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Headache Jul 2019
Topics: Epidemiologic Studies; Humans; Migraine Disorders; Observational Studies as Topic; Research Design
PubMed: 31297809
DOI: 10.1111/head.13572 -
JAMA Dec 2022
Topics: Causality; Comparative Effectiveness Research; Research Design; Observational Studies as Topic
PubMed: 36508210
DOI: 10.1001/jama.2022.21383 -
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 -
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 -
International Journal of Clinical... Jun 2016Observational studies have been recognised to be essential for investigating the safety profile of medications. Numerous observational studies have been conducted on the... (Review)
Review
Observational studies have been recognised to be essential for investigating the safety profile of medications. Numerous observational studies have been conducted on the platform of large population databases, which provide adequate sample size and follow-up length to detect infrequent and/or delayed clinical outcomes. Cohort and case-control are well-accepted traditional methodologies for hypothesis testing, while within-individual study designs are developing and evolving, addressing previous known methodological limitations to reduce confounding and bias. Respective examples of observational studies of different study designs using medical databases are shown. Methodology characteristics, study assumptions, strengths and weaknesses of each method are discussed in this review.
Topics: Databases, Pharmaceutical; Humans; Observational Studies as Topic; Pharmacoepidemiology; Research Design
PubMed: 27003827
DOI: 10.1007/s11096-016-0285-6 -
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 -
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 -
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 -
JAMA Surgery Aug 2021
Topics: Biomedical Research; Checklist; Guidelines as Topic; Humans; Meta-Analysis as Topic; Observational Studies as Topic; Research Design
PubMed: 33825847
DOI: 10.1001/jamasurg.2021.0522 -
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