A Study on the Effects of Different Levels of Data on the Overall Meta-Analysis Estimates

Idris, Nik Ruzni Nik; Abdullah, Mimi Hafizah

doi:10.17654/fjmsjan2015_073_086

Cited by 4 publications

(13 citation statements)

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“…These findings were consistent with earlier results (Riley et al, 2008;Idris and Abdullah, 2015) whose researchers compared the estimates from all-IPD and all-AD studies to the combined AD:IPD studies. We demonstrated that the value of combining AD:IPD was significant even if only 20% of the available studies were at the IPD level.…”

Section: Discussionsupporting

confidence: 92%

“…Riley et al (2008) results showed that the benefit of combining the IPD with AD increases as the proportion of IPD studies decreases and that the differences in the estimates from all AD and AD:IPD combined studies were relatively small. These findings are supported by another simulation-based study (Idris and Abdullah, 2015), for which the researchers concluded that the benefit of combining the data is greater if the majority of the studies to be combined are at AD-level and that if more than 80% of the studies are IPD, including the AD is not recommended as the statistical benefit is not significant. In fact, in this case, it would only serve to increase the overall SE.…”

Section: Introductionmentioning

confidence: 71%

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A Modified Two-Stage Method for Combining the Aggregate-Data and Individual-Patient-Data in Meta-Analysis

Idris¹,

Misran²

2015

J. of Applied Sciences

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A B S T R A C TThe objective of this study is to ascertain the efficacy of the existing two-stage method for combining the Aggregate Data (AD) and Individual Patient Data (IPD) studies in meta-analysis and to introduce some modifications to the existing twostage method for combining the these studies. We evaluated the effects of these modifications on the estimates of the overall treatment effect and investigated the influence of the number of studies included in the meta-analysis, N and the ratio of AD: IPD on these estimates. We used Percentage Relative Bias (PRB), Root Mean Square Error (RMSE) and coverage probability to assess the overall efficiency of these estimates. The results revealed that the current method for combining the AD:IPD studies exhibit rather poor coverage probability. We showed that the proposed methods had been able to improve the coverage probability by more than 40%, while maintaining the level of bias at par to their existing counterparts. These findings demonstrated the significance of proper technique for combining these studies in order to obtain better overall estimates, which is crucial for reliable overall conclusions.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 71%

A Modified Two-Stage Method for Combining the Aggregate-Data and Individual-Patient-Data in Meta-Analysis

Idris¹,

Misran²

2015

J. of Applied Sciences

View full text Add to dashboard Cite

show abstract

“…These findings are supported by another simulation-based study (Idris & Abdullah, 2015), for which the researchers concluded that the benefit of combining the data is greater if the majority of the studies to be combined are at AD-level and that if more than 80% of the studies are IPD, including the AD would only serve to increase the overall SE. Idris & Abdullah (2015) additionally noted that the while the bias and MSE was better for combined-level data, the coverage probability of the estmates were lower compared to those from the AD studies.…”

Section: Introductionmentioning

confidence: 66%

“…Idris & Abdullah (2015) additionally noted that the while the bias and MSE was better for combined-level data, the coverage probability of the estmates were lower compared to those from the AD studies.…”

Section: Introductionmentioning

confidence: 92%

“…Only two studies had examined the efficacy of the estimates that are based on combined-level studies (Riley et al, 2008;Idris & Abdullah, 2015). Riley et al (2008) took their data from a study on the effects of hypertension (Wang et al, 2005) and compared estimates obtained from a meta-analysis that combined IPD and AD studies using a two-stage method.…”

Section: Introductionmentioning

confidence: 99%

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Combining Aggregate Data and Individual Patient Data in Meta-Analysis: An Alternative Method

Idris

Misran

2015

MIJST

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Efficient integration of aggregate data and individual participant data in one‐way mixed models

Agarwala

Park

2022

Statistics in Medicine

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Often both aggregate data (AD) studies and individual participant data (IPD) studies are available for specific treatments. Combining these two sources of data could improve the overall meta‐analytic estimates of treatment effects. Moreover, often for some studies with AD, the associated IPD maybe available, albeit at some extra effort or cost to the analyst. We propose a method for combining treatment effects across trials when the response is from the exponential family of distribution and hence a generalized linear model structure can be used. We consider the case when treatment effects are fixed and common across studies. Using the proposed combination method, we study the relative efficiency of analyzing all IPD studies vs combining various percentages of AD and IPD studies. For many different models, design constraints under which the AD estimators are the IPD estimators, and hence fully efficient, are known. For such models, we advocate a selection procedure that chooses AD studies over IPD studies in a manner that force least departure from design constraints and hence ensures an efficient combined AD and IPD estimator.

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A Study on the Effects of Different Levels of Data on the Overall Meta-Analysis Estimates

Cited by 4 publications

References 0 publications

A Modified Two-Stage Method for Combining the Aggregate-Data and Individual-Patient-Data in Meta-Analysis

A Modified Two-Stage Method for Combining the Aggregate-Data and Individual-Patient-Data in Meta-Analysis

Combining Aggregate Data and Individual Patient Data in Meta-Analysis: An Alternative Method

Efficient integration of aggregate data and individual participant data in one‐way mixed models

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