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BACKGROUND: This study aimed to assess the efficacy of various supervised longitudinal learning approaches, comparing traditional statistical models and machine learning algorithms for prediction with longitudinal data. The primary objectives were to evaluate the predictive performance of different supervised longitudinal learning methods for low birth weight (LBW) and very low birth weight (VLBW) based on prenatal ultrasound measurements. Additionally, the study sought to extract interpretable risk features for disease prediction. METHODS: The evaluation involved benchmarking the performance of longitudinal models against conventional machine learning methods. Classification accuracy for LBW and VLBW at birth, as well as prediction accuracy for birth weight using prenatal sonographic ultrasound measurements, were assessed. RESULTS: Among the learning approaches we investigated in this study, the longitudinal machine learning approach, specifically, the mixed effect random forest (MERF), delivered the overall best performance in predicting birthweights and classifying LBW/VLBW disease status. CONCLUSION: The MERF combined the power of advanced machine learning algorithms to accommodate the inherent within-individual dependence in the observed data, delivering satisfactory performance in predicting the birthweight and classifying LBW/VLBW disease status. The study emphasized the importance of incorporating previous ultrasound measurements and considering correlations between repeated measurements for accurate prediction. The interpretable trees algorithm used for risk feature extraction proved reliable and applicable to other learning algorithms. These findings underscored the potential of longitudinal learning methods in improving birth weight prediction and highlighted the relevance of consistent risk features in line with established literature.

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The repair of DNA double-strand breaks is a crucial yet delicate process which is affected by a multitude of factors. In this study, our goal is to analyse the influence of the linear energy transfer (LET) on the DNA repair kinetics. By utilizing the database of repair of DNA and aggregating the results of 84 experiments, we conduct various model fits to evaluate and compare different hypothesis regarding the effect of LET on the rejoining of DNA ends. Despite the considerable research efforts dedicated to this topic over the past decades, our findings underscore the complexity of the relationship between LET and DNA repair kinetics. This study leverages big data analysis to capture overall trends that single experimental studies might miss, providing a valuable model for understanding how radiation quality impacts DNA damage and subsequent biological effects. Our results highlight the gaps in our current understanding, emphasizing the pressing need for further investigation into this phenomenon.

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Meta-analyses are commonly performed based on random-effects models, while in certain cases one might also argue in favor of a common-effect model. One such case may be given by the example of two "study twins" that are performed according to a common (or at least very similar) protocol. Here we investigate the particular case of meta-analysis of a pair of studies, for example, summarizing the results of two confirmatory clinical trials in phase III of a clinical development program. Thereby, we focus on the question of to what extent homogeneity or heterogeneity may be discernible and include an empirical investigation of published ("twin") pairs of studies. A pair of estimates from two studies only provide very little evidence of homogeneity or heterogeneity of effects, and ad hoc decision criteria may often be misleading.

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Much research has focused on executive function (EF) impairments in psychopathy, a severe personality disorder characterized by a lack of empathy, antisocial behavior, and a disregard for social norms and moral values. However, it is still unclear to what extent EF deficits are present across psychopathy factors and, more importantly, which EF domains are impaired. The current meta-analysis answers these questions by synthesizing the results of 50 studies involving 5,694 participants from 12 different countries. Using multilevel random-effects models, we pooled effect sizes (Cohen's d) for five different EF domains: overall EF, inhibition, planning, shifting, and working memory. Moreover, differences between psychopathy factors were evaluated. Our analyses revealed small deficits in overall EF, inhibition, and planning performance. However, a closer inspection of psychopathy factors indicated that EF deficits were specific to lifestyle/antisocial traits, such as disinhibition. Conversely, interpersonal/affective traits, such as boldness, showed no deficits and in some cases even improved EF performance. These findings suggest that EF deficits are not a key feature of psychopathy per se, but rather are related to antisociality and disinhibitory traits. Potential brain correlates of these findings as well as implications for future research and treatment are discussed.

