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.

Observational studies of cancer screening are subject to bias associated with the self-selection of screening participants for whom the underlying probability of cancer death may be different from those who do not participate. Dibden et al. reviewed data on mortality reduction from 27 observational studies of mammography screening expressed in terms of relative risk for women who were screened versus not screened. Results were given, both unadjusted and after application of a correction for self-selection. The correction was based on a constant (1.17)-the ratio of risks of death in screening non-attenders versus those not invited, derived from a Swedish study. For some of the studies this correction had a large effect in diminishing the measured mortality benefit associated with screening. In particular, application to The Pan-Canadian Study of Mammography Screening, a study whose authors had previously tested for and found no evidence of self-selection bias, caused the estimated benefit to decrease from 40% to 10%. The appropriateness of applying a correction based on a constant to a population whose healthcare environment and screening participation rates are very different from those from which it was derived is questionable.

Declining total fertility rates pose a severe challenge to the economy, society, culture, and politics of any region. Low fertility rates among China\'s rural floating population with strong fertility are aggravating these challenges. Previous research has confirmed the relationships between health insurance and fertility intention. However, it is still unclear whether the existing association is favorable or not. Moreover, the majority of existing studies in China employ data from either urban or rural populations, whereas evidence from rural floating populations remains scarce. Based on the \"China Migrants Dynamic Survey (CMDS)\" in 2016, the current study used the logistic regression model to explore the impact of health insurance policy on the fertility intention of the rural floating population in China. Propensity Score Matching (PSM) was used to address potential selection bias. Three important findings were observed: Firstly, participating in the Basic Medical Insurance System (BMISUR) significantly improved rural floating populations\' fertility intentions in China. Secondly, the association between age and the fertility intention of the floating population was \"inverted u-shaped\" with the highest fertility intention among those aged 25 to 34. There was also a positive correlation between personal income and fertility intention, and it was found between local housing purchase, formal employment, the co-residents scale, and the fertility intention in the rural floating population in China. Interprovincial mobility was positively associated with the fertility intention among rural migrants. Thirdly, the impact of health insurance policies on the fertility intention of the rural migrant population varies by gender, age, and inflow areas. The aforementioned findings can guide the Chinese government in its efforts to improve the fertility intention of the rural floating population, reform the social security system with a focus on \"targets\", and implement differentiated welfare policies aimed at promoting the equalization of basic public services, thereby contributing to China\'s population structure and long-term development.

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Many researchers have identified the issue of self-selection bias hindering the ability to detect nutrient effects in healthy populations. However, it appears that no effort has been made to mitigate this potential design flaw. By recruiting individuals on the basis of pre-trial dietary intake, the Memory and Attention Supplementation Trial aimed to capture a cohort of participants with a wide variety of dietary intake, thus increasing the likelihood of a diverse range of nutrient status. This perspective specifically examines the profile of these trial volunteers and in doing so, we present the first empirical evidence of self-selection bias when recruiting healthy volunteers for a randomized controlled trial of a nutrient-based supplement. These findings support the anecdotal proposal that traditional recruitment methods inherently attract trial volunteers who are vastly unrepresentative of the population and threatens the generalizability of this field of research. Alternative approaches to recruitment, including a-priori screening for baseline diet quality and nutrient status, are discussed as essential design recommendations to ensure accurate interpretation of nutrient effects within the context of baseline participant characteristics.

Research conducted in controlled laboratory settings plays an important role in understanding human eating behaviour. However, participant characteristics may affect motivation to participate in laboratory eating behaviour research and result in self-selection biases. First-year psychology students (n = 207, 90.8% female, m age = 18.8, m BMI = 21.9) were led to believe that they were choosing between enrolling in one of two laboratory-based studies: an \'advertising\' study, or an \'advertising and food\' study; the latter specified that participants would consume food and answer questions about their eating behaviour. Participants subsequently reported demographic information and completed a series of questionnaires assessing psychological characteristics hypothesised to predict self-selection in laboratory eating studies. The majority of participants preferred to participate in a study unrelated to eating over an eating-related study (70.5%, χ2 (1) = 34.90, p < .001). Participants choosing the eating-related study over the non-eating study had higher self-reported external eating tendencies and positive preoccupations with food, lower negative preoccupations with food and social appearance anxiety. Participants choosing the eating related study were also more likely to be male than female, although the number of males in the study was small (n = 19) and this finding should therefore be interpreted with caution. The present study provides some evidence that in university based samples of participants there are likely self-selection biases in laboratory-based eating behaviour studies and researchers may benefit from considering the potential influence of self-selection biases when conducting eating behaviour research.

