Motivational orientations of students towards internet dependent behavior and measures for its prevention.

OBJECTIVE: Aim: The aim is to study the motivation of students towards Internet dependent behavior and develop practical recommendations for improving a set of measures for its prevention. PATIENTS AND METHODS: Materials and Methods: The research involved 154 students of the National Academy of Internal Affairs. Research methods: analysis and generalization of literature sources, questionnaire, statistical methods. RESULTS: Results: The priority motives of students who manifest Internet dependence behavior were identif i ed. The motivational orientations of students determine their systematic stay in the virtual environment and include, first of all, compliance with modern world trends; accessibility of content; the need for recognition of personal results by other users; satisfaction with virtual communication with the social environment. CONCLUSION: Conclusions: The practical recommendations for improving a set of measures to counteract the spread of Internet dependence among students were developed. Overcoming Internet dependence involves influencing a person to change his or her motivational and value as well as communication spheres. Prevention of Internet dependence involves public health professionals conducting awareness-raising as well as psychological and correctional work with the most vulnerable categories of people.

Tumor-Agnostic Approvals: Insights and Practical Considerations.

Since the first approval of a tumor-agnostic indication in 2017, a total of seven tumor-agnostic indications involving six drugs have received approval from the FDA. In this paper, the master protocol subteam of the Statistical Methods in Oncology Scientific Working Group, Biopharmaceutical Session, American Statistical Association, provides a comprehensive summary of these seven tumor-agnostic approvals, describing their mechanisms of action; biomarker prevalence; study design; companion diagnostics; regulatory aspects, including comparisons of global regulatory requirements; and health technology assessment approval. Also discussed are practical considerations relating to the regulatory approval of tumor-agnostic indications, specifically (i) recommendations for the design stage to mitigate the risk that exceptions may occur if a treatment is initially hypothesized to be effective for all tumor types and (ii) because drug development continues after approval of a tumor-agnostic indication, recommendations for further development of tumor-specific indications in first-line patients in the setting of a randomized confirmatory basket trial, acknowledging the challenges in this area. These recommendations and practical considerations may provide insights for the future development of drugs for tumor-agnostic indications.

Statistical Considerations on the Use of RWD/RWE for Oncology Drug Approvals: Overview and Lessons Learned.

Despite increasing utilization of real-world data (RWD)/real-world evidence (RWE) in regulatory submissions, their application to oncology drug approvals has seen limited success. Real-world data is most commonly summarized as a benchmark control for a single arm study or used to augment the concurrent control in a randomized clinical trial (RCT). While there has been substantial research on usage of RWD/RWE, our goal is to provide a comprehensive overview of their use in oncology drug approval submissions to inform future RWD/RWE study design. We will review examples of applications and summarize the strengths and weaknesses of each example identified by regulatory agencies. A few noteworthy case studies will be reviewed in detail. Operational aspects of RWD/RWE study design/analysis will be also discussed.

Statistical and Operational Considerations for 2-Stage Adaptive Designs with Simultaneous Evaluation of Overall and Marker-Selected Populations in Oncology Confirmatory Trials.

In biomarker enrichment study designs that start with an all-comer population, simultaneous evaluation of the entire and the marker-selected populations can be more desirable than pre-specifying the testing order, when the degree of marker predictiveness is uncertain. While there has been substantial research on this approach, our goal is to provide a complete overview and guidance in all aspects of this approach, including the interim analysis potentially using different endpoints, combination tests with associated multiplicity control, and the final treatment effect estimation. Regulatory/operational aspects and actual cases demonstrating the potential advantage of this approach are also described.

Accuracy and Safety of Novel Designs for Phase I Drug-Combination Oncology Trials.

Despite numerous innovative designs having been published for phase I drug-combination dose finding trials, their use in real applications is rather limited. As a working group under the American Statistical Association Biopharmaceutical Section, our goal is to identify the unique challenges associated with drug combination, share industry's experiences with combination trials, and investigate the pros and cons of the existing designs. Toward this goal, we review seven existing designs and distinguish them based on the criterion of whether their primary objectives are to find a single maximum tolerated dose (MTD) or the MTD contour (i.e., multiple MTDs). Numerical studies, based on either industry-specified fixed scenarios or randomly generated scenarios, are performed to assess their relative accuracy, safety, and ease of implementation. We show that the algorithm-based 3+3 design has poor performance and often fails to find the MTD. The performance of model-based combination trial designs is mixed: some demonstrate high accuracy of finding the MTD but poor safety, while others are safe but with compromised identification accuracy. In comparison, the model-assisted designs, such as BOIN and waterfall designs, have competitive and balanced performance in the accuracy of MTD identification and patient safety, and are also simple to implement, thus offering an attractive approach to designing phase I drug-combination trials. By taking into consideration the design's operating characteristics, ease of implementation and regulation, the need for advanced infrastructures, as well as the risk of regulatory acceptance, our paper offers practical guidance on the selection of a suitable dose-finding approach for designing future combination trials.

Statistical Issues and Recommendations for Clinical Trials Conducted During the COVID-19 Pandemic.

