Search

Sunflower oil is an indispensable product of functional purpose, which at its reasonable consumption increases immunity, cleanses the body of toxins, improves the brain and heart, and as a result, having a complex effect on the whole body provides an undeniable benefit to human health. Modern development of sunflower oil purification technologies involves improvement of existing and development of new methods and technical means, environmentally safe, accompanied by minimum energy consumption and characterised by increased indicators of technological efficiency, providing the possibility of separation of unnecessary impurities and preservation of valuable components extracted from the oil for their further use. Such a promising method is the purification of sunflower oil in the electric field. The paper considers the possibility of cleaning sunflower oil by an electrostatic precipitator from suspended particles by means of an electric field acting on them. The planned experiment is described and the process of removal of unnecessary impurities is analysed. The justification of the factors and parameters of the experimental study is considered separately. The dependences of the electrical characteristics of oil on the parameters of the purification process are established. Analytical expressions for empirical dependences of energy parameters of the purification process, mode and parameters of ESP operation are determined. The adequacy of theoretical and experimental results has been checked. Having analysed the obtained results, the optimum parameters of effective cleaning and ESP operation were determined, which on the basis of the obtained regression equations should be the following: voltage at ESP electrodes U = 4.4 kV, interelectrode distance b = 0.01 m, oil temperature at the inlet θ = 42°С. The calculated energy consumption at these parameters is equal to 12 W-h/kg.

The article is aimed at creating a correct worldview about labor protection among engineering and technical workers of the agro-industrial complex engaged in the repair and maintenance of energy facilities. In our case, modern tractors and combines act as energy means. The main method is the analysis and conditions of operations for the repair and maintenance of tractors and combines, on the basis of which there is a need to properly organize the supervision of labor safety. As a result, the directions of work of the occupational safety specialist and the organization of his activities for the supervision of occupational safety have been developed and formulated, and the duties of engineering and technical workers in relation to this direction are also given. This article will be useful for masters and postgraduates studying the organization of maintenance and repair of tractors and combines, as well as engineering and technical workers of agro-industrial enterprises.

The article analyzes the factors affecting the soil, which directly affect the yield. Modern domestic agricultural machinery mainly has technical and technological solutions that make it possible to significantly advance in the direction of ecological balance of the impact of crop production on soils. These include non-fallow, combined techniques of basic and pre-sowing tillage, as well as the use of a new complex of heavy agrophilic running systems of heavy mobile power facilities.

The article provides the analysis of the criteria for the sustainable functioning of the automobile operating company in emergency situations (ES), indicating the factors that most influence this process, and the main characteristics of the company, on which the degree of its stability depends. The assessment of the stability of the automobile operating company (AOC) objects is carried out by the two most common damaging factors of emergency situations in peacetime: shock wave and heat of detonation. Tables provide characteristics of the constituent elements of such a automobile operating company and the damage rate of the elements of automobile operating companies, which characterize the damage rate and the corresponding pressure resulting from the explosion. The graph of the dependence of the destruction probability of the main production factors on the stability indicators of the object elements is presented. The characteristics of the damage rate of buildings are also presented, which will make it possible to coordinate properly the work on the stability of the object. The sequence of assessing the stability of the automobile operating company is determined. Recommendations for ensuring the protection of enterprise equipment are given. Examples of protective screens for protecting equipment and the basics of the methodology for their calculations are proposed.Basing on the results of the analysis, the constructive solution for the protective structure for AOC enterprises is proposed, designed to reduce the impact of secondary damaging factors of emergency situations on the surrounding area.

The article is intended for engineering and technological workers involved in the exploitation of road transport, for specialists of automobile enterprises who are responsible for organizing road safety. Prevention of diseases and increase of efficiency of drivers, development of such functions as attention, speed of reaction, perception of space and other professionally important psycho-physiological qualities, can be achieved only through purposeful and dosed application of specially designed complex sports and health-improving classes. The article presents the simplified methodology for determining the physical condition of the personnel of the car enterprise in order to design exercises and complexes based on it to achieve the goal. It also offers forms of physical education, which contributes to the optimal method of their organization and conduct. In addition, the frequency of classes is of great importance, which directly affects the speed of recovery of drivers. The purpose of this article was to determine the content, organizational and methodological features of complex physical exercises for drivers of vehicles. In the course of the research, the comprehensive program was designed to prevent fatigue, prevent occupational diseases and improve the efficiency of drivers. The following forms of physical exercises were included in the complex program: introductory gymnastics, physical culture minute, physical culture pause, self-massage, corrective exercises for the eyes, physical culture and health classes.

For many decades, any organization involved in the operation and maintenance of vehicles cannot imagine its activities without compressor units. Compressors are used in all sectors of the national economy, and are valued for their reliability, high efficiency, long service life. The scope of the equipment is very wide, without it the work of any large industrial enterprise is indispensable. Compressors are used in mechanical engineering, metallurgy, oil and gas industry, car services, construction and other industries. Each compressor unit is equipped with an emergency protection system that provides: automatic shutdown of the compressor; sound and light alarm. According to the requirements of the Rules, all compressor units are equipped with instrumentation: pressure gauges for measuring pressure, thermometers or other sensors for measuring temperature. The compressor is placed in a separate room, which should not be connected with the premises where explosive and chemically hazardous industries are located. In the premises of compressor units, it is not allowed to place equipment and equipment that are technologically and structurally not related to compressors.

