Dietary and movement behaviors [physical activity (PA), sedentary behavior (SED), and sleep] occur throughout a 24-h day and involve multiple contexts. Understanding the temporal patterning of these 24-h behaviors and their contextual determinants is key to determining their combined effect on health. A scoping review was conducted to identify novel analytic methods for determining temporal behavior patterns and their contextual correlates. We searched Embase, ProQuest, and EBSCOhost databases in July 2022 to identify studies published between 1997 and 2022 on temporal patterns and their contextual correlates (e.g., locational, social, environmental, personal). We included 14 studies after title and abstract (n = 33,292) and full-text (n = 135) screening, of which 11 were published after 2018. Most studies (n = 4 in adults; n = 5 in children and adolescents), examined waking behavior patterns (i.e., both PA and SED) of which 3 also included sleep and 6 included contextual correlates. PA and diet were examined together in only 1 study of adults. Contextual correlates of dietary, PA, and sleep temporal behavior patterns were also examined. Machine learning with various clustering algorithms and model-based clustering techniques were most used to determine 24-h temporal behavior patterns. Although the included studies used a diverse range of methods, behavioral variables, and assessment periods, results showed that temporal patterns characterized by high SED and low PA were linked to poorer health outcomes, than those with low SED and high PA. This review identified temporal behavior patterns, and their contextual correlates, which were associated with adiposity and cardiometabolic disease risk, suggesting these methods hold promise for the discovery of holistic lifestyle exposures important to health. Standardized reporting of methods and patterns and multidisciplinary collaboration among nutrition, PA, and sleep researchers; statisticians; and computer scientists were identified as key pathways to advance future research on temporal behavior patterns in relation to health.

Medical research offers potential for disease prediction, like Multiple Sclerosis (MS). This neurological disorder damages nerve cell sheaths, with treatments focusing on symptom relief. Manual MS detection is time-consuming and error prone. Though MS lesion detection has been studied, limited attention has been paid to clinical analysis and computational risk factor prediction. Artificial intelligence (AI) techniques and Machine Learning (ML) methods offer accurate and effective alternatives to mapping MS progression. However, there are challenges in accessing clinical data and interdisciplinary collaboration. By analyzing 103 papers, we recognize the trends, strengths and weaknesses of AI, ML, and statistical methods applied to MS diagnosis. AI/ML-based approaches are suggested to identify MS risk factors, select significant MS features, and improve the diagnostic accuracy, such as Rule-based Fuzzy Logic (RBFL), Adaptive Fuzzy Inference System (ANFIS), Artificial Neural Network methods (ANN), Support Vector Machine (SVM), and Bayesian Networks (BNs). Meanwhile, applications of the Expanded Disability Status Scale (EDSS) and Magnetic Resonance Imaging (MRI) can enhance MS diagnostic accuracy. By examining established risk factors like obesity, smoking, and education, some research tackled the issue of disease progression. The performance metrics varied across different aspects of MS studies: Diagnosis: Sensitivity ranged from 60 % to 98 %, specificity from 60 % to 98 %, and accuracy from 61 % to 97 %. Prediction: Sensitivity ranged from 76 % to 98 %, specificity from 65 % to 98 %, and accuracy from 62 % to 99 %. Segmentation: Accuracy ranged up to 96.7 %. Classification: Sensitivity ranged from 78 % to 97.34 %, specificity from 65 % to 99.32 %, and accuracy from 71 % to 97.94 %. Furthermore, the literature shows that combining techniques can improve efficiency, exploiting their strengths for better overall performance.

The Setting International Standards in Analyzing Patient-Reported Outcomes and Quality of Life Endpoints Data (SISAQOL) initiative was established in 2016 to assess the quality and standardization of patient-reported outcomes (PRO) data analysis in randomized controlled trials (RCTs) on advanced breast cancer. The initiative identified deficiencies in PRO data reporting, including nonstandardized methods for handling missing data. This study evaluated the reporting of health-related quality of life (HRQOL) in Japanese cancer RCTs to provide insights into the state of PRO reporting in Japan. The study reviewed PubMed articles published from 2010 to 2018. Eligible studies included Japanese cancer RCTs with ≥50 adult patients (≥50% were Japanese) with solid tumors receiving anticancer treatments. The evaluation criteria included clarity of the HRQOL hypotheses, multiplicity testing, primary analysis methods, and reporting of clinically meaningful differences. Twenty-seven HRQOL trials were identified. Only 15% provided a clear HRQOL hypothesis, and 63% examined multiple HRQOL domains without adjusting for multiplicity. Model-based methods were the most common statistical methods for the primary HRQOL analysis. Only 22% of the trials explicitly reported clinically meaningful differences in HRQOL. Baseline assessments were reported in most trials, but only 26% reported comparisons between the treatment groups. HRQOL analysis was based on the intention-to-treat population in 19% of the trials, and 74% reported compliance at follow-up; however, 41% did not specify how missing values were handled. Although the rates of reporting clinical hypotheses and clinically meaningful differences were relatively low, the current state of HRQOL evaluation in the Japanese cancer RCT appears comparable to that of previous studies.

