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Background & objectives: Due to lack of appropriate statistical knowledge, published research articles contain various errors related to the design, analysis and interpretation of results in the area of biomedical research. If research contains statistical error, however, costly, it may be of no use and the purpose of the investigation gets defeated. Many biomedical research articles published in different peer reviewed journals may retain several statistical errors and flaws in them. This study aimed to examine the trend and status of application of statistics in biomedical research articles. Study design, sample size estimation and statistical measures are crucial components of a study. These points were evaluated in published original research articles to understand the use or misuse of statistical tools. Methods: Three hundred original research articles from the latest issues of selected 37 journals were reviewed. These journals were from the five internationally recognized publication groups (CLINICAL KEY, BMJ Group, WILEY, CAMBRIDGE and OXFORD) accessible through the online library of SGPGI, Lucknow, India. Results: Among articles assessed under present investigation, 85.3 per cent (n=256) were observational, and 14.7 per cent (n=44) were interventional studies. In 93 per cent (n=279) of research articles, sample size estimation was not reproducible. The simple random sampling was encountered rarely in biomedical studies even though none of the articles was adjusted by design effect and, only five articles had used randomized test. The testing of assumption of normality was mentioned in only four studies before applying parametric tests. Interpretation & conclusions: In order to present biomedical research results with reliable and precise estimates based on data, the role of engaging statistical experts need to be appreciated. Journals must have standard rules for reporting study design, sample size and data analysis tools. Careful attention is needed while applying any statistical procedure as, it will not only help readers to trust in the published articles, but also rely on the inferences the published articles draw.
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Blood typing has revolutionized the field of medical science since its discovery about a century ago. Besides its established role in life-saving blood transfusions, researchers have always been curious about the relationship between blood groups and human ailments. The effect of blood groups on disease outcomes, susceptibility, and mortality has been widely explored. According to a particular school of thought, the endemicity of diseases shapes the distribution of blood group frequency in human populations and exert selection pressure favoring one blood type over another. Here we discuss the scope and association of different blood groups in the context of malaria.
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Abstract Introduction: there are several variations of randomized clinical trials. Trials can be classi fied by design as parallel, cross-over, factorial, assignment by group, n-of-1, paired, withdrawal, adaptive and pragmatic; and by purpose as superiority, non-inferiority and equivalence. Given this heterogeneity, the Consolidated Standards of Reporting Trials (CONSORT) were introduced in 1996 to provide a guideline for reporting randomized clinical trials. Objective: to describe the publication tendency of the various types of randomized clinical trials over 40 years, with reference to the publication of CONSORT and its extensions. Methods: the PubMed tool was used to search for randomized clinical trials published between 1979 and 2018, classifying them according to the varieties described. Results: a total of 472,114 published articles were found; 90.2% did not report the type of design and 98.2% did not report the purpose. Among the articles that reported the variety of randomized clinical trial, the predominant design was cross-over (5.9%), followed by parallel groups (2.34%); while the most common purpose was superiority (0.84%). After the launch of CONSORT, there was an increased proportion of articles published with the following designs: parallel groups; difference in proportions 1.89 95% CI (1.1-2.7); paired 1.07 95% CI (0.2-1.9); and pragmatic 4.73 95% CI (4.4-5.1); and for the purpose of non-inferiority 5.97 95% CI (5.6-6.3). Discussion: most articles on randomized clinical trials do not mention their type in the title and abstract. The proportion of articles that did, increased slightly after CONSORT was published. (Acta Med Colomb 2021; 46. DOI:https://doi.org/10.36104/amc.2021.1884).
