal regression tree algorithm compared with the parametric longitudinal models. Based on the results from both the real and simulated data, we recommend that researchers use the traj-mean method for imputing missing values of longitudinal data. Choosing the imputation method with the best performance is widely dependent on the models of interest and the data structure.

Fukushima study for Engaging people with type 2 Diabetes in Behaviour Associated Change (FEEDBACK): study protocol for a cluster randomised controlled trial

BACKGROUND

The growing burden of type 2 diabetes mellitus (T2DM) and the rising cost of healthcare worldwide make it imperative to identify interventions that can promote sustained self-management behaviour in T2DM populations while minimising costs for healthcare systems. The present FEEDBACK study (Fukushima study for Engaging people with type 2 Diabetes in Behaviour Associated Change) aims to evaluate the effects of a novel behaviour change intervention designed to be easily implemented and scaled across a wide range of primary care settings.

METHODS

A cluster randomised controlled trial (RCT) with a 6-month follow-up will be conducted to evaluate the effects of the FEEDBACK intervention. FEEDBACK is a personalised, multi-component intervention intended to be delivered by general practitioners during a routine diabetes consultation. It consists of five steps aimed at enhancing doctor-patient partnership to motivate self-management behaviour: (1) communication of cardiovascular risks using a 'heart age' tool, (2) goal setting, (3) action planning, (4) behavioural contracting, and (5) feedback on behaviour. We aim to recruit 264 adults with T2DM and suboptimal glycaemic control from 20 primary care practices in Japan (cluster units) that will be randomly assigned to either the intervention or control group. The primary outcome measure will be the change in HbA1c levels at 6-month follow-up. Secondary outcome measures include the change in cardiovascular risk score, the probability to achieve the recommended glycaemic target (HbA1c <7.0% [53mmol/mol]) at 6-month follow-up, and a range of behavioural and psychosocial variables. The planned primary analyses will be carried out at the individual level, according to the intention-to-treat principle. Between-group comparisons for the primary outcome will be analysed using mixed-effects models. This study protocol received ethical approval from the research ethics committee of Kashima Hospital, Fukushima, Japan (reference number: 2022002).

DISCUSSION

This article describes the design of a cluster RCT that will evaluate the effects of FEEDBACK, a personalised, multicomponent intervention aimed at enhancing doctor-patient partnership to engage adults with T2DM more effectively in self-management behaviour.

TRIAL REGISTRATION

The study protocol was prospectively registered in the UMIN Clinical Trials Registry (UMIN-CTR ID UMIN000049643 assigned on 29/11/2022). On submission of this manuscript, recruitment of participants is ongoing.

Practical considerations in utilizing cluster randomized controlled trials conducted in biopharmaceutical industry

Cluster randomized controlled trials (cluster RCTs), also known as parallel-arm group-randomized trials, are trials in which the randomized units are groups of participants, as opposed to individual participants. These trials have largely been implemented to address broad public health issues, but with the growing interest in use of real-world data in the regulatory setting, this design may be increasingly considered for industry trials. The key difference between cluster RCTs and traditional RCTs is the intraclass correlation coefficient (ICC) that needs to be considered in cluster RCTs. In this article, we discuss some key practical considerations that are related to ICC in the design, conduct, analysis, and report stages of a cluster RCT. These key considerations related to ICC can lead to improvement in how we translate research findings from cluster RCTs into practices in the biopharmaceutical industry.

Autopopulus: A Novel Framework for Autoencoder Imputation on Large Clinical Datasets

The adoption of electronic health records (EHRs) has made patient data increasingly accessible, precipitating the development of various clinical decision support systems and data-driven models to help physicians. However, missing data are common in EHR-derived datasets, which can introduce significant uncertainty, if not invalidating the use of a predictive model. Machine learning (ML)-based imputation methods have shown promise in various domains for the task of estimating values and reducing uncertainty to the point that a predictive model can be employed. We introduce Autopopulus, a novel framework that enables the design and evaluation of various autoencoder architectures for efficient imputation on large datasets. Autopopulus implements existing autoencoder methods as well as a new technique that outputs a range of estimated values (rather than point estimates), and demonstrates a workflow that helps users make an informed decision on an appropriate imputation method. To further illustrate Autopopulus' utility, we use it to identify not only which imputation methods can most accurately impute on a large clinical dataset, but to also identify the imputation methods that enable downstream predictive models to achieve the best performance for prediction of chronic kidney disease (CKD) progression.

Intention-to-treat analysis when only a baseline value is available

Objectives

How to perform an intention to treat (ITT) analysis when a patient has a baseline value but no follow-up measurements is problematic. The purpose of this study was to compare different methods that deal with this problem, i.e. no imputation (standard and alternative mixed model analysis), single imputation (i.e. baseline value carried forward), and multiple imputation (selective and non-selective).