BACKGROUND: Missing outcome data is common in trials, and robust methods to address this are needed. Most trial reports currently use methods applicable under a missing completely at random assumption (MCAR), although this strong assumption can often be inappropriate.
OBJECTIVE: To identify and summarise current literature on the analytical methods for handling missing outcome data in randomised controlled trials (RCTs), emphasising methods appropriate for data missing at random (MAR) or missing not at random (MNAR).
METHODS: We conducted a methodological scoping review and identified papers through searching four databases (MEDLINE, Embase, CENTRAL, and CINAHL) from January 2015 to March 2023. We also performed forward and backward citation searching. Eligible papers discussed methods or frameworks for handling missing outcome data in RCTs or simulation studies with an RCT design.
RESULTS: From 1878 records screened, our search identified 101 eligible papers. 90 (89%) papers described specific methods for addressing missing outcome data and 11 (11%) described frameworks for overall methodological approach. Of the 90 methods papers, 30 (33%) described methods under the MAR assumption, 48 (53%) explored methods under the MNAR assumption and 11 (12%) discussed methods under a hybrid of MAR and MNAR assumptions. Control-based methods under the MNAR assumption were the most common method explored, followed by multiple imputation under the MAR assumption.
CONCLUSIONS: This review provides guidance on available analytic approaches for handling missing outcome data, particularly under the MNAR assumption. These findings may support trialists in using appropriate methods to address missing outcome data.

Traditional environmental epidemiology has consistently focused on studying the impact of single exposures on specific health outcomes, considering concurrent exposures as variables to be controlled. However, with the continuous changes in environment, humans are increasingly facing more complex exposures to multi-pollutant mixtures. In this context, accurately assessing the impact of multi-pollutant mixtures on health has become a central concern in current environmental research. Simultaneously, the continuous development and optimization of statistical methods offer robust support for handling large datasets, strengthening the capability to conduct in-depth research on the effects of multiple exposures on health. In order to examine complicated exposure mixtures, we introduce commonly used statistical methods and their developments, such as weighted quantile sum, bayesian kernel machine regression, toxic equivalency analysis, and others. Delineating their applications, advantages, weaknesses, and interpretability of results. It also provides guidance for researchers involved in studying multi-pollutant mixtures, aiding them in selecting appropriate statistical methods and utilizing R software for more accurate and comprehensive assessments of the impact of multi-pollutant mixtures on human health.

OBJECTIVE: To describe statistical tools available for assessing publication integrity of groups of randomized controlled trials (RCTs).
METHODS: Narrative review.
RESULTS: Freely available statistical tools have been developed that compare the observed distributions of baseline variables with the expected distributions that would occur if successful randomization occurred. For continuous variables, the tools assess baseline means, baseline P values, and the occurrence of identical means and/or standard deviation. For categorical variables, they assess baseline P values, frequency counts for individual or all variables, numbers of trial participants randomized or withdrawing, and compare reported with independently calculated P values. The tools have been used to identify publication integrity concerns in RCTs from individual groups, and performed at an acceptable level in discriminating intentionally fabricated baseline summary data from genuine RCTs. The tools can be used when concerns have been raised about RCT(s) from an individual/group and when the whole body of their work is being examined, when conducting systematic reviews, and could be adapted to aid screening of RCTs at journal submission.
CONCLUSIONS: Statistical tools are useful for the assessment of publication integrity of groups of RCTs.

Biological processes underlying health and disease are inherently dynamic and are best understood when characterized in a time-informed manner. In this comprehensive review, we discuss challenges inherent in time-series microbiome data analyses and compare available approaches and methods to overcome them. Appropriate handling of longitudinal microbiome data can shed light on important roles, functions, patterns, and potential interactions between large numbers of microbial taxa or genes in the context of health, disease, or interventions. We present a comprehensive review and comparison of existing microbiome time-series analysis methods, for both preprocessing and downstream analyses, including differential analysis, clustering, network inference, and trait classification. We posit that the careful selection and appropriate utilization of computational tools for longitudinal microbiome analyses can help advance our understanding of the dynamic host-microbiome relationships that underlie health-maintaining homeostases, progressions to disease-promoting dysbioses, as well as phases of physiologic development like those encountered in childhood.

