Section 5: The limits of statistics in .NET Develop QRCode in .NET Section 5: The limits of statistics

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Section 5: The limits of statistics generate, create qr-codes none with .net projects Viual Cshap error. It is more e ectiv qr codes for .NET e to use descriptive statistics as above, which suggest some likelihood of higher manic switch risk at least with tricyclic antidepressants (TCAs) compared to placebo.

Thus, apparent agreement among studies hides major con icting results between the only adequately designed study using the most proven mood stabilizer, lithium, and the rest (either no mood stabilizer use or use of less proven agents).. Meta-analysis as interpretation The above example demonst QR Code ISO/IEC18004 for .NET rates the dangers of meta-analysis, as well as some of its benefits. Ultimately, meta-analysis is not the simple quantitative exercise that it may appear to be, and that some of its aficionados appear to believe is the case.

It involves many, many interpretive judgments, much more than in the usual application of statistical concepts to a single clinical trial. Its real danger, then, as Eysenck tried to emphasize (Eysenck, 1994), is that it can put an end to discussion, based on biased interpretations cloaked with quantitative authority, rather than leading to more accurate evaluation of available studies. At root, Eysenck points out that what matters is the quality of the studies, a matter that is not itself a quantitative question (Eysenck, 1994).

Meta-analysis can clarify, and it can obfuscate. By choosing one s inclusion and exclusion criteria carefully, one can still prove whatever point one wishes. Sometimes meta-analyses of the same topic, published by different researchers, directly conflict with each other.

Metaanalysis is a tool, not an answer. We should not let this method control us, doing metaanalyses willy-nilly on any and all topics (as unfortunately appears to be the habit of some researchers), but rather cautiously and selectively where the evidence seems amenable to this kind of methodology..

Meta-analysis is less valid than RCTs One last point deserves t QR for .NET o be re-emphasized, a point which meta-analysis mavens sometimes dispute, without justification: meta-analysis is never more valid than an equally large single RCT. This is because a single RCT of 500 patients means that the whole sample is randomized and confounding bias should be minimal.

But a meta-analysis of 5 different RCTs that add up to a total of 500 patients is no longer a randomized study. Meta-analysis is an observational pooling of data; the fact that the data were originally randomized no longer applies once they are pooled. So if they conflict, the results of meta-analysis, despite the fanciness of the word, should never be privileged over a large RCT.

In the case of the example above, that methodologically flawed meta-analysis does not come close to the validity of a recently published large RCT of 366 patients randomized to antidepressants versus placebo for bipolar depression, in which, contrary to the meta-analysis, there was no benefit with antidepressants (Sachs et al., 2007)..

Statistical alchemy Alvan Feinstein (Feinstei QR Code 2d barcode for .NET n, 1995) has thoughtfully critiqued meta-analysis in a way that pulls together much of the above discussion. He notes that, after much effort, scientists have come to a consensus about the nature of science; it must have four features: reproducibility, precise characterization, unbiased comparisons ( internal validity ), and appropriate generalization ( external validity ).

Readers will note that he thereby covers the same territory I use. 13: The alchemy of meta-analysis in this book as the three QR-Code for .NET organizing principles of statistics: bias, chance, and causation. Metaanalysis, Feinstein argues, ruins all this effort.

It does so because it seeks to convert existing things into something better. Significance can be attained statistically when small group sizes are pooled into big ones; and new scientific hypotheses, that had inconclusive results or that had not been originally tested, can be examined for special subgroups or other entities. These benefits come at the cost, though, of the removal or destruction of the scientific requirements that have been so carefully developed .

. . He makes the analogy to alchemy because of the idea of getting something for nothing, while simultaneously ignoring established scientific principles.

He calls this the free lunch principle, which makes meta-analysis suspect, along with the mixed salad principle, his metaphor for heterogeneity (implying even more drastic differences than apples and oranges). He notes that meta-analysis violates one of Hill s concepts of causation: the notion of consistency. Hill thought that studies should generally find the same result; meta-analysis accepts studies with differing results, and privileges some over others: With meta-analytic aggregates .

. . the important inconsistencies are ignored and buried in the statistical agglomeration.

Perhaps most importantly, Feinstein worried that researchers would stop doing better and better studies, and spend all their time trying to wrench truth from meta-analysis of poorly done studies. In effect, meta-analysis is unnecessary where it is valid, and unhelpful where it is needed: where studies are poorly done, meta-analysis is unhelpful, only combining highly heterogeneous and faulty data, thereby producing falsely precise but invalid meta-analytic results. Where studies are well done, meta-analysis is redundant: My chief complaint .

. . is that meta-analysis of randomized trials concentrates on a part of the scientific domain that is already reasonably well lit, while ignoring the much larger domain that lies either in darkness or in deceptive glitters.

As mentioned in 12, Feinstein s critique culminates in seeing meta-analysis as a symptom of EBM run amuck (Feinstein and Horwitz, 1997), with the Cochrane Collaboration in Oxford as its symbol, a new potential source of Galenic dogmatism, now in statistical guise. When RCTs are simply immediately put into meta-analysis software, and all other studies are ignored, then the only way in which meta-analysis can be legitimate careful assessment of quality and attention to heterogeneity is obviated. Quoting the statistician Richard Peto, Feinstein notes that the paintstaking detail of a good meta-analysis just isn t possible in the Cochrane collaboration when the procedures are done on an industrial scale.

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