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Potential Selection Bias (potential + selection_bias)
Selected AbstractsPotential selection bias in hospital-based studies of perinatal outcomePAEDIATRIC & PERINATAL EPIDEMIOLOGY, Issue 2 2004Article first published online: 1 MAR 200 No abstract is available for this article. [source] Health insurance and treatment seeking behaviour: evidence from a low-income countryHEALTH ECONOMICS, Issue 9 2004Matthew Jowett Abstract This paper analyses the effect of being insured under the voluntary component of Vietnamese Health Insurance, on patterns of treatment seeking behaviour. A multinomial logit model is estimated using household survey data from three provinces in Vietnam. Decisions regarding both the type of provider sought and type of care received are analysed. Insurance status is treated as both exogenous and endogenous to account for potential selection bias. The results indicate that, overall, insured patients are more likely to use outpatient facilities, and public providers, an effect that is particularly strong at lower income levels. Copyright © 2004 John Wiley & Sons, Ltd. [source] The Impact of Private Insurance Coverage on Veterans' Use of VA Care: Insurance and Selection EffectsHEALTH SERVICES RESEARCH, Issue 1p1 2008Yujing Shen Objective. To examine private insurance coverage and its impact on use of Veterans Health Administration (VA) care among VA enrollees without Medicare coverage. Data Sources. The 1999 National Health Survey of Veteran Enrollees merged with VA administrative data, with other information drawn from American Hospital Association data and the Area Resource File. Study Design. We modeled VA enrollees' decision of having private insurance coverage and its impact on use of VA care controlling for sociodemographic information, patients' health status, VA priority status and access to VA and non-VA alternatives. We estimated the true impact of insurance on the use of VA care by teasing out potential selection bias. Bias came from two sources: a security selection effect (sicker enrollees purchase private insurance for extra security and use more VA and non-VA care) and a preference selection effect (VA enrollees who prefer non-VA care may purchase private insurance and use less VA care). Principal Findings. VA enrollees with private insurance coverage were less likely to use VA care. Security selection dominated preference selection and naïve models that did not control for selection effects consistently underestimated the insurance effect. Conclusions. Our results indicate that prior research, which has not controlled for insurance selection effects, may have underestimated the potential impact of any private insurance policy change, which may in turn affect VA enrollees' private insurance coverage and consequently their use of VA care. From the decline in private insurance coverage from 1999 to 2002, we projected an increase of 29,400 patients and 158 million dollars for VA health care services. [source] Factors Affecting Plan Choice and Unmet Need among Supplemental Security Income Eligible Children with DisabilitiesHEALTH SERVICES RESEARCH, Issue 5p1 2005Jean M. Mitchell Objective. To evaluate factors affecting plan choice (partially capitated managed care [MC] option versus the fee-for-service [FFS] system) and unmet needs for health care services among children who qualified for supplemental security income (SSI) because of a disability. Data Sources. We conducted telephone interviews during the summer and fall of 2002 with a random sample of close to 1,088 caregivers of SSI eligible children who resided in the District of Columbia. Research Design. We employed a two-step procedure where we first estimated plan choice and then constructed a selectivity correction to control for the potential selection bias associated with plan choice. We included the selectivity correction, the dummy variable indicating plan choice and other exogenous regressors in the second stage equations predicting unmet need. The dependent variables in the second stage equations include: (1) having an unmet need for any service or equipment; (2) having an unmet need for physician or hospital services; (3) having an unmet need for medical equipment; (4) having an unmet need for prescription drugs; (5) having an unmet need for dental care. Principal Findings. More disabled children (those with birth defects, chronic conditions, and/or more limitations in activities of daily living) were more likely to enroll in FFS. Children of caregivers with some college education were more likely to opt for FFS, whereas children from higher income households were more prone to enroll in the partially capitated MC plan. Children in FFS were 9.9 percentage points more likely than children enrolled in partially capitated MC to experience an unmet need for any type of health care services (p<.01), while FFS children were 4.5 percentage points more likely than partially capitated MC enrollees to incur a medical equipment unmet need (p<.05). FFS children were also more likely than partially capitated MC enrollees to experience unmet needs for prescription drugs and dental care, however these differences were only marginally significant. Conclusions. We speculate that