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Statistics in Medicine May 2023Matching is a popular design for inferring causal effect with observational data. Unlike model-based approaches, it is a nonparametric method to group treated and... (Observational Study)
Observational Study
Matching is a popular design for inferring causal effect with observational data. Unlike model-based approaches, it is a nonparametric method to group treated and control subjects with similar characteristics together, hence to re-create a randomization-like scenario. The application of matched design for real world data may be limited by: (1) the causal estimand of interest; (2) the sample size of different treatment arms. We propose a flexible design of matching, based on the idea of template matching, to overcome these challenges. It first identifies the template group which is representative of the target population, then match subjects from the original data to this template group and make inference. We provide theoretical justification on how it unbiasedly estimates the average treatment effect using matched pairs and the average treatment effect on the treated when the treatment group has a bigger sample size. We also propose using the triplet matching algorithm to improve matching quality and devise a practical strategy to select the template size. One major advantage of matched design is that it allows both randomization-based or model-based inference, with the former being more robust. For the commonly used binary outcome in medical research, we adopt a randomization inference framework of attributable effects in matched data, which allows heterogeneous effects and can incorporate sensitivity analysis for unmeasured confounding. We apply our design and analytical strategy to a trauma care evaluation study.
Topics: Humans; Algorithms; Biomedical Research; Causality; Research Design; Sample Size; Observational Studies as Topic
PubMed: 36863006
DOI: 10.1002/sim.9698 -
BMJ Open Apr 2023The SARS-CoV-2 pandemic remains a threat to public health. Soon after its outbreak, it became apparent that children are less severely affected. Indeed, opposing...
INTRODUCTION
The SARS-CoV-2 pandemic remains a threat to public health. Soon after its outbreak, it became apparent that children are less severely affected. Indeed, opposing clinical manifestations between children and adults are observed for other infections. The SARS-CoV-2 outbreak provides the unique opportunity to study the underlying mechanisms. This protocol describes the methods of an observational study that aims to characterise age dependent differences in immune responses to primary respiratory infections using SARS-CoV-2 as a model virus and to assess age differences in clinical outcomes including lung function.
METHODS AND ANALYSIS
The study aims to recruit at least 120 children and 60 adults that are infected with SARS-CoV-2 and collect specimen for a multiomics analysis, including single cell RNA sequencing of nasal epithelial cells and peripheral blood mononuclear cells, mass cytometry of whole blood samples and nasal cells, mass spectrometry-based serum and plasma proteomics, nasal epithelial cultures with functional in vitro analyses, SARS-CoV-2 antibody testing, sequencing of the viral genome and lung function testing. Data obtained from this multiomics approach are correlated with medical history and clinical data. Recruitment started in October 2020 and is ongoing.
ETHICS AND DISSEMINATION
The study was reviewed and approved by the Ethics Committee of Charité - Universitätsmedizin Berlin (EA2/066/20). All collected specimens are stored in the central biobank of Charité - Universitätsmedizin Berlin and are made available to all participating researchers and on request.
TRIAL REGISTRATION NUMBER
DRKS00025715, pre-results publication.
Topics: Adult; Child; Humans; COVID-19; SARS-CoV-2; Leukocytes, Mononuclear; Specimen Handling; Nose; Observational Studies as Topic
PubMed: 37068896
DOI: 10.1136/bmjopen-2022-065221 -
BMC Medicine Dec 2021There have been ongoing efforts to understand when and how data from observational studies can be applied to clinical and regulatory decision making. The objective of... (Review)
Review
BACKGROUND
There have been ongoing efforts to understand when and how data from observational studies can be applied to clinical and regulatory decision making. The objective of this review was to assess the comparability of relative treatment effects of pharmaceuticals from observational studies and randomized controlled trials (RCTs).
METHODS
We searched PubMed and Embase for systematic literature reviews published between January 1, 1990, and January 31, 2020, that reported relative treatment effects of pharmaceuticals from both observational studies and RCTs. We extracted pooled relative effect estimates from observational studies and RCTs for each outcome, intervention-comparator, or indication assessed in the reviews. We calculated the ratio of the relative effect estimate from observational studies over that from RCTs, along with the corresponding 95% confidence interval (CI) for each pair of pooled RCT and observational study estimates, and we evaluated the consistency in relative treatment effects.
RESULTS
Thirty systematic reviews across 7 therapeutic areas were identified from the literature. We analyzed 74 pairs of pooled relative effect estimates from RCTs and observational studies from 29 reviews. There was no statistically significant difference (based on the 95% CI) in relative effect estimates between RCTs and observational studies in 79.7% of pairs. There was an extreme difference (ratio < 0.7 or > 1.43) in 43.2% of pairs, and, in 17.6% of pairs, there was a significant difference and the estimates pointed in opposite directions.
