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Annals of Internal Medicine Aug 2017Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure...
Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.
Topics: Confounding Factors, Epidemiologic; Epidemiologic Research Design; Humans; Observational Studies as Topic; Sensitivity and Specificity
PubMed: 28693043
DOI: 10.7326/M16-2607 -
Chest Jul 2020Sample size determination is an essential step in planning a clinical study. It is critical to understand that different study designs need different methods of sample... (Review)
Review
Sample size determination is an essential step in planning a clinical study. It is critical to understand that different study designs need different methods of sample size estimation. Although there is a vast literature discussing sample size estimation, incorrect or improper formulas continue to be applied. This article reviews basic statistical concepts in sample size estimation, discusses statistical considerations in the choice of a sample size for randomized controlled trials and observational studies, and provides strategies for reducing sample size when planning a study. To assist clinical researchers in performing sample size calculations, we have developed an online calculator for common clinical study designs. The calculator is available at http://riskcalc.org:3838/samplesize/. Finally, we offer our recommendations on reporting sample size determination in clinical studies.
Topics: Humans; Observational Studies as Topic; Randomized Controlled Trials as Topic; Research Design; Sample Size
PubMed: 32658647
DOI: 10.1016/j.chest.2020.03.010 -
JAMA Dec 2022
Topics: Causality; Comparative Effectiveness Research; Research Design; Observational Studies as Topic
PubMed: 36508210
DOI: 10.1001/jama.2022.21383 -
Nature Medicine Nov 2021The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics,... (Review)
Review
The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.
Topics: Computational Biology; Dysbiosis; Humans; Microbiota; Observational Studies as Topic; Research Design; Translational Science, Biomedical
PubMed: 34789871
DOI: 10.1038/s41591-021-01552-x -
Journal of Clinical Epidemiology Nov 2016Many analyses of observational data are attempts to emulate a target trial. The emulation of the target trial may fail when researchers deviate from simple principles... (Review)
Review
Many analyses of observational data are attempts to emulate a target trial. The emulation of the target trial may fail when researchers deviate from simple principles that guide the design and analysis of randomized experiments. We review a framework to describe and prevent biases, including immortal time bias, that result from a failure to align start of follow-up, specification of eligibility, and treatment assignment. We review some analytic approaches to avoid these problems in comparative effectiveness or safety research.
Topics: Bias; Comparative Effectiveness Research; Epidemiologic Research Design; Humans; Observational Studies as Topic; Selection Bias; Time Factors
PubMed: 27237061
DOI: 10.1016/j.jclinepi.2016.04.014 -
Journal of Traditional Chinese Medicine... Aug 2020To analyze clinical studies on correlations between Traditional Chinese Medicine (TCM) body constitution types and diseases published in the past 10 years, and to... (Review)
Review
OBJECTIVE
To analyze clinical studies on correlations between Traditional Chinese Medicine (TCM) body constitution types and diseases published in the past 10 years, and to provide an evidence base to support the use of such correlations for health maintenance and disease prevention.
METHODS
We searched five databases for the period April 2009 to December 2019: China National Knowledge Infrastructure Database, Wanfang Database, China Science and Technology Journal Database, PubMed and Embase. Three types of observational studies on correlation between constitution types and diseases were included: cross-sectional, case-control and cohort studies. Descriptive statistical methods were employed for data analysis.
RESULTS
A total of 1639 clinical studies were identified: 1452 (88.59%) cross-sectional studies, 115 (7.02%) case-control studies and 72 (4.39%) cohort studies covering 30 regions of China and five other countries (Malaysia, South Korea, Singapore, Thailand and France). The collection of studies comprised 19 disease categories and 333 different diseases. The 10 most commonly studied diseases were hypertension, diabetes, stroke, coronary atherosclerotic heart disease (CAHD), sleep disorders, neoplasm of the breast, dysmenorrhea, fatty liver disease, chronic viral hepatitis B and dyslipidemia. We found high distributions for each biased constitution type in different patient populations as follows: Qi-deficiency constitution in stroke, diabetes, chronic obstructive pulmonary disease, acquired immunodeficiency syndrome and hypertension; Yang-deficiency constitution in female infertility, osteoporosis, irritable bowel syndrome, gonarthrosis and dysmenorrhea; Yin-deficiency constitution in hypertension, diabetes, constipation, female climacteric states and osteoporosis; phlegm- dampness constitution in hypertension, stroke, fatty liver disease, diabetes and metabolic syndrome; damp-heat constitution in acne, chronic gastritis, chronic viral hepatitis B, human papillomavirus infection and hyperuricemia; blood-stasis constitution in CAHD, endometriosis and stroke; Qi-stagnation constitution in hyperplasia and neoplasms of the breast, insomnia, depression and thyroid nodules; and inherited-special constitution in asthma and allergic rhinitis.
