Central nervous system involvement in multiple myeloma.
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Adult↗
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Results for “Selection Bias”
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Biased selection within the social health insurance market in Colombia.
Reducing the impact of insurance market failures with regulations such as community-rated premiums, standardized benefit packages and open enrolment, yield limited effect because they create room for selection bias. The Colombian social health insurance system started a market approach in 1993 expecting to improve performance of preexisting monopolistic insurance funds by exposing them to competition by new entrants. This paper tests the hypothesis that market failures would lead to biased selection favoring new entrants. Two household surveys are analyzed using Self-Reported Health Status and the presence of chronic conditions as prospective indicators of individual risk. Biased selection is found to take place, leading to adverse selection among incumbents, and favorable selection among new entrants. This pattern is absent in 1997 but is evident in 2003. Given that the two incumbents analyzed are public organizations, the fiscal implications of the findings in terms of government bailouts, are analyzed.
Adolescent↗
Biased selection under an experimental enrollment and marketing Medicare HMO broker.
All studies conducted to date suggest that nearly all Medicare HMOs have experienced favorable risk selection in their Medicare HMO enrollments. While there is little definitive empiric knowledge about the extent to which Medicare HMOs can and do encourage favorable selection through marketing and enrollment activities, it has been speculated that centralizing all such functions through an independent broker could reduce enrollment selection bias. In 1985, the Health Care Financing Administration initiated a three-year demonstration of a HMO broker model in Portland, Oregon, known as Health Choice, Incorporated (HCI). This study reports empiric findings that provide no evidence to support claims of the efficacy of an enrollment brokerage function in reducing Medicare HMO enrollment selection bias.
Contract Services↗
Consumer information and biased selection in the demand for coverage supplementing Medicare.
This study examines how the relationship between health insurance knowledge and the health status of health insurance consumers influences their decisions to purchase insurance coverage. Data from the federal Medicare health insurance program for the elderly in the United States are used. The basic Medicare program provides a limited amount of coverage for health care services obtained from any provider in the private fee-for-service (FFS) market. Beneficiaries of this program may choose to supplement the basic coverage which they receive by two mechanisms: either they may purchase private insurance designed to fill some of the gaps left by the federal program ('Medigap' policies), thereby remaining in the FFS market and preserving their choice of provider, or they may enroll in health maintenance organizations (HMOs), thereby leaving the FFS market and agreeing to use only those providers affiliated with the HMO, and in return receiving broader coverage at little additional out-of-pocket cost. The study was made possible by a unique data set which combines measures of beneficiary knowledge of Medicare coverage with measures of perceived health status, socio-economic characteristics, and insurance coverage choices for a sample of Medicare beneficiaries who participated in an educational workshop about their insurance coverage options. These data were used to estimate a multinomial logistic model of the determinants of insurance choices, where the options included the two listed above and a basic Medicare option. The study explicitly recognizes the interaction between insurance information and health status in health plan choice. These results show that knowledge of coverage does have a differential impact on the decision to purchase health insurance depending on health status. With a high level of knowledge, sicker beneficiaries are less likely to have basic Medicare alone, compared with HMOs or Medigap policies, while healthier beneficiaries are less likely to be enrolled in HMOs, compared with Medigap policies. This finding has important implications for the use of health status measures to adjust capitated payment formulas when knowledgable consumers have the option to enroll in HMOs or remain in the FFS environment. In the absence of health status adjusters for the HMO capitation payments, high levels of coverage knowledge may exacerbate inherent selection bias among these coverage options by healthier and sicker consumers of health insurance.
Aged↗
Analysis of a molecular genetic neuro-oncology study with partially biased selection.
Oligodendrogliomas are a common variant of malignant brain tumors, and are unique for their relative sensitivity to chemotherapy and better prognosis. For these reasons, the identification of an objective oligodendroglial marker has been a long sought-after goal in the field of neuro-oncology. To this end, 75 patients who received chemotherapy at the London Regional Cancer Centre between 1984 and 1999 were studied (Ino et al., Clinical Cancer Research, 7, 839-845, 2001). Of these 75 patients, 50 were initially treated with chemotherapy (the current practice) and comprise a population-based sample. The remaining 25 patients were initially treated with radiation and were included in the study only because their tumor recurred, at which time they received chemotherapy. Because this group of 25 patients included neither those radiation patients whose tumors never recurred nor those radiation patients whose tumors recurred but were not treated with chemotherapy, issues of selection bias were of concern. For this reason, the initial analysis of these data included only the 50 population-based patients. This was unsatisfying given the rarity of this disease and of genetic information on this disease and led us to question whether we could undertake an analysis that includes all of the patients. Here we examine approaches for utilizing the entire study population, as well as the assumptions required for doing so. We illustrate that there are both costs and benefits to using the 25 selected patients.