CONCLUSIONS
Overall, our review shows that while there is no significant difference in the relative risk ratios between the majority of RCTs and observational studies compared, there is significant variation in about 20% of comparisons. The source of this variation should be the subject of further inquiry to elucidate how much of the variation is due to differences in patient populations versus biased estimates arising from issues with study design or analytical/statistical methods.
Topics: Humans; Observational Studies as Topic; Pharmaceutical Preparations; Randomized Controlled Trials as Topic; Research Design
PubMed: 34865623
DOI: 10.1186/s12916-021-02176-1 -
Deutsches Arzteblatt International Jul 2022Acute right heart failure is a life-threatening condition that can arise postoperatively. The options for symptomatic treatment have been markedly expanded in recent... (Review)
Review
BACKGROUND
Acute right heart failure is a life-threatening condition that can arise postoperatively. The options for symptomatic treatment have been markedly expanded in recent years through the introduction of percutaneously implantable mechanical cardiac support systems.
METHODS
This review is based on publications retrieved by a selective literature search in PubMed as well as on guidelines from Germany and abroad.
RESULTS
The diagnostic evaluation of right heart failure is chiefly based on echocardiography and pulmonary arterial catheteri - zation and is intended to lead to immediate treatment. Alongside treatment of the cause of the condition, supportive management is crucial to patient survival. A variety of ventilation strategies depending on the situation, catecholamine therapies, inhaled selective pulmonary vasodilators, and cardiac support systems are available for this purpose. The in-hospital mortality of postoperative right heart failure is 5-17 %. The results of the use of cardiac support systems reported in case series are dis - appointing, but nonetheless good compared to what these critically ill patients would face without such treatment. In one observational study, the 30-day survival rate was 73.3%.
CONCLUSION
Survival is aided by the rapid recognition of right heart failure, targeted multidisciplinary treatment, and contact with an extracorporeal life support (ECLS) center for additional supportive treatment measures. Further studies on the use of pharmacological and mechanical cardiac support systems must be carried out to provide stronger evidence on which treatment recommendations can be based.
Topics: Humans; Extracorporeal Membrane Oxygenation; Heart Failure; Critical Illness; Vasodilator Agents; Germany; Observational Studies as Topic
PubMed: 35583115
DOI: 10.3238/arztebl.m2022.0207 -
Scientific Reports Feb 2021Patients infected with SARS-CoV-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with...
Patients infected with SARS-CoV-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with mild COVID-19 to explore the potentials of wearable biosensors and machine learning-based analysis of physiology parameters to detect clinical deterioration. Thirty-four patients (median age: 32 years; male: 52.9%) with mild COVID-19 from Queen Mary Hospital were recruited. The mean National Early Warning Score 2 (NEWS2) were 0.59 ± 0.7. 1231 manual measurement of physiology parameters were performed during hospital stay (median 15 days). Physiology parameters obtained from wearable biosensors correlated well with manual measurement including pulse rate (r = 0.96, p < 0.0001) and oxygen saturation (r = 0.87, p < 0.0001). A machine learning-derived index reflecting overall health status, Biovitals Index (BI), was generated by autonomous analysis of physiology parameters, symptoms, and other medical data. Daily BI was linearly associated with respiratory tract viral load (p < 0.0001) and NEWS2 (r = 0.75, p < 0.001). BI was superior to NEWS2 in predicting clinical worsening events (sensitivity 94.1% and specificity 88.9%) and prolonged hospitalization (sensitivity 66.7% and specificity 72.7%). Wearable biosensors coupled with machine learning-derived health index allowed automated detection of clinical deterioration.
Topics: Adult; Biosensing Techniques; COVID-19; Female; Humans; Machine Learning; Male; Middle Aged; Observational Studies as Topic; Wearable Electronic Devices; Young Adult
PubMed: 33623096
DOI: 10.1038/s41598-021-82771-7 -
Medicine Jun 2020To investigate the gene rearrangement and mutation of lymphoma biomarkers including (Immunoglobulin H (IgH), Immunoglobulin kappa (IGK), Immunoglobulin lambda (IGL), and...
INTRODUCTION
To investigate the gene rearrangement and mutation of lymphoma biomarkers including (Immunoglobulin H (IgH), Immunoglobulin kappa (IGK), Immunoglobulin lambda (IGL), and TCR) in the lymphoma diagnosis.
METHODS AND ANALYSIS
Paraffin tissue samples from 240 cases diagnosed as suspected lymphoma in the department of pathology, Deyang City People's Hospital from June 2020 to June 2021 will be enrolled. Deoxyribonucleic acid extraction and Polymerase Chain Reaction (PCR) amplification will be performed in these paraffin tissue samples. Immunoglobulin and T cell receptor (TCR) rearrangement will be analyzed by hetero-double chain gel electrophoresis and BioMed-2 standardized immunoglobulin gene rearrangement detection system. In this study protocol IGH gene rearrangement, IGK gene rearrangement, both IGH and IGL gene rearrangement, both IGH and IGK gene rearrangement, both IGK and IGL gene rearrangement, both IGH, IGK and IGL gene rearrangement, TCR gene rearrangement and positive Ig/TCR rearrangement will be analyzed.
