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Journal of Clinical Epidemiology Dec 2023Recalled childhood adiposity is inversely associated with breast cancer observationally, including in Mendelian randomization (MR) studies. Breast cancer studies...
OBJECTIVES
Recalled childhood adiposity is inversely associated with breast cancer observationally, including in Mendelian randomization (MR) studies. Breast cancer studies recruited in adulthood only include survivors of childhood adiposity and breast cancer or a competing risk. We assessed recalled childhood adiposity on participant reported sibling and maternal breast cancer to ensure ascertainment of nonsurvivors.
STUDY DESIGN AND SETTING
We obtained independent strong genetic predictors of recalled childhood adiposity for women and their associations with participant reported own, sibling and maternal breast cancer from UK Biobank genome wide association studies.
RESULTS
Recalled childhood adiposity in women was inversely associated with own breast cancer using Mendelian randomization inverse variance weighting (odds ratio (OR) 0.66, 95% confidence interval (CI) 0.52-0.84) but less clearly related to participant reported sibling (OR 0.89, 95% CI 0.69-1.14) or maternal breast cancer (OR 0.84, 95% CI 0.67-1.05).
CONCLUSION
Weaker inverse associations of recalled childhood adiposity with breast cancer with more comprehensive ascertainment of cases before recruitment suggests the inverse association of recalled childhood adiposity with breast cancer could be partly selection bias from preferential selection of survivors. Greater consideration of survival bias in public health relevant causal inferences would be helpful.
Topics: Humans; Female; Breast Neoplasms; Adiposity; Selection Bias; Genome-Wide Association Study; Mendelian Randomization Analysis; Polymorphism, Single Nucleotide; Body Mass Index
PubMed: 37783402
DOI: 10.1016/j.jclinepi.2023.09.015 -
European Journal of Epidemiology Oct 2019Self-selection into prospective cohort studies and loss to follow-up can cause biased exposure-outcome association estimates. Previous investigations illustrated that...
Self-selection into prospective cohort studies and loss to follow-up can cause biased exposure-outcome association estimates. Previous investigations illustrated that such biases can be small in large prospective cohort studies. The structural approach to selection bias shows that general statements about bias are not possible for studies that investigate multiple exposures and outcomes, and that inverse probability of participation weighting (IPPW) but not adjustment for participation predictors generally reduces bias from self-selection and loss to follow-up. We propose to substantiate assumptions in structural models of selection bias through calculation of genetic correlations coefficients between participation predictors, outcome, and exposure, and to estimate a lower bound for bias due to self-selection and loss to follow-up by comparing effect estimates from IPP weighted and unweighted analyses. This study used data from the Norwegian Mother and Child Cohort Study and the Medical Birth Registry of Norway. Using the example of risk factors for ADHD, we find that genetic correlations between participation predictors, exposures, and outcome suggest the presence of bias. The comparison of exposure-outcome associations from regressions with and without IPPW revealed meaningful deviations. Assessment of selection bias for entire multi-exposure multi-outcome cohort studies is not possible. Instead, it has to be assessed and controlled on a case-by-case basis.
Topics: Bias; Child Development Disorders, Pervasive; Cohort Studies; Female; Follow-Up Studies; Humans; Male; Norway; Pregnancy; Prenatal Exposure Delayed Effects; Prospective Studies; Risk Factors; Selection Bias
PubMed: 31451995
DOI: 10.1007/s10654-019-00550-1 -
Journal of Alzheimer's Disease : JAD 2023Coronary atherosclerosis assessed in vivo was associated with cognitive impairment; however, conflicting findings have been reported in autopsy samples.
BACKGROUND
Coronary atherosclerosis assessed in vivo was associated with cognitive impairment; however, conflicting findings have been reported in autopsy samples.
OBJECTIVE
Our aims were to assess the association between atherosclerotic stenosis in the coronary arteries and cognitive impairment and to investigate the possibility of selection bias in an autopsy study.
METHODS
Coronary arteries were collected, and the largest luminal stenosis was measured. Sociodemographic, clinical, and cognitive information were reported by a reliable next-of-kin. The association was tested using logistic and linear regressions adjusted for sociodemographic and clinical variables. We restricted the sample to individuals that were born in 1935 or earlier and stratified the analysis by cause of death to investigate the role of selection bias.