Adult↗
Biased selection under the senior health plan prior use capitation formula.
A widely acknowledged shortcoming of the current AAPCC capitation formula for Medicare HMOs is its inability to adjust capitation levels for differences in health status among enrolled groups. Prior use models have been proposed as one alternative to the current AAPCC risk classes. From 1984 through 1987, Senior Health Plan (SHP) participated in a HCFA-sponsored demonstration project in which capitation payments were determined by a prior use formula incorporating information on inpatient hospital days and Part B deductibles. This paper contains findings about selection bias in SHP enrollment from analyses of preenrollment reimbursements and postenrollment mortality rates of SHP enrollees compared with Medicare FFS beneficiaries and enrollees of other Medicare HMOs in the Minneapolis-St. Paul area.
Capitation Fee↗
Misclassification and selection bias in case-control studies using an automated database.
Automated databases are increasingly being used for pharmacoepidemiological research and fieldwork is often carried out to supplement and validate information held within them. In MEMO's case-control studies, patients are identified using computerized ICD9 diagnosis codes, the original medical records are retrieved and checked, and only patients fulfilling case inclusion criteria are used. The 20-30% of patients for whom medical records cannot be found are usually excluded. The aim is to eliminate misclassification bias. However, selection bias may be introduced if availability of medical records is associated with exposure. This investigation was therefore carried out to assess the relative importance of misclassification and selection bias. Data from four previous case-control studies, investigating the associations between NSAIDs and hospitalization for colitis, acute renal failure, appendicitis and colorectal cancer, were used. To assess misclassification, odds ratios (with 95% CI) for recent exposure to NSAIDs were compared in repeated studies that used all patients identified by ICD9 codes, with studies using validated cases only. Selection bias was assessed by comparing results in studies using patients for whom records could and could not be found. Results were plotted and the graphs indicated that misclassification bias was relatively unimportant, but that selection bias could be introduced into a study in this way.
Journal Article↗
A structural approach to selection bias.
The term "selection bias" encompasses various biases in epidemiology. We describe examples of selection bias in case-control studies (eg, inappropriate selection of controls) and cohort studies (eg, informative censoring). We argue that the causal structure underlying the bias in each example is essentially the same: conditioning on a common effect of 2 variables, one of which is either exposure or a cause of exposure and the other is either the outcome or a cause of the outcome. This structure is shared by other biases (eg, adjustment for variables affected by prior exposure). A structural classification of bias distinguishes between biases resulting from conditioning on common effects ("selection bias") and those resulting from the existence of common causes of exposure and outcome ("confounding"). This classification also leads to a unified approach to adjust for selection bias.
Case-Control Studies↗
Quantifying the magnitude of baseline covariate imbalances resulting from selection bias in randomized clinical trials.
Selection bias is most common in observational studies, when patients select their own treatments or treatments are assigned based on patient characteristics, such as disease severity. This first-order selection bias, as we call it, is eliminated by randomization, but there is residual selection bias that may occur even in randomized trials which occurs when, subconsciously or otherwise, an investigator uses advance knowledge of upcoming treatment allocations as the basis for deciding whom to enroll. For example, patients more likely to respond may be preferentially enrolled when the active treatment is due to be allocated, and patients less likely to respond may be enrolled when the control group is due to be allocated. If the upcoming allocations can be observed in their entirety, then we will call the resulting selection bias second-order selection bias. Allocation concealment minimizes the ability to observe upcoming allocations, yet upcoming allocations may still be predicted (imperfectly), or even determined with certainty, if at least some of the previous allocations are known, and if restrictions (such as randomized blocks) were placed on the randomization. This mechanism, based on prediction but not observation of upcoming allocations, is the third-order selection bias that is controlled by perfectly successful masking, but without perfect masking is not controlled even by the combination of advance randomization and allocation concealment. Our purpose is to quantify the magnitude of baseline imbalance that can result from third-order selection bias when the randomized block procedure is used. The smaller the block sizes, the more accurately one can predict future treatment assignments in the same block as known previous assignments, so this magnitude will depend on the block size, as well as on the level of certainty about upcoming allocations required to bias the patient selection. We find that a binary covariate can, on average, be up to 50% unbalanced by third-order selection bias.
Analysis of Variance↗
Symptoms and selection bias: the influence of selection towards specialist care on the relationship between symptoms and diagnoses.
