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\nhttp:\/\/homepage3.nifty.com\/aihara\/meta_analysis3.html<\/strong><\/a><\/p>\n

\u56fa\u5b9a\u52b9\u679c\u30e2\u30c7\u30eb\u3092\u4f7f\u3063\u3066\u8a55\u4fa1\u3057\u3066\u307f\u3066\u7570\u8cea\u6027\u304c\u3042\u308b\u306e\u3067\u30e9\u30f3\u30c0\u30e0\u52b9\u679c\u30e2\u30c7\u30eb\u3092\u4f7f\u3046\u3068\u3044\u3046\u306e\u306f\u8aa4\u308a\u3067\u3059\u3002<\/span><\/p>\n

\u4e21\u8005\u306e\u9055\u3044\uff1a<\/strong>
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\u56fa\u5b9a\u52b9\u679c\u30e2\u30c7\u30eb\u306e\u5229\u7528<\/strong>
\nFixed effect model
\nIt makes sense to use the fixed-effect model if two conditions are met.
\nFirst, we believe that all the studies included in the analysis are functionally identical. Second, our goal is to compute the common effect size for the identified populatin, and not to generalize to other populations. \uff08Introduction to metaanalysis\u3088\u308a\uff09<\/p>\n

IOM\u306e\u30b7\u30b9\u30c6\u30de\u30c6\u30a3\u30c3\u30af\u30ec\u30d3\u30e5\u30fc\u306e\u305f\u3081\u306e\u57fa\u6e96<\/strong><\/a>
\nWhen pooling is feasible, investigators typically use one of two statistical techniques\u2014fixed-effects or random-effects models\u2014to 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.
\n\uff08Finding what works in health care)<\/p>\n","protected":false},"excerpt":{"rendered":"

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