BACKGROUND: In population-based research, pregnancy may be a repeated event. Despite published guidance on how to address repeated pregnancies to the same individual, a variety of approaches are observed in perinatal epidemiological studies. While some of these approaches are supported by the chosen research question, others are consequences of constraints inherent to a given dataset (e.g., missing parity information). These decisions determine how appropriately a given research question can be answered and overall generalizability.
OBJECTIVE: To compare common cohort selection and analytic approaches used for perinatal epidemiological research by assessing the prevalence of two perinatal outcomes and their association with a clinical and a social independent variable STUDY DESIGN: Using vital records linked to maternal hospital discharge records for singleton births, we created four cohorts: (1) all-births (2) randomly selected one birth per individual (3) first observed birth per individual (4) primiparous-births (parity 1). Sampling of births was not conditional on cluster (i.e., we did not sample all births by a given mother, but rather sampled individual births). Study outcomes were severe maternal morbidity and preeclampsia/eclampsia, and the independent variables were self-reported race/ethnicity (as a social factor) and systemic lupus erythematosus. Comparing the four cohorts, we assessed the distribution of maternal characteristics, the prevalence of outcomes, overall and stratified by parity, and risk ratios for the associations of outcomes with independent variables. Among all-births, we then compared risk ratios from three analytic strategies: with standard inference that assumes independently sampled births to the same mother in the model, with cluster-robust inference, and adjusting for parity.
RESULTS: We observed minor differences in the population characteristics between the all-birth (N=2,736,693), random-selection, and first-observed birth cohorts (both N=2,284,660), with more substantial differences between these cohorts and the primiparous-births cohort (N=1,054,684). Outcome prevalence was consistently lowest among all-births and highest among primiparous-births (e.g., severe maternal morbidity 18.9 per 1,000 births among primiparous-births vs. 16.6 per 1,000 births among all-births). When stratified by parity, outcome prevalence was always the lowest in births of parity 2 and highest among births of parity 1 for both outcomes. Risk ratios differed for study outcomes across all four cohorts, with the most pronounced differences between the primiparous-birth cohort and other cohorts. Among all-births, robust inference minimally impacted the confidence bounds of estimates, compared to the standard inference, i.e., crude estimates (e.g., lupus-severe maternal morbidity association: 4.01, 95% CI 3.54-4.55 vs. 4.01, 95% CI 3.53-4.56 for crude estimate), while adjusting for parity slightly shifted estimates, towards the null for severe maternal morbidity and away from the null for preeclampsia/eclampsia.
CONCLUSIONS: Researchers should consider the alignment between the methods they use, their sampling strategy, and their research question. This could include refining the research question to better match inference possible for available data, considering alternative data sources, and appropriately noting data limitations and resulting bias, as well as the generalizability of findings. If parity is an established effect modifier, stratified results should be presented.

BACKGROUND: Empirical evaluation of inverse probability weighting (IPW) for self-selection bias correction is inaccessible without the full source population. We aimed to: (i) investigate how self-selection biases frequency and association measures and (ii) assess self-selection bias correction using IPW in a cohort with register linkage.
METHODS: The source population included 17 936 individuals invited to the Copenhagen Aging and Midlife Biobank during 2009-11 (ages 49-63 years). Participants counted 7185 (40.1%). Register data were obtained for every invited person from 7 years before invitation to the end of 2020. The association between education and mortality was estimated using Cox regression models among participants, IPW participants and the source population.
RESULTS: Participants had higher socioeconomic position and fewer hospital contacts before baseline than the source population. Frequency measures of participants approached those of the source population after IPW. Compared with primary/lower secondary education, upper secondary, short tertiary, bachelor and master/doctoral were associated with reduced risk of death among participants (adjusted hazard ratio [95% CI]: 0.60 [0.46; 0.77], 0.68 [0.42; 1.11], 0.37 [0.25; 0.54], 0.28 [0.18; 0.46], respectively). IPW changed the estimates marginally (0.59 [0.45; 0.77], 0.57 [0.34; 0.93], 0.34 [0.23; 0.50], 0.24 [0.15; 0.39]) but not only towards those of the source population (0.57 [0.51; 0.64], 0.43 [0.32; 0.60], 0.38 [0.32; 0.47], 0.22 [0.16; 0.29]).
CONCLUSIONS: Frequency measures of study participants may not reflect the source population in the presence of self-selection, but the impact on association measures can be limited. IPW may be useful for (self-)selection bias correction, but the returned results can still reflect residual or other biases and random errors.

