To explore the multi-level Meta analysis model combining individual level data with aggregative level data and its application in medicine.
The difference in respect to "decreased hemoglobin A(1c)" between treatment with roglizatone and treatment without roglizatone was obtained by combining individual level data from clinical trial of roglizatone natrium with aggregative level data from literatures. This endeavor was regarded as an example to construct the multi-level Meta analysis model combining individual level data with aggregative level data.
The baseline hemoglobin A(1c) was 9% and the dose of drug was 4 mg per day. The average difference in "decreased hemoglobin A(1c)" between treatment with and without roglizatone was 0. 464%, with a 95% CI from 0.168% to 0.760%.
In case that individual data are available, the multi-level Meta analysis model combining individual level data with aggregative level data can utilize the data resources adequately and the results obtained should be more accurate.
[Show abstract][Hide abstract] ABSTRACT: Meta-analysis of individual patient data (IPD) is the gold-standard for synthesizing evidence across clinical studies. However, for some studies IPD may not be available and only aggregate data (AD), such as a treatment effect estimate and its standard error, may be obtained. In this situation, methods for combining IPD and AD are important to utilize all the available evidence. In this paper, we develop and assess a range of statistical methods for combining IPD and AD in meta-analysis of continuous outcomes from randomized controlled trials.
The methods take either a one-step or a two-step approach. The latter is simple, with IPD reduced to AD so that standard AD meta-analysis techniques can be employed. The one-step approach is more complex but offers a flexible framework to include both patient-level and trial-level parameters. It uses a dummy variable to distinguish IPD trials from AD trials and to constrain which parameters the AD trials estimate. We show that this is important when assessing how patient-level covariates modify treatment effect, as aggregate-level relationships across trials are subject to ecological bias and confounding. We thus develop models to separate within-trial and across-trials treatment–covariate interactions; this ensures that only IPD trials estimate the former, whilst both IPD and AD trials estimate the latter in addition to the pooled treatment effect and any between-study heterogeneity. Extension to multiple correlated outcomes is also considered. Ten IPD trials in hypertension, with blood pressure the continuous outcome of interest, are used to assess the models and identify the benefits of utilizing AD alongside IPD. Copyright
Statistics in Medicine 05/2008; 27(11):1870-93. DOI:10.1002/sim.3165 · 1.83 Impact Factor
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