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Advances in health information technology, such as electronic health records (EHRs) and disease registries, promise new sources of evidence on the effectiveness of health interventions. As these data sources become more readily accessible to investigators, they are likely to supplement or even replace clinical trials data in SRs of

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CER. Furthermore, as with other data sources, the potential for bias and confounding will need to be addressed.

The Food and Drug Administration Sentinel Initiative and related activities (e.g., Observational Medical Outcomes Partnership) may be an important new data source for future SRs. When operational, the Sentinel Initiative will be a national, integrated, electronic database built on EHRs and claims records databases for as many as 100 million individuals (HHS, 2010; Platt et al., 2009). Although the principal objective of the system is to detect adverse effects of drugs and other medical products, it may also be useful for SRs of CER questions. A “Mini-Sentinel” pilot is currently under development at Harvard Pilgrim Health Care (Platt, 2010) . The system will be a distributed network, meaning that separate data holders will contribute to the network, but the data will never be put into one common repository. Instead, all database holders will convert their data into a common data model and retain control over their own data. This allows a single “program” to be run (e.g., a statistical analysis in SAS) on all the disparate datasets, generating an estimated relative risk (or other measure) from each database. These then can be viewed as a type of meta-analysis.

The fact that available data are conducive to pooling is not in itself sufficient reason to conduct a meta-analysis (Fu et al., 2010). The meta-analysis should not be undertaken unless the anticipated results are likely to produce meaningful answers that are useful to patients, clinicians, or other decision makers. For example, if the same outcomes are measured differently in the individual studies and the measures cannot be converted to a common scale, doing a meta-analysis may not be appropriate (Cummings, 2004). This situation may occur in studies comparing the effect of an intervention on a variety of important patient outcomes such as pain, mental health status, or pulmonary function.

Conducting the Meta-Analysis

Good statistical analyses quantify the amount of variability in the data in order to obtain estimates of the precision with which estimates may be made. Large amounts of variability reduce our confidence that effects are accurately measured. In meta-analysis,

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variability arises from three sources—clinical diversity, methodological diversity, and statistical heterogeneity—which should be separately considered in presentation and discussion (Fu et al., 2010). Clinical diversity describes variability in study population characteristics, interventions, and outcome ascertainments. Methodological diversity encompasses variability in study design, conduct, and quality, such as blinding and concealment of allocation. Statistical heterogeneity, relating to the variability in observed treatment effects across studies, may occur because of random chance, but may also arise from real clinical and methodological diversity and bias.

Assessing the amount of variability is fundamental to determining the relevance of the individual studies to the SR’s research questions. It is also key to choosing which statistical model to use in the quantitative synthesis. Large amounts of variability may suggest a poorly formulated question or many sources of uncertainty that can influence effects. As noted above, if the individual studies are so diverse in terms of populations, interventions, comparators, outcomes, time lines, and/or settings, summary data will not yield clinically meaningful conclusions about the effect of an intervention for important subgroups of the population (West et al., 2010).

In general, quantifying heterogeneity helps determine whether and how the data may be combined, but specific tests of the presence of heterogeneity can be misleading and should not be used because of their poor statistical properties and because an assumption of complete homogeneity is nearly always unrealistic (Higgins et al., 2003). Graphical representations of among-study variation such as forest plots can be informative ( Figure 4-1 ) (Anzures-Cabrera and Higgins, 2010).

When pooling is feasible, investigators typically use one of two statistical techniques—fixed-effects or random-effects models—to analyze and integrate the data, depending on the extent of heterogeneity. Each model has strengths and limitations. A fixed-effects model assumes that the treatment effect is the same for each study. A random-effects model assumes that some heterogeneity is present and acceptable, and the data can be pooled. Exploring the potential sources of heterogeneity may be more important than a decision about the use of fixed- or random-effects models. Although the committee does not believe that any single statistical technique should be a methodological standard, it is essential that the SR team clearly explain and justify the reasons why it chose the technique actually used.

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FIGURE 4-1 Forest plot.

SOURCE: Schriger et al. (2010).

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In meta-analyses, the amount of within- and between-study variation determines how precisely study and aggregate treatment effects are estimated. Estimates of effects without accompanying measures of their uncertainty, such as confidence intervals, cannot be correctly interpreted. A forest plot can provide a succinct representation of the size and precision of individual study effects and aggregated effects. When effects are heterogeneous, more than one summary effect may be necessary to fully describe the data. Measures of uncertainty should also be presented for estimates of heterogeneity and for statistics that quantify relationships between treatment effects and sources of heterogeneity.

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By Anna Griffin Jul 18, 2018
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Sen. Jeff Merkley, D-Ore., right, accompanied by Sen. Debbie Stabenow, D-Mich., left, speaks during a news conference on the effects of the proposed Republican healthcare legislation on families on Capitol Hill in Washington, Thursday, June 22, 2017.

Andrew Harnik

Originally published on July 18, 2018 8:07 am

Oregon Sens. Jeff Merkley, Ron Wyden and other Senate Democrats spent three hoursTuesday nightprotestingthe Republican Party’s approach to confirming federal judges — and one Oregon lawyer's nomination in particular.

They’re objecting to the nomination of Ryan Bounds to serve on the influential 9th Circuit Court of Appeals and, more broadly, to what they describe as GOP efforts to pack federal courts with extremist jurists.

The Senate has traditionally not proceeded with judicial nominations without the support of a potential judge’s home state senators. But Senate Republicans have pushed ahead with Bounds’ nomination despite Wyden and Merkley’s concerns.

“This deed of putting forward this nomination on this floor tonight changes 100-year tradition of comity in the U.S. Senate, and a recognition that the home state senators have something important to say about the integrity of the individual being put forward,” Merkley said as the unusually long session kicked off.

The Oregon senators say they weren't adequately consulted about the Bounds nomination and that the White House pushed the nomination through quickly despite their requests to slow down. And they have raised questions about inflammatory writing Bounds, an assistant U.S. attorney, did while in college at Stanford University and whether Bounds was upfront about those writings later.

Other Senators objected to how Senate Republicans have handled other nominations and to the role patterned mesh sneakers Black Prada nw8qd2ZdRT
have played in coming up with lists of potential nominees for the Trump administration.

It's unlikely Senate Democrats can block Bounds’ nomination, but they hope to slowit as a form of protest.

"The debate over Ryan Bounds is not a typical debate over a typical nomination," Wyden said. "It is vital the Senate look at this nomination in a broader context, particularly as it relates to what I call the decline of principled bipartisanship here in the Senate."

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