Greening the agro-industrial complex became a problem in the last two decades of the 20th century, although some problems in this relatively new direction were raised in the 60s of the 20th century. In this article, the problem of the influence of technical means on the ecology of the agricultural engineering sphere is considered from the point of view of the impact on the human body of substances generated as a result of the technical work of the agro-industrial complex. The problem of greening the agro-industrial complex in modern conditions is of great importance in connection with the observed intensification of the development of agricultural production, which has a direct negative impact on the environment, which is the key to the health of people involved in the labor process. When studying human-machine systems in crop production, the place of ecology in the “external environment” block is determined. The dependences of the conceptual model of transformation of ecosystems under the influence of pollution are presented, which allows us to determine the vector of research in this direction.

In this paper, we review recent advances in statistical methods for the evaluation of the heterogeneity of treatment effects (HTE), including subgroup identification and estimation of individualized treatment regimens, from randomized clinical trials and observational studies. We identify several types of approaches using the features introduced in Lipkovich et al (Stat Med 2017;36: 136-196) that distinguish the recommended principled methods from basic methods for HTE evaluation that typically rely on rules of thumb and general guidelines (the methods are often referred to as common practices). We discuss the advantages and disadvantages of various principled methods as well as common measures for evaluating their performance. We use simulated data and a case study based on a historical clinical trial to illustrate several new approaches to HTE evaluation., (© 2024 John Wiley & Sons Ltd.)

Researchers are increasingly using insights derived from large-scale, electronic healthcare data to inform drug development and provide human validation of novel treatment pathways and aid in drug repurposing/repositioning. The objective of this study was to determine whether treatment of patients with multiple sclerosis with dimethyl fumarate, an activator of the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway, results in a change in incidence of type 2 diabetes and its complications. This retrospective cohort study used administrative claims data to derive four cohorts of adults with multiple sclerosis initiating dimethyl fumarate, teriflunomide, glatiramer acetate or fingolimod between January 2013 and December 2018. A causal inference frequentist model averaging framework based on machine learning was used to compare the time to first occurrence of a composite endpoint of type 2 diabetes, cardiovascular disease or chronic kidney disease, as well as each individual outcome, across the four treatment cohorts. There was a statistically significantly lower risk of incidence for dimethyl fumarate versus teriflunomide for the composite endpoint (restricted hazard ratio [95% confidence interval] 0.70 [0.55, 0.90]) and type 2 diabetes (0.65 [0.49, 0.98]), myocardial infarction (0.59 [0.35, 0.97]) and chronic kidney disease (0.52 [0.28, 0.86]). No differences for other individual outcomes or for dimethyl fumarate versus the other two cohorts were observed. This study effectively demonstrated the use of an innovative statistical methodology to test a clinical hypothesis using real-world data to perform early target validation for drug discovery. Although there was a trend among patients treated with dimethyl fumarate towards a decreased incidence of type 2 diabetes, cardiovascular disease and chronic kidney disease relative to other disease-modifying therapies-which was statistically significant for the comparison with teriflunomide-this study did not definitively support the hypothesis that Nrf2 activation provided additional metabolic disease benefit in patients with multiple sclerosis., Competing Interests: This work was funded by Eli Lilly and Company and all authors are employees of Eli Lilly and Company. This does not alter our adherence to PLOS ONE policies on sharing data and materials. Alan J.M. Brnabic was involved with the conceptualization, methodology, investigation and formal analysis of the data for the work and contributed to the original draft preparation, review and editing of the manuscript. Sarah E. Curtis was involved with the conceptualization, methodology, investigation and formal analysis of the data for the work and contributed to the review and editing of the manuscript. Joseph A. Johnston was involved with the conceptualization, methodology and investigation of the data for the work, and contributed to the original draft preparation, review and editing of the manuscript. Albert contributed to the review and editing of the manuscript. Anthony J. Zagar was involved with the methodology and investigation of the data for the work and contributed to the original draft preparation of the manuscript. Ilya Lipkovich was involved with the methodology and validation of the data for the work and contributed to the original draft preparation of the manuscript. Zbigniew Kadziola was involved with the formal analysis of the data for the work and contributed to the review and editing of the manuscript. Megan H. Murray was involved with the investigation, methodology and formal analysis of the data for the work and contributed to the original draft preparation of the manuscript. Timothy Ryan was involved with the conceptualization and investigation of the data for the work and contributed to the original draft preparation, review and editing of the manuscript. All authors have participated sufficiently in the work to agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All authors give final approval of the manuscript to be published., (Copyright: © 2024 Brnabic et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Digital health studies using electronic patient-reported outcomes (ePROs) and wearables bring new challenges, including the need for participants to consistently provide trial data., Objective: This study aims to characterize the engagement, protocol adherence, and data completeness among participants with rheumatoid arthritis enrolled in the Digital Tracking of Arthritis Longitudinally (DIGITAL) study., Methods: Participants were invited to participate in this app-based study, which included a 14-day run-in and an 84-day main study. In the run-in period, data were collected via the ArthritisPower mobile app to increase app familiarity and identify the individuals who were motivated to participate. Successful completers of the run-in period were mailed a wearable smartwatch, and automated and manual prompts were sent to participants, reminding them to complete app input or regularly wear and synchronize devices, respectively, during the main study. Study coordinators monitored participant data and contacted participants via email, SMS text messaging, and phone to resolve adherence issues per a priori rules, in which consecutive spans of missing data triggered participant contact. Adherence to data collection during the main study period was defined as providing requested data for >70% of 84 days (daily ePRO, ≥80% daily smartwatch data) or at least 9 of 12 weeks (weekly ePRO)., Results: Of the 470 participants expressing initial interest, 278 (59.1%) completed the run-in period and qualified for the main study. Over the 12-week main study period, 87.4% (243/278) of participants met the definition of adherence to protocol-specified data collection for weekly ePRO, and 57.2% (159/278) did so for daily ePRO. For smartwatch data, 81.7% (227/278) of the participants adhered to the protocol-specified data collection. In total, 52.9% (147/278) of the participants met composite adherence., Conclusions: Compared with other digital health rheumatoid arthritis studies, a short run-in period appears useful for identifying participants likely to engage in a study that collects data via a mobile app and wearables and gives participants time to acclimate to study requirements. Automated or manual prompts (ie, "It's time to sync your smartwatch") may be necessary to optimize adherence. Adherence varies by data collection type (eg, ePRO vs smartwatch data)., International Registered Report Identifier (irrid): RR2-10.2196/14665., (©William B Nowell, Jeffrey R Curtis, Hong Zhao, Fenglong Xie, Laura Stradford, David Curtis, Kelly Gavigan, Jessica Boles, Cassie Clinton, Ilya Lipkovich, Shilpa Venkatachalam, Amy Calvin, Virginia S Hayes. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 07.11.2023.)