Study design and setting

We used a simulation study with different scenarios regarding 1) the association between missingness and the baseline value, 2) whether the patients did or did not receive the treatment, and 3) the percentage of missing data, and two real life data sets.

Results

Bias and coverage were comparable between the two mixed model analyses and multiple imputation in most situations including the real life data examples. Only in the situation when the patients in the treatment group were simulated not to have received the treatment, selective imputation using this information outperformed all other methods.

Conclusions

In most situations a standard mixed model analysis without imputation is appropriate as ITT analysis. However, when patients with missing follow-up data allocated to the treatment group did not received treatment, it is advised to use selective imputation, using this information, although the results should be interpreted with caution.

The mixed model for repeated measures for cluster randomized trials: a simulation study investigating bias and type I error with missing continuous data

BACKGROUND

Cluster randomized trials (CRTs) are a design used to test interventions where individual randomization is not appropriate. The mixed model for repeated measures (MMRM) is a popular choice for individually randomized trials with longitudinal continuous outcomes. This model's appeal is due to avoidance of model misspecification and its unbiasedness for data missing completely at random or at random.

METHODS

We extended the MMRM to cluster randomized trials by adding a random intercept for the cluster and undertook a simulation experiment to investigate statistical properties when data are missing at random. We simulated cluster randomized trial data where the outcome was continuous and measured at baseline and three post-intervention time points. We varied the number of clusters, the cluster size, the intra-cluster correlation, missingness and the data-generation models. We demonstrate the MMRM-CRT with an example of a cluster randomized trial on cardiovascular disease prevention among diabetics.

RESULTS

When simulating a treatment effect at the final time point we found that estimates were unbiased when data were complete and when data were missing at random. Variance components were also largely unbiased. When simulating under the null, we found that type I error was largely nominal, although for a few specific cases it was as high as 0.081.

CONCLUSIONS

Although there have been assertions that this model is inappropriate when there are more than two repeated measures on subjects, we found evidence to the contrary. We conclude that the MMRM for CRTs is a good analytic choice for cluster randomized trials with a continuous outcome measured longitudinally.

TRIAL REGISTRATION

ClinicalTrials.gov, ID: NCT02804698.

Advancing successful implementation of task-shifted mental health care in low-resource settings (BASIC): protocol for a stepped wedge cluster randomized trial

BACKGROUND

The mental health treatment gap-the difference between those with mental health need and those who receive treatment-is high in low- and middle-income countries. Task-shifting has been used to address the shortage of mental health professionals, with a growing body of research demonstrating the effectiveness of mental health interventions delivered through task-shifting. However, very little research has focused on how to embed, support, and sustain task-shifting in government-funded systems with potential for scale up. The goal of the Building and Sustaining Interventions for Children (BASIC) study is to examine implementation policies and practices that predict adoption, fidelity, and sustainment of a mental health intervention in the education sector via teacher delivery and the health sector via community health volunteer delivery.

METHODS

BASIC is a Hybrid Type II Implementation-Effectiveness trial. The study design is a stepped wedge, cluster randomized trial involving 7 sequences of 40 schools and 40 communities surrounding the schools. Enrollment consists of 120 teachers, 120 community health volunteers, up to 80 site leaders, and up to 1280 youth and one of their primary guardians. The evidence-based mental health intervention is a locally adapted version of Trauma-focused Cognitive Behavioral Therapy, called Pamoja Tunaweza. Lay counselors are trained and supervised in Pamoja Tunaweza by local trainers who are experienced in delivering the intervention and who participated in a Train-the-Trainer model of skills transfer. After the first sequence completes implementation, in-depth interviews are conducted with initial implementing sites' counselors and leaders. Findings are used to inform delivery of implementation facilitation for subsequent sequences' sites. We use a mixed methods approach including qualitative comparative analysis to identify necessary and sufficient implementation policies and practices that predict 3 implementation outcomes of interest: adoption, fidelity, and sustainment. We also examine child mental health outcomes and cost of the intervention in both the education and health sectors.

DISCUSSION

The BASIC study will provide knowledge about how implementation of task-shifted mental health care can be supported in government systems that already serve children and adolescents. Knowledge about implementation policies and practices from BASIC can advance the science of implementation in low-resource contexts.

TRIAL REGISTRATION

Trial Registration: ClinicalTrials.gov Identifier: NCT03243396. Registered 9th August 2017, https://clinicaltrials.gov/ct2/show/NCT03243396.