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Meta-analysis (MA) is a fundamental statistical tool for combining the results of different studies to obtain potentially high-level evidence that can be implemented in clinical practice. Although its use in clinical research is increasing, MAs are still relatively rare in hand surgery. Therefore, it should be important for every hand surgeon to not only know how to interpret a MA, but also how to perform one. The purpose of this first of a two-part article is to introduce the principles of MA and describe the main models and methods used to pool effect estimates.

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This study aimed to assess the feasibility of applying two recent phase I meta-analyses methods to protein kinase inhibitors (PKIs) developed in oncology and to identify situations where these methods could be both feasible and useful. This ancillary study used data from a systematic review conducted to identify dose-finding studies for PKIs. PKIs selected for meta-analyses were required to have at least five completed dose-finding studies involving cancer patients, with available results, and dose escalation guided by toxicity assessment. To account for heterogeneity caused by various administration schedules, some studies were divided into study parts, considered as separate entities in the meta-analyses. For each PKI, two Bayesian random-effects meta-analysis methods were applied to model the toxicity probability distribution of the recommended dose and to estimate the maximum tolerated dose (MTD). Meta-analyses were performed for 20 PKIs including 96 studies corresponding to 115 study parts. The median posterior probability of toxicity probability was below the toxicity thresholds of 0.20 for 70% of the PKIs, even if the resulting credible intervals were very wide. All approved doses were below the MTD estimated for the minimum toxicity threshold, except for one, for which the approved dose was above the MTD estimated for the maximal threshold. The application of phase I meta-analysis methods has been feasible for the majority of PKI; nevertheless, their implementation requires multiple conditions. However, meta-analyses resulted in estimates with large uncertainty, probably due to limited patient numbers and/or between-study variability. This calls into question the reliability of the recommended doses.

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For personalized medicine, we propose a general method of evaluating the potential performance of an individualized treatment rule in future clinical applications with new patients. We focus on rules that choose the most beneficial treatment for the patient out of two active (nonplacebo) treatments, which the clinician will prescribe regularly to the patient after the decision. We develop a measure of the individualization potential (IP) of a rule. The IP compares the expected effectiveness of the rule in a future clinical individualization setting versus the effectiveness of not trying individualization. We illustrate our evaluation method by explaining how to measure the IP of a useful type of individualized rules calculated through a new parametric interaction model of data from parallel-group clinical trials with continuous responses. Our interaction model implies a structural equation model we use to estimate the rule and its IP. We examine the IP both theoretically and with simulations when the estimated individualized rule is put into practice in new patients. Our individualization approach was superior to outcome-weighted machine learning according to simulations. We also show connections with crossover and N-of-1 trials. As a real data application, we estimate a rule for the individualization of treatments for diabetic macular edema and evaluate its IP.

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Meta-analysis is a statistical method for combining quantitative results across studies. A fundamental decision in undertaking a meta-analysis is choosing an appropriate model for analysis. This is the second of two companion articles which have the joint aim of describing the different meta-analysis models. In the first article, we focused on the common-effect (also known as fixed-effect [singular]) model, and in this article, we focus on the random-effects model. We describe the key assumptions underlying the random-effects model, how it is related to the common-effect and fixed-effects [plural] models, and present some of the arguments for selecting one model over another. We outline some of the methods for fitting a random-effects model. Finally, we present an illustrative example to demonstrate how the results can differ depending on the chosen model and method. Understanding the assumptions of the different meta-analysis models, and the questions they address, is critical for meta-analysis model selection and interpretation.

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There has been growing research interest in developing methodology to evaluate the health care providers' performance with respect to a patient outcome. Random and fixed effects models are traditionally used for such a purpose. We propose a new method, using a fusion penalty to cluster health care providers based on quasi-likelihood. Without any priori knowledge of grouping information, our method provides a desirable data-driven approach for automatically clustering health care providers into different groups based on their performance. Further, the quasi-likelihood is more flexible and robust than the regular likelihood in that no distributional assumption is needed. An efficient alternating direction method of multipliers algorithm is developed to implement the proposed method. We show that the proposed method enjoys the oracle properties; namely, it performs as well as if the true group structure were known in advance. The consistency and asymptotic normality of the estimators are established. Simulation studies and analysis of the national kidney transplant registry data demonstrate the utility and validity of our method.

