Observational Studies in a Learning Health System Workshop Summary (2013) / Chapter Skim
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5 Detecting Treatment-Effect Heterogeneity
5 Detecting Treatment-Effect Heterogeneity
Pages 45-56
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From page 45... ...
• Well-planned observational studies conducted to answer spe cific questions with data from large databases can identify heterogeneity in treatment effects and enable individualized recommendations.
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From page 46... ...
Kent, director of the Clinical and Translational Science Program at the Tufts University Sackler School of Graduate Biomedical Sciences, presented an overview on the detection of heterogeneity in treatment effects; Mark A Hlatky discussed a specific example in which heterogeneity in treatment effects was observed in RCTs and observational studies; and Anirban Basu, associate professor and director of the Program in Health Economics and Outcomes Methodology at the University of Washington, spoke about the use of instrumental variables to identify heterogeneity in treatment effects.
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From page 47... ...
This type of subgroup analysis -- for example, of males versus females or diabetics versus nondiabetics -- is convenient but artificial because real patients simultaneously differ on multiple variables. As a result, the individuals in these one-variable-at-a-time subgroup analyses do not represent the full heterogeneity of patients that might be relevant for measurement of heterogeneous treatment effects that may be detected when combinations of variables are used (such as when multivariate risk models are employed)
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From page 48... ...
The two procedures have been compared in RCTs and observational studies, but these studies have focused on the general population and do not provide much guidance for specific patients. Hlatky noted that one of the early trials comparing the two procedures -- the Bypass Angioplasty Revascularization Investigation trial in diabetics -- did provide some evidence of a heterogeneous treatment effect.
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From page 49... ...
these trials were run at large medical centers with highly skilled staff, and the results may not represent those obtained by physicians in other clinical settings. To address some of these limitations, Hlatky and his collaborators examined observational data to see if they could replicate and extend the findings from their pooled RCT study (Hlatky et al., 2013)
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From page 50... ...
"We are really interested in how much the incremental outcome between two treatments varies across people," he explained. As a recap of the day's earlier presentation on instrumental variables, he reminded the workshop participants that instrumental variables are those that influence treatment choices but that are independent of factors that determine potential outcomes, that they are viewed as natural randomizers, and that they can be used to establish the causal effects of a treatment by accounting for both overt and hidden biases.
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From page 51... ...
In the presence of heterogeneity in the treatment effect, there is no reason to believe that the causal effect of a treatment that comes out of an observational study should be the same as the causal effect of a treatment that comes out of an RCT, that the average treatment effect is a relevant metric for evaluation, or that the effect of the instrumental variable has a relevant interpretation. The first step in addressing these issues is to develop a choice model that starts with the assumption that the choice of treatment is based on an underlying latent index; the latent index is a function of observed confounders and instrumental variables, as well as a function of unobserved confounders and stochastic error.
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From page 52... ...
He concluded his remarks by noting that differences between individual patients are of crucial importance and that the study of heterogeneity over broad subgroups may not be useful in comparative effectiveness research. He also noted that the use of algorithmic predictions generated from large sets of data from observational studies and then validated in confirmatory studies may be a promising way to guide clinical decision making.
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From page 53... ...
He agreed with Miguel A Hernán's view, presented in the prior session, that observational research and RCTs have a great deal in common and that one of the major issues in working in the observational domain is where and how to use instrumental variables or other strategies to effectively randomize the environment in which randomization is not occurring.
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From page 54... ...
DISCUSSION Platt said he was impressed that all five speakers were to some degree sanguine about the prospects for doing subgroup analysis. A workshop participant then remarked that to him, the idea of creating risk models to create subgroups or models that identify those who may have different absolute reductions in risk from a treatment is a good one but that it is not the right approach for finding factors that could modify the treatment effect, particularly biological risk factors.
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From page 55... ...
Charlson reiterated her earlier proposal that the field needs to develop methods to more systematically capture individual patient experiences beyond those recorded in electronic health records. Sheldon Greenfield agreed with this proposal because he believes that many of the variables related to heterogeneity that are now considered unobservable would be observable if both patients and physicians were queried more systematically.
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From page 56... ...
2007. Analysis of observational studies in the presence of treatment selection bias: Effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods.
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