Initial data analysis for longitudinal studies to build a solid foundation for reproducible analysis.

Initial data analysis (IDA) is the part of the data pipeline that takes place between the end of data retrieval and the beginning of data analysis that addresses the research question. Systematic IDA and clear reporting of the IDA findings is an important step towards reproducible research. A general framework of IDA for observational studies includes data cleaning, data screening, and possible updates of pre-planned statistical analyses. Longitudinal studies, where participants are observed repeatedly over time, pose additional challenges, as they have special features that should be taken into account in the IDA steps before addressing the research question. We propose a systematic approach in longitudinal studies to examine data properties prior to conducting planned statistical analyses. In this paper we focus on the data screening element of IDA, assuming that the research aims are accompanied by an analysis plan, meta-data are well documented, and data cleaning has already been performed. IDA data screening comprises five types of explorations, covering the analysis of participation profiles over time, evaluation of missing data, presentation of univariate and multivariate descriptions, and the depiction of longitudinal aspects. Executing the IDA plan will result in an IDA report to inform data analysts about data properties and possible implications for the analysis plan-another element of the IDA framework. Our framework is illustrated focusing on hand grip strength outcome data from a data collection across several waves in a complex survey. We provide reproducible R code on a public repository, presenting a detailed data screening plan for the investigation of the average rate of age-associated decline of grip strength. With our checklist and reproducible R code we provide data analysts a framework to work with longitudinal data in an informed way, enhancing the reproducibility and validity of their work.

Recoverability and estimation of causal effects under typical multivariable missingness mechanisms.

In the context of missing data, the identifiability or "recoverability" of the average causal effect (ACE) depends not only on the usual causal assumptions but also on missingness assumptions that can be depicted by adding variable-specific missingness indicators to causal diagrams, creating missingness directed acyclic graphs (m-DAGs). Previous research described canonical m-DAGs, representing typical multivariable missingness mechanisms in epidemiological studies, and examined mathematically the recoverability of the ACE in each case. However, this work assumed no effect modification and did not investigate methods for estimation across such scenarios. Here, we extend this research by determining the recoverability of the ACE in settings with effect modification and conducting a simulation study to evaluate the performance of widely used missing data methods when estimating the ACE using correctly specified g-computation. Methods assessed were complete case analysis (CCA) and various implementations of multiple imputation (MI) with varying degrees of compatibility with the outcome model used in g-computation. Simulations were based on an example from the Victorian Adolescent Health Cohort Study (VAHCS), where interest was in estimating the ACE of adolescent cannabis use on mental health in young adulthood. We found that the ACE is recoverable when no incomplete variable (exposure, outcome, or confounder) causes its own missingness, and nonrecoverable otherwise, in simplified versions of 10 canonical m-DAGs that excluded unmeasured common causes of missingness indicators. Despite this nonrecoverability, simulations showed that MI approaches that are compatible with the outcome model in g-computation may enable approximately unbiased estimation across all canonical m-DAGs considered, except when the outcome causes its own missingness or causes the missingness of a variable that causes its own missingness. In the latter settings, researchers may need to consider sensitivity analysis methods incorporating external information (e.g., delta-adjustment methods). The VAHCS case study illustrates the practical implications of these findings.

Adaptive platform trials rather than randomised controlled trials for paediatric sepsis.

Adaptive platform trials (APTs) offer a promising alternative to traditional randomised controlled trials for evaluating treatments for paediatric sepsis. Randomised controlled trials, despite being the gold standard for establishing causality between interventions and outcomes, make many assumptions about disease prevalence, severity and intervention effects, which are often incorrect. As a result, the evidence for most treatments for paediatric sepsis are based on low-quality evidence. APTs use accrued data rather than assumptions to power trial adaptations. They can assess multiple treatments simultaneously with shared research infrastructure. As such, APTs offer a more efficient, flexible and more effective way to identify optimal treatments. The proposed Paediatric Adaptive Sepsis Platform Trial, leveraging the Paediatric Research in Emergency Departments International Collaborative network's infrastructure, will evaluate resuscitation fluids, vasoactive medications, corticosteroids and antimicrobials. This trial has the potential to substantially impact clinical practice and reduce global sepsis mortality in children.

Statistical analyses of ordinal outcomes in randomised controlled trials: a scoping review.

