Dynamic longitudinal discriminant analysis using multiple longitudinal markers of different types

There is an emerging need in clinical research to accurately predict patients disease status and disease progression by optimally integrating multivariate clinical information. Clinical data is often collected over time for multiple biomarkers of different types (e.g. continuous, binary, counts). In this paper, we present a flexible and dynamic (time-dependent) discriminant analysis approach in which multiple biomarkers of various types are jointly modelled for classification purposes by the multivariate generalized linear mixed model. We propose a mixture of normal distributions for the random effects to allow additional flexibility when modelling the complex correlation between longitudinal biomarkers and to robustify the model and the classification procedure against misspecification of the random effects distribution. These longitudinal models are subsequently used in a multivariate time-dependent discriminant scheme to predict, at any time point, the probability of belonging to a particular risk group. The methodology is illustrated using clinical data from patients with epilepsy, where the aim is to identify patients who will not achieve remission of seizures within a 5-year follow up period

Fast approximate inference for multivariate longitudinal data

Collecting information on multiple longitudinal outcomes is increasingly common in many clinical settings. In many cases, it is desirable to model these outcomes jointly. However, in large data sets, with many outcomes, computational burden often prevents the simultaneous modeling of multiple outcomes within a single model. We develop a mean field variational Bayes algorithm, to jointly model multiple Gaussian, Poisson, or binary longitudinal markers within a multivariate generalized linear mixed model. Through simulation studies and clinical applications (in the fields of sight threatening diabetic retinopathy and primary biliary cirrhosis), we demonstrate substantial computational savings of our approximate approach when compared to a standard Markov Chain Monte Carlo, while maintaining good levels of accuracy of model parameters

The effect of random-effects misspecification on classification accuracy

Mixed models are a useful way of analysing longitudinal data. Random effects terms allow modelling of patient specific deviations from the overall trend over time. Correlation between repeated measurements are captured by specifying a joint distribution for all random effects in a model. Typically, this joint distribution is assumed to be a multivariate normal distribution. For Gaussian outcomes misspecification of the random effects distribution usually has little impact. However, when the outcome is discrete (e.g. counts or binary outcomes) generalised linear mixed models (GLMMs) are used to analyse longitudinal trends. Opinion is divided about how robust GLMMs are to misspecification of the random effects. Previous work explored the impact of random effects misspecification on the bias of model parameters in single outcome GLMMs. Accepting that these model parameters may be biased, we investigate whether this affects our ability to classify patients into clinical groups using a longitudinal discriminant analysis. We also consider multiple outcomes, which can significantly increase the dimensions of the random effects distribution when modelled simultaneously. We show that when there is severe departure from normality, more flexible mixture distributions can give better classification accuracy. However, in many cases, wrongly assuming a single multivariate normal distribution has little impact on classification accuracy

Imaging technologies for monitoring the safety, efficacy and mechanisms of action of cell-based regenerative medicine therapies in models of kidney disease

AbstractThe incidence of end stage kidney disease is rising annually and it is now a global public health problem. Current treatment options are dialysis or renal transplantation, which apart from their significant drawbacks in terms of increased morbidity and mortality, are placing an increasing economic burden on society. Cell-based Regenerative Medicine Therapies (RMTs) have shown great promise in rodent models of kidney disease, but clinical translation is hampered due to the lack of adequate safety and efficacy data. Furthermore, the mechanisms whereby the cell-based RMTs ameliorate injury are ill-defined. For instance, it is not always clear if the cells directly replace damaged renal tissue, or whether paracrine effects are more important. Knowledge of the mechanisms responsible for the beneficial effects of cell therapies is crucial because it could lead to the development of safer and more effective RMTs in the future. To address these questions, novel in vivo imaging strategies are needed to monitor the biodistribution of cell-based RMTs and evaluate their beneficial effects on host tissues and organs, as well as any potential adverse effects. In this review we will discuss how state-of-the-art imaging modalities, including bioluminescence, magnetic resonance, nuclear imaging, ultrasound and an emerging imaging technology called multispectral optoacoustic tomography, can be used in combination with various imaging probes to track the fate and biodistribution of cell-based RMTs in rodent models of kidney disease, and evaluate their effect on renal function

Visual Function Questionnaire as an outcome measure for homonymous hemianopia: subscales and supplementary questions, analysis from the VISION trial