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PURPOSE: This study aimed to assess the effectiveness of ozone therapy in treating Diabetes-related Foot Ulcer (DFU) and its outcomes. METHODS: A systematic search was conducted in PubMed/MEDLINE, Scopus, Web of Science, and ProQuest databases for published studies evaluating the use of ozone as an adjunct treatment for DFU, from inception to December 21, 2022. The primary outcome measure was the change in wound size after the intervention compared to pretreatment. Secondary outcomes included time to complete ulcer healing, number of healed patients, adverse events, amputation rates, and hospital length of stay. Quantitative data synthesis for the meta-analysis was performed using a random-effects model and generic inverse variance method, while overall heterogeneity analysis was conducted using a fixed-effects model. Interstudy heterogeneity was assessed using the I2 index (<50%) and the Cochrane Q statistic test. Sensitivity analysis was performed using the leave-one-out method. RESULTS: The meta-analysis included 11 studies comprising 960 patients with DFU. The results demonstrated a significant positive effect of ozone therapy on reducing foot ulcer size (Standardized Mean Difference (SMD): -25.84, 95% CI: -51.65 to -0.04, p = 0.05), shortening mean healing time (SMD: -38.59, 95% CI: -51.81 to -25.37, p < 0.001), decreasing hospital length of stay (SMD: -8.75, 95% CI: -14.81 to -2.69, p < 0.001), and reducing amputation rates (Relative Risk (RR): 0.46, 95% CI: 0.30-0.71, p < 0.001), compared to standard treatment. CONCLUSION: This meta-analysis indicates that ozone therapy has additional benefits in expediting complete DFU healing, reducing the amputation rates, and decreasing hospital length of stay, though its effects do not differ from standard treatments for complete ulcer resolution. Further research is needed to address the heterogeneity among studies and to better understand the potential beneficial effects of ozone therapy.

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Studies/trials assessing status and progression of periodontal disease (PD) usually focus on quantifying the relationship between the clustered (tooth within subjects) bivariate endpoints, such as probed pocket depth (PPD), and clinical attachment level (CAL) with the covariates. Although assumptions of multivariate normality can be invoked for the random terms (random effects and errors) under a linear mixed model (LMM) framework, violations of those assumptions may lead to imprecise inference. Furthermore, the response-covariate relationship may not be linear, as assumed under a LMM fit, and the regression estimates obtained therein do not provide an overall summary of the risk of PD, as obtained from the covariates. Motivated by a PD study on Gullah-speaking African-American Type-2 diabetics, we cast the asymmetric clustered bivariate (PPD and CAL) responses into a non-linear mixed model framework, where both random terms follow the multivariate asymmetric Laplace distribution (ALD). In order to provide a one-number risk summary, the possible non-linearity in the relationship is modeled via a single-index model, powered by polynomial spline approximations for index functions, and the normal mixture expression for ALD. To proceed with a maximum-likelihood inferential setup, we devise an elegant EM-type algorithm. Moreover, the large sample theoretical properties are established under some mild conditions. Simulation studies using synthetic data generated under a variety of scenarios were used to study the finite-sample properties of our estimators, and demonstrate that our proposed model and estimation algorithm can efficiently handle asymmetric, heavy-tailed data, with outliers. Finally, we illustrate our proposed methodology via application to the motivating PD study.

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This discussion contribution provides some subjective early history of network meta-analysis and also proposes a new bipartite graph structure to better represent multi-arm trials.

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Finlay-Wilkinson regression is a popular method for modeling genotype-environment interaction in plant breeding and crop variety testing. When environment is a random factor, this model may be cast as a factor-analytic variance-covariance structure, implying a regression on random latent environmental variables. This paper reviews such models with a focus on their use in the analysis of multi-environment trials for the purpose of making predictions in a target population of environments. We investigate the implication of random versus fixed effects assumptions, starting from basic analysis-of-variance models, then moving on to factor-analytic models and considering the transition to models involving observable environmental covariates, which promise to provide more accurate and targeted predictions than models with latent environmental variables.