Observations with respect to the relationship between symptoms and diseases can seriously be biased by selection phenomena. This selection may occur from the general population, via consultation behavior, diagnostic and therapeutic activities of the general practitioner, and by referral. Relationships may be suggested and reproduced even if they do not exist in unselected populations, as a product of diagnostic routines. Correction for selection bias can only be achieved by choosing proper comparison groups. While this can be done in a general practice setting, this is almost impossible after referral, as is demonstrated in this paper. Surprisingly, the most unbiased estimation of the relationship between symptoms and diseases after referral can be made from patient groups that are referred for reason unrelated to the disease under study. Definitive answers for the general practitioner can only be provided by prospective studies from a primary care setting. In the meantime, however, biased relationships can be maintained by teaching knowledge derived from a specialist experience.
Anemia↗
Reporting and selection bias in case-control studies of congenital malformations.
Retrospective studies of congenital malformations frequently rely on exposures reported by study subjects. Differential error in exposure reporting by cases and controls, which has alternatively been referred to as "recall bias" and "reporting bias," may result in a biased effect measure. Some authors have attempted to avoid reporting bias by comparing exposures between two malformed groups, rather than between cases and nonmalformed controls. This approach, however, may introduce its own bias, which we call selection bias. Both reporting bias and selection bias are shown to be algebraically equivalent to bias arising from exposure misclassification. The magnitudes of these biases are compared for a range of plausible parametric values. The case-control design is sensitive to both differential reporting and selection bias, and the choice of study design involves balancing these two sources of bias.
Case-Control Studies↗
Selection bias in semen studies due to self-selection of volunteers.
BACKGROUND: Reports of a secular decrease in semen quality remain controversial, particularly due to the possibility of selection bias. We aimed to describe the potential bias due to self-selection of volunteers in semen studies involving fecund men. METHODS: Using data from the French multicentre study REPRHOM, we compared the characteristics of the partners of pregnant women for three levels of participation: completion of a refusal questionnaire (n = 698), agreement to complete the study questionnaires only (n = 676) and agreement to complete the study questionnaires and give a semen sample (n = 331, 13% of the subjects approached). RESULTS: Poorly educated men refused more often to participate than highly educated men. Semen providers were more likely to have experienced unfavourable pregnancy outcomes (odds ratio 1.68, 95% confidence interval 1.14-2.49) compared with participants completing the questionnaires only. Time to pregnancy was similar for all participants. CONCLUSIONS: This study demonstrates the existence of selection bias in semen studies associated with fertility and socio-demographic characteristics of men. The results of semen analysis for this population sample cannot be extrapolated to the whole population from which the volunteers originate. More information is required on who participates, and participation rates should be reported in semen studies to make it possible to interpret the results correctly.
Adult↗
The role of health practices, health status, and prior health care claims in HMO selection bias.
To examine selection bias in terms of demographics, self-reported health practices, professionally evaluated health status, and prior health care utilization by employees and dependents, we examined health insurance choice between traditional insurance and a health maintenance organization (HMO) by central office employees of a large southwestern utility company. The HMO attracted a relatively younger and female population compared with the traditional plan. We found no difference in health practices and health status measures between the two groups. Consistent with recent studies, the HMO group experienced lower health claims cost the year prior to enrollment compared with persons who remained in the traditional plan. This difference was largely due to dependents' utilization-a factor that should be examined in future studies and considered by those structuring insurance premiums for HMOs and traditional plans.
Adult↗
Interpreting the term selection bias in medical research.
The issue of selection bias is often raised in the critical appraisal of medical studies, but it is often poorly defined and misunderstood. This paper, describes three common patterns of use of the term selection bias and their effects on study results. The three ways in which selection bias is used are related to 1) selection of representative subjects, 2) selection of subjects to exposures, and 3) selection of subjects at outcome. Avoidance of bias in the first of these issues, selection of representative subjects, enhances the ability to generalize a study's results. The other two uses of selection bias relate to the internal validity of studies. The selection of subjects to exposures without randomization in observational studies can distort results because of confounding variables. The selection of study subjects at outcome in case-control and cross-sectional studies can distort study findings if selection into the study is distorted according to exposure status. Readers of medical studies should understand the different implications of these uses to improve their critical evaluation of studies. Writers and discussants should be aware of these differences and provide clarifying details when they use the term.
Case-Control Studies↗
Evaluation of selection bias in a cross-sectional survey.
