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Journal of Health Psychology Oct 2019Myalgic encephalomyelitis/chronic fatigue syndrome has been a controversial diagnosis, resulting in tensions between patients and professionals providing them with care.... (Review)
Review
Myalgic encephalomyelitis/chronic fatigue syndrome has been a controversial diagnosis, resulting in tensions between patients and professionals providing them with care. A major constraint limiting progress has been the lack of a 'gold standard' for diagnosis; with a number of imperfect clinical and research criteria used, each defining different, though overlapping, groups of people with myalgic encephalomyelitis or chronic fatigue syndrome. We review basic epidemiological concepts to illustrate how the use of more specific and restrictive case definitions could improve research validity and drive progress in the field by reducing selection bias caused by diagnostic misclassification.
Topics: Fatigue Syndrome, Chronic; Humans; Male; Reproducibility of Results; Research Design; Selection Bias
PubMed: 28810428
DOI: 10.1177/1359105317695803 -
Journal of the Society For Integrative... 2006Randomized trials are an important method for deciding whether integrative oncology therapies do more good than harm. Many investigators do not pay sufficient attention... (Review)
Review
Randomized trials are an important method for deciding whether integrative oncology therapies do more good than harm. Many investigators do not pay sufficient attention to randomization procedures, and several studies have shown that only a fraction of trial reports describe randomization adequately. The purpose of randomization is to prevent selection bias: randomization procedures must therefore ensure that researchers are unable to predict the group to which a patient will be randomized until the patient is unambiguously registered on study; moreover, researchers must be unable to change a patient's allocation after the patients are registered. The use of telephone randomization and opaque envelopes has been suggested as a good randomization method, but both can be subverted. Randomization should be conducted either by a pharmaceutical company, which sends blinded medication to the hospital pharmacy, or by a secure, password-protected database system. Computer randomization can easily incorporate extensions of randomization, such as blocking, stratification, and minimization, which can help ensure balance between groups.
Topics: Humans; Medical Oncology; Patient Selection; Random Allocation; Randomized Controlled Trials as Topic; Research Design; Selection Bias
PubMed: 17022927
DOI: 10.2310/7200.2006.023 -
Nature Communications May 2017In real-world applications, observations are often constrained to a small fraction of a system. Such spatial subsampling can be caused by the inaccessibility or the...
In real-world applications, observations are often constrained to a small fraction of a system. Such spatial subsampling can be caused by the inaccessibility or the sheer size of the system, and cannot be overcome by longer sampling. Spatial subsampling can strongly bias inferences about a system's aggregated properties. To overcome the bias, we derive analytically a subsampling scaling framework that is applicable to different observables, including distributions of neuronal avalanches, of number of people infected during an epidemic outbreak, and of node degrees. We demonstrate how to infer the correct distributions of the underlying full system, how to apply it to distinguish critical from subcritical systems, and how to disentangle subsampling and finite size effects. Lastly, we apply subsampling scaling to neuronal avalanche models and to recordings from developing neural networks. We show that only mature, but not young networks follow power-law scaling, indicating self-organization to criticality during development.
Topics: Epidemics; Humans; Nerve Net; Selection Bias; Statistics as Topic
PubMed: 28469176
DOI: 10.1038/ncomms15140 -
BMJ (Clinical Research Ed.) Mar 2009To determine whether informed consent introduces selection bias in prospective observational studies using data from medical records, and consent rates for such studies. (Review)
Review
OBJECTIVES
To determine whether informed consent introduces selection bias in prospective observational studies using data from medical records, and consent rates for such studies.
DESIGN
Systematic review.
DATA SOURCES
Embase, Medline, and the Cochrane Library up to March 2008, reference lists from pertinent articles, and searches of electronic citations.
STUDY SELECTION
Prospective observational studies reporting characteristics of participants and non-participants approached for informed consent to use their medical records. Studies were selected independently in duplicate; a third reviewer resolved disagreements.
DATA EXTRACTION
Age, sex, race, education, income, or health status of participants and non-participants, the participation rate in each study, and susceptibility of these calculations to threats of selection and reporting bias.
RESULTS
Of 1650 citations 17 unique studies met inclusion criteria and had analysable data. Across all outcomes there were differences between participants and non-participants; however, there was a lack of consistency in the direction and the magnitude of effect. Of 161 604 eligible patients, 66.9% consented to use of data from their medical records.
CONCLUSIONS
Significant differences between participants and non-participants may threaten the validity of results from observational studies that require consent for use of data from medical records. To ensure that legislation on privacy does not unduly bias observational studies using medical records, thoughtful decision making by research ethics boards on the need for mandatory consent is necessary.
