Multiple Imputation of Composite Covariates in Survival Studies

Missing covariate values are a common problem in survival studies, and the method of choice when handling such incomplete data is often multiple imputation. However, it is not obvious how this can be used most effectively when an incomplete covariate is a function of other covariates. For example, body mass index (BMI) is the ratio of weight and height-squared. In this situation, the following question arises: Should a composite covariate such as BMI be imputed directly, or is it advantageous to impute its constituents, weight and height, first and to construct BMI afterwards? We address this question through a carefully designed simulation study that compares various approaches to multiple imputation of composite covariates in a survival context. We discuss advantages and limitations of these approaches for various types of missingness and imputation models. Our results are a first step towards providing much needed guidance to practitioners for analysing their incomplete survival data effectively

Genomic–transcriptomic evolution in lung cancer and metastasis

Intratumour heterogeneity (ITH) fuels lung cancer evolution, which leads to immune evasion and resistance to therapy. Here, using paired whole-exome and RNA sequencing data, we investigate intratumour transcriptomic diversity in 354 non-small cell lung cancer tumours from 347 out of the first 421 patients prospectively recruited into the TRACERx study. Analyses of 947 tumour regions, representing both primary and metastatic disease, alongside 96 tumour-adjacent normal tissue samples implicate the transcriptome as a major source of phenotypic variation. Gene expression levels and ITH relate to patterns of positive and negative selection during tumour evolution. We observe frequent copy number-independent allele-specific expression that is linked to epigenomic dysfunction. Allele-specific expression can also result in genomic–transcriptomic parallel evolution, which converges on cancer gene disruption. We extract signatures of RNA single-base substitutions and link their aetiology to the activity of the RNA-editing enzymes ADAR and APOBEC3A, thereby revealing otherwise undetected ongoing APOBEC activity in tumours. Characterizing the transcriptomes of primary–metastatic tumour pairs, we combine multiple machine-learning approaches that leverage genomic and transcriptomic variables to link metastasis-seeding potential to the evolutionary context of mutations and increased proliferation within primary tumour regions. These results highlight the interplay between the genome and transcriptome in influencing ITH, lung cancer evolution and metastasis

Bi-allelic Loss-of-Function CACNA1B Mutations in Progressive Epilepsy-Dyskinesia.

The occurrence of non-epileptic hyperkinetic movements in the context of developmental epileptic encephalopathies is an increasingly recognized phenomenon. Identification of causative mutations provides an important insight into common pathogenic mechanisms that cause both seizures and abnormal motor control. We report bi-allelic loss-of-function CACNA1B variants in six children from three unrelated families whose affected members present with a complex and progressive neurological syndrome. All affected individuals presented with epileptic encephalopathy, severe neurodevelopmental delay (often with regression), and a hyperkinetic movement disorder. Additional neurological features included postnatal microcephaly and hypotonia. Five children died in childhood or adolescence (mean age of death: 9 years), mainly as a result of secondary respiratory complications. CACNA1B encodes the pore-forming subunit of the pre-synaptic neuronal voltage-gated calcium channel Cav2.2/N-type, crucial for SNARE-mediated neurotransmission, particularly in the early postnatal period. Bi-allelic loss-of-function variants in CACNA1B are predicted to cause disruption of Ca2+ influx, leading to impaired synaptic neurotransmission. The resultant effect on neuronal function is likely to be important in the development of involuntary movements and epilepsy. Overall, our findings provide further evidence for the key role of Cav2.2 in normal human neurodevelopment.MAK is funded by an NIHR Research Professorship and receives funding from the Wellcome Trust, Great Ormond Street Children's Hospital Charity, and Rosetrees Trust. E.M. received funding from the Rosetrees Trust (CD-A53) and Great Ormond Street Hospital Children's Charity. K.G. received funding from Temple Street Foundation. A.M. is funded by Great Ormond Street Hospital, the National Institute for Health Research (NIHR), and Biomedical Research Centre. F.L.R. and D.G. are funded by Cambridge Biomedical Research Centre. K.C. and A.S.J. are funded by NIHR Bioresource for Rare Diseases. The DDD Study presents independent research commissioned by the Health Innovation Challenge Fund (grant number HICF-1009-003), a parallel funding partnership between the Wellcome Trust and the Department of Health, and the Wellcome Trust Sanger Institute (grant number WT098051). We acknowledge support from the UK Department of Health via the NIHR comprehensive Biomedical Research Centre award to Guy's and St. Thomas' National Health Service (NHS) Foundation Trust in partnership with King's College London. This research was also supported by the NIHR Great Ormond Street Hospital Biomedical Research Centre. J.H.C. is in receipt of an NIHR Senior Investigator Award. The research team acknowledges the support of the NIHR through the Comprehensive Clinical Research Network. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, Department of Health, or Wellcome Trust. E.R.M. acknowledges support from NIHR Cambridge Biomedical Research Centre, an NIHR Senior Investigator Award, and the University of Cambridge has received salary support in respect of E.R.M. from the NHS in the East of England through the Clinical Academic Reserve. I.E.S. is supported by the National Health and Medical Research Council of Australia (Program Grant and Practitioner Fellowship)