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In biomedical studies, continuous and ordinal longitudinal variables are frequently encountered. In many of these studies it is of interest to estimate the effect of one of these longitudinal variables on the other. Time-dependent covariates have, however, several limitations; they can, for example, not be included when the data is not collected at fixed intervals. The issues can be circumvented by implementing joint models, where two or more longitudinal variables are treated as a response and modeled with a correlated random effect. Next, by conditioning on these response(s), we can study the effect of one or more longitudinal variables on another. We propose a normal-ordinal(probit) joint model. First, we derive closed-form formulas to estimate the model-based correlations between the responses on their original scale. In addition, we derive the marginal model, where the interpretation is no longer conditional on the random effects. As a consequence, we can make predictions for a subvector of one response conditional on the other response and potentially a subvector of the history of the response. Next, we extend the approach to a high-dimensional case with more than two ordinal and/or continuous longitudinal variables. The methodology is applied to a case study where, among others, a longitudinal ordinal response is predicted with a longitudinal continuous variable.

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In Nordic countries and across Europe, breast cancer screening participation is high. However, a significant number of breast cancer cases are still diagnosed due to symptoms between screening rounds, termed "interval cancers". Radiologists use the interval cancer proportion as a proxy for the screening false negative rate (ie, 1-sensitivity). Our objective is to enhance our understanding of interval cancers by applying continuous tumour growth models to data from a study involving incident invasive breast cancer cases. Building upon previous findings regarding stationary distributions of tumour size and growth rate distributions in non-screened populations, we develop an analytical expression for the proportion of interval breast cancer cases among regularly screened women. Our approach avoids relying on estimated background cancer rates. We make specific parametric assumptions concerning tumour growth and detection processes (screening or symptoms), but our framework easily accommodates alternative assumptions. We also show how our developed analytical expression for the proportion of interval breast cancers within a screened population can be incorporated into an approach for fitting tumour growth models to incident case data. We fit a model on 3493 cases diagnosed in Sweden between 2001 and 2008. Our methodology allows us to estimate the distribution of tumour sizes at the most recent screening for interval cancers. Importantly, we find that our model-based expected incidence of interval breast cancers aligns closely with observed patterns in our study and in a large Nordic screening cohort. Finally, we evaluate the association between screening interval length and the interval cancer proportion. Our analytical expression represents a useful tool for gaining insights into the performance of population-based breast cancer screening programs.

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The dynamics and variability of protein conformations are directly linked to their functions. Many comparative studies of X-ray protein structures have been conducted to elucidate the relevant conformational changes, dynamics and heterogeneity. The rapid increase in the number of experimentally determined structures has made comparison an effective tool for investigating protein structures. For example, it is now possible to compare structural ensembles formed by enzyme species, variants or the type of ligands bound to them. In this study, the author developed a multilevel model for estimating two covariance matrices that represent inter- and intra-ensemble variability in the Cartesian coordinate space. Principal component analysis using the two estimated covariance matrices identified the inter-/intra-enzyme variabilities, which seemed to be important for the enzyme functions, with the illustrative examples of cytochrome P450 family 2 enzymes and class A $\beta$-lactamases. In P450, in which each enzyme has its own active site of a distinct size, an active-site motion shared universally between the enzymes was captured as the first principal mode of the intra-enzyme covariance matrix. In this case, the method was useful for understanding the conformational variability after adjusting for the differences between enzyme sizes. The developed method is advantageous in small ensemble-size problems and hence promising for use in comparative studies on experimentally determined structures where ensemble sizes are smaller than those generated, for example, by molecular dynamics simulations.

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Statistical models incorporating cluster-specific intercepts are commonly used in hierarchical settings, for example, observations clustered within patients or patients clustered within hospitals. Predicted values of these intercepts are often used to identify or "flag" extreme or outlying clusters, such as poorly performing hospitals or patients with rapid declines in their health. We consider a variety of flagging rules, assessing different predictors, and using different accuracy measures. Using theoretical calculations and comprehensive numerical evaluation, we show that previously proposed rules based on the 2 most commonly used predictors, the usual best linear unbiased predictor and fixed effects predictor, perform extremely poorly: the incorrect flagging rates are either unacceptably high (approaching 0.5 in the limit) or overly conservative (eg, much <0.05 for reasonable parameter values, leading to very low correct flagging rates). We develop novel methods for flagging extreme clusters that can control the incorrect flagging rates, including very simple-to-use versions that we call "self-calibrated." The new methods have substantially higher correct flagging rates than previously proposed methods for flagging extreme values, while controlling the incorrect flagging rates. We illustrate their application using data on length of stay in pediatric hospitals for children admitted for asthma diagnoses.