Selection bias is inherent in all occupational cohorts. Selection bias at entry has long been known and is commonly referred to as a "healthy worker effect." Less well appreciated is selection during the life of a cohort resulting from life-style factors (e.g., cigarette smoking); aging with accompanying chronic diseases, economic and demographic circumstances; and diseases that might result from exposures suffered by the cohort being studied, that influence whether individuals remain in a trade. These factors weigh differently at different times. Thus, at any point in time, "surviving" members of a cohort reflect an amalgam of selection factors. When such groups are studied in cross-sectional surveys there can be uncertainty whether clinical, radiological and physiological findings are necessarily representative for the trade or occupation as a whole. We analyzed the results of a large clinical field survey of long-term asbestos insulation workers to investigate whether the non-participants differed substantially from those who were examined. Five thousand three hundred and fifty-five (5,355) men, of an initial cohort of 17,800 established January 1, 1967, had reached 30 or more years from onset of their work by July 1, 1981. All were invited to come for examination. Two thousand and seventy-seven (2,077) came, and 3,278 did not. We questioned a sample of 1,393 non-responders to see why they failed to appear. The answers did not give evidence of significant health-related selection influence. Sickness only infrequently kept them away. We then followed both groups--those examined and those not examined--to the end of 1987 for their mortality experience. There was no great difference. The non-responders had somewhat fewer deaths overall and proportionately fewer of asbestos-associated cancers, such as mesothelioma and lung cancer. The results indicated that, in this cohort, there did not seem to be health-related selection bias that determined whether or not cohort members responded to invitations for examinations.
Asbestos↗
Case-control studies in clinical research: mechanism and prevention of selection bias.
The mechanism by which selection bias occurs in case-control studies is explained to an audience of clinicians using a simple conceptual framework and a graphical presentation. A case-control study consists in comparing the frequency of exposure in a group of subjects having the studied disease (the cases) relative to another group free of that disease (the controls). Cases and controls can be thought of as arising from a hypothetical cohort study. Thus, enrolled cases are a fraction F1 of the exposed who developed the disease plus a fraction F3 of the unexposed who developed the disease during a given period. Similarly, enrolled controls are a fraction F2 of the exposed who did not develop disease plus a fraction F4 of the unexposed who did not develop the disease. A selection process is inherent to the design of case-control studies but it leads to selection bias only when the ratio of F1 x F4/F2 x F3 is not equal to unity. Examples demonstrate the implication of sampling fractions for designing and interpreting case-control studies performed in clinical settings.
Case-Control Studies↗
Lack of concealment may lead to selection bias in cluster randomized trials of palliative care.
Comprehensive palliative care programs are often implemented on a community level, and to evaluate such interventions, randomization by cluster (community) may be the only feasible method. In trials randomizing individual subjects, the importance of proper concealment has been stressed. In cluster randomized trials, however, concealment of individual patient allocation is often impossible. The following risk of selection bias has been given little attention. In the present study, comparing palliative care to conventional care, community health care districts were defined as clusters and randomized. The patients' treatment assignment was determined by the allocation of the cluster in which they resided, and hence predictable by their address. A biased selection based on practical considerations related to patients' diagnoses and hospital departments was suspected. To explore this, cancer diagnoses were grouped according to local tradition for sharing of treatment responsibility among hospital departments. A significant difference between trial arms in distribution of these groups was revealed and strongly supported our suspicion. The finding carries an important message to future researchers: when using cluster randomization, any evidence of selection bias should be carefully checked and reported.
Adult↗
Subject selection biases in clinical trials: data from a multicenter schizophrenia treatment study.
To evaluate subject selection biases in clinical trials, demographic characteristics (gender, race, and age) of subjects at different phases of evaluation for a multicenter maintenance trial in schizophrenia were examined. Six thousand twelve diagnostically appropriate subjects were screened for the study; of these, 1,320 met eligibility criteria and 528 (9% of the screened sample) entered the study. Women, blacks, and older subjects were more likely not to meet eligibility criteria; women and older subjects were more likely and blacks were less likely to refuse study participation. Overall, compared with the screened population, the sample of subjects who entered the study contained proportionately fewer women (33 vs. 43%), more blacks (48.5 vs. 41%), and fewer older subjects (mean age of the entered sample was 29.4 +/- 7.4 vs. 34.8 +/- 11.3 years for the screened population). Having identified these selection factors, a second goal was to assess the potential clinical relevance of selection biases of these magnitudes on clinical trials using models of hypothetical studies with different degrees of selection bias. These showed that selection biases would rarely change overall study outcomes to a clinically relevant degree. However, in our models, selection biases did limit the ability to make inferences about results for select small subgroups of the study population. Investigators should consider collecting data on the recruitment process to allow estimation of the effects of selection biases on the generalizability of their findings.
Acute Disease↗