The tenure gap in electoral participation: instrumental motivation or selection bias? Comparing homeowners and tenants across four housing regimes.
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ABSTRACT:
SUBMITTER: Andre S
PROVIDER: S-EPMC5476201 | biostudies-other | 2017 Jun
REPOSITORIES: biostudies-other
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Project description:Instrumental variables (IV) are used to draw causal conclusions about the effect of exposure E on outcome Y in the presence of unmeasured confounders. IV assumptions have been well described: (1) IV affects E; (2) IV affects Y only through E; (3) IV shares no common cause with Y. Even when these assumptions are met, biased effect estimates can result if selection bias allows a noncausal path from E to Y. We demonstrate the presence of bias in IV analyses on a sample from a simulated dataset, where selection into the sample was a collider on a noncausal path from E to Y. By applying inverse probability of selection weights, we were able to eliminate the selection bias. IV approaches may protect against unmeasured confounding but are not immune from selection bias. Inverse probability of selection weights used with IV approaches can minimize bias.
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Project description:Participants in epidemiologic and genetic studies are rarely true random samples of the populations they are intended to represent, and both known and unknown factors can influence participation in a study (known as selection into a study). The circumstances in which selection causes bias in an instrumental variable (IV) analysis are not widely understood by practitioners of IV analyses. We use directed acyclic graphs (DAGs) to depict assumptions about the selection mechanism (factors affecting selection) and show how DAGs can be used to determine when a two-stage least squares IV analysis is biased by different selection mechanisms. Through simulations, we show that selection can result in a biased IV estimate with substantial confidence interval (CI) undercoverage, and the level of bias can differ between instrument strengths, a linear and nonlinear exposure-instrument association, and a causal and noncausal exposure effect. We present an application from the UK Biobank study, which is known to be a selected sample of the general population. Of interest was the causal effect of staying in school at least 1 extra year on the decision to smoke. Based on 22,138 participants, the two-stage least squares exposure estimates were very different between the IV analysis ignoring selection and the IV analysis which adjusted for selection (e.g., risk differences, 1.8% [95% CI, -1.5%, 5.0%] and -4.5% [95% CI, -6.6%, -2.4%], respectively). We conclude that selection bias can have a major effect on an IV analysis, and further research is needed on how to conduct sensitivity analyses when selection depends on unmeasured data.
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Project description:We can be motivated when reward depends on performance, or merely by the prospect of a guaranteed reward. Performance-dependent (contingent) reward is instrumental, relying on an internal action-outcome model, whereas motivation by guaranteed reward may minimise opportunity cost in reward-rich environments. Competing theories propose that each type of motivation should be dependent on dopaminergic activity. We contrasted these two types of motivation with a rewarded saccade task, in patients with Parkinson's disease (PD). When PD patients were ON dopamine, they had greater response vigour (peak saccadic velocity residuals) for contingent rewards, whereas when PD patients were OFF medication, they had greater vigour for guaranteed rewards. These results support the view that reward expectation and contingency drive distinct motivational processes, and can be dissociated by manipulating dopaminergic activity. We posit that dopamine promotes goal-directed motivation, but dampens reward-driven vigour, contradictory to the prediction that increased tonic dopamine amplifies reward expectation.
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Project description:There is a fast-growing literature on estimating optimal treatment regimes based on randomized trials or observational studies under a key identifying condition of no unmeasured confounding. Because confounding by unmeasured factors cannot generally be ruled out with certainty in observational studies or randomized trials subject to noncompliance, we propose a general instrumental variable approach to learning optimal treatment regimes under endogeneity. Specifically, we establish identification of both value function E[YD(L)] for a given regime D and optimal regimes argmaxDE[YD(L)] with the aid of a binary instrumental variable, when no unmeasured confounding fails to hold. We also construct novel multiply robust classification-based estimators. Furthermore, we propose to identify and estimate optimal treatment regimes among those who would comply to the assigned treatment under a monotonicity assumption. In this latter case, we establish the somewhat surprising result that complier optimal regimes can be consistently estimated without directly collecting compliance information and therefore without the complier average treatment effect itself being identified. Our approach is illustrated via extensive simulation studies and a data application on the effect of child rearing on labor participation.
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Project description:BackgroundNon-participation in aetiologic studies of pubertal timing is frequent. However, little effort has been given to explore the risk and potential impact of selection bias in studies of pubertal timing.ObjectiveWe aimed to explore the risk of selection bias due to non-participation in a newly established puberty cohort.MethodsWe evaluated whether three maternal exposures chosen a priori (pre-pregnancy obesity, smoking, and alcohol drinking during pregnancy) were associated with participation, whether pubertal timing was associated with participation, and whether selection bias influenced the associations between these exposures and pubertal timing. In total, 22 439 children from the Danish National Birth Cohort born 2000-2003 were invited to the Puberty Cohort and 15 819 (70%) participated. Exposures were self-reported during pregnancy. Pubertal timing was measured using a previously validated marker, "the height difference in standard deviations" (HD:SDS), which is the difference between pubertal height and adult height, both in standard deviations. For this study, pubertal height at around 13 years in sons and around 11 years in daughters was obtained from an external database, and adult height was predicted based on parental height reported by mothers.ResultsParticipation was associated with most exposures but not with pubertal timing, measured by HD:SDS. The associations between exposures and HD:SDS were comparable for participants only and all invited for participation.ConclusionIn conclusion, the risk of selection bias in aetiologic studies on pubertal timing in the Puberty Cohort appears minimal.