BACKGROUND: Randomised controlled trials (RCTs) aim to estimate the causal effect of one or more interventions relative to a control. One type of outcome that can be of interest in an RCT is an ordinal outcome, which is useful to answer clinical questions regarding complex and evolving patient states. The target parameter of interest for an ordinal outcome depends on the research question and the assumptions the analyst is willing to make. This review aimed to provide an overview of how ordinal outcomes have been used and analysed in RCTs. METHODS: The review included RCTs with an ordinal primary or secondary outcome published between 2017 and 2022 in four highly ranked medical journals (the British Medical Journal, New England Journal of Medicine, The Lancet, and the Journal of the American Medical Association) identified through PubMed. Details regarding the study setting, design, the target parameter, and statistical methods used to analyse the ordinal outcome were extracted. RESULTS: The search identified 309 studies, of which 144 were eligible for inclusion. The most used target parameter was an odds ratio, reported in 78 (54%) studies. The ordinal outcome was dichotomised for analysis in 47 ( 33 % ) studies, and the most common statistical model used to analyse the ordinal outcome on the full ordinal scale was the proportional odds model (64 [ 44 % ] studies). Notably, 86 (60%) studies did not explicitly check or describe the robustness of the assumptions for the statistical method(s) used. CONCLUSIONS: The results of this review indicate that in RCTs that use an ordinal outcome, there is variation in the target parameter and the analytical approaches used, with many dichotomising the ordinal outcome. Few studies provided assurance regarding the appropriateness of the assumptions and methods used to analyse the ordinal outcome. More guidance is needed to improve the transparent reporting of the analysis of ordinal outcomes in future trials.

Overcoming aggregation with laser heated nanoelectrospray mass spectrometry: thermal stability and pathways for loss of bicarbonate from carbonic anhydrase II.

Variable temperature electrospray mass spectrometry is useful for multiplexed measurements of the thermal stabilities of biomolecules, but the ionization process can be disrupted by aggregation-prone proteins/complexes that have irreversible unfolding transitions. Resistively heating solutions containing a mixture of bovine carbonic anhydrase II (BCAII), a CO2 fixing enzyme involved in many biochemical pathways, and cytochrome c leads to complete loss of carbonic anhydrase signal and a significant reduction in cytochrome c signal above ∼72 °C due to aggregation. In contrast, when the tips of borosilicate glass nanoelectrospray emitters are heated with a laser, complete thermal denaturation curves for both proteins are obtained in <1 minute. The simultaneous measurements of the melting temperature of BCAII and BCAII bound to bicarbonate reveal that the bicarbonate stabilizes the folded form of this protein by ∼6.4 °C. Moreover, the temperature dependences of different bicarbonate loss pathways are obtained. Although protein analytes are directly heated by the laser for only 140 ms, heat conduction further up the emitter leads to a total analyte heating time of ∼41 s. Pulsed laser heating experiments could reduce this time to ∼0.5 s for protein aggregation that occurs on a faster time scale. Laser heating provides a powerful method for studying the detailed mechanisms of cofactor/ligand loss with increasing temperature and promises a new tool for studying the effect of ligands, drugs, growth conditions, buffer additives, or other treatments on the stabilities of aggregation-prone biomolecules.

Advancing randomized controlled trial methodologies: The place of innovative trial design in eating disorders research.

Randomized controlled trials can be used to generate evidence on the efficacy and safety of new treatments in eating disorders research. Many of the trials previously conducted in this area have been deemed to be of low quality, in part due to a number of practical constraints. This article provides an overview of established and more innovative clinical trial designs, accompanied by pertinent examples, to highlight how design choices can enhance flexibility and improve efficiency of both resource allocation and participant involvement. Trial designs include individually randomized, cluster randomized, and designs with randomizations at multiple time points and/or addressing several research questions (master protocol studies). Design features include the use of adaptations and considerations for pragmatic or registry-based trials. The appropriate choice of trial design, together with rigorous trial conduct, reporting and analysis, can establish high-quality evidence to advance knowledge in the field. It is anticipated that this article will provide a broad and contemporary introduction to trial designs and will help researchers make informed trial design choices for improved testing of new interventions in eating disorders. PUBLIC SIGNIFICANCE: There is a paucity of high quality randomized controlled trials that have been conducted in eating disorders, highlighting the need to identify where efficiency gains in trial design may be possible to advance the eating disorder research field. We provide an overview of some key trial designs and features which may offer solutions to practical constraints and increase trial efficiency.

Facial Function in Bell Palsy in a Cohort of Children Randomized to Prednisolone or Placebo 12 Months After Diagnosis.