Topics: Humans; Informed Consent; Medical Records; Research Design; Selection Bias
PubMed: 19282440
DOI: 10.1136/bmj.b866 -
The Journal of Thoracic and... Apr 2020
Topics: Humans; Lung Neoplasms; Mesothelioma; Selection Bias
PubMed: 32035644
DOI: 10.1016/j.jtcvs.2019.11.083 -
American Journal of Epidemiology Nov 2019Caregivers have lower mortality rates than noncaregivers in population-based studies, which contradicts the caregiver-stress model and raises speculation about selection...
Caregivers have lower mortality rates than noncaregivers in population-based studies, which contradicts the caregiver-stress model and raises speculation about selection bias influencing these findings. We examined possible selection bias due to 1) sampling decisions and 2) selective participation among women (baseline mean age = 79 years) in the Caregiver-Study of Osteoporotic Fractures (Caregiver-SOF) (1999-2009), an ancillary study to the Study of Osteoporotic Fractures (SOF). Caregiver-SOF includes 1,069 SOF participants (35% caregivers) from 4 US geographical areas (Baltimore, Maryland; Minneapolis, Minnesota; the Monongahela Valley, Pennsylvania; and Portland, Oregon). Participants were identified by screening all SOF participants for caregiver status (1997-1999; n = 4,036; 23% caregivers) and rescreening a subset of caregivers and noncaregivers matched on sociodemographic factors 1-2 years later. Adjusted hazard ratios related caregiving to 10-year mortality in all women initially screened, subsamples representing key points in constructing Caregiver-SOF, and Caregiver-SOF. Caregivers had better functioning than noncaregivers at each screening. The association between caregiving and mortality among women invited to participate in Caregiver-SOF (41% died; adjusted hazard ratio (aHR) = 0.73, 95% confidence interval (CI): 0.61, 0.88) was slightly more protective than that in all initially screened women (37% died; aHR = 0.83, 95% CI: 0.73, 0.95), indicating little evidence of selection bias due to sampling decisions, and was similar to that in Caregiver-SOF (39% died; aHR = 0.71, 95% CI: 0.57, 0.89), indicating no participation bias. These results add to a body of evidence that informal caregiving may impart health benefits.
Topics: Aged; Aged, 80 and over; Caregivers; Female; Humans; Mortality; Selection Bias
PubMed: 31429867
DOI: 10.1093/aje/kwz173 -
BMC Medical Research Methodology Aug 2023When conducting randomised controlled trials is impractical, an alternative is to carry out an observational study. However, making valid causal inferences from... (Review)
Review
BACKGROUND
When conducting randomised controlled trials is impractical, an alternative is to carry out an observational study. However, making valid causal inferences from observational data is challenging because of the risk of several statistical biases. In 2016 Hernán and Robins put forward the 'target trial framework' as a guide to best design and analyse observational studies whilst preventing the most common biases. This framework consists of (1) clearly defining a causal question about an intervention, (2) specifying the protocol of the hypothetical trial, and (3) explaining how the observational data will be used to emulate it.
METHODS
The aim of this scoping review was to identify and review all explicit attempts of trial emulation studies across all medical fields. Embase, Medline and Web of Science were searched for trial emulation studies published in English from database inception to February 25, 2021. The following information was extracted from studies that were deemed eligible for review: the subject area, the type of observational data that they leveraged, and the statistical methods they used to address the following biases: (A) confounding bias, (B) immortal time bias, and (C) selection bias.
RESULTS
The search resulted in 617 studies, 38 of which we deemed eligible for review. Of those 38 studies, most focused on cardiology, infectious diseases or oncology and the majority used electronic health records/electronic medical records data and cohort studies data. Different statistical methods were used to address confounding at baseline and selection bias, predominantly conditioning on the confounders (N = 18/49, 37%) and inverse probability of censoring weighting (N = 7/20, 35%) respectively. Different approaches were used to address immortal time bias, assigning individuals to treatment strategies at start of follow-up based on their data available at that specific time (N = 21, 55%), using the sequential trial emulations approach (N = 11, 29%) or the cloning approach (N = 6, 16%).
CONCLUSION
Different methods can be leveraged to address (A) confounding bias, (B) immortal time bias, and (C) selection bias. When working with observational data, and if possible, the 'target trial' framework should be used as it provides a structured conceptual approach to observational research.
