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# 可以幫忙翻譯英文期刊的文章嗎(醫學與統計) 2

Logistic regression was used to estimate the odds ration and 95% confidence interval. Univariate analyses were performed between the candidate independent variable and ESRD using simple logistic regression value of less than 0.05 was considered to be statistically significant. Independent variables associated with ESRD with a p value of lees than or equal to 0.05 in the univariate logistic regression analysis were considered in the multivariate modeling of ESRD, using multiple logistic regression. To increase the precision of the estimation without sacrificing validity, we used a backwards procedure to find a “core model” of “important” predictors. Initially all hypothesized risk factors (exposures) were included as covariates in the model, then the factor with the smallest correlation coefficient was dropped, etc. Thus, each covariate in the core model had an observed effect on ESRD adjusting for all the other predictors in that model. In addition, we tested whether these data fit the model using Hosmer-Lemeshow goodness-fit test. Because income and education were closely correlated (test for linear association using the extended Mantel-Haenszel test, P< 0.0001), attempting to use income and education in the same model to predict ESRD was inconvenient because they were collinear. We separately examined the association between the 2 factors(education and income) and ESRD in 2 multiple logistic regressions.

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這是我的翻譯

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把下面的英文

Logistic regression was used to estimate the odds ration and 95% confidence interval. Univariate analyses were performed between the candidate independent variable and ESRD using simple logistic regression value of less than 0.05 was considered to be statistically significant. Independent variables associated with ESRD with a p value of lees than or equal to 0.05 in the univariate logistic regression analysis were considered in the multivariate modeling of ESRD, using multiple logistic regression. To increase the precision of the estimation without sacrificing validity, we used a backwards procedure to find a “core model” of “important” predictors. Initially all hypothesized risk factors (exposures) were included as covariates in the model, then the factor with the smallest correlation coefficient was dropped, etc. Thus, each covariate in the core model had an observed effect on ESRD adjusting for all the other predictors in that model. In addition, we tested whether these data fit the model using Hosmer-Lemeshow goodness-fit test. Because income and education were closely correlated (test for linear association using the extended Mantel-Haenszel test, P< 0.0001), attempting to use income and education in the same model to predict ESRD was inconvenient because they were collinear. We separately examined the association between the 2 factors(education and income) and ESRD in 2 multiple logistic regressions.

翻譯成中文:

Logistic回歸來估計的可能性口糧和95％的置信區間。單因素分析是候選人之間進行的自變量和ESRD使用簡單的邏輯回歸值小於0.05被認為是統計學意義。自變量與終末期腎病的酒糟鴨價值大於或等於0.05的單因素logistic回歸分析，認為在多元建模終末期腎病，使用多個邏輯回歸。為了提高估計精度不犧牲的有效性，我們用反向程序找到一個“核心模式”的“重要”的預測。最初所有假設的風險因素（張）被列為協變量的模型，然後因素，最小的相關係數為下降等，因此，每個協變量的核心模型觀察到的影響有一個調整的終末期腎病的所有其他預測的這一模式。此外，我們測試的這些數據是否符合該機型採用霍斯默- Lemeshow擬合優度檢驗。由於收入和教育是密切相關（線性協會測試使用擴展Mantel - Haenszel法檢驗，P“0.0001），企圖利用收入和教育在同一模型預測終末期腎病的不便，因為他們是共線。我們分別檢測了2之間的關聯因素（教育和收入）和終末期腎病的多因素Logistic回歸2。

後勤退化被用于估計可能性定量和95%信赖区间。 單變量的分析執行了在候選人独立变量之間，並且使用簡單的後勤退化價值的ESRD少于0.05认為統計地重大的。 独立变量联合與渣滓的p價值的ESRD使用多後勤退化，比或均等到0.05在單變量的後勤回归分析在多維分佈塑造被考慮了ESRD。 要增加估計的精確度，无需犧牲有效性，我們向後使用一個做法發現「核心模型」「重要」預報因子。 最初所有被假設的风险因素(曝光)包括作為covariates在模型，然后與最小的相关系数的因素滴下了等等。 因此，在核心模型的每covariate有對调整為所有其他預報因子的ESRD的被觀察的作用在那個模型。 另外，我們測試這些數據是否適合了模型使用Hosmer-Lemeshow善良適合了測試。 由于收入和教育嚴密地被關聯了(線性協會的使用延長的壁爐臺Haenszel測試， P<測試; 因為他們是在同一直線上的， 0.0001)，試圖使用收入和教育在同一個模型預言ESRD是不便的。 我們分別地審查了在2個因素(教育和收入)和ESRD之間的協會在2多後勤退化。

2009-10-02 15:56:53 補充：

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Source(s): 字典

大大,你的翻譯看起來怪怪的,醫學的的名詞我是不懂,

但是統計的名詞我比較熟,比如95%的信賴區間,

統計結果小於0.05,顯示統計結果是顯著的

給大大參考囉