Abstract-The COVID-19 pandemic has had and continues to have major impacts on planned and ongoing clinical trials. Its effects on trial data create multiple potential statistical issues. The scale of impact is unprecedented, but when viewed individually, many of the issues are well defined and feasible to address. A number of strategies and recommendations are put forward to assess and address issues related to estimands, missing data, validity and modifications of statistical analysis methods, need for additional analyses, ability to meet objectives and overall trial interpretability.

Sources of Safety Data and Statistical Strategies for Design and Analysis: Real World Insights.

BACKGROUND: Although randomized controlled clinical trials provide necessary information and serve as the basis for regulatory decision making, a significant gap exists between the evidence these trials provide and what the biomedical community needs. It is recognized that a wealth of data are routinely collected outside clinical trials. Such real-world data (RWD) are not of comparable quality, it does not have similar immunity from bias and confounding as data collected in randomized clinical trials, but it might offer additional understanding of the benefit-risk, provide new insights to different stakeholders, and aid in regulatory decision making. This can be especially true when rare but serious adverse events are considered because randomized clinical trials are often not large enough and have insufficient duration to address safety concerns fully. Also, the passage of the 21st Century Cures bill passed by Congress in 2016 means that several data sources outside traditional clinical trials will play a greater role in regulatory decision making. This manuscript is third in a series of articles from the American Statistical Association Biopharmaceutical Section Safety Working Group. METHODS: In this manuscript, authors reviewed some RWD sources and shared considerations for statistical strategies and methodologies needed to design and analyze observational safety studies and pragmatic trials. RESULTS: Authors presented case studies and shared recommendations for statistical methods necessary to design and analyze safety trials using RWD. CONCLUSIONS: RWD is an important source of safety data that contribute to the totality of safety information available to generate evidence for regulators, sponsors, payers, physicians, and patients. However, it is important to determine if such data are fit for purpose.

Sources of Safety Data and Statistical Strategies for Design and Analysis: Clinical Trials.

BACKGROUND: There has been an increased emphasis on the proactive and comprehensive evaluation of safety endpoints to ensure patient well-being throughout the medical product life cycle. In fact, depending on the severity of the underlying disease, it is important to plan for a comprehensive safety evaluation at the start of any development program. Statisticians should be intimately involved in this process and contribute their expertise to study design, safety data collection, analysis, reporting (including data visualization), and interpretation. METHODS: In this manuscript, we review the challenges associated with the analysis of safety endpoints and describe the safety data that are available to influence the design and analysis of premarket clinical trials. RESULTS: We share our recommendations for the statistical and graphical methodologies necessary to appropriately analyze, report, and interpret safety outcomes, and we discuss the advantages and disadvantages of safety data obtained from clinical trials compared to other sources. CONCLUSIONS: Clinical trials are an important source of safety data that contribute to the totality of safety information available to generate evidence for regulators, sponsors, payers, physicians, and patients. This work is a result of the efforts of the American Statistical Association Biopharmaceutical Section Safety Working Group.

Sources of Safety Data and Statistical Strategies for Design and Analysis: Transforming Data Into Evidence.

BACKGROUND: Safety evaluation is a key aspect of medical product development. It is a continual and iterative process requiring thorough thinking, and dedicated time and resources. METHODS: In this article, we discuss how safety data are transformed into evidence to establish and refine the safety profile of a medical product, and how the focus of safety evaluation, data sources, and statistical methods change throughout a medical product's life cycle. RESULTS: Some challenges and statistical strategies for medical product safety evaluation are discussed. Examples of safety issues identified in different periods, that is, premarketing and postmarketing, are discussed to illustrate how different sources are used in the safety signal identification and the iterative process of safety assessment. The examples highlighted range from commonly used pediatric vaccine given to healthy children to medical products primarily used to treat a medical condition in adults. These case studies illustrate that different products may require different approaches, and once a signal is discovered, it could impact future safety assessments. CONCLUSIONS: Many challenges still remain in this area despite advances in methodologies, infrastructure, public awareness, international harmonization, and regulatory enforcement. Innovations in safety assessment methodologies are pressing in order to make the medical product development process more efficient and effective, and the assessment of medical product marketing approval more streamlined and structured. Health care payers, providers, and patients may have different perspectives when weighing in on clinical, financial and personal needs when therapies are being evaluated.

Bridging data across studies using frequentist and Bayesian estimation.

In drug development programs, an experimental treatment is evaluated across different populations and/or disease types using multiple studies conducted in countries around the world. In order to show the efficacy and safety in a specific population, a bridging study may be required. There are therapeutic areas for which enrolling patients to a trial is very challenging. Therefore, it is of interest to utilize the available historical information from previous studies. However, treatment effect may vary across different subpopulations/disease types; therefore, directly utilizing outcomes from historical studies may result in a biased estimation of treatment effect under investigation in the target trial. In this article, we propose novel approaches using both frequentist and Bayesian frameworks that allow borrowing information from historical studies while accounting for relevant patient's covariates via a propensity-based weighting. We evaluate the operating characteristics of the proposed methods in a simulation study and demonstrate that under certain conditions these methods may lead to improved estimation of a treatment effect.

