An exploratory instrumental variable analysis of the outcomes of localized breast cancer treatments in a medicare population

Hadley, Jack; Polsky, Daniel; Mandelblatt, Jeanne S.; Mitchell, Jean M.; Weeks, Jane C.; Wang, Qin; Hwang, Yi Ting

doi:10.1002/hec.710

Cited by 44 publications

(45 citation statements)

References 19 publications

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“…38–40 However, finding an appropriate instrumental variable is often difficult, the estimates may vary depending on the instrument chosen, 41 and the estimates often lack precision. 42,43 Hence, further development of the method is warranted.…”

Section: Discussionmentioning

confidence: 99%

Validation of Disability Status, a Claims-based Measure of Functional Status for Cancer Treatment and Outcomes Studies

Davidoff

Gardner²,

Zuckerman

et al. 2014

Medical Care

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Background In prior research, we developed a claims-based prediction model for poor patient disability status (DS), a proxy measure for performance status, commonly used by oncologists to summarize patient functional status and assess ability of a patient to tolerate aggressive treatment. In this study, we implemented and validated the DS measure in 4 cohorts of cancer patients: early and advanced non-small cell lung cancers (NSCLC), stage IV estrogen-receptor (ER-) negative breast cancer, and myelodysplastic syndromes (MDS). Data and methods 1999–2007 SEER-Medicare data for the four cohorts of cancer patients. Bivariate and multivariate logistic regression tested the association of the DS measure with designated cancer-directed treatments: early NSCLC (surgery), advanced NSCLC (chemotherapy), stage IV ER- breast cancer (chemotherapy), and MDS (erythropoiesis-stimulating agents). Treatment model fit was compared across model iterations. Results In both unadjusted and adjusted results, predicted poor DS was strongly associated with a lower likelihood of cancer treatment receipt in all four cohorts [early NSCLC (N=20,280), advanced NSCLC (N=31,341), stage IV ER- breast cancer (N=1,519), and MDS (N=6,058)] independent of other patient, contextual, and disease characteristics, as well as the Charlson Comorbidity Index (CCI). Inclusion of the DS measure into models already controlling for other variables did not significantly improve model fit across the cohorts. Conclusions The DS measure is a significant independent predictor of cancer-directed treatment. Small changes in model fit associated with both DS and the CCI suggest that unobserved factors continue to play a role in determining cancer treatments.

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Section: Discussionmentioning

confidence: 99%

Validation of Disability Status, a Claims-based Measure of Functional Status for Cancer Treatment and Outcomes Studies

Davidoff

Gardner²,

Zuckerman

et al. 2014

Medical Care

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“…7 One of the earliest applications of IV in the medical field is probably the research of Permutt and Hebel, 8 who estimated the effect of smoking of pregnant women on their child's birth weight, using an encouragement to stop smoking as the instrumental variable. More recent examples can be found in Beck et al, 9 Brooks et al, 10 Earle et al, 11 Hadley et al, 12 Leigh and Schembri, 13 McClellan, 14 and McIntosh. 15 However, it has been argued that the application of this method is limited because of its strong assumptions, making it difficult in practice to find a suitable instrumental variable.…”

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confidence: 97%

Instrumental Variables

Martens¹,

Pestman²,

Boer³

et al. 2006

Epidemiology

362

119

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To correct for confounding, the method of instrumental variables (IV) has been proposed. Its use in medical literature is still rather limited because of unfamiliarity or inapplicability. By introducing the method in a nontechnical way, we show that IV in a linear model is quite easy to understand and easy to apply once an appropriate instrumental variable has been identified. We also point out some limitations of the IV estimator when the instrumental variable is only weakly correlated with the exposure. The IV estimator will be imprecise (large standard error), biased when sample size is small, and biased in large samples when one of the assumptions is only slightly violated. For these reasons, it is advised to use an IV that is strongly correlated with exposure. However, we further show that under the assumptions required for the validity of the method, this correlation between IV and exposure is limited. Its maximum is low when confounding is strong, such as in case of confounding by indication. Finally, we show that in a study in which strong confounding is to be expected and an IV has been used that is moderately or strongly related to exposure, it is likely that the assumptions of IV are violated, resulting in a biased effect estimate. We conclude that instrumental variables can be useful in case of moderate confounding but are less useful when strong confounding exists, because strong instruments cannot be found and assumptions will be easily violated.

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“…9,10 Its use has been increasing over the past 15 years in health services and medical care research. 16,[21][22][23][24][25] Because quasi-experimental and observational research designs are likely to dominate research on the effects of palliative care on patient, family and health system outcomes, palliative care researchers using these nonexperimental designs can strengthen the scientific rigor of their findings by using instrumental variable methods when possible. Palliative care patients and their families are challenging to study but their care is in desperate need of an improved evidence base to assure that the highest quality of care can be provided.…”

Section: Resultsmentioning

confidence: 99%