Statistical Analysis with Missing Data

Jeffrey L. Anderson, MD, FACC, FAHA, Chair 

Jonathan L. Halperin, MD, FACC, FAHA, Chair-Elect 

Alice K. Jacobs, MD, FACC, FAHA, Immediate Past Chair 2009–2011 [§§][1]

Sidney C. Smith, Jr, MD, FACC, FAHA, Past Chair 2006–2008 [§§][1]

Cynthia D. Adams, MSN, APRN-BC, FAHA[§§][1]

Nancy M

2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease: executive summary: a report of the American College of Cardiology Foundation/American Heart Association task force on practice guidelines, and the American College of Physicians, American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons.

The smearing estimate is proposed as a nonparametric estimate of the expected response on the untransformed scale after fitting a linear regression model on a transformed scale. The estimate is consistent under mild regularity conditions, and usually attains high efficiency relative to parametric estimates. It can be viewed as a low-premium insurance policy against departures from parametric distributional assumptions. A real-world example of predicting medical expenditures shows that the smearing estimate can outperform parametric estimates even when the parametric assumption is nearly satisfied.

https://www.jstor.org/stable/pdfplus/2288126.pdf

Smearing Estimate: A Nonparametric Retransformation Method

We estimate how cost sharing, the portion of the bill the patient pays, affects the demand for medical services. The data come from a randomized experiment. A catastrophic insurance plan reduces expenditures 31 percent relative to zero out-of-pocket price. The price elasticity is approximately -0.2. We reject the hypothesis that less favorable coverage of outpatient services increases total expenditure (for example, by deterring preventive care or inducing hospitalization).

/pdf/health-insurance-and-the-demand-for-medical-care-evidence-13gpg9hn2l.pdf

Health insurance and the demand for medical care: evidence from a randomized experiment.

Survey questions asking about taboo topics such as sexual activities, illegal behaviour such as social fraud, or unsocial attitudes such as racism, often generate inaccurate survey estimates which are distorted by social desirability bias. Due to self-presentation concerns, survey respondents underreport socially undesirable activities and overreport socially desirable ones. This article reviews theoretical explanations of socially motivated misreporting in sensitive surveys and provides an overview of the empirical evidence on the effectiveness of specific survey methods designed to encourage the respondents to answer more honestly. Besides psychological aspects, like a stable need for social approval and the preference for not getting involved into embarrassing social interactions, aspects of the survey design, the interviewer’s characteristics and the survey situation determine the occurrence and the degree of social desirability bias. The review shows that survey designers could generate more valid data by selecting appropriate data collection strategies that reduce respondents’ discomfort when answering to a sensitive question.

Determinants of social desirability bias in sensitive surveys: a literature review

A total of 7706 persons are participating in a controlled trial of alternative health-insurance policies. Interim results indicate that persons fully covered for medical services spend about 50 per cent more than do similar persons with income-related catastrophe insurance. Full coverage leads to more people using services and to more services per user. Both ambulatory services and hospital admissions increase. Once patients are admitted to the hospital, however, expenditures per admission do not differ significantly among the experimental insurance plans. In addition, hospital admissions for children do not vary by plan. The income-related cost sharing in the experimental plans affects expenditure by different income groups similarly, but adults' total expenditure varies more than children's. Sufficient data are not available on whether higher use by persons with free care reflects overuse, or whether lower use by those with income-related catastrophe coverage reflects underuse. Both may well be true.

/pdf/some-interim-results-from-a-controlled-trial-of-cost-sharing-45u8qrpdi8.pdf

Some Interim Results from a Controlled Trial of Cost Sharing in Health Insurance

Worker demand for health insurance in the non-group market

https://www.rand.org/content/dam/rand/pubs/reports/2007/R1149-1.pdf

On Having Your Cake and Eating It Too: Econometric Problems in Estimating the Demand for Health Services

Estimates of survey response bias and reliability are presented for six topics: receipt of welfare, income, alcohol use, drug use, criminal history, and embarrassing medical conditions. The estimates are derived from published full-design criterion validity studies. The common assumption that these characteristics are underreported is, in part, based on partial validity studies; the bias in estimating response parameters using partial designs is demonstrated. Evidence from the full-design studies suggests that the response biases for these topics center near zero but that the responses are unreliable or noisy. Implications for survey design and methodological research are considered.

Response Bias and Reliability in Sensitive Topic Surveys

Data from three recent surveys indicate that about 40 percent of workers with employment-based health insurance are enrolled in plans that their employers self-insure. Despite the conside...

M. Susan Marquis

Papers

Some Interim Results from a Controlled Trial of Cost Sharing in Health Insurance

Worker demand for health insurance in the non-group market

On Having Your Cake and Eating It Too: Econometric Problems in Estimating the Demand for Health Services

Response Bias and Reliability in Sensitive Topic Surveys

Self-Insured Employer Health Plans: Prevalence, Profile, Provisions, and Premiums