the case management services available under the MC option, low Medicaid FFS reimbursements and provider availability account for some of the differences in unmet need that exist between partially capitated MC and FFS enrollees. [source] Occupational exposure to crystalline silica and risk of systemic lupus erythematosus: A population-based, case,control study in the Southeastern United StatesARTHRITIS & RHEUMATISM, Issue 7 2002Christine G. Parks Objective Crystalline silica may act as an immune adjuvant to increase inflammation and antibody production, and findings of occupational cohort studies suggest that silica exposure may be a risk factor for systemic lupus erythematosus (SLE). We undertook this population-based study to examine the association between occupational silica exposure and SLE in the southeastern US. Methods SLE patients (n = 265; diagnosed between January 1, 1995 and July 31, 1999) were recruited from 4 university rheumatology practices and 30 community-based rheumatologists in 60 contiguous counties. Controls (n = 355), frequency-matched to patients by age, sex, and state of residence, were randomly selected from driver's license registries. The mean age of the patients at diagnosis was 39 years; 91% were women and 60% were African American. Detailed occupational and farming histories were collected by in-person interviews. Silica exposure was determined through blinded assessment of job histories by 3 industrial hygienists, and potential medium- or high-level exposures were confirmed through followup telephone interviews. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were estimated by logistic regression. Results More patients (19%) than controls (8%) had a history of medium- or high-level silica exposure from farming or trades. We observed an association between silica and SLE (medium exposure OR 2.1 [95% CI 1.1,4.0], high exposure OR 4.6 [95% CI 1.4,15.4]) that was seen in separate analyses by sex, race, and at different levels of education. Conclusion These results suggest that crystalline silica exposure may promote the development of SLE in some individuals. Additional research is recommended in other populations, using study designs that minimize potential selection bias and maximize the quality of exposure assessment. [source] Sensitivity Analyses Comparing Outcomes Only Existing in a Subset Selected Post-Randomization, Conditional on Covariates, with Application to HIV Vaccine TrialsBIOMETRICS, Issue 2 2006Bryan E. Shepherd Summary In many experiments, researchers would like to compare between treatments and outcome that only exists in a subset of participants selected after randomization. For example, in preventive HIV vaccine efficacy trials it is of interest to determine whether randomization to vaccine causes lower HIV viral load, a quantity that only exists in participants who acquire HIV. To make a causal comparison and account for potential selection bias we propose a sensitivity analysis following the principal stratification framework set forth by Frangakis and Rubin (2002, Biometrics58, 21,29). Our goal is to assess the average causal effect of treatment assignment on viral load at a given baseline covariate level in the always infected principal stratum (those who would have been infected whether they had been assigned to vaccine or placebo). We assume stable unit treatment values (SUTVA), randomization, and that subjects randomized to the vaccine arm who became infected would also have become infected if randomized to the placebo arm (monotonicity). It is not known which of those subjects infected in the placebo arm are in the always infected principal stratum, but this can be modeled conditional on covariates, the observed viral load, and a specified sensitivity parameter. Under parametric regression models for viral load, we obtain maximum likelihood estimates of the average causal effect conditional on covariates and the sensitivity parameter. We apply our methods to the world's first phase III HIV vaccine trial. [source] Matched Case,Control Data Analysis with Selection BiasBIOMETRICS, Issue 4 2001I-Feng Lin Summary. Case-control studies offer a rapid and efficient way to evaluate hypotheses. On the other hand, proper selection of the controls is challenging, and the potential for selection bias is a major weakness. Valid inferences about parameters of interest cannot be drawn if selection bias exists. Furthermore, the selection bias is difficult to evaluate. Even in situations where selection bias can be estimated, few methods are available. In the matched case-control Northern Manhattan Stroke Study (NOMASS), stroke-free controls are sampled in two stages. First, a telephone survey ascertains demographic and exposure status from a large random sample. Then, in an in-person interview, detailed information is collected for the selected controls to be used in a matched case,control study. The telephone survey data provides information about the selection probability and the potential selection bias. In this article, we propose bias-corrected estimators in a case-control study using a joint estimating equation approach. The proposed bias-corrected estimate and its standard error can be easily obtained by standard statistical software. [source] | ||||||||||