Evaluation of the Victorian Healthy Homes Program: protocol for a randomised controlled trial.

INTRODUCTION: The evaluation of the Victorian Healthy Homes Program (VHHP) will generate evidence about the efficacy and cost-effectiveness of home upgrades to improve thermal comfort, reduce energy use and produce health and economic benefits to vulnerable households in Victoria, Australia. METHODS AND ANALYSIS: The VHHP evaluation will use a staggered, parallel group clustered randomised controlled trial to test the home energy intervention in 1000 households. All households will receive the intervention either before (intervention group) or after (control group) winter (defined as 22 June to 21 September). The trial spans three winters with differing numbers of households in each cohort. The primary outcome is the mean difference in indoor average daily temperature between intervention and control households during the winter period. Secondary outcomes include household energy consumption and residential energy efficiency, self-reported respiratory symptoms, health-related quality of life, healthcare utilisation, absences from school/work and self-reported conditions within the home. Linear and logistic regression will be used to analyse the primary and secondary outcomes, controlling for clustering of households by area and the possible confounders of year and timing of intervention, to compare the treatment and control groups over the winter period. Economic evaluation will include a cost-effectiveness and cost-benefit analysis. ETHICS AND DISSEMINATION: Ethical approval was received from Victorian Department of Human Services Human Research Ethics Committee (reference number: 04/17), University of Technology Sydney Human Research Ethics Committee (reference number: ETH18-2273) and Australian Government Department of Veterans Affairs. Study results will be disseminated in a final report and peer-reviewed journals. TRIAL REGISTRATION NUMBER: ACTRN12618000160235.

Strategies for Imputation of High-Resolution Environmental Data in Clinical Randomized Controlled Trials.

Time series data collected in clinical trials can have varying degrees of missingness, adding challenges during statistical analyses. An additional layer of complexity is introduced for missing data in randomized controlled trials (RCT), where researchers must remain blinded between intervention and control groups. Such restriction severely limits the applicability of conventional imputation methods that would utilize other participants' data for improved performance. This paper explores and compares various methods to impute high-resolution temperature logger data in RCT settings. In addition to the conventional non-parametric approaches, we propose a spline regression (SR) approach that captures the dynamics of indoor temperature by time of day that is unique to each participant. We investigate how the inclusion of external temperature and energy use can improve the model performance. Results show that SR imputation results in 16% smaller root mean squared error (RMSE) compared to conventional imputation methods, with the gap widening to 22% when more than half of data is missing. The SR method is particularly useful in cases where missingness occurs simultaneously for multiple participants, such as concurrent battery failures. We demonstrate how proper modelling of periodic dynamics can lead to significantly improved imputation performance, even with limited data.