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Instrumental variable methods to assess quality of care the marginal effects of process-of-care on blood pressure change and treatment costs.

ABSTRACT: BACKGROUND:Hypertension is poorly controlled. Team-based care and changes in the process of care have been proposed to address these quality problems. However, assessing care processes is difficult because they are often confounded even in randomized behavioral studies by unmeasured confounders based on discretion of health care providers. OBJECTIVE:To evaluate the effects of process measures including number of counseling sessions about lifestyle modification and number of antihypertensive medications on blood pressure change and payer-perspective treatment costs. METHODS:Data were obtained from two prospective, cluster randomized controlled clinical trials (Trial A and B) implementing physician-pharmacist collaborative interventions compared with usual care over six months in community-based medical offices in the Midwest. Multivariate linear regression models with both instrumental variable methods and as-treated methods were utilized. Instruments were indicators for trial and study arms. Models of blood pressure change and costs included both process measures, demographic variables, and clinical variables. RESULTS:The analysis included 496 subjects. As-treated methods showed no significant associations between process and outcomes. The instruments used in the study were insufficient to simultaneously identify distinct process effects. However, the post-hoc instrumental variable models including one process measure at a time while controlling for the other process demonstrated significant associations between the processes and outcomes with estimates considerably larger than as-treated estimates. CONCLUSIONS:Instrumental variable methods with combined randomized behavioral studies may be useful to evaluate the effects of different care processes. However, substantial distinct process variation across studies is needed to fully capitalize on this approach. Instrumental variable methods focusing on individual processes provided larger and stronger outcome relationships than those found using as-treated methods which are subject to confounding.

SUBMITTER: Kulchaitanaroaj P

PROVIDER: S-EPMC5774669 | biostudies-literature | 2015 Mar-Apr

REPOSITORIES: biostudies-literature

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Project description:The 'Mendelian randomization' approach uses genotype as an instrumental variable to distinguish between causal and non-causal explanations of biomarker-disease associations. Classical methods for instrumental variable analysis are limited to linear or probit models without latent variables or missing data, rely on asymptotic approximations that are not valid for weak instruments and focus on estimation rather than hypothesis testing. We describe a Bayesian approach that overcomes these limitations, using the JAGS program to compute the log-likelihood ratio (lod score) between causal and non-causal explanations of a biomarker-disease association. To demonstrate the approach, we examined the relationship of plasma urate levels to metabolic syndrome in the ORCADES study of a Scottish population isolate, using genotype at six single-nucleotide polymorphisms in the urate transporter gene SLC2A9 as an instrumental variable. In models that allow for intra-individual variability in urate levels, the lod score favouring a non-causal over a causal explanation was 2.34. In models that do not allow for intra-individual variability, the weight of evidence against a causal explanation was weaker (lod score 1.38). We demonstrate the ability to test one of the key assumptions of instrumental variable analysis--that the effects of the instrument on outcome are mediated only through the intermediate variable--by constructing a test for residual effects of genotype on outcome, similar to the tests of 'overidentifying restrictions' developed for classical instrumental variable analysis. The Bayesian approach described here is flexible enough to deal with any instrumental variable problem, and does not rely on asymptotic approximations that may not be valid for weak instruments. The approach can easily be extended to combine information from different study designs. Statistical power calculations show that instrumental variable analysis with genetic instruments will typically require combining information from moderately large cohort and cross-sectional studies of biomarkers with information from very large genetic case-control studies.

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Instrumental variable methods to assess quality of care the marginal effects of process-of-care on blood pressure change and treatment costs.

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