Topics: Humans; Biomedical Research; Selection Bias; Databases, Factual; MEDLINE; Medical Oncology; Observational Studies as Topic
PubMed: 37587484
DOI: 10.1186/s12874-023-02000-9 -
Bioinformatics (Oxford, England) Sep 2022Synthetic lethality (SL) between two genes occurs when simultaneous loss of function leads to cell death. This holds great promise for developing anti-cancer...
MOTIVATION
Synthetic lethality (SL) between two genes occurs when simultaneous loss of function leads to cell death. This holds great promise for developing anti-cancer therapeutics that target synthetic lethal pairs of endogenously disrupted genes. Identifying novel SL relationships through exhaustive experimental screens is challenging, due to the vast number of candidate pairs. Computational SL prediction is therefore sought to identify promising SL gene pairs for further experimentation. However, current SL prediction methods lack consideration for generalizability in the presence of selection bias in SL data.
RESULTS
We show that SL data exhibit considerable gene selection bias. Our experiments designed to assess the robustness of SL prediction reveal that models driven by the topology of known SL interactions (e.g. graph, matrix factorization) are especially sensitive to selection bias. We introduce selection bias-resilient synthetic lethality (SBSL) prediction using regularized logistic regression or random forests. Each gene pair is described by 27 molecular features derived from cancer cell line, cancer patient tissue and healthy donor tissue samples. SBSL models are built and tested using approximately 8000 experimentally derived SL pairs across breast, colon, lung and ovarian cancers. Compared to other SL prediction methods, SBSL showed higher predictive performance, better generalizability and robustness to selection bias. Gene dependency, quantifying the essentiality of a gene for cell survival, contributed most to SBSL predictions. Random forests were superior to linear models in the absence of dependency features, highlighting the relevance of mutual exclusivity of somatic mutations, co-expression in healthy tissue and differential expression in tumour samples.
AVAILABILITY AND IMPLEMENTATION
https://github.com/joanagoncalveslab/sbsl.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Topics: Humans; Synthetic Lethal Mutations; Selection Bias; Neoplasms; Genes, Synthetic
PubMed: 35876858
DOI: 10.1093/bioinformatics/btac523 -
BMC Medical Research Methodology Sep 2021The lung allocation system in the U.S. prioritizes lung transplant candidates based on estimated pre- and post-transplant survival via the Lung Allocation Scores (LAS)....
BACKGROUND
The lung allocation system in the U.S. prioritizes lung transplant candidates based on estimated pre- and post-transplant survival via the Lung Allocation Scores (LAS). However, these models do not account for selection bias, which results from individuals being removed from the waitlist due to receipt of transplant, as well as transplanted individuals necessarily having survived long enough to receive a transplant. Such selection biases lead to inaccurate predictions.
METHODS
We used a weighted estimation strategy to account for selection bias in the pre- and post-transplant models used to calculate the LAS. We then created a modified LAS using these weights, and compared its performance to that of the existing LAS via time-dependent receiver operating characteristic (ROC) curves, calibration curves, and Bland-Altman plots.
RESULTS
The modified LAS exhibited better discrimination and calibration than the existing LAS, and led to changes in patient prioritization.
CONCLUSIONS
Our approach to addressing selection bias is intuitive and can be applied to any organ allocation system that prioritizes patients based on estimated pre- and post-transplant survival. This work is especially relevant to current efforts to ensure more equitable distribution of organs.
Topics: Humans; Lung Transplantation; Patient Selection; Retrospective Studies; Selection Bias; Tissue and Organ Procurement; Waiting Lists
PubMed: 34548017
DOI: 10.1186/s12874-021-01379-7 -
The American Psychologist Oct 2008This article proposes that psychological research published in APA journals focuses too narrowly on Americans, who comprise less than 5% of the world's population. The...
This article proposes that psychological research published in APA journals focuses too narrowly on Americans, who comprise less than 5% of the world's population. The result is an understanding of psychology that is incomplete and does not adequately represent humanity. First, an analysis of articles published in six premier APA journals is presented, showing that the contributors, samples, and editorial leadership of the journals are predominantly American. Then, a demographic profile of the human population is presented to show that the majority of the world's population lives in conditions vastly different from the conditions of Americans, underlining doubts of how well American psychological research can be said to represent humanity. The reasons for the narrowness of American psychological research are examined, with a focus on a philosophy of science that emphasizes fundamental processes and ignores or strips away cultural context. Finally, several suggestions for broadening the scope of American psychology are offered.
Topics: Bibliometrics; Demography; Global Health; Humans; Internationality; Psychology; Research; Selection Bias; United States
PubMed: 18855491
DOI: 10.1037/0003-066X.63.7.602