Observational Studies in a Learning Health System Workshop Summary (2013) / Chapter Skim
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6 Predicting Individual Responses
6 Predicting Individual Responses
Pages 57-68
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From page 57... ...
• Implicit propensity score matching can be used to overcome limitations related to sparse information on confounders in databases of spontaneous reports of drug adverse events.
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From page 58... ...
Kattan, chair of the Quantitative Health Sciences Department at the Cleveland Clinic, described one method of predicting individual risk from treatment. Mitchell H
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should promote an effort to develop approximate matching strategies and then conduct tests to see what happens when physicians make treatment decisions using these strategies. Singer noted that heterogeneity among both patients and physician practice will play a large role in this effort.
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In the aftermath of the Vioxx and Avandia incidents, there was a public call for the establishment of a public database to monitor drug safety, but FDA has been maintaining such a database for more than 30 years as part of the Adverse Events Reporting System. Today, this database comprises more than 3 million reports, an enormous observational database, but it is of limited utility because it is only sparsely populated with data on patient age, sex, weight, and country; the drugs that a patient was taking at the time of the adverse event; or the conditions for which a patient was receiving treatment.
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From page 61... ...
However, if the control cohort is restricted by use of the IPSM technique, the frequency at which hyperglycemia would be an expected adverse event reported in association with all other drugs that a patient is taking would be 17.6 percent, revealing the false association with diabetes drugs. He described another example in which IPSM corrects for the association between arrhythmia and antiarrhythmic drugs, in which 10 of 13 drugs identified without correction by the use of IPSM were no longer associated with what is known as the "prorhythmic effect." The three drugs whose proportional reporting ratio still exceeded the significance threshold after correction by the use of IPSM do, in fact, have prorhythmic effects that limit their use.
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From page 62... ...
The urologists were provided with PSA, biopsy Gleason grades, clinical stage, patient age, systematic biopsy details, previous biopsy results, and PSA history. They were also provided with the preoperative nomogram and were asked to make their own predictions of the probability of 5-year progression-free cancer with or without
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He indicated that he would like to see the field develop comparative effectiveness tables. He acknowledged that tailoring such tables to an individual is difficult, but he added, "I think efficiencies and better decision making would take place if we could get that type of information handed to us." He showed an example of a risk calculator developed at the Cleveland Clinic that uses its EHR to fill in an individual's information on age, gender, comorbid conditions, medications, blood pressure, lipid levels, smoking status, and other personal characteristics to produce a table that provides 6-year probabilities of mortality, stroke, coronary artery disease, liver injury, heart failure, renal insufficiency, and diabetic nephropathy for each of four classes of diabetes drugs (see Table 6-1)
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From page 64... ...
He then commended Tatonetti for his assessment of how observational data from large data sets can be used to identify adverse events. This approach to controlling for confounding by indication was interesting, but he took a wait-and-see attitude as to whether this method works, pending further studies to gain more experience with IPSM.
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"The number of patients you need is simply too large and the costs are too large, so these technologies need to be investigated," said Tatonetti. "The problem is, we do not have a lot of validation that they produce reliable effect risk estimates." Horwitz asked the panel if the current risk models are providing data that may be misleading patients when they make decisions.
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Kent, commenting on Kattan's use of risk models in concert with clinical trials, said that his research has been finding what he called a surprising degree of risk variation even in efficacy trials and that the typical patient typically has a much lower risk than the summary effect in the overall trial results. He also noted that although Kattan's work showed that the gestalt of physicians often does not agree with the actual risk, prediction models often disagree with one another as well and produce different recommendations.
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In response to a question from Horwitz about what needs to be done to provide predictions that reflect longitudinal changes in treatment, condition, or comorbidities, Gail said that RCTs or adaptive RCTs can be designed to address those issues in some cases, but doing so requires that the intervention and clinical question be carefully designed at the very outset of the project. He added that researchers are developing approaches to answering some of these longitudinal questions using observational data, "and to the extent that they do account for confounding by indication and for the longitudinal nature of confounding, they may be getting closer to giving good advice." Singer agreed that adaptive trial designs are a good start toward addressing longitudinal questions.
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From page 68... ...
2003. A nomogram for predicting the likeli hood of additional nodal metastases in breast cancer patients with a positive sentinel node biopsy.
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