Background: Obesity is associated with an increased risk of multiple conditions, ranging from heart disease to cancer. However, there are few predictive models for these outcomes that have been developed specifically for people with overweight/obesity., Objective: To develop predictive models for obesity-related complications in patients with overweight and obesity., Methods: Electronic health record data of adults with body mass index 25-80 kg/m 2 treated in primary care practices between 2000 and 2019 were utilized to develop and evaluate predictive models for nine long-term clinical outcomes using a) Lasso-Cox models and b) a machine-learning method random survival forests (RSF). Models were trained on a training dataset and evaluated on a test dataset over 100 replicates. Parsimonious models of <10 variables were also developed using Lasso-Cox., Results: Over a median follow-up of 5.6 years, study outcome incidence in the cohort of 433,272 patients ranged from 1.8% for knee replacement to 11.7% for atherosclerotic cardiovascular disease. Harrell C-index averaged over replicates ranged from 0.702 for liver outcomes to 0.896 for death for RSF, and from 0.694 for liver outcomes to 0.891 for death for Lasso-Cox. The Harrell C-index for parsimonious models ranged from 0.675 for liver outcomes to 0.850 for knee replacement., Conclusions: Predictive modeling can identify patients at high risk of obesity-related complications. Interpretable Cox models achieve results close to those of machine learning methods and could be helpful for population health management and clinical treatment decisions., Competing Interests: Turchin reports equity in Brio Systems, consulting for Novo Nordisk and Proteomics International, and research support from Astra‐Zeneca, Eli Lilly and Company and Novo Nordisk. Lipkovich, Shinde, Ahmad and Kan are employees and stockholders of Eli Lilly and Company. None of the other authors report any conflicts of interest., (© 2023 The Authors. Obesity Science & Practice published by World Obesity and The Obesity Society and John Wiley & Sons Ltd.)

There has been much interest in the evaluation of heterogeneous treatment effects (HTE) and multiple statistical methods have emerged under the heading of personalized/precision medicine combining ideas from hypothesis testing, causal inference, and machine learning over the past 10-15 years. We discuss new ideas and approaches for evaluating HTE in randomized clinical trials and observational studies using the features introduced earlier by Lipkovich, Dmitrienko, and D'Agostino that distinguish principled methods from simplistic approaches to data-driven subgroup identification and estimating individual treatment effects and use a case study to illustrate these approaches. We identified and provided a high-level overview of several classes of modern statistical approaches for personalized/precision medicine, elucidated the underlying principles and challenges, and compared findings for a case study across different methods. Different approaches to evaluating HTEs may produce (and actually produced) highly disparate results when applied to a specific data set. Evaluating HTE with machine learning methods presents special challenges since most of machine learning algorithms are optimized for prediction rather than for estimating causal effects. An additional challenge is in that the output of machine learning methods is typically a "black box" that needs to be transformed into interpretable personalized solutions in order to gain acceptance and usability.