UNASSIGNED: Longitudinal studies are key to understanding risk factors for health, well-being, and disease, yet associations may be biased if study invitation and participation are non-random. Religious/spiritual beliefs and behaviours (RSBB) are increasingly recognised as having potentially important relationships with health. However, it is unclear whether RSBB is associated with study participation. We examine whether RSBB is associated with participation in the longitudinal birth cohort ALSPAC (Avon Longitudinal Study of Parents and Children).
UNASSIGNED: Three RSBB factors were used: religious belief (belief in God/a divine power; yes/not sure/no), religious affiliation (Christian/none/other), and religious attendance (frequency of attendance at a place of worship). Participation was measured in three ways: i) total number of questionnaires/clinics completed (linear and ordinal models); ii) completion of the most recent questionnaire (logistic model); and iii) length of participation (survival model). Analyses were repeated for the ALSPAC mothers, their partners, and the study children, and were adjusted for relevant socio-demographic confounders.
UNASSIGNED: Religious attendance was positively associated with participation in all adjusted models in all three cohorts. For example, study mothers who attended a place of worship at least once a month on average completed two more questionnaires (out of a possible 50), had 50% greater odds of having completed the most recent questionnaire, and had 25% reduced risk of drop-out, relative to those who did not attend. In the adjusted analyses, religious belief and attendance were not associated with participation. However, the majority of unadjusted models showed associations between RSBB and participation.
UNASSIGNED: After adjusting for confounders, religious attendance - not religious belief or affiliation - was associated with participation in ALSPAC. These results indicate that use of RSBB variables (and religious attendance in particular) may result in selection bias and spurious associations; these potential biases should be explored and discussed in future studies using these data.

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Web-based testing of cognitive abilities allows for large-scale assessments without geographical constraints. Yet, the extent to which it can reach populations beyond the typical demographic groups recruited for cognitive studies is unclear. This study focused on comparing the characteristics of individuals from a general population study (HUNT4) who chose to participate in a cognitive study (HUNT4-Hjernetrim) with those who did not. Additionally, we investigated participants\' engagement and user experience. We obtained data on socio-demographics, health conditions (both physical and mental), self-reported cognitive or learning difficulties, and lifestyle factors of Hjernetrim participants and non-participants from the HUNT4 database. Hjernetrim involved 13 cognitive tests, administered through the online platform Memoro. We used logistic regressions to assess participation biases and linear regressions to assess participants\' engagement and user experience. Of 65,851 HUNT4 participants invited via regular mail to Hjernetrim, 5634 (9.4%, aged 13-97, 54% women) participated. The best represented in the sample were 50-79-year-olds, women, tertiary educated, living alone, from urban areas, not occupationally active, and reporting memory complaints. Individuals who were aged 80+, had motor or vision impairments, and teenagers with learning disabilities, were underrepresented. Participants were more likely to have mental health problems, have or survived cancer and less likely to have cardiovascular disease. Participants logged on mainly during weekdays, the preferred time of day varied by age. On average, participants used 42 min and completed 78% of the tasks. Using PCs provided the most complete data. In terms of user experiences, 65% were positive while 14% were negative or reported technical difficulties. Overall, the study demonstrated that web-based methodology allowed for a relatively well-represented sample that included groups typically difficult to reach. The presence of somatic and mental diseases had a variable influence on participation. Participants finished most tests and reported positive experiences overall.

UNASSIGNED: To summarize recent literature on selection bias in disparities research addressing either descriptive or causal questions, with examples from dementia research.
UNASSIGNED: Defining a clear estimand, including the target population, is essential to assess whether generalizability bias or collider-stratification bias are threats to inferences. Selection bias in disparities research can result from sampling strategies, differential inclusion pipelines, loss to follow-up, and competing events. If competing events occur, several potentially relevant estimands can be estimated under different assumptions, with different interpretations. The apparent magnitude of a disparity can differ substantially based on the chosen estimand. Both randomized and observational studies may misrepresent health disparities or heterogeneity in treatment effects if they are not based on a known sampling scheme.
UNASSIGNED: Researchers have recently made substantial progress in conceptualization and methods related to selection bias. This progress will improve the relevance of both descriptive and causal health disparities research.

