Biomarker-Guided Non-Adaptive Trial Designs in Phase II and Phase III: A Methodological Review (vol 7, 1, 2017)

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Fixed and Adaptive Parallel Subgroup-Specific Design for Survival Outcomes: Power and Sample Size

Biomarker-guided clinical trial designs, which focus on testing the effectiveness of a biomarker-guided approach to treatment in improving patient health, have drawn considerable attention in the era of stratified medicine with many different designs being proposed in the literature. However, planning such trials to ensure they have sufficient power to test the relevant hypotheses can be challenging and the literature often lacks guidance in this regard. In this study, we focus on the parallel subgroup-specific design, which allows the evaluation of separate treatment effects in the biomarker-positive subgroup and biomarker-negative subgroup simultaneously. We also explore an adaptive version of the design, where an interim analysis is undertaken based on a fixed percentage of target events, with the option to stop each biomarker-defined subgroup early for futility or efficacy. We calculate the number of events and patients required to ensure sufficient power in each of the biomarker-defined subgroups under different scenarios when the primary outcome is time-to-event. For the adaptive version, stopping probabilities are also explored. Since multiple hypotheses are being tested simultaneously, and multiple interim analyses are undertaken, we also focus on controlling the overall type I error rate by way of multiplicity adjustment

Fixed and Adaptive Parallel Subgroup-Specific Design for Survival Outcomes: Power and Sample Size (vol 7, 19, 2017)

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joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes

Background: Joint modelling of longitudinal and time-to-event outcomes has received considerable attention over recent years. Commensurate with this has been a rise in statistical software options for fitting these models. However, these tools have generally been limited to a single longitudinal outcome. Here, we describe the classical joint model to the case of multiple longitudinal outcomes, propose a practical algorithm for fitting the models, and demonstrate how to fit the models using a new package for the statistical software platform R, joineRML. Results: A multivariate linear mixed sub-model is specified for the longitudinal outcomes, and a Cox proportional hazards regression model with time-varying covariates is specified for the event time sub-model. The association between models is captured through a zero-mean multivariate latent Gaussian process. The models are fitted using a Monte Carlo Expectation-Maximisation algorithm, and inferences are based on approximate standard errors from the empirical profile information matrix, which are contrasted to an alternative bootstrap estimation approach. We illustrate the model and software on a real data example for patients with primary biliary cirrhosis with three repeatedly measured biomarkers. Conclusions: An open-source software package capable of fitting multivariate joint models is available. The underlying algorithm and source code makes use of several methods to increase computational speed

Urinary Biomarkers of Aminoglycoside-Induced Nephrotoxicity in Cystic Fibrosis: Kidney Injury Molecule-1 and Neutrophil Gelatinase-Associated Lipocalin

Aminoglycosides are commonly used for the treatment of pulmonary exacerbations in patients with cystic fibrosis (CF). However, they are potentially nephrotoxic. This prospective observational cohort study aimed to investigate the potential validity of two urinary renal biomarkers, Kidney Injury Molecule-1 (KIM-1) and Neutrophil Gelatinase-associated Lipocalin (NGAL), in identifying aminoglycoside-induced nephrotoxicity in children with CF. Children and young adults up to 20 years of age with a confirmed diagnosis of CF were recruited from ten United Kingdom hospitals. Participants provided urine samples for measurement of KIM-1 and NGAL concentrations, at baseline, at regular outpatient appointments, and before, during and after exposure to clinically-indicated treatment with the aminoglycoside tobramycin. 37/158 patients recruited (23.4%) received at least one course of IV tobramycin during the study. The median peak fold-change during tobramycin exposure for KIM-1 was 2.28 (IQR 2.69) and 4.02 (IQR 7.29) for NGAL, in the absence of serum creatinine changes. Baseline KIM-1 was positively associated with cumulative courses of IV aminoglycosides (R2 = 0.11; β = 0.03; p < 0.0001). KIM-1, in particular, may be a useful, non-invasive, biomarker of acute and chronic proximal tubular injury associated with exposure to aminoglycosides in patients with CF, but its clinical utility needs to be further evaluated in prospective studies

Warfarin dosing algorithms: A systematic review

AIMS:Numerous algorithms have been developed to guide warfarin dosing and improve clinical outcomes. We reviewed the algorithms available for various populations and the covariates, performances and risk of bias of these algorithms. METHODS:We systematically searched MEDLINE until 20 May 2020 and selected studies describing the development, external validation, or clinical utility of a multivariable warfarin dosing algorithm. Two investigators conducted data extraction and quality assessment. RESULTS:Of 10,035 screened records, 266 articles were included in the review, describing the development of 433 dosing algorithms, 481 external validations and 52 clinical utility assessments. Most developed algorithms were for dose-initiation (86%), developed by multiple linear regression (65%) and mostly applicable to Asians (49%) or Whites (43%). The most common demographic/clinical/environmental covariates were age (included in 401 algorithms), concomitant medications (270 algorithms) and weight (229 algorithms) while CYP2C9 (329 algorithms), VKORC1 (319 algorithms) and CYP4F2 (92 algorithms) variants were the most common genetic covariates. Only 26% and 7% algorithms were externally validated and evaluated for clinical utility, respectively, with less than 2% of algorithm developments and external validations being rated as having a low risk of bias. CONCLUSION:Most warfarin dosing algorithms have been developed in Asians and Whites and may not be applicable to under-served populations. Few algorithms have been externally validated, assessed for clinical utility, and/or have a low risk of bias which makes them unreliable for clinical use. Algorithm development and assessment should follow current methodological recommendations to improve reliability and applicability, and under-represented populations should be prioritized

A comparison of HMGB1 concentrations between cerebrospinal fluid and blood in patients with neurological disease

AIMS: To determine whether a correlation exists between paired cerebrospinal fluid (CSF) and serum levels of a novel inflammatory biomarker, high-mobility group box 1 (HMGB1), in different neurological conditions. METHODS: HMGB1 was measured in the serum and CSF of 46 neurological patients (18 idiopathic intracranial hypertension [IIH], 18 neurological infection/inflammation [NII] and 10 Rasmussen's encephalitis [RE]). RESULTS: Mean serum (± SD) HMGB1 levels were 1.43 ± 0.54, 25.28 ± 27.9 and 1.89 ± 1.49 ng/ml for the patients with IIH, NII and RE, respectively. Corresponding mean (± SD) CSF levels were 0.35 ± 0.22, 4.48 ± 6.56 and 2.24 ± 2.35 ng/ml. Both CSF and serum HMGB1 was elevated in NII. Elevated CSF HMGB1 was demonstrated in RE. There was no direct correlation between CSF and serum levels of HMGB1. CONCLUSION: Serum HMGB1 cannot be used as a surrogate measure for CSF levels. CSF HMGB1 was elevated in NII and RE, its role as a prognostic/stratification biomarker needs further study