In this paper, we consider randomized controlled clinical trials comparing two treatments in efficacy assessment using a time to event outcome. We assume a relatively small number of candidate biomarkers available in the beginning of the trial, which may help define an efficacy subgroup which shows differential treatment effect. The efficacy subgroup is to be defined by one or two biomarkers and cut-offs that are unknown to the investigator and must be learned from the data. We propose a two-stage adaptive design with a pre-planned interim analysis and a final analysis. At the interim, several subgroup-finding algorithms are evaluated to search for a subgroup with enhanced survival for treated versus placebo. Conditional powers computed based on the subgroup and the overall population are used to make decision at the interim to terminate the study for futility, continue the study as planned, or conduct sample size recalculation for the subgroup or the overall population. At the final analysis, combination tests together with closed testing procedures are used to determine efficacy in the subgroup or the overall population. We conducted simulation studies to compare our proposed procedures with several subgroup-identification methods in terms of a novel utility function and several other measures. This research demonstrated the benefit of incorporating data-driven subgroup selection into adaptive clinical trial designs., (© 2022 John Wiley & Sons Ltd.)

The ICH E9(R1) addendum (2019) proposed principal stratification (PS) as one of five strategies for dealing with intercurrent events. Therefore, understanding the strengths, limitations, and assumptions of PS is important for the broad community of clinical trialists. Many approaches have been developed under the general framework of PS in different areas of research, including experimental and observational studies. These diverse applications have utilized a diverse set of tools and assumptions. Thus, need exists to present these approaches in a unifying manner. The goal of this tutorial is threefold. First, we provide a coherent and unifying description of PS. Second, we emphasize that estimation of effects within PS relies on strong assumptions and we thoroughly examine the consequences of these assumptions to understand in which situations certain assumptions are reasonable. Finally, we provide an overview of a variety of key methods for PS analysis and use a real clinical trial example to illustrate them. Examples of code for implementation of some of these approaches are given in Supplemental Materials., (© 2022 John Wiley & Sons Ltd.)

Objectives: To explore the associations among activation, physical activity, hemoglobin A1c (HbA1c), and healthy days in older adults with type 2 diabetes (T2D) who participated in wellness programs., Study Design: Observational, longitudinal cohort study utilizing survey, claims, and wellness program data., Methods: From January to May 2018, individuals enrolled in a commercial or Medicare Advantage and prescription drug plan with T2D (aged 55-89 years) and SilverSneakers or step count data were eligible. Three waves of surveys were mailed (n = 5000) to collect information on activation (Consumer Health Activation Index; Influence, Motivation, and Patient Activation for Diabetes) and health-related quality of life (Healthy Days). Generalized linear models and predictive models evaluated the associations of unhealthy days and HbA1c with physical activity and activation factors. Additional models tested the relationship between physical activity and future acute care visits, accounting for potential confounders via inverse probability of treatment weighting., Results: Respondents to all 3 waves (n = 1147) had higher comorbidity indices but lower HbA1c than individuals with T2D without physical activity data (P < .0001). Individuals with moderate and high activation levels had 67.4% to 74.0% and 71.6% to 85.6% fewer unhealthy days, respectively, than those with lower activation (P < .01). Individuals with high (> 8000/day) step counts at baseline were predicted to have 2.04 fewer unhealthy days/month at follow-up (P < .05) and 0.19% (P < .02) lower HbA1c units, respectively, compared with those with less than 4000 steps per day. High SilverSneakers activity (> 2 activities per week) reduced subsequent acute care visits by 49%., Conclusions: Increasing patient activation levels encourages physical activity, which can help improve glycemic control and health-related quality of life, especially among older adults.

Unlike in randomized clinical trials (RCTs), confounding control is critical for estimating the causal effects from observational studies due to the lack of treatment randomization. Under the unconfoundedness assumption, matching methods are popular because they can be used to emulate an RCT that is hidden in the observational study. To ensure the key assumption hold, the effort is often made to collect a large number of possible confounders, rendering dimension reduction imperative in matching. Three matching schemes based on the propensity score (PSM), prognostic score (PGM), and double score (DSM, ie, the collection of the first two scores) have been proposed in the literature. However, a comprehensive comparison is lacking among the three matching schemes and has not made inroads into the best practices including variable selection, choice of caliper, and replacement. In this article, we explore the statistical and numerical properties of PSM, PGM, and DSM via extensive simulations. Our study supports that DSM performs favorably with, if not better than, the two single score matching in terms of bias and variance. In particular, DSM is doubly robust in the sense that the matching estimator is consistent requiring either the propensity score model or the prognostic score model is correctly specified. Variable selection on the propensity score model and matching with replacement is suggested for DSM, and we illustrate the recommendations with comprehensive simulation studies. An R package is available at https://github.com/Yunshu7/dsmatch., (© 2021 John Wiley & Sons Ltd.)

Estimating a treatment effect from observational data requires modeling treatment and outcome subject to uncertainty/misspecification. A previous research has shown that it is not possible to find a uniformly best strategy. In this article we propose a novel Frequentist Model Averaging (FMA) framework encompassing any estimation strategy and accounting for model uncertainty by computing a cross-validated estimate of Mean Squared Prediction Error (MSPE). We present a simulation study with data mimicking an observational database. Model averaging over 15+ strategies was compared with individual strategies as well as the best strategy selected by minimum MSPE. FMA showed robust performance (Bias, Mean Squared Error (MSE), and Confidence Interval (CI) coverage). Other strategies, such as linear regression, did well in simple scenarios but were inferior to the FMA in a scenario with complex confounding.

