{"title":"Systematic review and meta-analysis in clinical trials","authors":"Anthony Lockett","doi":"10.1016/j.mpmed.2025.04.004","DOIUrl":"10.1016/j.mpmed.2025.04.004","url":null,"abstract":"<div><div>A systematic review aims to evaluate the effectiveness of interventions by comprehensively analysing published studies. The basis is a thorough search across multiple databases to identify studies meeting predefined inclusion criteria, with data extracted on study design, participant characteristics and key outcomes. Results are often pooled using meta-analysis where appropriate, revealing the key findings. Systematic reviews and meta-analyses are cornerstone methodologies in evidence-based research, providing a structured and comprehensive approach to synthesizing existing evidence on a specific topic. Although they are related, they are distinct concepts. A systematic review is a type of literature search that uses repeatable steps to find evaluate and synthesize evidence to answer a research question. Meta-analyses use statistical techniques to pool data from multiple studies, providing a quantitative summary of the evidence. These methods are widely used across disciplines including medicine, public health, psychology, education and social sciences, to inform policy, practice and further research. While they are separate methods, systematic review and meta-analysis share common features and the need for common standards.</div></div>","PeriodicalId":74157,"journal":{"name":"Medicine (Abingdon, England : UK ed.)","volume":"53 6","pages":"Pages 364-367"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Interpreting clinical guidelines","authors":"Shams Al-Ani, Anthony Lockett","doi":"10.1016/j.mpmed.2025.04.003","DOIUrl":"10.1016/j.mpmed.2025.04.003","url":null,"abstract":"<div><div>Clinical guidelines are an essential part of clinical and patient decision-making. To be relevant clinical guidelines must incorporate evidence and the opinion of clinicians and patients. Guidelines should be developed to quality standards and regularly updated if they are to be of value. Guidelines should be departed from if there is new evidence or depending on individual circumstances.</div></div>","PeriodicalId":74157,"journal":{"name":"Medicine (Abingdon, England : UK ed.)","volume":"53 6","pages":"Pages 392-395"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Statistics and data in health economics","authors":"Anthony Lockett","doi":"10.1016/j.mpmed.2025.03.007","DOIUrl":"10.1016/j.mpmed.2025.03.007","url":null,"abstract":"<div><div>Health economics is the study of value for money to guide decision-making. The conduct of heath economic analysis involves a wide variety of data types, collected from both publicly available sources and by specific data collection. Specialist and non-specialist methods of analysis are used to conduct the different types of analysis. These include cost evaluation, cost minimization, cost-effectiveness and cost–benefit analysis.</div></div>","PeriodicalId":74157,"journal":{"name":"Medicine (Abingdon, England : UK ed.)","volume":"53 6","pages":"Pages 396-398"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Clinical measurement in clinical trials","authors":"Anthony Lockett","doi":"10.1016/j.mpmed.2025.03.012","DOIUrl":"10.1016/j.mpmed.2025.03.012","url":null,"abstract":"<div><div>Clinical measurement is a cornerstone of healthcare, enabling accurate diagnosis, monitoring and treatment evaluation. It encompasses a wide range of tools and techniques, from vital sign assessments to advanced imaging and biomarker analysis. Key challenges include ensuring accuracy, precision and reliability while minimizing the variability caused by human error, device limitations or patient factors. The standardization and calibration of instruments are critical to maintaining data integrity. Innovations such as wearable devices and point-of-care testing are transforming clinical measurement, offering real-time, patient-centred data. Addressing these challenges and leveraging technological advancements are essential for improving patient outcomes and advancing evidence-based medical practices.</div></div>","PeriodicalId":74157,"journal":{"name":"Medicine (Abingdon, England : UK ed.)","volume":"53 6","pages":"Pages 385-387"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The effect of web-based clinical misinformation on patient interactions","authors":"David Wandless","doi":"10.1016/j.mpmed.2025.03.011","DOIUrl":"10.1016/j.mpmed.2025.03.011","url":null,"abstract":"<div><div>The proliferation of online misinformation has reshaped patient–provider interactions, posing risks to trust, adherence and effective healthcare delivery. With the accessibility of Web 2.0 platforms, patients increasingly turn to online sources for health information, often encountering unverified and misleading content. This article examines the impact of web-based clinical misinformation on patient interactions, highlighting how false claims influence trust, decision-making and adherence to treatment. Key contributing factors, such as social media dynamics, cognitive biases and spread of misinformation, are explored, alongside the role of healthcare professionals in mitigating the effects of misinformation. Strategies to address misinformation, including digital literacy education, collaborative efforts with technology platforms and improved patient guidance, are discussed. This article underscores the need for a unified approach to combating misinformation, fostering a healthcare environment that supports informed, evidence-based patient engagement.