@article {12508, title = {Reducing Nonresponse and Data Linkage Consent Bias in Large-Scale Panel Surveys.}, journal = {Forum for Health Economics and Policy}, year = {2022}, abstract = {

Selection bias is an ongoing concern in large-scale panel surveys where the cumulative effects of unit nonresponse increase at each subsequent wave of~data collection. A second source of selection bias in panel studies is the inability to link respondents to supplementary administrative records, either because respondents do not consent to link or the matching algorithm fails to locate their administrative records. Both sources of selection bias can affect the validity of conclusions drawn from these data sources. In this article, I discuss recently proposed methods of reducing both sources of selection bias in panel studies, with a special emphasis on reducing selection bias in the US Health and Retirement Study.

}, keywords = {post-survey adjustments, questionnaire design, Selection Bias}, issn = {1558-9544}, doi = {10.1515/fhep-2021-0060}, author = {Sakshaug, Joseph W} } @article {10.1214/21-AOAS1453, title = {Assessing selection bias in regression coefficients estimated from nonprobability samples with applications to genetics and demographic surveys}, journal = {The Annals of Applied Statistics}, volume = {15}, year = {2021}, pages = {1556-1581}, abstract = {Selection bias is a serious potential problem for inference about relationships of scientific interest based on samples without well-defined probability sampling mechanisms. Motivated by the potential for selection bias in: (a) estimated relationships of polygenic scores (PGSs) with phenotypes in genetic studies of volunteers and (b) estimated differences in subgroup means in surveys of smartphone users, we derive novel measures of selection bias for estimates of the coefficients in linear and probit regression models fitted to nonprobability samples, when aggregate-level auxiliary data are available for the selected sample and the target population. The measures arise from normal pattern-mixture models that allow analysts to examine the sensitivity of their inferences to assumptions about nonignorable selection in these samples. We examine the effectiveness of the proposed measures in a simulation study and then use them to quantify the selection bias in: (a) estimated PGS-phenotype relationships in a large study of volunteers recruited via Facebook and (b) estimated subgroup differences in mean past-year employment duration in a nonprobability sample of low-educated smartphone users. We evaluate the performance of the measures in these applications using benchmark estimates from large probability samples.}, keywords = {Linear regression, nonprobability samples, polygenic scores, probit regression, Selection Bias}, doi = {https://doi.org/10.1214/21-AOAS1453}, author = {Brady T. West and Roderick J.A. Little and Rebecca R. Andridge and Philip S. Boonstra and Erin B Ware and Anita Pandit and Fernanda Alvarado-Leiton} } @article {12201, title = {Reducing Nonresponse and Linkage Consent Bias in Large-Scale Panel Surveys}, year = {2021}, publisher = {National Institute on Aging}, address = {Bethesda, MD}, abstract = {Selection bias is an ongoing concern in large-scale panel surveys, such as the US Health and Retirement Study, where the cumulative effects of unit nonresponse increase at each subsequent wave of data collection. Another source of selection bias in panel studies is the inability to link respondents to target administrative databases, either because respondents do not provide consent to link or the matching algorithm fails to locate their administrative record. Both sources of selection bias can adversely affect the validity of conclusions drawn from these data sources. In this article, I discuss recently proposed methods for reducing both sources of selection bias in panel studies, with a special emphasis on reducing selection bias in the US Health and Retirement Study.}, keywords = {post-survey adjustments, questionnaire design, Selection Bias}, author = {Joseph W Sakshaug} } @article {7116, title = {Dual use of Medicare and the Veterans Health Administration: are there adverse health outcomes?}, journal = {BMC Health Serv Res}, volume = {6}, year = {2006}, month = {2006 Oct 09}, pages = {131}, publisher = {6}, abstract = {

BACKGROUND: Millions of veterans are eligible to use the Veterans Health Administration (VHA) and Medicare because of their military service and age. This article examines whether an indirect measure of dual use based on inpatient services is associated with increased mortality risk.

METHODS: Data on 1,566 self-responding men (weighted N = 1,522) from the Survey of Assets and Health Dynamics among the Oldest Old (AHEAD) were linked to Medicare claims and the National Death Index. Dual use was indirectly indicated when the self-reported number of hospital episodes in the 12 months prior to baseline was greater than that observed in the Medicare claims. The independent association of dual use with mortality was estimated using proportional hazards regression.