Data-driven subgroup analysis plays an important role in clinical trials. This paper focuses on practical considerations in post-hoc subgroup investigations in the context of confirmatory clinical trials. The analysis is aimed at assessing the heterogeneity of treatment effects across the trial population and identifying patient subgroups with enhanced treatment benefit. The subgroups are defined using baseline patient characteristics, including demographic and clinical factors. Much progress has been made in the development of reliable statistical methods for subgroup investigation, including methods based on global models and recursive partitioning. The paper provides a review of principled approaches to data-driven subgroup identification and illustrates subgroup analysis strategies using a family of recursive partitioning methods known as the SIDES (subgroup identification based on differential effect search) methods. These methods are applied to a Phase III trial in patients with metastatic colorectal cancer. The paper discusses key considerations in subgroup exploration, including the role of covariate adjustment, subgroup analysis at early decision points and interpretation of subgroup search results in trials with a positive overall effect., (© 2021. The Drug Information Association, Inc.)

The current COVID-19 pandemic poses numerous challenges for ongoing clinical trials and provides a stress-testing environment for the existing principles and practice of estimands in clinical trials. The pandemic may increase the rate of intercurrent events (ICEs) and missing values, spurring a great deal of discussion on amending protocols and statistical analysis plans to address these issues. In this article, we revisit recent research on estimands and handling of missing values, especially the ICH E9 (R1) Addendum on Estimands and Sensitivity Analysis in Clinical Trials. Based on an in-depth discussion of the strategies for handling ICEs using a causal inference framework, we suggest some improvements in applying the estimand and estimation framework in ICH E9 (R1). Specifically, we discuss a mix of strategies allowing us to handle ICEs differentially based on reasons for ICEs. We also suggest ICEs should be handled primarily by hypothetical strategies and provide examples of different hypothetical strategies for different types of ICEs as well as a road map for estimation and sensitivity analyses. We conclude that the proposed framework helps streamline translating clinical objectives into targets of statistical inference and automatically resolves many issues with defining estimands and choosing estimation procedures arising from events such as the pandemic., (© 2021. The Drug Information Association, Inc.)

Background: The aim of this study was to determine how clusters or subgroups of insulin-treated people with diabetes, based upon healthcare resource utilization, select social demographic and clinical characteristics, and diabetes management parameters, are related to health outcomes including acute care visits and hospital admissions., Methods: This was a non-experimental, retrospective cluster analysis. We utilized Aetna administrative claims data to identify insulin-using people with diabetes with service dates from 01 January 2015 to 30 June 2018. The study included adults over the age of 18 years who had a diagnosis of type 1 (T1DM) or type 2 diabetes mellitus (T2DM) on insulin therapy and had Aetna medical and pharmacy coverage for at least 18 months (6 months prior and 12 months after their index date, defined as either their first insulin prescription fill date or their earliest date allowing for 6 months' prior coverage). We used K-means clustering methods to identify relevant subgroups of people with diabetes based on 13 primary outcome variables., Results: A total of 100,650 insulin-using people with diabetes were identified in the Aetna administrative claims database and met study criteria, including 11,826 (11.7%) with T1DM and 88,824 (88.3%) with T2DM. Of these 79,053 (78.5%) people were existing insulin users. Seven distinct clusters were identified with different characteristics and potential risks of diabetes complications. Overall, clusters were significantly associated with differences in healthcare utilization (emergency room visits, inpatient admissions, and total inpatient days) after multivariable adjustment., Conclusions: This analysis of healthcare claims data using clustering methodologies identified meaningful subgroups of patients with diabetes using insulin. The subgroups differed in comorbidity burden, healthcare utilization, and demographic factors which could be used to identify higher risk patients and/or guide the management and treatment of diabetes.

Aim: To predict optimal treatments maximizing overall survival (OS) and time to treatment discontinuation (TTD) for patients with metastatic breast cancer (MBC) using machine learning methods on electronic health records. Patients/methods: Adult females with HR+/HER2- MBC on first- or second-line systemic therapy were eligible. Random survival forest (RSF) models were used to predict optimal regimen classes for individual patients and each line of therapy based on baseline characteristics. Results: RSF models suggested greater use of CDK4 & 6 inhibitor-based therapies may maximize OS and TTD. RSF-predicted optimal treatments demonstrated longer OS and TTD compared with nonoptimal treatments across line of therapy (hazard ratios = 0.44∼0.79). Conclusion: RSF may help inform optimal treatment choices and improve outcomes for patients with HR+/HER2- MBC.