RESULTS
In 253 participants (mean age = 78.0±8.5 years old, 48% male), stenosis was not associated with cognitive impairment (OR = 0.85, 95% CI = 0.69; 1.06, p = 0.15). In individuals who were born before 1936 in the absence of cardiovascular disease as the cause of death, greater stenosis was associated with cognitive impairment (OR = 4.02, 95% CI = 1.39; 11.6, p = 0.01). On the other hand, this association was not present among those born in 1935 or earlier who died of cardiovascular diseases (OR = 0.83, 95% CI = 0.60; 1.16, p = 0.28).
CONCLUSION
We found that higher coronary stenosis was associated with cognitive impairment only in individuals born in 1935 or earlier and who had not died from cardiovascular diseases. Selection bias may be an important issue when investigating risk factors for chronic degenerative diseases in older individuals using autopsy samples.
Topics: Humans; Male; Aged, 80 and over; Aged; Female; Coronary Artery Disease; Cardiovascular Diseases; Selection Bias; Cognitive Dysfunction; Atherosclerosis
PubMed: 37182864
DOI: 10.3233/JAD-220820 -
Journal of Pain and Symptom Management May 2021Palliative care (PC) programs are typically evaluated using observational data, raising concerns about selection bias.
CONTEXT
Palliative care (PC) programs are typically evaluated using observational data, raising concerns about selection bias.
OBJECTIVES
To quantify selection bias because of observed and unobserved characteristics in a PC demonstration program.
METHODS
Program administrative data and 100% Medicare claims data in two states and a 20% sample in eight states (2013-2017). The sample included 2983 Medicare fee-for-service beneficiaries aged 65+ participating in the PC program and three matched cohorts: regional; two states; and eight states. Confounding because of observed factors was measured by comparing patient baseline characteristics. Confounding because of unobserved factors was measured by comparing days of follow-up and six-month and one-year mortality rates.
RESULTS
After matching, evidence for observed confounding included differences in observable baseline characteristics, including race, morbidity, and utilization. Evidence for unobserved confounding included significantly longer mean follow-up in the regional, two-state, and eight-state comparison cohorts, with 207 (P < 0.001), 192 (P < 0.001), and 187 (P < 0.001) days, respectively, compared with the 162 days for the PC cohort. The PC cohort had higher six-month and one-year mortality rates of 53.5% and 64.5% compared with 43.5% and 48.0% in the regional comparison, 53.4% and 57.4% in the two-state comparison, and 55.0% and 59.0% in the eight-state comparison.
CONCLUSION
This case study demonstrates that selection of comparison groups impacts the magnitude of measured and unmeasured confounding, which may change effect estimates. The substantial impact of confounding on effect estimates in this study raises concerns about the evaluation of novel serious illness care models in the absence of randomization. We present key lessons learned for improving future evaluations of PC using observational study designs.
Topics: Aged; Cohort Studies; Fee-for-Service Plans; Humans; Medicare; Palliative Care; Selection Bias; United States
PubMed: 32947017
DOI: 10.1016/j.jpainsymman.2020.09.011 -
BMC Cancer Jun 2022With the aim of obtaining more uniformity and quality in the treatment of corpus uteri cancer in Belgium, the EFFECT project has prospectively collected detailed...
BACKGROUND
With the aim of obtaining more uniformity and quality in the treatment of corpus uteri cancer in Belgium, the EFFECT project has prospectively collected detailed information on the real-world clinical care offered to 4063 Belgian women with primary corpus uteri cancer. However, as data was collected on a voluntary basis, data may be incomplete and biased. Therefore, this study aimed to assess the completeness and potential selection bias of the EFFECT database.
METHODS
Five databases were deterministically coupled by use of the patient's national social security number. Participation bias was assessed by identifying characteristics associated with hospital participation in EFFECT, if any. Registration bias was assessed by identifying patient, tumor and treatment characteristics associated with patient registration by participating hospitals, if any. Uni- and multivariable logistic regression were applied.
