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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 -
Acta Obstetricia Et Gynecologica... Feb 2020
Topics: Cluster Analysis; Female; Humans; Intention to Treat Analysis; Randomized Controlled Trials as Topic; Research Design; Selection Bias
PubMed: 31953858
DOI: 10.1111/aogs.13776 -
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 -
JMIR Public Health and Surveillance Jul 2022Selection bias and unmeasured confounding are fundamental problems in epidemiology that threaten study internal and external validity. These phenomena are particularly...
BACKGROUND
Selection bias and unmeasured confounding are fundamental problems in epidemiology that threaten study internal and external validity. These phenomena are particularly dangerous in internet-based public health surveillance, where traditional mitigation and adjustment methods are inapplicable, unavailable, or out of date. Recent theoretical advances in causal modeling can mitigate these threats, but these innovations have not been widely deployed in the epidemiological community.
OBJECTIVE
The purpose of our paper is to demonstrate the practical utility of causal modeling to both detect unmeasured confounding and selection bias and guide model selection to minimize bias. We implemented this approach in an applied epidemiological study of the COVID-19 cumulative infection rate in the New York City (NYC) spring 2020 epidemic.
METHODS
We collected primary data from Qualtrics surveys of Amazon Mechanical Turk (MTurk) crowd workers residing in New Jersey and New York State across 2 sampling periods: April 11-14 and May 8-11, 2020. The surveys queried the subjects on household health status and demographic characteristics. We constructed a set of possible causal models of household infection and survey selection mechanisms and ranked them by compatibility with the collected survey data. The most compatible causal model was then used to estimate the cumulative infection rate in each survey period.
RESULTS
There were 527 and 513 responses collected for the 2 periods, respectively. Response demographics were highly skewed toward a younger age in both survey periods. Despite the extremely strong relationship between age and COVID-19 symptoms, we recovered minimally biased estimates of the cumulative infection rate using only primary data and the most compatible causal model, with a relative bias of +3.8% and -1.9% from the reported cumulative infection rate for the first and second survey periods, respectively.
CONCLUSIONS
We successfully recovered accurate estimates of the cumulative infection rate from an internet-based crowdsourced sample despite considerable selection bias and unmeasured confounding in the primary data. This implementation demonstrates how simple applications of structural causal modeling can be effectively used to determine falsifiable model conditions, detect selection bias and confounding factors, and minimize estimate bias through model selection in a novel epidemiological context. As the disease and social dynamics of COVID-19 continue to evolve, public health surveillance protocols must continue to adapt; the emergence of Omicron variants and shift to at-home testing as recent challenges. Rigorous and transparent methods to develop, deploy, and diagnosis adapted surveillance protocols will be critical to their success.
Topics: COVID-19; Confounding Factors, Epidemiologic; Humans; Internet; New York City; SARS-CoV-2; Selection Bias
PubMed: 35605128
DOI: 10.2196/31306 -
CMAJ : Canadian Medical Association... May 2017
Topics: Humans; Selection Bias
PubMed: 28483851
DOI: 10.1503/cmaj.732964 -
Forum For Health Economics & Policy Dec 2022Selection bias is an ongoing concern in large-scale panel surveys where the cumulative effects of unit nonresponse increase at each subsequent wave of data collection....
Selection bias is an ongoing concern in large-scale panel surveys where the cumulative effects of unit nonresponse increase at each subsequent wave of data collection. A second source of selection bias in panel studies is the inability to link respondents to supplementary administrative records, either because respondents do not consent to link or the matching algorithm fails to locate their administrative records. Both sources of selection bias can affect the validity of conclusions drawn from these data sources. In this article, I discuss recently proposed methods of reducing both sources of selection bias in panel studies, with a special emphasis on reducing selection bias in the US Health and Retirement Study.
Topics: Bias; Surveys and Questionnaires; Selection Bias; Longitudinal Studies; Information Storage and Retrieval
PubMed: 35728803
DOI: 10.1515/fhep-2021-0060 -
BMJ (Clinical Research Ed.) Jun 2023Effect estimates may be biased when the study design or the data analysis is conditional on a collider—a variable that is caused by two other variables. Causal...
Effect estimates may be biased when the study design or the data analysis is conditional on a collider—a variable that is caused by two other variables. Causal directed acyclic graphs are a helpful tool to identify colliders that may introduce selection bias in observational research.
Topics: Humans; Selection Bias; Bias
PubMed: 37286200
DOI: 10.1136/bmj.p1135 -
Journal of the National Cancer... Jul 2020Low-dose, penetrating photon radiation exposure is ubiquitous, yet our understanding of cancer risk at low doses and dose rates derives mainly from high-dose studies....
BACKGROUND
Low-dose, penetrating photon radiation exposure is ubiquitous, yet our understanding of cancer risk at low doses and dose rates derives mainly from high-dose studies. Although a large number of low-dose cancer studies have been recently published, concern exists about the potential for confounding to distort findings. The aim of this study was to describe and assess the likely impact of confounding and selection bias within the context of a systematic review.
METHODS
We summarized confounding control methods for 26 studies published from 2006 to 2017 by exposure setting (environmental, medical, or occupational) and identified confounders of potential concern. We used information from these and related studies to assess evidence for confounding and selection bias. For factors in which direct or indirect evidence of confounding was lacking for certain studies, we used a theoretical adjustment to determine whether uncontrolled confounding was likely to have affected the results.
RESULTS
For medical studies of childhood cancers, confounding by indication (CBI) was the main concern. Lifestyle-related factors were of primary concern for environmental and medical studies of adult cancers and for occupational studies. For occupational studies, other workplace exposures and healthy worker survivor bias were additionally of interest. For most of these factors, however, review of the direct and indirect evidence suggested that confounding was minimal. One study showed evidence of selection bias, and three occupational studies did not adjust for lifestyle or healthy worker survivor bias correlates. Theoretical adjustment for three factors (smoking and asbestos in occupational studies and CBI in childhood cancer studies) demonstrated that these were unlikely to explain positive study findings due to the rarity of exposure (eg, CBI) or the relatively weak association with the outcome (eg, smoking or asbestos and all cancers).
CONCLUSION
Confounding and selection bias are unlikely to explain the findings from most low-dose radiation epidemiology studies.
Topics: Asbestos; Bias; Confounding Factors, Epidemiologic; Epidemiologic Studies; Humans; Occupational Exposure; Selection Bias; Smoking
PubMed: 32657349
DOI: 10.1093/jncimonographs/lgaa008 -
American Journal of Epidemiology Jun 2017In causal analyses, conditioning on a collider generally results in selection bias. Conditioning on a prognostic factor that is independent of the exposure-and therefore...
In causal analyses, conditioning on a collider generally results in selection bias. Conditioning on a prognostic factor that is independent of the exposure-and therefore is not a collider-can also result in selection bias when 1) the exposure has a non-null effect on the outcome and 2) the association between the noncollider and the outcome is heterogenous across levels of the exposure. This result was empirically demonstrated by Greenland in 1977 (Am J Epidemiol. 1977;106(3):184-187).
Topics: Causality; Epidemiologic Research Design; Humans; Models, Theoretical; Selection Bias
PubMed: 28535177
DOI: 10.1093/aje/kwx077 -
Journal of the American Heart... Mar 2022
Topics: Bias; Data Interpretation, Statistical; Selection Bias
PubMed: 34632821
DOI: 10.1161/JAHA.121.023234