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Calorie Restriction with Optimum Nutrition Forum
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CRON4healthyfuture
Guest
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 Posted: Tue Feb 14, 2006 5:19 am Post subject: Low-fat vs. Low-carb; No winner yet? |
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A recent meta-analysis in the Archives of Internal Medicine suggests that low-carbohydrate diets, although beneficial for weight loss, do not seem to always change things for the better.
I personally am not a huge fan of "meta-analyses", but, for what it's worth......
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http://archinte.ama-assn.org/cgi/content/short/166/3/285
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| "Conclusions Low-carbohydrate, non–energy-restricted diets appear to be at least as effective as low-fat, energy-restricted diets in inducing weight loss for up to 1 year. However, potential favorable changes in triglyceride and high-density lipoprotein cholesterol values should be weighed against potential unfavorable changes in low-density lipoprotein cholesterol values when low-carbohydrate diets to induce weight loss are considered." - Archives of Internal Medicine |
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A1CR
Site Admin
Joined: 18 Jan 2006
Posts: 559
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| Posted: Wed Feb 15, 2006 12:08 am Post subject: Problems with Meta Analysis |
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| CRON4healthyfuture wrote: |
| I personally am not a huge fan of "meta-analyses"... |
From [ http://www.tufts.edu/~gdallal/meta.htm ] it is noted:
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Meta analysis always struggles with two issues:
1. publication bias (also known as the file drawer problem) and
2. the varying quality of the studies.
Publication bias is "the systematic error introduced in a statistical inference by conditioning on publication status." For example, studies showing an effect may be more likely to be published than studies showing no effect. (Studies showing no effect are often considered unpublishable and are just filed away, hence the name file drawer problem.) Publication bias can lead to misleading results when a statistical analysis is performed after assembling all of the published literature on some subject.
When assembling the available literature, it can be difficult to determine the amount of care that went into each study. Thus, poorly designed studies end up being given the same weight as well designed studies. This, too, can lead to misleading results when the data are summarized.
When large, high quality randomized, double-blind, controlled trials are available, they are the gold standard basis for action. Publication bias and the varying quality of other studies are not issues because there is no need to assemble the research in the area. So, to the two primary concerns about meta-analysis--publication bias and varying quality of the studies--I have added a third:
(3) Meta analysis is used only when problems (1) and (2) are all but certain to cause the most trouble! That is, meta-analysis is employed only when no large-scale, high quality trials are available and the problems of publication bias and the varying quality and outcomes of available studies all but guarantee it will be impossible to draw a clear conclusion! |
Some solutions to this problem are also presented at the Tufts site...
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Those who perform meta analyses are aware of these problems and have proposed a number of guidelines to minimize their impact.
* A formal protocol should be written specifying the exact question under investigation and describing the studies that will be included in the analysis.
* All research, not just published research, should be included.
* Registries should be established so that studies can be tracked from their inception and not just on publication. This idea has been given a push so that drug companies would not be able to publish trials showing benefit from their products while suppressing those that do not.
* Many meta analytic techniques should be used and all results should be reported. A result would be considered reliable only if all of the techniques give the same result. |
However the author "continue[s] to be skeptical and remain unconvinced that these procedures are sufficient to overcome the problems they seek to address." |
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