</div></div>","PeriodicalId":74157,"journal":{"name":"Medicine (Abingdon, England : UK ed.)","volume":"53 6","pages":"Pages 407-410"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Audit and quality improvement in statistics and data in healthcare","authors":"Anthony Lockett, Elizabeth Lockett","doi":"10.1016/j.mpmed.2025.04.002","DOIUrl":"10.1016/j.mpmed.2025.04.002","url":null,"abstract":"<div><div>Audit and quality improvement are central to the governance of healthcare and research. They are cyclical in nature, with audit having shorter cycles than quality improvement. The type of audit and improvement depends on the objectives and the data available. However, both use the ‘model for improvement’ system to achieve objectives.</div></div>","PeriodicalId":74157,"journal":{"name":"Medicine (Abingdon, England : UK ed.)","volume":"53 6","pages":"Pages 402-406"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Statistical analysis and significance tests for clinical trial data","authors":"Gregory L Ginn, Clare Campbell-Cooper","doi":"10.1016/j.mpmed.2025.04.005","DOIUrl":"10.1016/j.mpmed.2025.04.005","url":null,"abstract":"<div><div>The analysis of clinical trial data is vital for determining the true effects of treatments and differentiating these effects from random variation. Two key statistical methodologies are discussed: descriptive and inferential. Descriptive statistics provide insights by summarizing participant characteristics, treatment outcomes, and variable distributions using measures such as the mean, median, standard deviation and interquartile range. These summaries set the stage for hypothesis testing and assumption validation. Inferential statistics extend this foundation by enabling generalizations about a broader population, employing methods such as hypothesis testing, confidence intervals and regression models. Hypothesis testing evaluates the evidence for treatment effects, often using statistical tests such as <em>t</em>-tests, analysis of variance or chi-squared tests, while confidence intervals quantify the precision of these effects. Survival analysis, such as Kaplan–Meier curves and Cox models, is employed for time-to-event data. Adjusting for covariates is crucial for controlling confounding factors and is often paired with methods to manage multiple comparisons, such as Bonferroni corrections and false discovery rate (FDR) procedures. Proper power calculations ensure adequate sample sizes to detect meaningful effects, minimizing type I and type II errors. This comprehensive approach strengthens the reliability of clinical trial conclusions, supporting evidence-based decision-making in medical research.</div></div>","PeriodicalId":74157,"journal":{"name":"Medicine (Abingdon, England : UK ed.)","volume":"53 6","pages":"Pages 376-379"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Clinical trial design and conduct","authors":"Anthony Lockett","doi":"10.1016/j.mpmed.2025.04.001","DOIUrl":"10.1016/j.mpmed.2025.04.001","url":null,"abstract":"<div><div>This article provides an overview of the clinical and scientific challenges that must be considered in developing a clinical trial. Here, the clinical, scientific and regulatory setting of clinical trials are considered. In particular, the article discusses the issues surrounding the identification of the target population, definition of the treatment, choice of clinical outcomes and choice of comparators. It includes a discussion of the choice of randomization strategies, blinding, conduct and monitoring of the study, and plans for reporting the results. The importance of a well-defined study protocol is emphasized.</div></div>","PeriodicalId":74157,"journal":{"name":"Medicine (Abingdon, England : UK ed.)","volume":"53 6","pages":"Pages 355-357"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Cognitive bias and human factors in statistics and data in healthcare","authors":"Anthony Lockett","doi":"10.1016/j.mpmed.2025.03.008","DOIUrl":"10.1016/j.mpmed.2025.03.008","url":null,"abstract":"<div><div>Bias and human factors play a major role in the way in which data are interpreted and the resultant decisions. Six common sources of bias and the link that they have to human factors are presented. An understanding of bias and human factors is essential if information and data are to be correctly used. Testing strategies and cultural factors play a major role in preventing the misinterpretation of data.</div></div>","PeriodicalId":74157,"journal":{"name":"Medicine (Abingdon, England : UK ed.)","volume":"53 6","pages":"Pages 399-401"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Choosing statistical methods for clinical trials","authors":"Gregory L Ginn, Clare Campbell-Cooper","doi":"10.1016/j.mpmed.2025.04.007","DOIUrl":"10.1016/j.mpmed.2025.04.007","url":null,"abstract":"<div><div>Choosing the appropriate statistical methods is vital for ensuring the integrity, validity and efficiency of clinical trials. The selection of the methods to be used depends on trial-specific factors, including the nature of the data collected, hypothesis to be tested, sample size, duration, complexity and nature of the intervention. Once the overall methods are established, power analysis (balancing type I and II error risks, typically targeting 80–90% power to detect true treatment effects) needs to be considered. Variability, allocation ratio, study design and potential attrition also influence the methods selected. Incorporating these methodologies into clinical trial design ensures statistical rigour, resource efficiency and ethical integrity, enabling researchers to generate reliable, impactful evidence to guide clinical practice and advance medical knowledge.</div></div>","PeriodicalId":74157,"journal":{"name":"Medicine (Abingdon, England : UK ed.)","volume":"53 6","pages":"Pages 380-384"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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