CONCLUSION
Eight biased TCM constitutions were closely related to specific diseases, and could be used to guide individualized prevention and treatment. More rigorously designed studies are recommended to further verify the constitution-disease relationship.
Topics: Drug Therapy; Drugs, Chinese Herbal; Humans; Medicine, Chinese Traditional; Observational Studies as Topic; Treatment Outcome
PubMed: 32744037
DOI: 10.19852/j.cnki.jtcm.2020.04.019 -
Statistics in Medicine Dec 2015The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse... (Review)
Review
Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies.
The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in which we found that the use of IPTW has increased rapidly in recent years, but that in the most recent year, a majority of studies did not formally examine whether weighting balanced measured covariates between treatment groups. We then proceed to describe a suite of quantitative and qualitative methods that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample. The quantitative methods use the weighted standardized difference to compare means, prevalences, higher-order moments, and interactions. The qualitative methods employ graphical methods to compare the distribution of continuous baseline covariates between treated and control subjects in the weighted sample. Finally, we illustrate the application of these methods in an empirical case study. We propose a formal set of balance diagnostics that contribute towards an evolving concept of 'best practice' when using IPTW to estimate causal treatment effects using observational data.
Topics: Humans; Models, Statistical; Monte Carlo Method; Observational Studies as Topic; Outcome Assessment, Health Care; Propensity Score
PubMed: 26238958
DOI: 10.1002/sim.6607 -
BMJ (Clinical Research Ed.) Jul 2018Mendelian randomisation uses genetic variation as a natural experiment to investigate the causal relations between potentially modifiable risk factors and health...
Mendelian randomisation uses genetic variation as a natural experiment to investigate the causal relations between potentially modifiable risk factors and health outcomes in observational data. As with all epidemiological approaches, findings from Mendelian randomisation studies depend on specific assumptions. We provide explanations of the information typically reported in Mendelian randomisation studies that can be used to assess the plausibility of these assumptions and guidance on how to interpret findings from Mendelian randomisation studies in the context of other sources of evidence
Topics: Causality; Confounding Factors, Epidemiologic; Effect Modifier, Epidemiologic; Genetic Variation; Genome-Wide Association Study; Humans; Mendelian Randomization Analysis; Observational Studies as Topic; Outcome Assessment, Health Care; Public Health
PubMed: 30002074
DOI: 10.1136/bmj.k601 -
Respiratory Care Nov 2023Studies can be observational or experimental. With an observational study, the investigator does not determine the assignment of subjects, and there might not be a...
Studies can be observational or experimental. With an observational study, the investigator does not determine the assignment of subjects, and there might not be a control group. If there is a control group, assignment of the independent variable (exposure or intervention) is not under the control of the investigator. Observational studies can be rigorously conducted, but the lack of random assignment of the exposure/intervention introduces confounding and bias. Thus, the quality of evidence resulting from observational studies is lower than that of experimental randomized controlled trials (RCTs). An observational study might be performed if an RCT is unethical, impractical, or outside the control of the investigator. There are many types of prospective and retrospective observational study designs. However, an observational study design should be avoided if an experimental study is possible. Sophisticated statistical approaches can be used, but this does not elevate an observational study to the level of an RCT. Regardless of quality, an observational study cannot establish causality.
Topics: Research Design; Observational Studies as Topic
PubMed: 37339891
DOI: 10.4187/respcare.11170 -
BMJ (Clinical Research Ed.) Oct 2021Mendelian randomisation (MR) studies allow a better understanding of the causal effects of modifiable exposures on health outcomes, but the published evidence is often...
Mendelian randomisation (MR) studies allow a better understanding of the causal effects of modifiable exposures on health outcomes, but the published evidence is often hampered by inadequate reporting. Reporting guidelines help authors effectively communicate all critical information about what was done and what was found. STROBE-MR (strengthening the reporting of observational studies in epidemiology using mendelian randomisation) assists authors in reporting their MR research clearly and transparently. Adopting STROBE-MR should help readers, reviewers, and journal editors evaluate the quality of published MR studies. This article explains the 20 items of the STROBE-MR checklist, along with their meaning and rationale, using terms defined in a glossary. Examples of transparent reporting are used for each item to illustrate best practices.
Topics: Epidemiologic Research Design; Guidelines as Topic; Humans; Mendelian Randomization Analysis; Observational Studies as Topic
PubMed: 34702754
DOI: 10.1136/bmj.n2233