Журнал биометрии и биостатистики

Fractional Vegetation Coverage (FVC) is a critical parameter in ecological and environmental studies. It represents the proportion of ground covered by green vegetation, providing essential information for understanding ecosystem dynamics, monitoring environmental changes, and managing natural resources. Traditionally, FVC estimation relied on field surveys and remote sensing techniques. However, the advent of Machine Learning (ML) has revolutionized this field, offering enhanced accuracy and efficiency in FVC analysis. This essay delves into the integration of machine learning for enhanced fractional vegetation coverage analysis, exploring its methodologies, applications, benefits, and challenges. Before the integration of machine learning, FVC estimation primarily relied on field-based methods and remote sensing techniques. Field-based methods involve direct measurement of vegetation coverage through ground surveys. While these methods are accurate, they are labor-intensive, time-consuming, and limited in spatial coverage. Remote sensing techniques, on the other hand, utilize satellite or aerial imagery to estimate FVC over large areas. These techniques include spectral vegetation indices (such as NDVI), image classification, and regression analysis. Although remote sensing offers broader spatial coverage, it faces challenges like atmospheric interference, sensor limitations, and complex data processing requirements.

The National Center for Health Statistics (NCHS) employs the Research and Development Survey (RANDS) to test new questions, methodologies, and survey designs. As technology advances, the role of digital tools and methodologies has become integral in refining RANDS data collection processes. This paper explores how technology enhances the efficiency, accuracy, and scope of RANDS surveys. It delves into the specific technological innovations employed, their impact on survey results, and the challenges faced in integrating these tools. Through a comprehensive analysis, the paper underscores the transformative role of technology in modern survey data collection.

Meteorological models are crucial tools for predicting weather and climate patterns. However, these models often exhibit biases due to imperfections in model physics, initial conditions, and parameterizations. Bias correction methods are employed to adjust model outputs, enhancing their accuracy and reliability. This review examines various bias-correction techniques used in meteorological modeling, evaluating their effectiveness, advantages, and limitations. We explore statistical methods, dynamical approaches, and machine learning techniques, providing a comprehensive overview of current practices and future directions in the field. The review aims to guide researchers and practitioners in selecting appropriate bias-correction methods for improving meteorological predictions.

Power analysis for sample size calculation (power calculation) plays an important role in clinical research to guarantee that we have sufficient power for detecting a clinically meaningful difference (treatment effect) at a pre-specified level of significance. In practice, however, there may be little or no information regarding the test treatment under study available. In this case, it is suggested that power calculation for detecting an anticipated effect size adjusted for standard deviation be performed, reducing a two-parameter problem into a single parameter problem by taking both mean response and variability into consideration. This study systematically analyzes estimating sample sizes across diverse endpoints, including relative/absolute change, risk metrics, exponential and proportional hazards models. Findings underscore the distinct nature of these metrics, reinforcing the necessity of an effect size measure as a standardized framework. Notably, analysis suggests it is possible to translate continuous and binary outcomes through a common effect size metric, which could facilitate meta-analyses involving heterogeneous outcome types. However, extending such translations to time-to-event outcomes presents additional complexities warranting advanced modeling techniques and hazard-based metrics. Through critical examination of effect size-based power calculations, this study contributes insights into efficient sample size estimation. It highlights the importance of standardized effect sizes as a unifying measure and the potential for outcome translation across endpoints.