RESULTS: 96 (11\%) of the veterans were classified as dual users. 766 men (50.3\%) had died by December 31, 2002, including 64.9\% of the dual users and 49.3\% of all others, for an attributable mortality risk of 15.6\% (p < .003). Adjusting for demographics, socioeconomics, comorbidity, hospitalization status, and selection bias at baseline, as well as subsequent hospitalization for ambulatory care sensitive conditions, the independent effect of dual use was a 56.1\% increased relative risk of mortality (AHR = 1.561; p = .009).

CONCLUSION: An indirect measure of veterans{\textquoteright} dual use of the VHA and Medicare systems, based on inpatient services, was associated with an increased risk of death. Further examination of dual use, especially in the outpatient setting, is needed, because dual inpatient and dual outpatient use may be different phenomena.

}, keywords = {Aged, Aged, 80 and over, Deductibles and Coinsurance, Episode of Care, Health Services Research, Hospitals, Veterans, Humans, Male, Medicare, Mortality, Outcome Assessment, Health Care, Proportional Hazards Models, Quality Indicators, Health Care, Risk Assessment, Selection Bias, Surveys and Questionnaires, United States, United States Department of Veterans Affairs, Veterans}, issn = {1472-6963}, doi = {10.1186/1472-6963-6-131}, author = {Frederic D Wolinsky and Thomas R Miller and An, Hyonggin and Paul R Brezinski and Thomas E Vaughn and Gary E Rosenthal} } @article {6832, title = {Breast cancer and women{\textquoteright}s labor supply.}, journal = {Health Serv Res}, volume = {37}, year = {2002}, month = {2002 Oct}, pages = {1309-28}, publisher = {37}, abstract = {

OBJECTIVE: To investigate the effect of breast cancer on women{\textquoteright}s labor supply. DATE SOURCE/STUDY SETTING: Using the 1992 Health and Retirement Study, we estimate the probability of working using probit regression and then, for women who are employed, we estimate regressions for average weekly hours worked using ordinary least squares (OLS). We control for health status by using responses to perceived health status and comorbidities. For a sample of married women, we control for spouses{\textquoteright} employer-based health insurance. We also perform additional analyses to detect selection bias in our sample.

PRINCIPAL FINDINGS: We find that the probability of breast cancer survivors working is 10 percentage points less than that for women without breast cancer. Among women who work, breast cancer survivors work approximately three more hours per week than women who do not have cancer. Results of similar magnitude persist after health status is controlled in the analysis, and although we could not definitively rule out selection bias, we could not find evidence that our results are attributable to selection bias.

CONCLUSIONS: For some women, breast cancer may impose an economic hardship because it causes them to leave theirjobs. However, for women who survive and remain working, this study failed to show a negative effect on hours worked associated with breast cancer. Perhaps the morbidity associated with certain types and stages of breast cancer and its treatment does not interfere with work.

}, keywords = {Breast Neoplasms, Comorbidity, Cost of Illness, Decision making, Employment, Family Characteristics, Female, Health Benefit Plans, Employee, Health Status, Humans, Marital Status, Middle Aged, Probability, Selection Bias, Survivors, United States, Women, Working}, issn = {0017-9124}, doi = {10.1111/1475-6773.01041}, author = {Cathy J. Bradley and Bednarek, Heather and David Neumark} } @article {6680, title = {The effect of smoking on health using a sequential self-selection model.}, journal = {Health Econ}, volume = {9}, year = {2000}, note = {ProCite field 3 : SUNY, Albany; SUNY, Albany}, month = {2000 Sep}, pages = {491-511}, publisher = {9}, abstract = {

We estimate a structural model of individual smoking behaviour emphasizing the role of individual risk belief on smoking choices. Our model consists of five equations: two selection equations for initiation and cessation decisions, and three switching outcome regressions for nonsmokers, ex-smokers, and current smokers. The presence of significant self-selectivity implies that the health effects of smoking based on sample proportions do not correctly indicate the true risk of cigarette smoking. Further, our evidence suggests that the self-selection in the cessation decision, but not in the initiation decision, is consistent with economic rationality. We estimate the model by full information maximum likelihood (FIML) with starting values from heteroskedasticity corrected Heckman-Lee two-step method using newly released Health and Retirement Study (HRS) data.

}, keywords = {Adult, Aged, Aged, 80 and over, Attitude to Health, Decision making, Health Behavior, Health Status, Health Surveys, Humans, Middle Aged, Models, Econometric, Predictive Value of Tests, Reproducibility of Results, Risk Factors, Risk-Taking, Selection Bias, Smoking, Smoking cessation, Surveys and Questionnaires, United States}, issn = {1057-9230}, doi = {10.1002/1099-1050(200009)9:6<491::aid-hec541>3.0.co;2-$\#$}, author = {Kajal Chatterji Lahiri and Song, Jae G.} }