The Effects of Imputing the Missing Standard Deviations on the Standard Error of Meta Analysis Estimates

Idris, Nik Ruzni Nik; Robertson, Chris

doi:10.1080/03610910802556106

Cited by 21 publications

(20 citation statements)

References 16 publications

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“…Comparisons of PRB were made between the estimates based on the random effect model and the fixed effect model. The main conclusions drawn from this project support many of the findings from the previous literature [4,5,6]. We have illustrated that whether the estimates of overall effect size is based on the fixed or random effect model, imputation is a good approach in handling the problem of missing study-level SDs.…”

Section: B Random Effect Modelssupporting

confidence: 79%

“…In contrast, imputation is always recommended to estimate the SE of the effect size in data with missing SDs as otherwise serious bias may be introduced [4]. The results show that if the random effect model is used to estimate the SE of the estimate in the data where there is some missing SDs, both the non-parametric MI and mean imputation will give equally good estimates (no difference in estimates based on ; p < 0.337).…”

Section: B Random Effect Modelsmentioning

confidence: 55%

“…A popular approach in handling this problem is through imputation of the missing SDs [3]. Earlier studies which examined the effects of imputing the missing SDs on the overall meta analysis estimates [4,5,6] concludes that imputation recovers most of the lost information in the estimate of effect size and the corresponding SE. These studies however did not look at the effect of the model used to estimate the effect size and the corresponding SE.…”

Section: Introductionmentioning

confidence: 99%

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The Effects of the Choice of Meta Analysis Model on the Overall Estimates for Continuous Data with Missing Standard Deviations

Idris

Saidin

2010

2010 Second International Conference on Computer Engineering and Applications

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The choice between the fixed and random effects model for providing an overall meta analysis estimate in continuous data may affect the accuracy of these estimates. For studies with complete information, the Cochrane's Q-test could provide some guide on the choice, although the power of this test is quite low. If the study-level standard deviations (SDs) are not completely reported or "missing", selection of meta analysis model should be done with more caution. Many studies suggest that imputation is a good way of recovering the lost information in the effect size estimate and the corresponding standard error. In this article, we compare empirically, the effects of imputation of the missing SDs on the overall meta analysis estimates based on both the fixed and random effect model. The results suggest imputation is recommended to estimate the overall effect size. However, to estimate its corresponding standard error (SE), imputation is recommended for the estimates based on the random effect model. If the fixed effect model is used, imputation may lead to bias estimates of the SE.

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Section: B Random Effect Modelssupporting

confidence: 79%

Section: B Random Effect Modelsmentioning

confidence: 55%

Section: Introductionmentioning

confidence: 99%

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The Effects of the Choice of Meta Analysis Model on the Overall Estimates for Continuous Data with Missing Standard Deviations

Idris

Saidin

2010

2010 Second International Conference on Computer Engineering and Applications

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“…). The few studies that examined imputation in a meta‐analytic framework did so either using simple imputation processes, restricted modelling scenarios, or gauged against limited metrics (Thiessen Philbrook, Barrowman & Garg ; Idris & Robertson ; Idris & Sarudin ). A few manuscripts have described or used multiple imputation or similar techniques to recover missing data in ecological meta‐analyses (Cleasby & Nakagawa ; Nakagawa & Santos ).…”

Section: Introductionmentioning

confidence: 99%

“…Although multiple imputation techniques for meta-analysis have been used for some time in the medical and social sciences Robertson, Idris & Boyle 2004), its use is still inconsistent (Wiebe et al 2006). The few studies that examined imputation in a meta-analytic framework did so either using simple imputation processes, restricted modelling scenarios, or gauged against limited metrics (Thiessen Philbrook, Barrowman & Garg 2007;Idris & Robertson 2009;Idris & Sarudin 2011). A few manuscripts have described or used multiple imputation or similar techniques to recover missing data in ecological meta-analyses (Cleasby & Nakagawa 2012;Nakagawa & Santos 2012).…”

Section: Introductionmentioning

confidence: 99%

Using multiple imputation to estimate missing data in meta‐regression

et al. 2014

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Summary1. There is a growing need for scientific synthesis in ecology and evolution. In many cases, meta-analytic techniques can be used to complement such synthesis. However, missing data are a serious problem for any synthetic efforts and can compromise the integrity of meta-analyses in these and other disciplines. Currently, the prevalence of missing data in meta-analytic data sets in ecology and the efficacy of different remedies for this problem have not been adequately quantified. 2. We generated meta-analytic data sets based on literature reviews of experimental and observational data and found that missing data were prevalent in meta-analytic ecological data sets. We then tested the performance of complete case removal (a widely used method when data are missing) and multiple imputation (an alternative method for data recovery) and assessed model bias, precision and multimodel rankings under a variety of simulated conditions using published meta-regression data sets. 3. We found that complete case removal led to biased and imprecise coefficient estimates and yielded poorly specified models. In contrast, multiple imputation provided unbiased parameter estimates with only a small loss in precision. The performance of multiple imputation, however, was dependent on the type of data missing. It performed best when missing values were weighting variables, but performance was mixed when missing values were predictor variables. Multiple imputation performed poorly when imputing raw data which were then used to calculate effect size and the weighting variable. 4. We conclude that complete case removal should not be used in meta-regression and that multiple imputation has the potential to be an indispensable tool for meta-regression in ecology and evolution. However, we recommend that users assess the performance of multiple imputation by simulating missing data on a subset of their data before implementing it to recover actual missing data.

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Consequences of multiple imputation of missing standard deviations and sample sizes in meta‐analysis

Kambach

Bruelheide

Gerstner

et al. 2020

Ecology and Evolution

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Meta‐analyses often encounter studies with incompletely reported variance measures (e.g., standard deviation values) or sample sizes, both needed to conduct weighted meta‐analyses. Here, we first present a systematic literature survey on the frequency and treatment of missing data in published ecological meta‐analyses showing that the majority of meta‐analyses encountered incompletely reported studies. We then simulated meta‐analysis data sets to investigate the performance of 14 options to treat or impute missing SDs and/or SSs . Performance was thereby assessed using results from fully informed weighted analyses on (hypothetically) complete data sets. We show that the omission of incompletely reported studies is not a viable solution. Unweighted and sample size‐based variance approximation can yield unbiased grand means if effect sizes are independent of their corresponding SD s and SSs . The performance of different imputation methods depends on the structure of the meta‐analysis data set, especially in the case of correlated effect sizes and standard deviations or sample sizes. In a best‐case scenario, which assumes that SDs and/or SSs are both missing at random and are unrelated to effect sizes, our simulations show that the imputation of up to 90% of missing data still yields grand means and confidence intervals that are similar to those obtained with fully informed weighted analyses. We conclude that multiple imputation of missing variance measures and sample sizes could help overcome the problem of incompletely reported primary studies, not only in the field of ecological meta‐analyses. Still, caution must be exercised in consideration of potential correlations and pattern of missingness.

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The Effects of Imputing the Missing Standard Deviations on the Standard Error of Meta Analysis Estimates

Cited by 21 publications

References 16 publications

The Effects of the Choice of Meta Analysis Model on the Overall Estimates for Continuous Data with Missing Standard Deviations

The Effects of the Choice of Meta Analysis Model on the Overall Estimates for Continuous Data with Missing Standard Deviations

Using multiple imputation to estimate missing data in meta‐regression

Consequences of multiple imputation of missing standard deviations and sample sizes in meta‐analysis

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