The COVID-19 pandemic has impacted ongoing clinical trials. We consider particular impacts on noninferiority clinical trials, which aim to show that an investigational treatment is not markedly worse than an existing active control with known benefit. Because interpretation of noninferiority trials requires cross-trial validation involving untestable assumptions, it is vital that they be run to very high standards. The COVID-19 pandemic has introduced an unexpected impact on clinical trials, with subjects possibly missing treatment or assessments due to unforeseen intercurrent events. The resulting data must be carefully considered to ensure proper statistical inference. Missing data can often, but not always, be considered missing completely at random (MCAR). We discuss ways to ensure validity of the analyses through study conduct and data analysis, with focus on the hypothetical strategy for constructing estimands. We assess various analytic strategies of analyzing longitudinal binary data with dropouts where outcomes may be MCAR or missing at random (MAR). Simulations show that certain multiple imputation strategies control the Type I error rate and provide additional power over analysis of observed data when data are MCAR or MAR, with weaker assumptions about the missing data mechanism.Abstract- The COVID-19 pandemic has impacted ongoing clinical trials. We consider particular impacts on noninferiority clinical trials, which aim to show that an investigational treatment is not markedly worse than an existing active control with known benefit. Because interpretation of noninferiority trials requires cross-trial validation involving untestable assumptions, it is vital that they be run to very high standards. The COVID-19 pandemic has introduced an unexpected impact on clinical trials, with subjects possibly missing treatment or assessments due to unforeseen intercurrent events. The resulting data must be carefully considered to ensure proper statistical inference. Missing data can often, but not always, be considered missing completely at random (MCAR). We discuss ways to ensure validity of the analyses through study conduct and data analysis, with focus on the hypothetical strategy for constructing estimands. We assess various analytic strategies of analyzing longitudinal binary data with dropouts where outcomes may be MCAR or missing at random (MAR). Simulations show that certain multiple imputation strategies control the Type I error rate and provide additional power over analysis of observed data when data are MCAR or MAR, with weaker assumptions about the missing data mechanism., (© 2020 American Statistical Association.)

Objective: To examine the impact of missing data when evaluating the confirmed disability worsening (CDW) endpoint in multiple sclerosis clinical trials and explore analytical methods for handling censored participants (those with missing confirmation data)., Methods: CDW risk factors were assessed among participants with an initial disability worsening (≥ 1.0-point increase in Expanded Disability Status Scale [EDSS] score from a baseline score of ≥ 1.0; ≥ 1.5-point increase from a baseline of 0) using data from the DECIDE trial of daclizumab beta. A post-hoc simulation study was performed to evaluate three strategies for imputing confirmation status in censored participants: assume all were confirmed; assume none were confirmed (standard analytical approach); or use an observed rate multiple imputation (ORMI) approach based on treatment group and similar participant risk factors. Simulation study results were used to evaluate pre-specified analyses in DECIDE., Results: In DECIDE, larger change from baseline to initial disability worsening in EDSS score (p = 0.0003), higher baseline EDSS score (p = 0.0013), age (p = 0.004), and preceding relapse (p < 0.0001) were associated with 12-week CDW. In the simulation study, relative to the full dataset (no missing data), the strategy of assuming no censored participants were confirmed underestimated the treatment effect, and the strategy of assuming all censored participants were confirmed overestimated the treatment effect (hazard ratio 0.749 and 0.713 vs 0.733). ORMI correctly estimated treatment effect and increased study power by ~5-10% compared with the standard analytical approach., Conclusion: The ORMI approach based on CDW risk factors minimizes bias and is expected to provide the most accurate treatment effect estimate for the CDW endpoint., Competing Interests: Declaration of Competing Interest Katherine Riester is an employee of and holds stock/stock options in Biogen. Ludwig Kappos’ institution has received in the last 3 years, and used exclusively for research support: steering committee/consulting fees from Actelion, Addex, Bayer HealthCare, Biogen, Biotica, Genzyme, Lilly, Merck, Mitsubishi, Novartis, Ono, Pfizer, Receptos, Sanofi-Aventis, Santhera, Siemens, Teva, UCB, and XenoPort; speaker fees from Bayer HealthCare, Biogen, Merck, Novartis, Sanofi-Aventis, and Teva; support of educational activities from Bayer HealthCare, Biogen, CSL Behring, Genzyme, Merck, Novartis, Sanofi-Aventis, and Teva; license fees for Neurostatus products; and grants from Bayer HealthCare, Biogen, the European Union, Merck, Novartis, Roche, Roche Research Foundations, the Swiss Multiple Sclerosis Society, and the Swiss National Research Foundation. Krzysztof Selmaj has received consulting fees from Genzyme, Novartis, Ono, Roche, Synthon, and Teva, and speaker fees from Biogen. Stacy Lindborg is an employee of and holds stock/stock options in Biogen. Ilya Lipkovich is a former employee of IQVIA (formerly Quintiles); he performed statistical analysis as part of the contract between Biogen and IQVIA. Jacob Elkins is an employee of and holds stock/stock options in Biogen., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

The National Research Council (NRC) Expert Panel Report on Prevention and Treatment of Missing Data in Clinical Trials highlighted the need for clearly defining objectives and estimands. That report sparked considerable discussion and literature on estimands and how to choose them. Importantly, consideration moved beyond missing data to include all postrandomization events that have implications for estimating quantities of interest (intercurrent events, aka ICEs). The ICH E9(R1) draft addendum builds on that research to outline key principles in choosing estimands for clinical trials, primarily with focus on confirmatory trials. This paper provides additional insights, perspectives, details, and examples to help put ICH E9(R1) into practice. Specific areas of focus include how the perspectives of different stakeholders influence the choice of estimands; the role of randomization and the intention-to-treat principle; defining the causal effects of a clearly defined treatment regimen, along with the implications this has for trial design and the generalizability of conclusions; detailed discussion of strategies for handling ICEs along with their implications and assumptions; estimands for safety objectives, time-to-event endpoints, early-phase and one-arm trials, and quality of life endpoints; and realistic examples of the thought process involved in defining estimands in specific clinical contexts.

