Assessing response bias from missing quality of life data: The Heckman method

Health and Quality of Life Outcomes

John S Rumsfeld²  John A Spertus³  David J Magid⁴  Mary E Plomondon²  Anne E Sales¹ 

[1] Department of Health Services, University of Washington, Seattle, WA, USA;Cardiovascular Outcomes Research, Denver VA Medical Center, and Division of Cardiology, University of Colorado Health Sciences Center, Denver, CO, USA;Cardiovascular Outcomes Research, Mid-America Heart Institute, and Department of Medicine, University of Missouri, Kansas City, MO, USA;Clinical Research Unit, Colorado Permanente Medical Group, and Department of Preventive Medicine and Biometrics, University of Colorado Health Sciences Center, Denver, CO, USA

关键词: health-related quality of life;    cardiovascular disease;    Selection bias;   

Others  :  1216940
DOI  :  10.1186/1477-7525-2-49

 received in 2004-03-01, accepted in 2004-09-16,  发布年份 2004

【 摘 要 】

Background

The objective of this study was to demonstrate the use of the Heckman two-step method to assess and correct for bias due to missing health related quality of life (HRQL) surveys in a clinical study of acute coronary syndrome (ACS) patients.

Methods

We analyzed data from 2,733 veterans with a confirmed diagnosis of acute coronary syndromes (ACS), including either acute myocardial infarction or unstable angina. HRQL outcomes were assessed by the Short-Form 36 (SF-36) health status survey which was mailed to all patients who were alive 7 months following ACS discharge. We created multivariable models of 7-month post-ACS physical and mental health status using data only from the 1,660 survey respondents. Then, using the Heckman method, we modeled survey non-response and incorporated this into our initial models to assess and correct for potential bias. We used logistic and ordinary least squares regression to estimate the multivariable selection models.

Results

We found that our model of 7-month mental health status was biased due to survey non-response, while the model for physical health status was not. A history of alcohol or substance abuse was no longer significantly associated with mental health status after controlling for bias due to non-response. Furthermore, the magnitude of the parameter estimates for several of the other predictor variables in the MCS model changed after accounting for bias due to survey non-response.

Conclusion

Recognition and correction of bias due to survey non-response changed the factors that we concluded were associated with HRQL seven months following hospital admission for ACS as well as the magnitude of some associations. We conclude that the Heckman two-step method may be a valuable tool in the assessment and correction of selection bias in clinical studies of HRQL.

【 授权许可】

   
2004 Sales et al; licensee BioMed Central Ltd.

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