Joint modeling of time to recurrence and cancer stage at recurrence in oncology trials.

This research was motivated by a clinical trial with bladder cancer patients who went through a surgery and were followed up for cancer recurrence. One of the main objectives of the trial was to evaluate the time to cancer recurrence in patients in control and experimental groups. At the time of recurrence, the disease stage was also evaluated. Because the stage of cancer at recurrence significantly impacts future treatment and patient prognosis of survival, analyzing the time to cancer recurrence and the stage at recurrence jointly provides more clinically relevant information than analyzing the time to recurrence alone. In this paper, we propose a stochastic model for the joint distribution of time to recurrence and cancer stage that (1) accounts for the recurrence caused by cancer cells surviving a treatment or a surgery and for the recurrence caused by spontaneous carcinogenesis, and (2) incorporates parameters that have biological meaning. To estimate the parameters, we use the maximum-likelihood method combined with the EM algorithm. To demonstrate the performance of our modeling, we evaluate the data from a clinical trial in patients with bladder cancer. We also use simulations to assess the sensitivity of the method.

Comparing oncology clinical programs by use of innovative designs and expected net present value optimization: Which adaptive approach leads to the best result?

Designing an oncology clinical program is more challenging than designing a single study. The standard approaches have been proven to be not very successful during the last decade; the failure rate of Phase 2 and Phase 3 trials in oncology remains high. Improving a development strategy by applying innovative statistical methods is one of the major objectives of a drug development process. The oncology sub-team on Adaptive Program under the Drug Information Association Adaptive Design Scientific Working Group (DIA ADSWG) evaluated hypothetical oncology programs with two competing treatments and published the work in the Therapeutic Innovation and Regulatory Science journal in January 2014. Five oncology development programs based on different Phase 2 designs, including adaptive designs and a standard two parallel arm Phase 3 design were simulated and compared in terms of the probability of clinical program success and expected net present value (eNPV). In this article, we consider eight Phase2/Phase3 development programs based on selected combinations of five Phase 2 study designs and three Phase 3 study designs. We again used the probability of program success and eNPV to compare simulated programs. For the development strategies, we considered that the eNPV showed robust improvement for each successive strategy, with the highest being for a three-arm response adaptive randomization design in Phase 2 and a group sequential design with 5 analyses in Phase 3.

Practical designs for Phase I combination studies in oncology.

Phase I trials evaluating the safety of multidrug combinations are becoming more common in oncology. Despite the emergence of novel methodology in the area, it is rare that innovative approaches are used in practice. In this article, we review three methods for Phase I combination studies that are easy to understand and straightforward to implement. We demonstrate the operating characteristics of the designs through illustration in a single trial, as well as through extensive simulation studies, with the aim of increasing the use of novel approaches in Phase I combination studies. Design specifications and software capabilities are also discussed.

Nine-year change in statistical design, profile, and success rates of Phase II oncology trials.

We investigated nine-year trends in statistical design and other features of Phase II oncology clinical trials published in 2005, 2010, and 2014 in five leading oncology journals: Cancer, Clinical Cancer Research, Journal of Clinical Oncology, Annals of Oncology, and Lancet Oncology. The features analyzed included cancer type, multicenter vs. single-institution, statistical design, primary endpoint, number of treatment arms, number of patients per treatment arm, whether or not statistical methods were well described, whether the drug was found effective based on rigorous statistical testing of the null hypothesis, and whether the drug was recommended for future studies.

Advances in Statistical Approaches Oncology Drug Development.

We describe some recent developments in statistical methodology and practice in oncology drug development from an academic and an industry perspective. Many adaptive designs were pioneered in oncology, and oncology is still at the forefront of novel methods to enable better and faster Go/No-Go decision making while controlling the cost.

Adaptive Clinical Trials: Overview of Early-Phase Designs and Challenges.

In this paper, we describe developments in adaptive design methodology and discuss implementation strategies and operational challenges in early phase adaptive clinical trials. The BATTLE trial - the first completed, biomarker-based, Bayesian adaptive randomized study in lung cancer - is presented as a case study to illustrate main ideas and share learnings.

Adaptive Design Applied to Identification of the Minimum Effective Dose in Schizophrenia: Simulations of Scientific and Commercial Value.

The International Society for CNS Clinical Trials and Methodology (ISCTM) Adaptive Design Working Group (IADWG) designed a case study simulation exercise to compare the value of traditional versus adaptive design approaches to phase II clinical trial design in schizophrenia in statistical and economic terms. Operational characteristics of both designs were compared across 7 likely dose-response curves. Based on IADWG members' recent research experience in schizophrenia, estimates of expected net present value (eNPV) for the molecule were compared for the traditional and adaptive designs. Across dose-response curve scenarios with a minimum effective dose (MED), the adaptive design was more likely to show proof of concept and correctly identify the MED than was the traditional design. Even with a conservative weighting of possible dose-response curves, using an adaptive design in phase II resulted in higher eNPV. This simulation supports the statistical and economic value for decision makers exploring the use of adaptive approaches to phase II research in schizophrenia.