BACKGROUND: Findings from studies assessing Long Covid in children and young people (CYP) need to be assessed in light of their methodological limitations. For example, if non-response and/or attrition over time systematically differ by sub-groups of CYP, findings could be biased and any generalisation limited. The present study aimed to (i) construct survey weights for the Children and young people with Long Covid (CLoCk) study, and (ii) apply them to published CLoCk findings showing the prevalence of shortness of breath and tiredness increased over time from baseline to 12-months post-baseline in both SARS-CoV-2 Positive and Negative CYP.
METHODS: Logistic regression models were fitted to compute the probability of (i) Responding given envisioned to take part, (ii) Responding timely given responded, and (iii) (Re)infection given timely response. Response, timely response and (re)infection weights were generated as the reciprocal of the corresponding probability, with an overall \'envisioned population\' survey weight derived as the product of these weights. Survey weights were trimmed, and an interactive tool developed to re-calibrate target population survey weights to the general population using data from the 2021 UK Census.
RESULTS: Flexible survey weights for the CLoCk study were successfully developed. In the illustrative example, re-weighted results (when accounting for selection in response, attrition, and (re)infection) were consistent with published findings.
CONCLUSIONS: Flexible survey weights to address potential bias and selection issues were created for and used in the CLoCk study. Previously reported prospective findings from CLoCk are generalisable to the wider population of CYP in England. This study highlights the importance of considering selection into a sample and attrition over time when considering generalisability of findings.

UNASSIGNED: The Korea HIV/AIDS Cohort Study has been conducted prospectively for 18 years. However, it faces limitations in representing the entire population of patients with HIV in Korea. To address these limitations and validate the study design, we analyzed characteristics across several HIV datasets.
UNASSIGNED: We compared epidemiological and clinical characteristics from 3 datasets: the Korea HIV/AIDS Cohort Study (dataset 1, n=1,562), retrospective cohort data (dataset 2, n=2,665), and the national HIV reporting system of the Korea Disease Control and Prevention Agency (KDCA) (dataset 3, n=17,403).
UNASSIGNED: The demographic characteristics of age, sex, and age at HIV diagnosis did not differ significantly across datasets. However, dataset 3 contained a higher percentage of patients diagnosed after 2008 (69.5%) than the other datasets. Regarding transmission routes, same-sex contact accounted for a greater proportion of dataset 1 (59.8%) compared to datasets 2 (20.9%) and 3 (32.6%). The percentage of patients with CD4 T-cell counts below 200/mm3 at HIV diagnosis was higher in datasets 1 (39.4%) and 2 (33.3%) compared to dataset 3 (16.3%). Initial HIV viral load measurements were not obtained for dataset 3.
UNASSIGNED: The Korea HIV/AIDS Cohort Study demonstrated representativeness regarding the demographic characteristics of Korean patients. Of the sources, dataset 1 contained the most data on transmission routes. While the KDCA data encompassed all HIV patients, it lacked detailed clinical information. To improve the representativeness of the Korea HIV/AIDS Cohort Study, we propose expanding and revising the cohort design and enrolling more patients who have been recently diagnosed.

Recently, there has been considerable progress in developing new technologies and equipment for the medical field, including minimally invasive surgeries. Evaluating the effectiveness of these treatments requires study designs like randomized controlled trials. However, due to the nature of certain treatments, randomization is not always feasible, leading to the use of observational studies. The effect size estimated from observational studies is subject to selection bias caused by confounders. One method to reduce this bias is propensity scoring. This study aimed to introduce a propensity score matching process between two groups using a practical example with R. Additionally, Rex, an Excel add-in graphical user interface statistical program, is provided for researchers unfamiliar with R programming. Further techniques, such as matching with three or more groups, propensity score weighting and stratification, and imputation of missing values, are summarized to offer approaches for more complex studies not covered in this tutorial.

OBJECTIVE: A test method is proposed for identifying potential selection bias risk in single prospective controlled clinical therapy trials that can be applied by trial reviewers.
METHODS: The method is described in detail and was tested on eight randomised controlled trials (RCTs) with reported negative Berger-Exner test results as negative and on eight prospective, controlled cohort studies as positive controls. All 16 studies were identified by systematic literature search.
RESULTS: The test method yielded negative results for all RCTs and positive results for six out of the eight cohort studies.
CONCLUSIONS: All test results remained within the expected limits for both study types, suggesting a reasonably high accuracy for correctly identifying selection bias risk. However, the method does not provide the possibility to establish whether such bias risk has actually altered trial outcomes. Instead, a positive test result may provide an empirical basis for rating a trial as of high selection bias risk during trial appraisal.