Multiple imputation of a derived variable in a survival analysis context

A data set contains variables that are directly measured, and can be expanded by non-trivial transformations of the measured variable; e.g., dichotomising a continuous variable. Additionally, a new variable can be constructed from several measured variables; e.g., body mass index (BMI) is the ratio of weight and height-squared. The transformed or constructed variable is a derived variable, and the measured variable(s) that build the derived variable are constituents. A complication in a derived variable arises if at least one value in the constituents is not stored, that is, the derived variable is incomplete. Incomplete variables are a common problem when analysing data and can lead to incorrect inferences in the analysis if mishandled. One approach to deal with them is multiple imputation (MI). In MI, each missing value is replaced several times, yielding several complete multiply imputed data sets. Each data set is analysed, with the results subsequently combined. Two approaches to impute an incomplete derived variable are active and passive imputation. In active imputation, the derived variable is directly imputed, so the functional relationship with the constituents is ignored. In passive imputation, the constituents are imputed and the derived variable is later constructed.Previous literature finds that the performance of active and passive MI can depend on the model fitted to the multiply imputed data. One gap in the literature is in the performance of active and passive MI in a survival analysis context.In this thesis, a simulation study is run to investigate the performance of active and passive imputation for three functional forms in a survival analysis context: ratio, additive, and index. In an additive form, the derived variable is a weighted sum of the constituents. In an index form, a numerical variable is categorised as a factor.Conditions investigated include how the missingness is imposed, and the number of predictors to impute the missing values. A special case of passive imputation outperforms active imputation for a ratio and additive functional form. Active imputation outperforms passive imputation for an index functional form

Integrating intimate partner violence prevention content into a digital parenting chatbot intervention during COVID-19: Intervention development and remote data collection

Abstract Background Intimate partner violence (IPV) is a serious public health issue which experienced a sharp incline during the onset of COVID-19. Increases in other forms of violence, such as violence against children (VAC), have also been linked to the pandemic, and there have been calls for greater prevention efforts that tackle both forms of violence concurrently. The COVID-19 crisis has highlighted the urgent need for evidence-based and scalable violence prevention interventions that target multiple forms of family violence. Parenting programmes have shown promising results in preventing various forms of family violence, including IPV and VAC, and have recently experienced an expansion in delivery, with digital intervention formats growing. This paper describes the development and evaluation of the IPV prevention content designed and integrated into ParentText, a chatbot parenting intervention adapted from Parenting for Lifelong Health programmes. Methods The ParentText IPV prevention content was developed using the Six Steps in Quality Intervention Development (6SQuID) framework. This involved targeted literature searches for key studies to identify causal factors associated with IPV and determining those with greatest scope for change. Findings were used to develop the intervention content and theory of change. Consultations were held with academic researchers (n = 5), practitioners (n = 5), and local community organisations (n = 7), who reviewed the content. A formative evaluation was conducted with parents in relationships (n = 96) in Jamaica to better understand patterns in user engagement with the intervention and identify strategies to further improve engagement. Results Using the 6SQuID model, five topics on IPV prevention were integrated into the ParentText chatbot. Text-messages covering each topic, including additional materials such as cartoons and videos, were also developed. The formative evaluation revealed an average user-engagement length of 14 days, 0.50 chatbot interactions per day, and over half of participants selected to view additional relationship content. Conclusions This article provides a unique contribution as the first to integrate IPV prevention content into a remotely delivered, digital parenting intervention for low-resource settings. The findings from this research and formative evaluation shed light on the promising potential of chatbots as scalable and accessible forms of violence prevention, targeting multiple types of family violence

Optimising engagement in a digital parenting intervention to prevent violence against adolescents in Tanzania: protocol for a cluster randomised factorial trial

Abstract Background Violence against adolescents is a universal reality, with severe individual and societal costs. There is a critical need for scalable and effective violence prevention strategies such as parenting programmes, particularly in low- and middle-income countries where rates of maltreatment are highest. Digital interventions may be a scalable and cost-effective alternative to in-person delivery, yet maximising caregiver engagement is a substantial challenge. This trial employs a cluster randomised factorial experiment and a novel mixed-methods analytic approach to assess the effectiveness, cost-effectiveness, and feasibility of intervention components designed to optimise engagement in an open-source parenting app, ParentApp for Teens. The app is based on the evidence-based Parenting for Lifelong Health for Teens programme, developed collaboratively by academic institutions in the Global South and North, the WHO, and UNICEF. Methods/design Sixteen neighbourhoods, i.e., clusters, will be randomised to one of eight experimental conditions which consist of any combination of three components (Support: self-guided/moderated WhatsApp groups; App Design: sequential workshops/non-sequential modules; Digital Literacy Training: on/off). The study will be conducted in low-income communities in Tanzania, targeting socioeconomically vulnerable caregivers of adolescents aged 10 to 17 years (16 clusters, 8 conditions, 640 caregivers, 80 per condition). The primary objective of this trial is to estimate the main effects of the three components on engagement. Secondary objectives are to explore the interactions between components, the effects of the components on caregiver behavioural outcomes, moderators and mediators of programme engagement and impact, and the cost-effectiveness of components. The study will also assess enablers and barriers to engagement qualitatively via interviews with a subset of low, medium, and high engaging participants. We will combine quantitative and qualitative data to develop an optimised ParentApp for Teens delivery package. Discussion This is the first known cluster randomised factorial trial for the optimisation of engagement in a digital parenting intervention in a low- and middle-income country. Findings will be used to inform the evaluation of the optimised app in a subsequent randomised controlled trial. Trial registration Pan African Clinical Trial Registry, PACTR202210657553944. Registered 11 October 2022, https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=24051