Pooled effect sizes were calculated using random effects models.
利用随机效应模型计算了混合效应的大小。
Pooled relative risks (RRs) were estimated by random effects models.
混合相对危险度(RRs)通过随机效果模型进行估测。
The effect of deep tube well use on the incidence of childhood diarrhoea was assessed using a random effects negative binomial regression model.
采用随机影响负二项回归模型，评估深管井的使用对儿童发生腹泻的影响。
Fixed and random effects models were used to pool the data.
使用固定与随机效果模型整合资料。
The summary relative risks (RRs) were estimated by the random effects model.
利用随机效应模型来估计相对危险度。
The detection of multiple outliers in random effects models of the unbalanced one-way classifications is studied in this paper.
研究了非平衡单向分类随机效应模型中多个异常值的检验问题。
It is difficult to establish the validity of any distributional assumption, and this is a common criticism of random effects meta-analyses.
建立一个有效的分布形式是很困难的，这也是随机效应模型通常被批评的原因。
When the answers do not coincide, the random effects estimate may not reflect the actual effect in any particular population being studied.
当答案不一致时，在任何一个被研究的总体中，随机效应估计也许不能反应真正的效应。
Results Repeated measurement obtained reasonable results by the fixed and random effects along with efficient estimate of covariance matrix.
结果通过固定效应与随机效应及对协方差矩阵的估计，使重复测量数据得以合理的分析。
Treatment effects were summarised as relative risks or weighted mean differences with 95% confidence intervals by using a random effects model.
治疗效果采用相对危险度和加权均数差(采用随机效应模型的95%可信区间)概括。
Where significant or apparent heterogeneity was found, results were reported using the random effects model and potential causes of the heterogeneity were sought.
若结果有显著差异则报告以随机效应模式呈现，并探讨可能的原因。
A random effects meta-analysis model involves an assumption that the effects being estimated in the different studies are not identical, but follow some distribution.
随机效应的Meta分析模型涉及这样一个假定，在各个研究中的被估计效应是不一样的，但遵循某种分布。
Based on panel data, the fixed effects model and the random effects model are used to estimate the performance of agriculture loan in the period from 1999 to 2002 in China.
文章基于面板数据，利用固定效应估计法和随机效应估计法，对我国在1999年至2002年间农业信贷的绩效作出实证研究。
CONCLUSION: multivariate random coefficients model can effectively analyze the dynamic change trend and random effects of multivariate repeated measures data in medical research.
结论：多变量随机系数模型可有效地进行多变量重复观测数据的动态变化趋势分析以及随机效应分析。
The precision of mixed model considering plot random effects, time series error autocorrelation and different plantation density at one time is better than that of ordinary regression analysis method.
同时考虑样地的随机效应、观测数据的时间序列相关性及不同初植密度的混合模型模拟精度比传统的非线性回归方法模拟精度高。
Two reviewers extracted data, which were aggregated using random-effects models.
由两名评论员通过随机效应模型提取数据。
Where there was significant statistical heterogeneity, we reported risk ratios (RRs) using the random-effects model. For other outcomes we used the fixed-effect model.
在有显著的统计异质性时，使用随机效应模型来呈现相对风险值，其他的预后值则使用固定效应模型。
Pooled relative risk (RR) and 95% confidence interval (CI) were calculated using a random-effects model.
共同的相对危险率和95%的可信区间用一种随机效应模型被计算出来。
Random-effects model meta-regression analyses were performed to assess sources of intertrial heterogeneity.
进行随机效果模型统合回归分析以评估试验间异质性的来源。
Preplanned sensitivity and subgroup analyses were performed after data were analysed with a random-effects model.
在采用随机效应模型分析数据后实施预先计划的敏感性和亚组分析。
Then, for each available comparison, we conducted a random-effects meta-analysis.
对于每一项可取得的比较项目，我们进行随机效应统合分析。
We meta-analysed data using a random-effects model on the primary and key secondary outcomes and formally assessed heterogeneity.
我们在主要与关键次级结果中，以随机效应模式将资料进行统合分析，并正式地评估其异质性。
We meta-analysed data using a random-effects model on the primary and key secondary outcomes and formally assessed heterogeneity.
我们在主要与关键次级结果中，以随机效应模式将资料进行统合分析，并正式地评估其异质性。
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