RESULTS
EFFECT covers 56% of all Belgian women diagnosed with primary corpus uteri cancer between 2012 and 2016. These women were registered by 54% of hospitals, which submitted a median of 86% of their patients. Participation of hospitals was found to be biased: low-volume and Walloon-region centers were less likely to participate. Registration of patients by participating hospitals was found to be biased: patients with a less favorable risk profile, with missing data for several clinical-pathological risk factors, that did not undergo curative surgery, and were not discussed in a multidisciplinary tumor board were less likely to be registered.
CONCLUSIONS
Due to its voluntary nature, the EFFECT database suffers from a selection bias, both in terms of the hospitals choosing to participate and the patients being included by participating institutions. This study, therefore, highlights the importance of assessing the selection bias that may be present in any study that voluntarily collects clinical data not otherwise routinely collected. Nevertheless, the EFFECT database covers detailed information on the real-world clinical care offered to 56% of all Belgian women diagnosed with corpus uteri cancer between 2012 and 2016, and may therefore act as a powerful tool for measuring and improving the quality of corpus uteri cancer care in Belgium.
Topics: Belgium; Bias; Endometrial Neoplasms; Female; Humans; Selection Bias; Uterine Neoplasms
PubMed: 35650593
DOI: 10.1186/s12885-022-09671-5 -
Nature and extent of selection bias resulting from convenience sampling in the emergency department.Emergency Medicine Journal : EMJ Apr 2022To compare the clinical and demographic variables of patients who present to the ED at different times of the day in order to determine the nature and extent of... (Observational Study)
Observational Study
BACKGROUND
To compare the clinical and demographic variables of patients who present to the ED at different times of the day in order to determine the nature and extent of potential selection bias inherent in convenience sampling METHODS: We undertook a retrospective, observational study of data routinely collected in five EDs in 2019. Adult patients (aged ≥18 years) who presented with abdominal or chest pain, headache or dyspnoea were enrolled. For each patient group, the discharge diagnoses (primary outcome) of patients who presented during the day (08:00-15:59), evening (16:00-23:59), and night (00:00-07:59) were compared. Demographics, triage category and pain score, and initial vital signs were also compared.
RESULTS
2500 patients were enrolled in each of the four patient groups. For patients with abdominal pain, the diagnoses differed significantly across the time periods (p<0.001) with greater proportions of unspecified/unknown cause diagnoses in the evening (47.4%) compared with the morning (41.7%). For patients with chest pain, heart rate differed (p<0.001) with a mean rate higher in the evening (80 beats/minute) than at night (76). For patients with headache, mean patient age differed (p=0.004) with a greater age in the daytime (46 years) than the evening (41). For patients with dyspnoea, discharge diagnoses differed (p<0.001). Asthma diagnoses were more common at night (12.6%) than during the daytime (7.5%). For patients with dyspnoea, there were also differences in gender distribution (p=0.003), age (p<0.001) and respiratory rates (p=0.003) across the time periods. For each patient group, the departure status differed across the time periods (p<0.001).
CONCLUSION
Patients with abdominal or chest pain, headache or dyspnoea differ in a range of clinical and demographic variables depending upon their time of presentation. These differences may potentially introduce selection bias impacting upon the internal validity of a study if convenience sampling of patients is undertaken.
Topics: Adolescent; Adult; Chest Pain; Emergency Service, Hospital; Humans; Middle Aged; Retrospective Studies; Selection Bias; Triage
PubMed: 34706898
DOI: 10.1136/emermed-2021-211390 -
Demography Oct 2023Recent studies have shown that U.S. Census- and American Community Survey (ACS)-based estimates of income segregation are subject to upward finite sampling bias (Logan...
Recent studies have shown that U.S. Census- and American Community Survey (ACS)-based estimates of income segregation are subject to upward finite sampling bias (Logan et al. 2018; Logan et al. 2020; Reardon et al. 2018). We identify two additional sources of bias that are larger and opposite in sign to finite sampling bias: measurement error-induced attenuation bias and temporal pooling bias. The combination of these three sources of bias make it unclear how income segregation has trended. We formalize the three types of bias, providing a method to correct them simultaneously using public data from the decennial census and ACS from 1990 to 2015-2019. We use these methods to produce bias-corrected estimates of income segregation in the United States from 1990 to 2019. We find that (1) segregation is on the order of 50% greater than previously believed; (2) the increase from 2000 to the 2005-2009 period was much greater than indicated by previous estimates; and (3) segregation has declined since 2005-2009. Correcting these biases requires good estimates of the reliability of self-reported income and of the year-to-year volatility in neighborhood mean incomes.