DISCUSSION
In this study, we will use B and T cell lymphoma analysis focusing on IgH, IGK, IGL, and TCR gene rearrangement, so as to provide early guidance for the diagnosis of lymphoma. Second generation sequencing technology is helpful in the differential diagnosis of lymphoma.
TRIAL REGISTRATION
Chinese Clinical trial registry: ChiCTR2000032366.
Topics: Gene Rearrangement; Humans; Lymphoma; Mutation; Observational Studies as Topic; Prospective Studies; Research Design
PubMed: 32541525
DOI: 10.1097/MD.0000000000020733 -
Research and Reporting Considerations for Observational Studies Using Electronic Health Record Data.Annals of Internal Medicine Jun 2020Electronic health records (EHRs) are an increasingly important source of real-world health care data for observational research. Analyses of data collected for purposes... (Review)
Review
Electronic health records (EHRs) are an increasingly important source of real-world health care data for observational research. Analyses of data collected for purposes other than research require careful consideration of data quality as well as the general research and reporting principles relevant to observational studies. The core principles for observational research in general also apply to observational research using EHR data, and these are well addressed in prior literature and guidelines. This article provides additional recommendations for EHR-based research. Considerations unique to EHR-based studies include assessment of the accuracy of computer-executable cohort definitions that can incorporate unstructured data from clinical notes and management of data challenges, such as irregular sampling, missingness, and variation across time and place. Principled application of existing research and reporting guidelines alongside these additional considerations will improve the quality of EHR-based observational studies.
Topics: Data Collection; Electronic Health Records; Humans; Observational Studies as Topic; Primary Health Care
PubMed: 32479175
DOI: 10.7326/M19-0873 -
Journal of Crohn's & Colitis Nov 2023Participatory research, also referred to as patient and public involvement, is an approach that involves collaborating with patients affected by the focus of the... (Review)
Review
Participatory research, also referred to as patient and public involvement, is an approach that involves collaborating with patients affected by the focus of the research, on the design, development and delivery of research to improve outcomes. There are two broad justifications for this: first, that it enhances the quality and relevance of research, and second, that it satisfies the ethical argument for patient inclusion in decisions about them. This synergistic and collaborative effort, which bridges the divide between researchers and participants with the lived condition, is now a mainstream activity and widely accepted as best practice. Although there has been a substantial increase in the literature over the past two decades, little has been published on how participatory research has been used in inflammatory bowel disease [IBD] research and little guidance as to how researchers should go about this. With an increasing incidence and prevalence worldwide, combined with declining study enrolment in an era of perennial unmet need, there are a multitude of benefits of participatory research to IBD patients and investigators, including research output that is informed and relevant to the real world. A key example of participatory research in IBD is the I-CARE study, a large-scale, pan-European observational study assessing the safety of advanced therapies, which had significant patient involvement throughout the study. In this review, we provide a comprehensive overview of the benefits and challenges of participatory research and discuss opportunities of building strategic alliances between IBD patients, healthcare providers and academics to strengthen research outcomes.
Topics: Humans; Inflammatory Bowel Diseases; Patient Participation; Observational Studies as Topic
PubMed: 37220886
DOI: 10.1093/ecco-jcc/jjad090 -
Chest Jul 2020Causal directed acyclic graphs (cDAGs) have become popular tools for researchers to better examine biases related to causal questions. DAGs comprise a series of arrows... (Review)
Review
Causal directed acyclic graphs (cDAGs) have become popular tools for researchers to better examine biases related to causal questions. DAGs comprise a series of arrows connecting nodes that represent variables and in doing so can demonstrate the causal relation between different variables. cDAGs can provide researchers with a blueprint of the exposure and outcome relation and the other variables that play a role in that causal question. cDAGs can be helpful in the design and interpretation of observational studies in pulmonary, critical care, sleep, and cardiovascular medicine. They can also help clinicians and researchers to better identify the structure of different biases that can affect the validity of observational studies. Most of the available literature on cDAGs and their function use language that might be unfamiliar to clinicians. This article explains cDAG terminology and the principles behind how they work. We use cDAGs and clinical examples that are mostly focused in the area of pulmonary medicine to describe the structure of confounding, selection bias, overadjustment bias, and detection bias. These principles are then applied to a more complex published case study on the use of statins and COPD mortality. We also introduce readers to other resources for a more in-depth discussion of causal inference principles.
Topics: Bias; Biomedical Research; Causality; Epidemiologic Studies; Humans; Observational Studies as Topic; Research Design
PubMed: 32658648
DOI: 10.1016/j.chest.2020.03.011 -
Recenti Progressi in Medicina Nov 2019
Topics: Humans; Observational Studies as Topic; Patient Selection; Randomized Controlled Trials as Topic; Selection Bias; Treatment Outcome
PubMed: 31808436
DOI: 10.1701/3265.32352