Non-compliance is a common challenge for researchers and may reduce the power of an intention-to-treat analysis. Whilst a per protocol approach attempts to deal with this issue, it can result in biased estimates. Several methods to resolve this issue have been identified in previous reviews, but there is limited evidence supporting their use. This review aimed to identify simulation studies which compare such methods, assess the extent to which certain methods have been investigated and determine their performance under various scenarios.
A systematic search of several electronic databases including MEDLINE and Scopus was carried out from conception to 30th November 2022. Included papers were published in a peer-reviewed journal, readily available in the English language and focused on comparing relevant methods in a superiority randomised controlled trial under a simulation study. Articles were screened using these criteria and a predetermined extraction form used to identify relevant information. A quality assessment appraised the risk of bias in individual studies. Extracted data was synthesised using tables, figures and a narrative summary. Both screening and data extraction were performed by two independent reviewers with disagreements resolved by consensus.
Of 2325 papers identified, 267 full texts were screened and 17 studies finally included. Twelve methods were identified across papers. Instrumental variable methods were commonly considered, but many authors found them to be biased in some settings. Non-compliance was generally assumed to be all-or-nothing and only occurring in the intervention group, although some methods considered it as time-varying. Simulation studies commonly varied the level and type of non-compliance and factors such as effect size and strength of confounding. The quality of papers was generally good, although some lacked detail and justification. Therefore, their conclusions were deemed to be less reliable.
It is common for papers to consider instrumental variable methods but more studies are needed that consider G-methods and compare a wide range of methods in realistic scenarios. It is difficult to make conclusions about the best method to deal with non-compliance due to a limited body of evidence and the difficulty in combining results from independent simulation studies.
CRD42022370910.

UNASSIGNED: Traditionally, within dose-finding clinical trials, treatment toxicity and tolerability are assessed by clinicians. Research has shown that clinician reporting may have inadequate inter-rater reliability, poor correlation with patient reported outcomes, and under capture the true toxicity burden. The introduction of patient-reported outcomes (PROs), where the patient can assess their own symptomatic adverse events or quality of life, has potential to complement current practice to aid dose optimisation. There are no international recommendations offering guidance for the inclusion of PROs in dose-finding trial design and analysis. Our review aimed to identify and describe current statistical methods and data visualisation techniques employed to analyse and visualise PRO data in published early phase dose-finding oncology trials (DFOTs).
UNASSIGNED: DFOTs published from June 2016-December 2022, which presented PRO analysis methods, were included in this methodological review. We extracted 35 eligible papers indexed in PubMed. Study characteristics extracted included: PRO objectives, PRO measures, statistical analysis and visualisation techniques, and whether the PRO was involved in interim and final dose selection decisions.
UNASSIGNED: Most papers (30, 85.7%) did not include clear PRO objectives. 20 (57.1%) papers used inferential statistical techniques to analyse PROs, including survival analysis and mixed-effect models. One trial used PROs to classify a clinicians\' assessed dose-limiting toxicities (DLTs). Three (8.6%) trials used PROs to confirm the tolerability of the recommended dose. 25 trial reports visually presented PRO data within a figure or table within their publication, of which 12 papers presented PRO score longitudinally.
UNASSIGNED: This review highlighted that the statistical methods and reporting of PRO analysis in DFOTs are often poorly described and inconsistent. Many trials had PRO objectives which were not clearly described, making it challenging to evaluate the appropriateness of the statistical techniques used. Drawing conclusions based on DFOTs which are not powered for PROs may be misleading. With no guidance and standardisation of analysis methods for PROs in early phase DFOTs, it is challenging to compare study findings across trials. Therefore, there is a crucial need to establish international guidance to enhance statistical methods and graphical presentation for PRO analysis in the dose-finding setting.
UNASSIGNED: EA has been supported to undertake this work as part of a PhD studentship from the Institute of Cancer Research within the MRC/NIHR Trials Methodology Research Partnership. AM is supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at the Royal Marsden NHS Foundation Trust, the Institute of Cancer Research and Imperial College.

Many scientific papers are published each year and substantial resources are spent to develop biomarker-based tests for precision oncology. However, only a handful of tests is currently used in daily clinical practice, since development is challenging. In this situation, the application of adequate statistical methods is essential, but little is known about the scope of methods used.
A PubMed search identified clinical studies among women with breast cancer comparing at least two different treatment groups, one of which chemotherapy or endocrine treatment, by levels of at least one biomarker. Studies presenting original data published in 2019 in one of 15 selected journals were eligible for this review. Clinical and statistical characteristics were extracted by three reviewers and a selection of characteristics for each study was reported.
Of 164 studies identified by the query, 31 were eligible. Over 70 different biomarkers were evaluated. Twenty-two studies (71%) evaluated multiplicative interaction between treatment and biomarker. Twenty-eight studies (90%) evaluated either the treatment effect in biomarker subgroups or the biomarker effect in treatment subgroups. Eight studies (26%) reported results for one predictive biomarker analysis, while the majority performed multiple evaluations, either for several biomarkers, outcomes and/or subpopulations. Twenty-one studies (68%) claimed to have found significant differences in treatment effects by biomarker level. Fourteen studies (45%) mentioned that the study was not designed to evaluate treatment effect heterogeneity.
Most studies evaluated treatment heterogeneity via separate analyses of biomarker-specific treatment effects and/or multiplicative interaction analysis. There is a need for the application of more efficient statistical methods to evaluate treatment heterogeneity in clinical studies.