Resumen Introducción: existen múltiples variantes del ensayo clínico aleatorizado; según diseño: grupos paralelos, cruzado, factorial, asignación por grupos, N de 1, pareado, retiro, adaptativo y pragmático; y por propósito: superioridad, no inferioridad y equivalencia. Debido a esta heterogeneidad, en 1996 se introdujo el CONSORT (Consolidated Standards of Reporting Trials) para suministrar una guía para el reporte de los ensayos clínicos aleatorizados. Objetivo: describir la tendencia de publicación de los tipos de ensayos clínicos aleatorizados durante 40 años, en relación con la publicación del CONSORT y sus extensiones. Métodos: se utilizó la herramienta PubMed para realizar una búsqueda de ensayos clínicos aleatorizados publicados entre 1979 y 2018, discriminándolos según las variantes descritas. Resultados: se encontraron 472 114 artículos publicados, el 90.2% no reportó tipo de diseño y 98.2% no reportó propósito. Entre los artículos que reportaron la variante de ensayos clínicos aleatorizados, el diseño predominante fue el cruzado (5.9%), seguido por grupos paralelos (2.34%); mientras que el propósito más frecuente fue el de superioridad (0.84%). Hubo un aumento en la proporción de artículos por variante publicados después del lanzamiento del CONSORT para los diseños de: grupos paralelos; diferencia de proporciones 1.89 IC 95% (1.1-2.7); pareado 1.07 IC 95% (0.2-1.9); pragmático 4.73 IC 95% (4.4-5.1); y para el propósito de no inferioridad 5.97 IC 95% (5.6-6.3). Discusión: la mayoría de los artículos sobre ensayos clínicos aleatorizados no mencionan en su título y resumen la variante de estos. La proporción de artículos que sí lo hicieron, aumentó discre tamente después de la publicación del CONSORT. (Acta Med Colomb 2021; 46. DOI:https://doi.org/10.36104/amc.2021.1884).
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El diseño y la planificación de una investigación requiere conocer detalladamente el problema de estudio y las formas que se han abordado para investigarlo. Identificar el propósito del estudio ayudará a elegir el diseño más apropiado, determinar el o los grupos a estudiar, el conjunto suficiente de variables a evaluar y, en el caso de los pediatras o profesionales clínicos que trabajan con niños, tomar en cuenta las consideraciones especiales que se deben tener cuando se realiza investigación en pediatría. En este artículo se describen los tipos de diseño de estudio clínicos y epidemiológicos, con enfoque en la investigación en pediatría.
The design and planning of an investigation requires a detailed knowledge of the study problem and the ways that have been approached to investigate it. Identifying the purpose of the study will help choose the most appropriate design, determine the group (s) to study, the sufficient set of variables to evaluate and, in the case of pediatricians or clinical professionals who work with children, take into account special considerations. to have when conducting pediatric research. This article describes the types of clinical and epidemiological study design with a focus on pediatric research.
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Humans , Child , Pediatrics , Research Design , Epidemiologic Studies , Biomedical Research/methods , Scientific and Technical PublicationsABSTRACT
Objective To evaluate the feasibility and effectiveness of flipped classroom teaching in medical research design class with more than 50 undergraduate. Methods Using randomized parallel-control study, two classes were chosen for this study:flipped classroom teaching group (i.e.FC group) and traditional teaching group (i.e.control group).Except for different teaching modes, all the other conditions of these two groups remained the same, including learning materials, network reference materials and teaching equipment. Results The FC group had 52 students and the control group had 69 students.After teaching, the final test score on study design in FC group was 11.53 higher (95%CI:4.75-18.32, t=3.37, P=0.001).However, there was no statistically significant difference between the test scores of other medical statistic teaching units (t=0.223, P=0.824). Conclusion FC teaching can be used in large class (more than 50 students) in medical undergraduate education.For flipped classroom teaching with large number of students, teachers should strengthen the arrangement of students′ self-study before class, organize effective team discussion, and make full use of new technologies to support teaching.
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Objective: To analyze the registration characteristics of registered clinical research, and find the potential scientific and feasibility problems of clinical research on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), providing reference for the good management of follow-up research registration. Methods: The key words of coronavirus disease 2019 (COVID-19) were identified and retrieved from Chinese Clinical Trial Registry and ClinicalTrials website. The registration characteristics of ethical status, number of recruits, total time, number of groups, intervention, study endpoint type, withdrawal, randomized controlled trial (RCT), stage, registration type, provinces distribution and patients' condition were summarized. IBM SPSS 22.0 software was used to analyze the above characteristics. Results: A total of 400 registered clinical studies were collected. Among them, 59 studies were not ethically approved, 15 studies were withdrawn, and stages of 303 studies were unclear. The differences of the three registration characteristics on the two official websites were statistically significant (all P<0.05). Fourteen studies recruited more than 1 000 people, the total time of 189 studies exceeded 6 months, and the number of groups in 22 studies exceeded 4 groups. There was no significant difference in the three registration characteristics on the two official websites. Only 15 studies were industry-sponsored trial. Most registered clinical studies were distributed in Hubei Province. Conclusion: The awareness of Chinese investigator initiating trial registration has increased. However, by collating and analyzing the registration information, it is found that the study design is not rigorous, so it is necessary to strengthen the registration quality management and study design methodological demonstration.