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Project description:ObjectivesLittle is known about influences of sample selection on estimation in propensity score matching. The purpose of the study was to assess potential selection bias using one-to-one greedy matching versus optimal full matching as part of an evaluation of supportive housing in New York City (NYC).Study design and settingsData came from administrative data for 2 groups of applicants who were eligible for an NYC supportive housing program in 2007-09, including chronically homeless adults with a substance use disorder and young adults aging out of foster care. We evaluated the 2 matching methods in their ability to balance covariates and represent the original population, and in how those methods affected outcomes related to Medicaid expenditures.ResultsIn the population with a substance use disorder, only optimal full matching performed well in balancing covariates, whereas both methods created representative populations. In the young adult population, both methods balanced covariates effectively, but only optimal full matching created representative populations. In the young adult population, the impact of the program on Medicaid expenditures was attenuated when one-to-one greedy matching was used, compared with optimal full matching.ConclusionGiven covariate balancing with both methods, attenuated program impacts in the young adult population indicated that one-to-one greedy matching introduced selection bias.
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Project description:BACKGROUND:Participation in epidemiologic studies is steadily declining, which may result in selection bias. It is therefore an ongoing challenge to clarify the determinants of participation to judge possible selection effects and to derive measures to minimise that bias. We evaluated the potential for selection bias in a recent population-based cohort study with low baseline participation and investigated reasons for nonparticipation. METHODS:LIFE-Adult is a cohort study in the general population of the city of Leipzig (Germany) designed to gain insights into the distribution and development of civilisation diseases. Nine thousand one hundred forty-five participants aged 40-79 years were randomly sampled in 2011-2014. We compared LIFE-Adult participants with both the Leipzig population and nonparticipants using official statistics and short questionnaire data. We applied descriptive statistics and logistic regression analysis to evaluate the determinants of study participation. RESULTS:Thirty-one percent of the invited persons participated in the LIFE-Adult baseline examination. Study participants were less often elderly women and more often married, highly educated, employed, and current nonsmokers compared to both the Leipzig population and nonparticipants. They further reported better health than nonparticipants. The observed differences were considerable in education and health variables. They were generally stronger in men than in women. For example, in male study participants aged 50-69, the frequency of high education was 1.5 times that of the general population, and the frequency of myocardial infarction was half that of nonparticipants. Lack of time and interest, as well as health problems were the main reasons for nonparticipation. CONCLUSIONS:Our investigation suggests that the low baseline participation in LIFE-Adult is associated with the typical selection of study participants with higher social status and healthier lifestyle, and additionally less disease. Notably, education and health status seem to be crucial selection factors. Consequently, frequencies of major health conditions in the general population will likely be underestimated. A differential selection related to sex might also distort effect estimates. The extent of the assessment, the interest in the research topic, and health problems of potential participants should in future be considered in LIFE-Adult and in similar studies to raise participation and to minimise selection bias.
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Project description:BackgroundNatural history data are essential for trial design in Duchenne (DMD) and Becker muscular dystrophy (BMD), but recruitment for observational studies can be challenging.ObjectiveWe reviewed reasons why patients or caregivers declined participation, and compared characteristics of participants and non-participants to assess possible selection bias in four observational studies, three on DMD and one on BMD.MethodsThree pediatric DMD studies focused on cross-sectional cognitive function and brain MRI (DMDbrain, n = 35 and DMDperfusion, n = 12), and on longitudinal upper extremity function and muscle MRI (DMDarm, n = 22). One adult BMD study assessed longitudinal functioning (n = 36). Considerations for non-participation were retrospectively reviewed from screening logs. Age, travel-time, DMD gene mutations and age at loss of ambulation (DMDarm and BMD study only), of participants and non-participants were derived from the Dutch Dystrophinopathy Database and compared using nonparametric tests (p < 0.05).ResultsThe perceived burden of the protocol (38.2%), use of MRI (30.4%), and travel-time to the study site (19.1%) were the most frequently reported considerations for non-participation. Only few patients reported lack of personal gain (0.0- 5.9%). Overall, participating patients were representative for the studied sub-populations, except for a younger age of DMDarm study participants and a complete lack of participants with a mutation beyond exon 63.ConclusionOptimizing patient involvement in protocol design, improving MRI experiences, and integrating research into clinics are important factors to decrease burden and facilitate participation. Nationwide registries are essential to compare participants and non-participants and ensure representative observational research. Specific effort is needed to include patients with distal mutations in cognitive studies.
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Project description:Drawing causal inference with observational studies is the central pillar of many disciplines. One sufficient condition for identifying the causal effect is that the treatment-outcome relationship is unconfounded conditional on the observed covariates. It is often believed that the more covariates we condition on, the more plausible this unconfoundedness assumption is. This belief has had a huge impact on practical causal inference, suggesting that we should adjust for all pretreatment covariates. However, when there is unmeasured confounding between the treatment and outcome, estimators adjusting for some pretreatment covariate might have greater bias than estimators without adjusting for this covariate. This kind of covariate is called a bias amplifier, and includes instrumental variables that are independent of the confounder, and affect the outcome only through the treatment. Previously, theoretical results for this phenomenon have been established only for linear models. We fill in this gap in the literature by providing a general theory, showing that this phenomenon happens under a wide class of models satisfying certain monotonicity assumptions. We further show that when the treatment follows an additive or multiplicative model conditional on the instrumental variable and the confounder, these monotonicity assumptions can be interpreted as the signs of the arrows of the causal diagrams.
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