This paper provides examples of defining estimands in real-world scenarios following ICH E9(R1) guidelines. Detailed discussions on choosing the estimands and estimators can be found in our companion papers. Three scenarios of increasing complexity are illustrated. The first example is a proof-of-concept trial in major depressive disorder where the estimand is chosen to support the sponsor decision on whether to continue development. The second and third examples are confirmatory trials in severe asthma and rheumatoid arthritis respectively. We discuss the intercurrent events expected during each trial and how they can be handled so as to be consistent with the study objectives. The estimands discussed in these examples are not the only acceptable choices for their respective scenarios. The intent is to illustrate the key concepts rather than focus on specific choices. Emphasis is placed on following a study development process where estimands link the study objectives with data collection and analysis in a coherent manner, thereby avoiding disconnect between objectives, estimands, and analyses.

Background: Tralokinumab is an anti-interleukin (IL)-13 monoclonal antibody investigated for the treatment of severe, uncontrolled asthma in two Phase III clinical trials, STRATOS 1 and 2. The STRATOS 1 biomarker analysis plan was developed to identify biomarker(s) indicative of IL-13 activation likely to predict tralokinumab efficacy and define a population in which there was an enhanced treatment effect; this defined population was then tested in STRATOS 2., Methods: The biomarkers considered were blood eosinophil counts, fractional exhaled nitric oxide (FeNO), serum dipeptidyl peptidase-4, serum periostin and total serum immunoglobulin E. Tralokinumab efficacy was measured as the reduction in annualised asthma exacerbation rate (AAER) compared with placebo (primary endpoint measure of STRATOS 1 and 2). The biomarker analysis plan included negative binomial and generalised additive models, and the Subgroup Identification based on Differential Effect Search (SIDES) algorithm, supported by robustness and sensitivity checks. Effects on the key secondary endpoints of STRATOS 1 and 2, which included changes from baseline in standard measures of asthma outcomes, were also investigated. Prior to the STRATOS 1 read-out, numerous simulations of the methodology were performed with hypothetical data., Results: FeNO and periostin were identified as the only biomarkers potentially predictive of treatment effect, with cut-offs chosen by the SIDES algorithm of > 32.3 ppb and > 27.4 ng/ml, respectively. The FeNO > 32.3 ppb subgroup was associated with greater AAER reductions and improvements in key secondary endpoints compared with the periostin > 27.4 ng/ml subgroup. Upon further evaluation of AAER reductions at different FeNO cut-offs, ≥37 ppb was chosen as the best cut-off for predicting tralokinumab efficacy., Discussion: A rigorous statistical approach incorporating multiple methods was used to investigate the predictive properties of five potential biomarkers and to identify a participant subgroup that demonstrated an enhanced tralokinumab treatment effect. Using STRATOS 1 data, our analyses identified FeNO at a cut-off of ≥37 ppb as the best assessed biomarker for predicting enhanced treatment effect to be tested in STRATOS 2. Our findings were inconclusive, which reflects the complexity of subgroup identification in the severe asthma population., Trial Registration: STRATOS 1 and 2 are registered on ClinicalTrials.gov ( NCT02161757 registered on June 12, 2014, and NCT02194699 registered on July 18, 2014).

Subgroup by treatment interaction assessments are routinely performed when analysing clinical trials and are particularly important for phase 3 trials where the results may affect regulatory labelling. Interpretation of such interactions is particularly difficult, as on one hand the subgroup finding can be due to chance, but equally such analyses are known to have a low chance of detecting differential treatment effects across subgroup levels, so may overlook important differences in therapeutic efficacy. EMA have therefore issued draft guidance on the use of subgroup analyses in this setting. Although this guidance provided clear proposals on the importance of pre-specification of likely subgroup effects and how to use this when interpreting trial results, it is less clear which analysis methods would be reasonable, and how to interpret apparent subgroup effects in terms of whether further evaluation or action is necessary. A PSI/EFSPI Working Group has therefore been investigating a focused set of analysis approaches to assess treatment effect heterogeneity across subgroups in confirmatory clinical trials that take account of the number of subgroups explored and also investigating the ability of each method to detect such subgroup heterogeneity. This evaluation has shown that the plotting of standardised effects, bias-adjusted bootstrapping method and SIDES method all perform more favourably than traditional approaches such as investigating all subgroup-by-treatment interactions individually or applying a global test of interaction. Therefore, these approaches should be considered to aid interpretation and provide context for observed results from subgroup analyses conducted for phase 3 clinical trials., (© 2018 John Wiley & Sons, Ltd.)

This article focuses on 2 objectives in the analysis of efficacy in long-term extension studies of chronic diseases: (1) defining and discussing estimands of interest in such studies and (2) evaluating the performance of several multiple imputation methods that may be useful in estimating some of these estimands. Specifically, 4 estimands are defined and their clinical utility and inferential ramifications discussed. The performance of several multiple imputation methods and approaches were evaluated using simulated data. Results suggested that when interest is in a binary outcome derived from an underlying continuous measurement, it is preferable to impute the underlying continuous value that is subsequently dichotomized rather than to directly impute the binary outcome. Results also demonstrated that multivariate Gaussian models with Markov chain Monte Carlo imputation and sequential regression have minimal bias and the anticipated confidence interval coverage, even in settings with ordinal data where departures from normality are a concern. These approaches are further illustrated using a long-term extension study in psoriasis., (© 2018 John Wiley & Sons, Ltd.)