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Detection bias is an information bias.It was first proposed by Horwitz from the study investigating the association of the administration of estrogen after menopause with the occurrence of endometrial cancer, which still prevails in most epidemiological studies.We use the Directed Acyclic Graph to analyze the effect of a given exposure on a specific outcome with the association estimates between the measured exposure and outcome.Detection bias occurs when there is additional open paths irrelevant to the target path of interest which is originated from measured exposure to measured outcome.We further analyzed how the detection bias was formed under different study designs, including cohort study, randomized clinical trial and case-control study in order to further investigate its potential influence on the effect/association estimation.
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Detection bias is an information bias.It was first proposed by Horwitz from the study investigating the association of the administration of estrogen after menopause with the occurrence of endometrial cancer, which still prevails in most epidemiological studies.We use the Directed Acyclic Graph to analyze the effect of a given exposure on a specific outcome with the association estimates between the measured exposure and outcome.Detection bias occurs when there is additional open paths irrelevant to the target path of interest which is originated from measured exposure to measured outcome.We further analyzed how the detection bias was formed under different study designs, including cohort study, randomized clinical trial and case-control study in order to further investigate its potential influence on the effect/association estimation.
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Recent studies have highlighted the potential of ‘translational’ microbiome research in addressing real-world challengespertaining to human health, nutrition and disease. Additionally, outcomes of microbiome research have also positivelyimpacted various aspects pertaining to agricultural productivity, fuel or energy requirements, and stability/preservation ofvarious ecological habitats. Microbiome data is multi-dimensional with various types of data comprising nucleic andprotein sequences, metabolites as well as various metadata related to host and or environment. This poses a major challengefor computational analysis and interpretation of data to reach meaningful, reproducible (and replicable) biological conclusions. In this review, we first describe various aspects of microbiomes that make them an attractive tool/target fordeveloping various translational applications. The challenge of deciphering signatures from an information-rich resourcelike the microbiome is also discussed. Subsequently, we present three case-studies that exemplify the potential of microbiome-based solutions in solving real-world problems. The final part of the review attempts to familiarize readers with theimportance of a robust study design and the diligence required during every stage of analysis for achieving solutions withpotential translational value.
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Preclinical animal studies are mandatory before new treatments can be tested in clinical trials. However, their use in developing new therapies for sepsis has been controversial because of limitations of the models and inconsistencies with the clinical conditions. In consideration of the revised definition for clinical sepsis and septic shock (Sepsis-3), a Wiggers-Bernard Conference was held in Vienna in May 2017 to propose standardized guidelines on preclinical sepsis modeling. The participants conducted a literature review of 260 most highly cited scientific articles on sepsis models published between 2003 and 2012. The review showed, for example, that mice were used in 79% and euthanasia criteria were defined in 9% of the studies. PartⅠof this report details the recommendations for study design and humane modeling endpoints that should be addressed in sepsis models. The first recommendation is that survival follow-up should reflect the clinical time course of the infectious agent used in the sepsis model. Furthermore, it is recommended that therapeutic interventions should be initiated after the septic insult replicating clinical care. To define an unbiased and reproducible association between a new treatment and outcome, a randomization and blinding of treatments as well as inclusion of all methodological details in scientific publications is essential. In all preclinical sepsis studies, the high standards of animal welfare must be implemented. Therefore, development and validation of specific criteria for monitoring pain and distress, and euthanasia of septic animals, as well as the use of analgesics are recommended. A set of four considerations is also proposed to enhance translation potential of sepsis models. Relevant biological variables and comorbidities should be included in the study design and sepsis modeling should be extended to mammalian species other than rodents. In addition, the need for source control (in case of a defined infection focus) should be considered. These recommendations and considerations are proposed as "best practices" for animal models of sepsis that should be implemented.
