Sirolimus for Pediatric Cervicofacial Lymphatic Malformation: A Systematic Review and Meta-Analysis.

OBJECTIVE: This study is a systematic review and meta-analysis of the efficacy and safety of sirolimus in the management of pediatric cervicofacial lymphatic malformations (LMs). DATA SOURCES: EMBASE, Medline, Scopus, and Cochrane databases were searched, along with the reference list of all included articles. REVIEW METHODS: The study protocol was registered with PROSPERO and a systematic literature search strategy was designed and conducted with the aid of a medical librarian. All studies including case reports were included, with pooled analysis of raw data. A meta-analysis was conducted of magnetic resonance imaging (MRI), clinical, and airway outcomes. RESULTS: Thirteen case series and five individual case reports were included. Meta-analysis showed 78% (95% CI 57%-94%) of 62 patients had a reduction in LM volume, on MRI criteria, by 20% or more, and 32% (95% CI 11%-57%) had a reduction of 50% or more. Further meta-analysis showed 97% (95% CI 88%-100%) of 78 patients reported some clinical improvement on sirolimus. Sirolimus may be of particular value in management of airway LMs; out of 27 tracheostomy-dependent patients, meta-analysis showed 33% (95% CI 1%-78%) were decannulated after starting sirolimus. Individual patient meta-analysis on 24 individuals showed a statistically significant better response to sirolimus when initiated under the age of 2 years. CONCLUSION: This review and meta-analysis support the efficacy of sirolimus in pediatric LMs of the head, neck, and airway. A large multi-center trial is needed to further explore its role and limitations. Laryngoscope, 134:2038-2047, 2024.

A latent class model to multiply impute missing treatment indicators in observational studies when inferences of the treatment effect are made using propensity score matching.

Analysts often estimate treatment effects in observational studies using propensity score matching techniques. When there are missing covariate values, analysts can multiply impute the missing data to create m completed data sets. Analysts can then estimate propensity scores on each of the completed data sets, and use these to estimate treatment effects. However, there has been relatively little attention on developing imputation models to deal with the additional problem of missing treatment indicators, perhaps due to the consequences of generating implausible imputations. However, simply ignoring the missing treatment values, akin to a complete case analysis, could also lead to problems when estimating treatment effects. We propose a latent class model to multiply impute missing treatment indicators. We illustrate its performance through simulations and with data taken from a study on determinants of children's cognitive development. This approach is seen to obtain treatment effect estimates closer to the true treatment effect than when employing conventional imputation procedures as well as compared to a complete case analysis.

An overview of synthetic administrative data for research.

Use of administrative data for research and for planning services has increased over recent decades due to the value of the large, rich information available. However, concerns about the release of sensitive or personal data and the associated disclosure risk can lead to lengthy approval processes and restricted data access. This can delay or prevent the production of timely evidence. A promising solution to facilitate more efficient data access is to create synthetic versions of the original datasets which are less likely to hold confidential information and can minimise disclosure risk. Such data may be used as an interim solution, allowing researchers to develop their analysis plans on non-disclosive data, whilst waiting for access to the real data. We aim to provide an overview of the background and uses of synthetic data and describe common methods used to generate synthetic data in the context of UK administrative research. We propose a simplified terminology for categories of synthetic data (univariate, multivariate, and complex modality synthetic data) as well as a more comprehensive description of the terminology used in the existing literature and illustrate challenges and future directions for research.

Multiply imputing missing values arising by design in transplant survival data.

In this article, we address a missing data problem that occurs in transplant survival studies. Recipients of organ transplants are followed up from transplantation and their survival times recorded, together with various explanatory variables. Due to differences in data collection procedures in different centers or over time, a particular explanatory variable (or set of variables) may only be recorded for certain recipients, which results in this variable being missing for a substantial number of records in the data. The variable may also turn out to be an important predictor of survival and so it is important to handle this missing-by-design problem appropriately. Consensus in the literature is to handle this problem with complete case analysis, as the missing data are assumed to arise under an appropriate missing at random mechanism that gives consistent estimates here. Specifically, the missing values can reasonably be assumed not to be related to the survival time. In this article, we investigate the potential for multiple imputation to handle this problem in a relevant study on survival after kidney transplantation, and show that it comprehensively outperforms complete case analysis on a range of measures. This is a particularly important finding in the medical context as imputing large amounts of missing data is often viewed with scepticism.

D-optimal designs for multiarm trials with dropouts.

Multiarm trials with follow-up on participants are commonly implemented to assess treatment effects on a population over the course of the studies. Dropout is an unavoidable issue especially when the duration of the multiarm study is long. Its impact is often ignored at the design stage, which may lead to less accurate statistical conclusions. We develop an optimal design framework for trials with repeated measurements, which takes potential dropouts into account, and we provide designs for linear mixed models where the presence of dropouts is noninformative and dependent on design variables. Our framework is illustrated through redesigning a clinical trial on Alzheimer's disease, whereby the benefits of our designs compared with standard designs are demonstrated through simulations.