System-integrated technology-enabled model of care (SINEMA) to improve the health of stroke patients in rural China: Statistical analysis plan for a cluster-randomized controlled trial

BACKGROUND

The system-integrated technology-enabled model of care (SINEMA) trial aimed to evaluate the effectiveness of a community-based multi-component intervention for secondary prevention of stroke in rural China.

OBJECTIVE

To present the detailed statistical analysis plan for the trial prior to database locking and data analysis.

METHODS

The detailed analysis plan outlines primary and secondary outcome measures, describes the over-arching data analysis principles to be adopted as well as more detailed descriptions of specific analytical approaches for effectiveness analyses, as well strategies to handle missing outcome data.

DISCUSSION

Publication of the statistical analysis plan increases the transparency of the data analysis procedure and reduces potential bias in trial reporting.

TRIAL REGISTRATION

The trial was registered with clinicaltrials.gov (NCT03185858).

System-integrated technology-enabled model of care to improve the health of stroke patients in rural China: protocol for SINEMA-a cluster-randomized controlled trial

BACKGROUND

Despite the significant burden of stroke in rural China, secondary prevention of stroke is suboptimal. This study aims to develop a SINEMA for the secondary prevention of stroke in rural China and to evaluate the effectiveness of the model compared with usual care.

METHODS

The SINEMA model is being implemented and evaluated through a 1-year cluster-randomized controlled trial in Nanhe County, Hebei Province in China. Fifty villages from 5 townships are randomized in a 1:1 ratio to either the intervention or the control arm (usual care) with a target to enroll 25 stroke survivors per village. Village doctors in the intervention arm (1) receive systematic cascade training by stroke specialists on clinical guidelines, essential medicines and behavior change; (2) conduct monthly follow-up visits with the support of a mobile phone application designed for this study; (3) participate in virtual group activities with other village doctors; 4) receive performance feedback and payment. Stroke survivors participate in a health education and project briefing session, receive monthly follow-up visits by village doctors and receive a voice message call daily as reminders for medication use and physical activities. Baseline and 1-year follow-up survey will be conducted in all villages by trained staff who are blinded of the randomized allocation of villages. The primary outcome will be systolic blood pressure and the secondary outcomes will include diastolic blood pressure, medication adherence, mobility, physical activity level and quality of life. Process and economic evaluation will also be conducted.

DISCUSSION

This study is one of very few that aim to promote secondary prevention of stroke in resource-constrained settings and the first to incorporate mobile technologies for both healthcare providers and patients in China. The SINEMA model is innovative as it builds the capacity of primary healthcare workers in the rural area, uses mobile health technologies at the point of care, and addresses critical health needs for a vulnerable community-dwelling patient group. The findings of the study will provide translational evidence for other resource-constrained settings in developing strategies for the secondary prevention of stroke.

Meta Salud Diabetes study protocol: a cluster-randomised trial to reduce cardiovascular risk among a diabetic population of Mexico

INTRODUCTION

Northern Mexico has among the highest rates of cardiovascular disease (CVD) and diabetes in the world. This research addresses core gaps in implementation science to develop, test and scale-up CVD risk-reduction interventions in diabetics through a national primary care health system.

METHODS AND ANALYSIS

The Meta Salud Diabetes (MSD) research project is a parallel two-arm cluster-randomised clinical behavioural trial based in 22 (n=22) health centres in Sonora, Mexico. MSD aims to evaluate the effectiveness of the MSD intervention for the secondary prevention of CVD risk factors among a diabetic population (n=320) compared with the study control of usual care. The MSD intervention consists of 2-hour class sessions delivered over a 13-week period providing educational information to encourage sustainable behavioural change to prevent disease complications including the adoption of physical activity. MSD is delivered within the context of Mexico's national primary care health centre system by health professionals, including nurses, physicians and community health workers via existing social support groups for individuals diagnosed with chronic disease. Mixed models are used to estimate the effect of MSD by comparing cardiovascular risk, as measured by the Framingham Risk Score, between the trial arms. Secondary outcomes include hypertension, behavioural risk factors and psychosocial factors.

ETHICS AND DISSEMINATION

This work is supported by the National Institutes of Health, National Heart Lung and Blood Institute (1R01HL125996-01) and approved by the University of Arizona Research Institutional Review Board (Protocol 1508040144) and the Research Bioethics Committee at the University of Sonora. The first Internal Review Board approval date was 31 August 2015 with five subsequent approved amendments. This article refers to protocol V.0.2, dated 30 January 2017. Results will be disseminated via peer-reviewed publication and presentation at international conferences and will be shared through meetings with health systems officials.

TRIAL REGISTRATION NUMBER

NCT0280469; Pre-results.