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We consider estimating the effect of a treatment on a given outcome measured on subjects tested both before and after treatment assignment in observational studies. A vast literature compares the competing approaches of modelling the post-test score conditionally on the pre-test score versus modelling the difference, namely, the gain score. Our contribution lies in analyzing the merits and drawbacks of two approaches in a multilevel setting. This is relevant in many fields, such as education, where students are nested within schools. The multilevel structure raises peculiar issues related to contextual effects and the distinction between individual-level and cluster-level treatments. We compare the two approaches through a simulation study. For individual-level treatments, our findings align with existing literature. However, for cluster-level treatments, the scenario is more complex, as the cluster mean of the pre-test score plays a key role. Its reliability crucially depends on the cluster size, leading to potentially unsatisfactory estimators with small clusters.

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AIM: Prediction intervals are a useful measure of uncertainty for meta-analyses that capture the likely effect size of a new (similar) study based on the included studies. In comparison, confidence intervals reflect the uncertainty around the point estimate but provide an incomplete summary of the underlying heterogeneity in the meta-analysis. This study aimed to estimate (i) the proportion of meta-analysis studies that report a prediction interval in sports medicine; and (ii) the proportion of studies with a discrepancy between the reported confidence interval and a calculated prediction interval. METHODS: We screened, at random, 1500 meta-analysis studies published between 2012 and 2022 in highly ranked sports medicine and medical journals. Articles that used a random effect meta-analysis model were included in the study. We randomly selected one meta-analysis from each article to extract data from, which included the number of estimates, the pooled effect, and the confidence and prediction interval. RESULTS: Of the 1500 articles screened, 866 (514 from sports medicine) used a random effect model. The probability of a prediction interval being reported in sports medicine was 1.7% (95% CI = 0.9%, 3.3%). In medicine the probability was 3.9% (95% CI = 2.4%, 6.6%). A prediction interval was able to be calculated for 220 sports medicine studies. For 60% of these studies, there was a discrepancy in study findings between the reported confidence interval and the calculated prediction interval. Prediction intervals were 3.4 times wider than confidence intervals. CONCLUSION: Very few meta-analyses report prediction intervals and hence are prone to missing the impact of between-study heterogeneity on the overall conclusions. The widespread misinterpretation of random effect meta-analyses could mean that potentially harmful treatments, or those lacking a sufficient evidence base, are being used in practice. Authors, reviewers, and editors should be aware of the importance of prediction intervals.

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Temporal network data is often encoded as time-stamped interaction events between senders and receivers, such as co-authoring scientific articles or communication via email. A number of relational event frameworks have been proposed to address specific issues raised by complex temporal dependencies. These models attempt to quantify how individual behaviour, endogenous and exogenous factors, as well as interactions with other individuals modify the network dynamics over time. It is often of interest to determine whether changes in the network can be attributed to endogenous mechanisms reflecting natural relational tendencies, such as reciprocity or triadic effects. The propensity to form or receive ties can also, at least partially, be related to actor attributes. Nodal heterogeneity in the network is often modelled by including actor-specific or dyadic covariates. However, comprehensively capturing all personality traits is difficult in practice, if not impossible. A failure to account for heterogeneity may confound the substantive effect of key variables of interest. This work shows that failing to account for node level sender and receiver effects can induce ghost triadic effects. We propose a random-effect extension of the relational event model to deal with these problems. We show that it is often effective over more traditional approaches, such as in-degree and out-degree statistics. These results that the violation of the hierarchy principle due to insufficient information about nodal heterogeneity can be resolved by including random effects in the relational event model as a standard.

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