Data are usually collected in a raw format and thus the inherent information is difficult to understand. Therefore, raw data need to be summarized, processed, and analyzed. However, no matter how well manipulated, the information derived from the raw data should be presented in an effective format, otherwise, it would be a great loss for both authors and readers. In this article, the techniques of data and information presentation in textual, tabular, and graphical forms are introduced. Text is the principal method for explaining findings, outlining trends, and providing contextual information. A table is best suited for representing individual information and represents both quantitative and qualitative information. A graph is a very effective visual tool as it displays data at a glance, facilitates comparison, and can reveal trends and relationships within the data such as changes over time, frequency distribution, and correlation or relative share of a whole. Text, tables, and graphs for data and information presentation are very powerful communication tools. They can make an article easy to understand, attract and sustain the interest of readers, and efficiently present large amounts of complex information. Moreover, as journal editors and reviewers glance at these presentations before reading the whole article, their importance cannot be ignored.
Methods

No abstract available.

As a follow-up to a previous article, this review provides several in-depth concepts regarding a survival analysis. Also, several codes for specific survival analysis are listed to enhance the understanding of such an analysis and to provide an applicable survival analysis method. A proportional hazard assumption is an important concept in survival analysis. Validation of this assumption is crucial for survival analysis. For this purpose, a graphical analysis method and a goodness-of-fit test are introduced along with detailed codes and examples. In the case of a violated proportional hazard assumption, the extended models of a Cox regression are required. Simplified concepts of a stratified Cox proportional hazard model and time-dependent Cox regression are also described. The source code for an actual analysis using an available statistical package with a detailed interpretation of the results can enable the realization of survival analysis with personal data. To enhance the statistical power of survival analysis, an evaluation of the basic assumptions and the interaction between variables and time is important. In doing so, survival analysis can provide reliable scientific results with a high level of confidence.
Follow-Up Studies ; Humans ; Methods ; Proportional Hazards Models ; Statistics as Topic ; Survival Analysis

Length of time is a variable often encountered during data analysis. Survival analysis provides simple, intuitive results concerning time-to-event for events of interest, which are not confined to death. This review introduces methods of analyzing time-to-event. The Kaplan-Meier survival analysis, log-rank test, and Cox proportional hazards regression modeling method are described with examples of hypothetical data.
Methods ; Sample Size ; Statistics as Topic ; Survival Analysis*

As a follow-up to a previous article, this review provides several in-depth concepts regarding a survival analysis. Also, several codes for specific survival analysis are listed to enhance the understanding of such an analysis and to provide an applicable survival analysis method. A proportional hazard assumption is an important concept in survival analysis. Validation of this assumption is crucial for survival analysis. For this purpose, a graphical analysis method and a goodness-of-fit test are introduced along with detailed codes and examples. In the case of a violated proportional hazard assumption, the extended models of a Cox regression are required. Simplified concepts of a stratified Cox proportional hazard model and time-dependent Cox regression are also described. The source code for an actual analysis using an available statistical package with a detailed interpretation of the results can enable the realization of survival analysis with personal data. To enhance the statistical power of survival analysis, an evaluation of the basic assumptions and the interaction between variables and time is important. In doing so, survival analysis can provide reliable scientific results with a high level of confidence.

The statistical significance of a clinical trial analysis result is determined by a mathematical calculation and probability based on null hypothesis significance testing. However, statistical significance does not always align with meaningful clinical effects; thus, assigning clinical relevance to statistical significance is unreasonable. A statistical result incorporating a clinically meaningful difference is a better approach to present statistical significance. Thus, the minimal clinically important difference (MCID), which requires integrating minimum clinically relevant changes from the early stages of research design, has been introduced. As a follow-up to the previous statistical round article on P values, confidence intervals, and effect sizes, in this article, we present hands-on examples of MCID and various effect sizes and discuss the terms statistical significance and clinical relevance, including cautions regarding their use.

Osteoarthritis is the most common form of chronic inflammatory arthritis, and its prevalence is steadily increasing owing to its association with aging. Therefore, understanding and implementing appropriate treatments for osteoarthritis in clinical practice is becoming increasingly important. Additionally, there is active research on a new approach for treating osteoarthritis: disease-modifying osteoarthritis drugs (DMOADs).Current Concepts: Several global osteoarthritis treatment guidelines exist; this article introduces the guidelines of the American College of Rheumatology and Osteoarthritis Research Society International, which are among the most widely recognized. A common theme across various guidelines is that exercise and weight loss are the primary recommended treatments. As for pharmacotherapy, the top recommendations include topical nonsteroidal anti-inflammatory drugs (NSAIDs) and oral NSAIDs. Although multiple classes of drugs such as DMOADs are being actively researched to slow the progression of osteoarthritis, no drug has yet been confirmed to be clinically effective or approved for use.Discussion and Conclusion: A deeper understanding of osteoarthritis treatment can help prevent malpractice and improve patient outcomes. While current treatments focus primarily on symptom management, the development of effective DMOADs holds promise for fundamentally altering the disease course and improving joint function and quality of life.

Osteoarthritis (OA) is the most prevalent chronic joint disorder that leads to pain, disability, and functional impairment. The OA prevalence among those aged ≥65 years in Korea was 30.2% between 2017 and 2021. Owing to a rapidly aging population, OA is becoming a significant public health and socioeconomic concern.Current Concepts: The onset of OA is characterized by cartilage injury, which affects the surrounding tissues and progresses to joint destruction. The pathophysiology varies across joints; hand OA is associated with genetic factors, whereas knee and hip OA are associated with mechanical stress. Nonsteroidal anti-inflammatory drugs are the primary treatment; however, considering their adverse effects and limited efficacy in halting disease progression, further research is warranted for more effective therapies. Natural medicines have been investigated; however, their clinical efficacy is inadequate.Discussion and Conclusion: Currently, no disease-modifying treatments are validated for OA. Animal models fail to reflect the slow progression and complexity of human OA; therefore, drug development remains challenging. In view of increasing healthcare costs and rapid population aging, further research and broader socioeconomic strategies are essential to manage the growing burden of OA.