There is growing interest and investment in precision medicine as a means to provide the best possible health care. A treatment regime formalizes precision medicine as a sequence of decision rules, one per clinical intervention period, that specify if, when and how current treatment should be adjusted in response to a patient's evolving health status. It is standard to define a regime as optimal if, when applied to a population of interest, it maximizes the mean of some desirable clinical outcome, such as efficacy. However, in many clinical settings, a high-quality treatment regime must balance multiple competing outcomes; eg, when a high dose is associated with substantial symptom reduction but a greater risk of an adverse event. We consider the problem of estimating the most efficacious treatment regime subject to constraints on the risk of adverse events. We combine nonparametric Q-learning with policy-search to estimate a high-quality yet parsimonious treatment regime. This estimator applies to both observational and randomized data, as well as settings with variable, outcome-dependent follow-up, mixed treatment types, and multiple time points. This work is motivated by and framed in the context of dosing for chronic pain; however, the proposed framework can be applied generally to estimate a treatment regime which maximizes the mean of one primary outcome subject to constraints on one or more secondary outcomes. We illustrate the proposed method using data pooled from 5 open-label flexible dosing clinical trials for chronic pain., (© 2018 The Authors. Statistics in Medicine Published by John Wiley & Sons, Ltd.)

The general topic of subgroup identification has attracted much attention in the clinical trial literature due to its important role in the development of tailored therapies and personalized medicine. Subgroup search methods are commonly used in late-phase clinical trials to identify subsets of the trial population with certain desirable characteristics. Post-hoc or exploratory subgroup exploration has been criticized for being extremely unreliable. Principled approaches to exploratory subgroup analysis based on recent advances in machine learning and data mining have been developed to address this criticism. These approaches emphasize fundamental statistical principles, including the importance of performing multiplicity adjustments to account for selection bias inherent in subgroup search. This article provides a detailed review of multiplicity issues arising in exploratory subgroup analysis. Multiplicity corrections in the context of principled subgroup search will be illustrated using the family of SIDES (subgroup identification based on differential effect search) methods. A case study based on a Phase III oncology trial will be presented to discuss the details of subgroup search algorithms with resampling-based multiplicity adjustment procedures.

This paper deals with the general topic of subgroup analysis in late-stage clinical trials with emphasis on multiplicity considerations. The discussion begins with multiplicity issues arising in the context of exploratory subgroup analysis, including principled approaches to subgroup search that are applied as part of subgroup exploration exercises as well as in adaptive biomarker-driven designs. Key considerations in confirmatory subgroup analysis based on one or more pre-specified patient populations are reviewed, including a survey of multiplicity adjustment methods recommended in multi-population phase III clinical trials. Guidelines for interpretation of significant findings in several patient populations are introduced to facilitate the decision-making process and achieve consistent labeling across development programs. Copyright © 2017 John Wiley & Sons, Ltd., (Copyright © 2017 John Wiley & Sons, Ltd.)

It is well known that both the direction and magnitude of the treatment effect in clinical trials are often affected by baseline patient characteristics (generally referred to as biomarkers). Characterization of treatment effect heterogeneity plays a central role in the field of personalized medicine and facilitates the development of tailored therapies. This tutorial focuses on a general class of problems arising in data-driven subgroup analysis, namely, identification of biomarkers with strong predictive properties and patient subgroups with desirable characteristics such as improved benefit and/or safety. Limitations of ad-hoc approaches to biomarker exploration and subgroup identification in clinical trials are discussed, and the ad-hoc approaches are contrasted with principled approaches to exploratory subgroup analysis based on recent advances in machine learning and data mining. A general framework for evaluating predictive biomarkers and identification of associated subgroups is introduced. The tutorial provides a review of a broad class of statistical methods used in subgroup discovery, including global outcome modeling methods, global treatment effect modeling methods, optimal treatment regimes, and local modeling methods. Commonly used subgroup identification methods are illustrated using two case studies based on clinical trials with binary and survival endpoints. Copyright © 2016 John Wiley & Sons, Ltd., (Copyright © 2016 John Wiley & Sons, Ltd.)

Since the introduction of the propensity score (PS), methods for estimating treatment effects with observational data have received growing attention in the literature. Recent research has added substantially to the number of available statistical approaches for controlling confounding in such analyses. However, researchers need guidance to decide on the optimal analytic strategy for any given scenario. To address this gap, we conducted simulations evaluating both well-established methods (regression, PS weighting, stratification, and matching) and more recently proposed approaches (tree-based methods, local control, entropy balancing, genetic matching, prognostic scoring). The simulation scenarios included tree-based and smooth regression models as true data-generation mechanisms. We evaluated an extensive number of analysis strategies combining different treatment choices and outcome models. Key findings include 1) the lack of a single best strategy across all potential scenarios; 2) the importance of appropriately addressing interactions in the treatment choice model and/or outcome model; and 3) a tree-structured treatment choice model and a polynomial outcome model with second-order interactions performed well. One limitation to this initial assessment is the lack of heterogeneous simulation scenarios allowing treatment effects to vary by patient.