BACKGROUND: Information on the medium-term recovery of children with Bell palsy or acute idiopathic lower motor neuron facial paralysis is limited. METHODS: We followed up children aged 6 months to <18 years with Bell palsy for 12 months after completion of a randomized trial on the use of prednisolone. We assessed facial function using the clinician-administered House-Brackmann scale and the modified parent-administered House-Brackmann scale. RESULTS: One hundred eighty-seven children were randomized to prednisolone (n = 93) or placebo (n = 94). At six months, the proportion of patients who had recovered facial function based on the clinician-administered House-Brackmann scale was 98% (n = 78 of 80) in the prednisolone group and 93% (n = 76 of 82) in the placebo group. The proportion of patients who had recovered facial function based on the modified parent-administered House-Brackmann scale was 94% (n = 75 of 80) vs 89% (n = 72 of 81) at six months (OR 1.88; 95% CI 0.60, 5.86) and 96% (n = 75 of 78) vs 92% (n = 73 of 79) at 12 months (OR 3.12; 95% CI 0.61, 15.98). CONCLUSIONS: Although the vast majority had complete recovery of facial function at six months, there were some children without full recovery of facial function at 12 months, regardless of prednisolone use.

Handling missing data when estimating causal effects with targeted maximum likelihood estimation.

Targeted maximum likelihood estimation (TMLE) is increasingly used for doubly robust causal inference, but how missing data should be handled when using TMLE with data-adaptive approaches is unclear. Based on data (1992-1998) from the Victorian Adolescent Health Cohort Study, we conducted a simulation study to evaluate 8 missing-data methods in this context: complete-case analysis, extended TMLE incorporating an outcome-missingness model, the missing covariate missing indicator method, and 5 multiple imputation (MI) approaches using parametric or machine-learning models. We considered 6 scenarios that varied in terms of exposure/outcome generation models (presence of confounder-confounder interactions) and missingness mechanisms (whether outcome influenced missingness in other variables and presence of interaction/nonlinear terms in missingness models). Complete-case analysis and extended TMLE had small biases when outcome did not influence missingness in other variables. Parametric MI without interactions had large bias when exposure/outcome generation models included interactions. Parametric MI including interactions performed best in bias and variance reduction across all settings, except when missingness models included a nonlinear term. When choosing a method for handling missing data in the context of TMLE, researchers must consider the missingness mechanism and, for MI, compatibility with the analysis method. In many settings, a parametric MI approach that incorporates interactions and nonlinearities is expected to perform well.

A comparison of strategies for selecting auxiliary variables for multiple imputation.

Multiple imputation (MI) is a popular method for handling missing data. Auxiliary variables can be added to the imputation model(s) to improve MI estimates. However, the choice of which auxiliary variables to include is not always straightforward. Several data-driven auxiliary variable selection strategies have been proposed, but there has been limited evaluation of their performance. Using a simulation study we evaluated the performance of eight auxiliary variable selection strategies: (1, 2) two versions of selection based on correlations in the observed data; (3) selection using hypothesis tests of the "missing completely at random" assumption; (4) replacing auxiliary variables with their principal components; (5, 6) forward and forward stepwise selection; (7) forward selection based on the estimated fraction of missing information; and (8) selection via the least absolute shrinkage and selection operator (LASSO). A complete case analysis and an MI analysis using all auxiliary variables (the "full model") were included for comparison. We also applied all strategies to a motivating case study. The full model outperformed all auxiliary variable selection strategies in the simulation study, with the LASSO strategy the best performing auxiliary variable selection strategy overall. All MI analysis strategies that we were able to apply to the case study led to similar estimates, although computational time was substantially reduced when variable selection was employed. This study provides further support for adopting an inclusive auxiliary variable strategy where possible. Auxiliary variable selection using the LASSO may be a promising alternative when the full model fails or is too burdensome.

Electronic Structure and Photophysics of Low Spin d5 Metallocenes.

The electronic structure and photophysics of two low spin metallocenes, decamethylmanganocene (MnCp*2) and decamethylrhenocene (ReCp*2), were investigated to probe their promise as photoredox reagents. Computational studies support the assignment of 2E2 ground state configurations and low energy ligand-to-metal charge transfer transitions for both complexes. Weak emission is observed at room temperature for ReCp*2 with τ = 1.8 ns in pentane, whereas MnCp*2 is not emissive. Calculation of the excited state reduction potentials for both metallocenes reveal their potential potency as excited state reductants (E°'([MnCp*2]+/0*) = -3.38 V and E°'([ReCp*2]+/0*) = -2.61 V vs Fc+/0). Comparatively, both complexes exhibit mild potentials for photo-oxidative processes (E°'([MnCp*2]0*/-) = -0.18 V and E°'([ReCp*2]0*/-) = -0.20 V vs Fc+/0). These results showcase the rich electronic structure of low spin d5 metallocenes and their promise as excited state reductants.