A comparison of two methods of estimating propensity scores after multiple imputation.

In many observational studies, analysts estimate treatment effects using propensity scores, e.g. by matching or sub-classifying on the scores. When some values of the covariates are missing, analysts can use multiple imputation to fill in the missing data, estimate propensity scores based on the m completed datasets, and use the propensity scores to estimate treatment effects. We compare two approaches to implement this process. In the first, the analyst estimates the treatment effect using propensity score matching within each completed data set, and averages the m treatment effect estimates. In the second approach, the analyst averages the m propensity scores for each record across the completed datasets, and performs propensity score matching with these averaged scores to estimate the treatment effect. We compare properties of both methods via simulation studies using artificial and real data. The simulations suggest that the second method has greater potential to produce substantial bias reductions than the first, particularly when the missing values are predictive of treatment assignment.

Biocatalytic conversion of aloeresin A to aloesin.

Leaf exudates from Aloe species, such as the Southern African Aloe ferox, are used in traditional medicines for both humans and livestock. This includes aloesin, a skin bleaching product that inhibits the synthesis of melanin. Aloesin, (a C-glycoside-5-methylchromone) can be released from aloeresin A, an ester of aloesin, through hydrolysis. The objective of the current study was to identify an enzymatic hydrolysis method for converting aloeresin A to aloesin, resulting in increased concentrations of aloesin in the aloe bitters extract. More than 70 commercially available hydrolytic enzymes were screened for the conversion of aloeresin A. An esterase (ESL001-02) from Diversa, a lipase (Novozym 388) and a protease (Aspergillus oryzae) preparation were identified during screening as being capable of providing conversion of pure aloeresin A, with the protease giving the best conversion (~100%). It was found that a contaminating enzyme in Novo 388 was responsible for the conversion of aloeresin A to aloesin. This contaminating enzyme, possibly a protease, was able to give almost complete conversion using crude aloe bitters extract, doubling the concentration of aloesin in aloe bitters extract via the hydrolysis of aloeresin A.

Estimating propensity scores with missing covariate data using general location mixture models.

In many observational studies, analysts estimate causal effects using propensity scores, e.g. by matching, sub-classifying, or inverse probability weighting based on the scores. Estimation of propensity scores is complicated when some values of the covariates are missing. Analysts can use multiple imputation to create completed data sets from which propensity scores can be estimated. We propose a general location mixture model for imputations that assumes that the control units are a latent mixture of (i) units whose covariates are drawn from the same distributions as the treated units' covariates and (ii) units whose covariates are drawn from different distributions. This formulation reduces the influence of control units outside the treated units' region of the covariate space on the estimation of parameters in the imputation model, which can result in more plausible imputations. In turn, this can result in more reliable estimates of propensity scores and better balance in the true covariate distributions when matching or sub-classifying. We illustrate the benefits of the latent class modeling approach with simulations and with an observational study of the effect of breast feeding on children's cognitive abilities.

Two-level stochastic search variable selection in GLMs with missing predictors.

Stochastic search variable selection (SSVS) algorithms provide an appealing and widely used approach for searching for good subsets of predictors while simultaneously estimating posterior model probabilities and model-averaged predictive distributions. This article proposes a two-level generalization of SSVS to account for missing predictors while accommodating uncertainty in the relationships between these predictors. Bayesian approaches for allowing predictors that are missing at random require a model on the joint distribution of the predictors. We show that predictive performance can be improved by allowing uncertainty in the specification of predictor relationships in this model. The methods are illustrated through simulation studies and analysis of an epidemiologic data set.

Multi-copy expression and fed-batch production of Rhodotorula araucariae epoxide hydrolase in Yarrowia lipolytica.

Epoxide hydrolases (EHs) of fungal origin have the ability to catalyze the enantioselective hydrolysis of epoxides to their corresponding diols. However, wild type fungal EHs are limited in substrate range and enantioselectivity. Additionally, the production of fungal epoxide hydrolase (EH) by wild-type strains is typically very low. In the present study, the EH-encoding gene from Rhodotorula araucariae was functionally expressed in Yarrowia lipolytica, under the control of a growth phase inducible hp4d promoter, in a multi-copy expression cassette. The transformation experiments yielded a positive transformant, with a final EH activity of 220 U/g dw in shake-flask cultures. Evaluation of this transformant in batch fermentations resulted in approximately 7-fold improvement in EH activity over the flask scale. Different constant specific feed rates were tested in fed-batch fermentations, resulting in an EH activity of 1,750 U/g dw at a specific feed rate of approximately 0.1 g/g/h, in comparison to enzyme production levels of 0.3 U/g dw for the wild type R. araucariae and 52 U/g dw for an Escherichia coli recombinant strain expressing the same gene. The expression of EH in Y. lipolytica using a multi-copy cassette demonstrates potential for commercial application.