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Epidemiology is a branch of science that mainly involves in the etiology studies of non-randomness phenomenon among homogenous populations. In this paper, we use causal-thinking, supported by its tool-Directed Acyclic Graphs, to illustrate how the estimation of effects is affected by the issues as relations between effect and association, time sequences between variables and their measured counterparts, natural picture of dynamic population, formation of susceptible population, selection of study population, impact of covariates and types of cases etc., on the estimation of effects. This type of thinking may help us to re-capture the epidemiological theories, methods and related applications. Thus, causal-thinking should be strengthened.
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Preclinical animal studies are mandatory before new treatments can be tested in clinical trials. However, their use in developing new therapies for sepsis has been controversial because of limitations of the models and inconsistencies with the clinical conditions. In consideration of the revised definition for clinical sepsis and septic shock (Sepsis-3), a Wiggers-Bernard Conference was held in Vienna in May 2017 to propose standardized guidelines on preclinical sepsis modeling. The participants conducted a literature review of 260 most highly cited scientific articles on sepsis models published between 2003 and 2012. The review showed, for example, that mice were used in 79% and euthanasia criteria were defined in 9% of the studies. PartⅠof this report details the recommendations for study design and humane modeling endpoints that should be addressed in sepsis models. The first recommendation is that survival follow-up should reflect the clinical time course of the infectious agent used in the sepsis model. Furthermore, it is recommended that therapeutic interventions should be initiated after the septic insult replicating clinical care. To define an unbiased and reproducible association between a new treatment and outcome, a randomization and blinding of treatments as well as inclusion of all methodological details in scientific publications is essential. In all preclinical sepsis studies, the high standards of animal welfare must be implemented. Therefore, development and validation of specific criteria for monitoring pain and distress, and euthanasia of septic animals, as well as the use of analgesics are recommended. A set of four considerations is also proposed to enhance translation potential of sepsis models. Relevant biological variables and comorbidities should be included in the study design and sepsis modeling should be extended to mammalian species other than rodents. In addition, the need for source control (in case of a defined infection focus) should be considered. These recommendations and considerations are proposed as "best practices" for animal models of sepsis that should be implemented.
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Preclinical animal studies precede the majority of clinical trials. While the clinical definitions of sepsis and recommended treatments are regularly updated, a systematic review of preclinical models of sepsis has not been done and clear modeling guidelines are lacking. To address this deficit, a Wiggers-Bernard Conference on preclinical sepsis modeling was held in Vienna in May, 2017. The goal of the conference was to identify limitations of preclinical sepsis models and to propose a set of guidelines, defined as the "Minimum Quality Threshold in Pre-clinical Sepsis Studies" (MQTiPSS), to enhance translational value of these models. A total of 31 experts from 13 countries participated and were divided into six thematic Working Groups: Study Design, Humane modeling, Infection types, Organ failure/dysfunction, Fluid resuscitation, and Antimicrobial therapy endpoints. As basis for the MQTiPSS discussions, the participants conducted a literature review of the 260 most highly cited scientific articles on sepsis models (2003-2012). Overall, the participants reached consensus on 29 points; 20 at "recommendation" and nine at "consideration" strength. This Executive Summary provides a synopsis of the MQTiPSS consensus. We believe that these recommendations and considerations will serve to bring a level of standardization to preclinical models of sepsis and ultimately improve translation of preclinical findings. These guideline points are proposed as "best practices" for animal models of sepsis that should be implemented. To encourage its wide dissemination, this article is freely accessible on the Intensive Care Medicine Experimental and Infection journal websites. In order to encourage its wide dissemination, this article is freely accessible in Shock, Infection, and Intensive Care Medicine Experimental.