Observational treatment studies provide a valuable alternative to RCTs but are often criticized due to potential self-selection biases. Studies comparing those who do and do not participate in research on eating disorder treatment are scarce, but necessary to evaluate the impact of self-selection bias on outcomes. All consecutive underweight adult first admissions (N = 392) to an integrated inpatient (IP)-partial hospital (PH) behavioral specialty program were invited to participate in a longitudinal study of eating disorder treatment. Demographic and hospital course data were collected on participants (n = 234) and non-participants (n = 158). Participants and non-participants had similar BMI at admission, lengths of stay, and weight gain rates. Participants were less likely than non-participants to end treatment prematurely from IP and were discharged at a higher BMI; the effect size was small. Few differences in hospital course were observed between participants and non-participants. Although participants were more likely to transition to PH and were discharged at a higher BMI, completion of step-down to PH within this integrated IP-PH program rather than research participation status at admission was a better indicator of discharge BMI, which remains the strongest predictor of long-term weight-maintenance in eating disorders.

The objective of this study was to evaluate if mammography screening attendance is associated with a reduction in late-stage breast cancer incidence.
The cohort included over 400,000 Italian women who were first invited to participate in regional screening programmes during the 1990s and were followed for breast cancer incidence for 13 years. We obtained individual data on their exposure to screening and correlated this with total and stage-specific breast cancer incidence. Socio-economic status and pre-screening incidence data were used to assess the presence of self-selection bias.
Overall, screening attendance was associated with a 10% excess risk of in situ and invasive breast cancer (IRR = 1.10; 95% confidence interval (CI): 1.06-1.14), which dropped to 5% for invasive cancers only (IRR = 1.05; 95% CI: 1.01-1.09). There were significant reductions among attenders for specific cancer stages; we observed a 39% reduction for T2 or larger (IRR = 0.61; 95% CI: 0.57-0.66), 19% for node positives (IRR = 0.81; 95% CI: 0.76-0.86) and 28% for stage II and higher (IRR = 0.72; 95% CI: 0.68-0.76). Our data suggest that the presence of self-selection bias is limited and, overall, invited women experienced a 17% reduction of advanced cancers compared with pre-screening rates.
Comparing attenders\' and non-attenders\' stage-specific breast cancer incidence, we have estimated that screening attendance is associated with a reduction of nearly 30% for stages II+.

Understanding the relationships between influential factors and incident clearance time is crucial to make effective countermeasures for incident management agencies. Although there have been a certain number of achievements on incident clearance time modeling, limited effort is made to investigate the relative role of incident response time and its self-selection in influencing the clearance time. To fill this gap, this study uses the endogenous switching model to explore the influential factors in incident clearance time, and aims to disentangle causation from self-selection bias caused by response process. Under the joint two-stage model framework, the binary probit model and switching regression model are formulated for both incident response time and clearance time, respectively. Based on the freeway incident data collected in Washington State, full information maximum likelihood (FIML) method is utilized to estimate the endogenous switching model parameters. Significant factors affecting incident response time and clearance time can be identified, including incident, temporal, geographical, environmental, traffic and operational attributes. The estimate results reveal the influential effects of incident, temporal, geographical, environmental, traffic and operational factors on incident response time and clearance time. In addition, the causality of incident response time itself and its self-selection correction on incident clearance time are found to be indispensable. These findings suggest that the causal effect of response time on incident clearance time will be overestimated if the self-selection bias is not considered.

Estimates of the effect of employment on women\'s risk of partner violence in cross-sectional studies are subject to potential \"self-selection bias.\" Women\'s personal choice of whether to pursue employment or not may create fundamental differences between the group of women who are employed and those who are not employed that standard regression methods cannot account for even after adjusting for confounding. The aim of this study is to demonstrate the utility of propensity score matching (PSM), a technique used widely in econometrics, to address this bias in cross-sectional studies. We use PSM to estimate an unbiased effect-size of women\'s employment on their risk of experiencing partner violence in urban and rural Tanzania using data from the 2010 Tanzania Demographic and Health Survey (DHS). Three different measures of women\'s employment were analyzed: whether they had engaged in any productive work outside of the home in the past year, whether they received payment in cash for this productive work, and whether their employment was stable. Women who worked outside of the home were significantly different from those who did not. In both urban and rural Tanzania, women\'s risk of violence appears higher among women who worked in the past year than among those who did not, even after using PSM to account for underlying differences in these two groups of women. Being paid in cash reversed this effect in rural areas whereas stability of employment reduced this risk in urban centers. The estimated size of effect varied by type of matching estimator, but the direction of the association remained largely consistent. This study\'s findings suggest substantial self-selection into employment. PSM methods, by compensating for this bias, appear to be a useful tool for estimating the relationship between women\'s employment and partner violence in cross-sectional studies.