Background: We conduct supplementary analyses of the NEI VFQ-25 data to evaluate where changes occurred within subscales of the NEI VFQ-25 leading to change in the composite scores between the three treatment arms, and evaluate the NEI VFQ-25 with and without the Neuro 10 supplement. Methods: A prospective, multicentre, parallel, single-blind, three-arm RCT of fourteen UK acute stroke units was conducted. Stroke survivors with homonymous hemianopia were recruited. Interventions included: Fresnel prisms for minimum 2 h, 5 days/week over 6-weeks (Arm a), Visual search training for minimum 30 min, 5 days/week over 6-weeks (Arm b) and standard care-information only (Arm c). Primary and secondary outcomes (including NEI VFQ-25 data) were measured at baseline, 6, 12 and 26 weeks after randomisation. Results: Eighty seven patients were recruited (69% male; mean age (SD) equal to 69 (12) years). At 26 weeks, outcomes for 24, 24 and 22 patients, respectively, were compared to baseline. NEI VFQ-25 (with and without Neuro 10) responses improved from baseline to 26 weeks with visual search training compared to Fresnel prisms and standard care. In subscale analysis, the most impacted across all treatment arms was ‘driving’ whilst the least impacted were ‘colour vision’ and ‘ocular pain’. Conclusions: Composite scores differed systematically for the NEI VFQ-25 (Neuro 10) versus NEI VFQ-25 at all time points. For subscale scores, descriptive statistics suggest clinically relevant improvement in distance activities and vision-specific dependency subscales for NEI VFQ-25 scores in the visual search treatment arm. Trial Registration: Current Controlled Trials ISRCTN05956042

Telmisartan to reduce insulin resistance in HIV-positive individuals on combination antiretroviral therapy: the TAILoR dose-ranging Phase II RCT

BackgroundCombination antiretroviral therapy (cART) is the standard for human immunodeficiency virus (HIV) infection treatment but can result in metabolic abnormalities, such as insulin resistance, dyslipidaemia and lipodystrophy, which can increase the risk of cardiovascular disease.ObjectiveThe objective of the trial was to evaluate whether or not telmisartan, an angiotensin II receptor antagonist and a peroxisome proliferator-activated receptor-γ partial agonist, could reduce insulin resistance in HIV-positive individuals on cART, and affect blood and imaging biomarkers of cardiometabolic disease.DesignA Phase II, multicentre, randomised, open-labelled, dose-ranging trial of telmisartan over a period of 48 weeks with an adaptive design comprising two stages was used to identify the optimal dose of telmisartan. Participants were randomised to receive one of the three doses of telmisartan (20, 40 and 80 mg) or no intervention (control).SettingRecruitment was from 19 HIV specialist centres in the UK.ParticipantsA total of 377 patients infected with HIV who met the prespecified inclusion/exclusion criteria.Interventions20-, 40- and 80-mg tablets of telmisartan.Main outcome measuresThe primary outcome measure was reduction in the homeostatic model assessment of insulin resistance (HOMA-IR), a marker of insulin resistance, at 24 weeks. Secondary outcome measures were changes in plasma lipid profile; Quantitative Insulin Sensitivity Check Index (QUICKI) and revised QUICKI, alternative markers of insulin resistance, plasma adipokines (adiponectin, leptin, interleukin 8, tumour necrosis factor alpha, resistin); high-sensitivity C-reactive protein (hs-CRP); body fat redistribution, as measured by magnetic resonance imaging/proton magnetic resonance spectroscopy; changes in renal markers (albumin-to-creatinine ratio, neutrophil gelatinase-associated lipocalin); and tolerability to telmisartan.ResultsAt the interim analysis, 80 mg of telmisartan was taken forward into the second stage of the study. Baseline characteristics were balanced across treatment arms. There were no differences in HOMA-IR [0.007, standard error (SE) 0.106], QUICKI (0.001, SE 0.001) and revised QUICKI (0.002, SE 0.002) at 24 weeks between the telmisartan (80 mg; n = 106) and non-intervention (n = 105) arms. Longitudinal analysis over 48 weeks showed that there was no change in HOMA-IR, lipid or adipokine levels; however, but there were significant, but marginal, improvements in revised QUICKI [0.004, 95% confidence interval (CI) 0.000 to 0.008] and plasma hs-CRP (–0.222, 95% CI –0.433 to –0.011) over 48 weeks. Substudies also showed a significant reduction in the liver fat content at 24 weeks (1.714, 95% CI –2.787 to –0.642; p = 0.005) and urinary albumin excretion at 48 weeks (–0.665, 95% CI –1.31 to –0.019; p = 0.04). There were no differences in serious adverse events between the telmisartan and control arms.LimitationsThe patients had modest elevations of HOMA-IR at baseline, and our trial could have been under-powered to detect smaller improvements in insulin resistance over time.ConclusionsUsing a novel adaptive design, we demonstrated that there was no significant effect of telmisartan (80 mg) on the primary outcome measure of HOMA-IR and some secondary outcomes (plasma lipids and adipokines). Telmisartan did lead to favourable, and biologically plausible, changes of the secondary longitudinal outcome measures: revised QUICKI, hs-CRP, hepatic fat accumulation and urinary albumin excretion. Taken collectively, our findings showed that telmisartan did not reduce insulin resistance in patients infected with HIV on antiretrovirals.Future workThe mechanistic basis of adipocyte regulation will be studied to allow for development of biomarkers and interventions.Trial registrationCurrent Controlled Trials ISRCTN51069819.FundingThis project was funded by the Efficacy and Mechanism Evaluation (EME) programme, a Medical Research Council and National Institute for Health Research partnership