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Objective Clinical research is a critical procedure for the development of medicine.Reliability of the clinical research finding is determined by the quality of study design and analysis courses.It will also further impact the guideline development and clinical practice.This study was focus on the evaluation of clinical research quality during its whole process.Methods Subjects of this study were the clinical summary reports from a government funded project which were submitted in 2016.Standardized data collecting form had been used to capture the key features regarding to the quality of study design and data analysis.After the review of data accuracy,descriptive analysis had been carried to interpret the observed findings both for design and analysis aspects.Results There were 67 project summary reports included in our analysis.The top three investigated therapeutic areas were oncology,cardiovascular/cerebrovascular diseases and orthopedics (19.4 ％,11.9 ％ and 11.9 ％).Most of studies fulfilled the evaluation criteria according to their original plan.94 ％ studies were strictly compliance with the original protocol with no interim amendment.Meanwhile,the report on sample size determination and appropriate use of multi-variable analysis should be improved.Conclusions Usually,clinical research program can fulfill the evaluate goal according to funding requirements.But the methodology quality should be paid more attention.It is highly suggested to cooperate with the professional statistical team and do continuous improvement effort to enhance the validity of study findings.
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In health the most frequent researches are done in the form of observational studies. In this type of scientific research the researchers do not interfere with the phenomena under study, only observe in a systematic and standardized manner, collecting and recording information, data or materials that spontaneously occur at a particular time of the health-disease process, or along its natural evolution, and then proceed with its description and/or analysis. In observational studies normally four types of study design are used: case series studies, cross-section studies, case-control studies and cohort studies. Thus, cross-sectional studies are very useful in descriptive studies when used in studies that are proposed to be analytical, the results must be interpreted by researchers with good experience in that specific field of knowledge, using a lot of caution and common sense.
No campo da saúde as pesquisas mais frequentes são feitas na forma de estudos observacionais. Nesse tipo de investigação científica os pesquisadores não interferem nos fenômenos em estudo, apenas os observam de maneira sistemática e padronizada, coletando e registrando informações, dados ou materiais que ocorrem espontaneamente num determinado momento do processo saúde-doença, ou ao longo de sua evolução natural, para posteriormente proceder à sua descrição e/ou análise. Nos Estudos observacionais normalmente quatro tipos de desenho de estudo são passíveis de utilização: estudos de series de casos, estudos de corte transversal. estudos de caso-controle e estudos de coorte. Assim, salienta-se que os estudos de corte transversal têm sua grande utilidade em estudos descritivos ao mesmo tempo que, quando utilizados em estudos que se propõem a serem analíticos, os resultados devem ser interpretados por pesquisadores com boa experiência naquele campo específico de conhecimento, valendo-se de muita cautela e bom senso.
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In epidemiology,intervention is normally used to define what experiment is intervention studies are equaled to experimental studies.Experimental studies are also considered scientifically more rigorous than observational ones.Intervention is generally referred to human activities that can interfere or change natural conditions.The intervention by definition may not necessarily be beneficial to the study subjects (although exposing harmful interventions to humans are unethical) and activities by the researcher,by the subject himself,or by any third party and either now or in the past can all form "effective" interventions.For example,interventions that can damage the optic nerve by any of the three parties can all help the researcher establish the relation between the optic nerve and vision.In the same sense,an activity that a study subject initiated in the past,such as smoking,would also constitute a valid intervention.As a result,a cohort study on smoking and lung cancer would also be an experiment.From the above arguments,we can see that intervention alone does not suffice to distinguish between experiment and observation.As we equal experiment to higher scientific rigorousness than observation,only can study designing features of intervention studies be used to define experiment.In intervention trials,randomization is the defining feature that makes randomized controlled trials differ from,and scientifically more rigorous than,controlled observational studies and has been commonly used to define experiment.If we have to divide clinical research into experiment and observation,randomized controlled trials would be experimental and non-randomized studies of intervention are trials but not experiment.Big data,real-world studies are not experiment and cannot replace randomized trials in confirmation of efficacy if comparison groups are not formed by randomization.Real world studies cannot replace randomized controlled trials.This is the most important message this paper wishes to convey.