Topics: Humans; United States; Reproducibility of Results; Income; Residence Characteristics; Bias; Selection Bias; Social Segregation
PubMed: 37605929
DOI: 10.1215/00703370-10932629 -
Annals of Epidemiology Oct 2023Placental histopathology is a resource for investigating obesity-associated pregnancy conditions. However, studies oversample adverse pregnancies, biasing findings. We...
PURPOSE
Placental histopathology is a resource for investigating obesity-associated pregnancy conditions. However, studies oversample adverse pregnancies, biasing findings. We examine the association between prepregnancy obesity (risk factor for inflammation) and histologic placental inflammation (correlated with impaired infant neurodevelopment) and how selection bias may influence the association.
METHODS
Singleton term deliveries between 2008 and 2012 from the Magee Obstetric Maternal and Infant database were analyzed. Prepregnancy body mass index (BMI) was categorized as underweight, lean (referent), overweight, and obese. Outcomes were diagnoses of acute (acute chorioamnionitis and fetal inflammation) and chronic placental inflammation (chronic villitis). Risk ratios for associations between BMI and placental inflammation were estimated using selection bias approaches: complete case, exclusion of pregnancy complications, multiple imputation, and inverse probability weighting. E-values approximated how susceptible estimates were to residual selection bias.
RESULTS
Across methods, obesity was associated with an 8-15% lower risk of acute chorioamnionitis, a 7%-14% lower risk of acute fetal inflammation, and a 12%-30% higher risk of chronic villitis relative to lean women. E-values indicated modest residual selection bias could explain away associations, though few measured indications of placental evaluations met this threshold.
CONCLUSIONS
Obesity may contribute to placental inflammation, and we highlight robust methods to analyze clinical data susceptible to selection bias.
Topics: Female; Pregnancy; Humans; Placenta; Chorioamnionitis; Selection Bias; Obesity; Inflammation; Body Mass Index
PubMed: 37302673
DOI: 10.1016/j.annepidem.2023.06.003 -
Cancer Science Mar 2023Patients with advanced cancer undergo comprehensive genomic profiling in Japan only after treatment options have been exhausted. Patients with a very poor prognosis were...
Patients with advanced cancer undergo comprehensive genomic profiling in Japan only after treatment options have been exhausted. Patients with a very poor prognosis were not able to undergo profiling tests, resulting in a selection bias called length bias, which makes accurate survival analysis impossible. The actual impact of length bias on the overall survival of patients who have undergone profiling tests is unclear, yet appropriate methods for adjusting for length bias have not been developed. To assess the length bias in overall survival, we established a simulation-based model for length bias adjustment. This study utilized clinicogenomic data of 8813 patients with advanced cancer who underwent profiling tests at hospitals throughout Japan between June 2019 and April 2022. Length bias was estimated by the conditional Kendall τ statistics and was significantly positive for 13 of the 15 cancer subtypes, suggesting a worse prognosis for patients who underwent profiling tests in early timing. The median overall survival time in colorectal, breast, and pancreatic cancer from the initial survival-prolonging chemotherapy with adjustment for length bias was 937 (886-991), 1225 (1152-1368), and 585 (553-617) days, respectively (median; 95% credible interval). Adjusting for length bias made it possible to analyze the prognostic relevance of oncogenic mutations and treatments. In total, 12 tumor-specific oncogenic mutations correlating with poor survival were detected after adjustment. There was no difference in survival between FOLFIRINOX (leucovorin, fluorouracil, irinotecan, and oxaliplatin) or gemcitabine with nab-paclitaxel-treated groups as first-line chemotherapy for pancreatic cancer. Adjusting for length bias is an essential part of utilizing real-world clinicogenomic data.
Topics: Humans; Selection Bias; Antineoplastic Combined Chemotherapy Protocols; Pancreatic Neoplasms; Japan; Genomics
PubMed: 36369895
DOI: 10.1111/cas.15651 -
Journal of the American Heart... Mar 2022
Topics: Bias; Data Interpretation, Statistical; Selection Bias
PubMed: 34632821
DOI: 10.1161/JAHA.121.023234