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In epidemiology,intervention is normally used to define what experiment is intervention studies are equaled to experimental studies.Experimental studies are also considered scientifically more rigorous than observational ones.Intervention is generally referred to human activities that can interfere or change natural conditions.The intervention by definition may not necessarily be beneficial to the study subjects (although exposing harmful interventions to humans are unethical) and activities by the researcher,by the subject himself,or by any third party and either now or in the past can all form "effective" interventions.For example,interventions that can damage the optic nerve by any of the three parties can all help the researcher establish the relation between the optic nerve and vision.In the same sense,an activity that a study subject initiated in the past,such as smoking,would also constitute a valid intervention.As a result,a cohort study on smoking and lung cancer would also be an experiment.From the above arguments,we can see that intervention alone does not suffice to distinguish between experiment and observation.As we equal experiment to higher scientific rigorousness than observation,only can study designing features of intervention studies be used to define experiment.In intervention trials,randomization is the defining feature that makes randomized controlled trials differ from,and scientifically more rigorous than,controlled observational studies and has been commonly used to define experiment.If we have to divide clinical research into experiment and observation,randomized controlled trials would be experimental and non-randomized studies of intervention are trials but not experiment.Big data,real-world studies are not experiment and cannot replace randomized trials in confirmation of efficacy if comparison groups are not formed by randomization.Real world studies cannot replace randomized controlled trials.This is the most important message this paper wishes to convey.
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Ten acupuncture-related articles were published in The Journal of the American Medical Association (JAMA) between 1998 and 2017. Five studies showed positive results in terms of the effectiveness of acupuncture/Chinese medicine (CM); five studies showed negative results. This article summarizes the acupuncturerelated clinical trials published over the last 20 years in JAMA, and addresses what seems to be a fundamental ambivalence in Western medical journals regarding the scientific validity of acupuncture/CM. As yet there has been no consensus on the role of acupuncture in healthcare in Western countries. This is hardly surprising, considering the conflicting evidence found in published studies. Skepticism regarding acupuncture/CM is largely grounded in the fact that an accurate model for assessing the true clinical effects of acupuncture has yet to be created. This article discusses some of the pitfalls which result from applying Western-based scientific principles to CM, and suggests that in many cases, "negative" studies have been misinterpreted. The clinical experience of acupuncture practitioners is often in direct conflict with many of the negative conclusions published in journals. We are in need of an accurate model for sham and placebo treatments, and must analyze all published studies for design flaws and faulty conclusions.
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Two randomized controlled trials of acupuncture concerning polycystic ovary syndrome (PCOS) and stress urinary incontinence (SUI) were published simultaneously in the 24th issue, 2017 of The Journal of the American Medical Association (JAMA). A trial involving PCOS indicated that active acupuncture did not increase live birth compared with sham acupuncture; meanwhile, another trial referring to SUI showed that electroacupuncture resulted in less urine leakage compared with sham electroacupuncture. With an eye to the negative and positive results of acupuncture, three pivotal factors should be contemplated: (1) proper illness for acupuncture, that is, a problem need to be solved in current medical science, and acupuncture may really work for it; (2) proper pre-studied primary outcome, which is better be objective and repeatedly measurable to reveal the therapeutic effect of acupuncture truly and objectively; (3) proper sham control, which can blind the patients to the upmost extent with minimal biological effects. Through the publication of clinical trials of acupuncture in high-impact journals in recent years, researchers should have confidence in their clinical trials by pondering over these three pivotal factors.
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Modern epidemiology is the art and science of investigating quantitatively regularities or general laws regarding applied healthcare issues.The validity of epidemiological studies is primarily determined by the study design and the precision by the sample size.Large randomized controlled trial (RCT) is thus the most rigorous and most precise epidemiological study design.Due to ethical concerns,RCTs can however be used only to evaluate medical interventions.Rigorousness of study design and sample size required for a study are inversely related to the anticipated size of effect to be evaluated:the smaller the effect,the more rigorous the study design and larger the sample size are required.Thus,large RCTs are necessary and called upon when and only when the effectiveness to be proved is relatively small;large effectiveness can be verified with small or medium-sized RCTs or even observational studies.In the stages of scientific research,large RCTs are confirmatory rather than original investigations on new hypotheses,whereas the value of a study is ultimately determined by the importance and novelty of the research question rather than methodology and the P value.Overemphasis on large RCTs has been causing:1) overemphasis on interventions of small or moderate effect;2) overemphasis on confirmatory studies and on size of study and funding and weakening original creative work;3) increasing the risk of research resources,medical activities,and patients' well-being being hijacked by pharmaceutical companies.