Performing meta-analysis with incomplete statistical information in clinical trials

<p>Abstract</p> <p>Background</p> <p>Results from clinical trials are usually summarized in the form of sampling distributions. When full information (mean, SEM) about these distributions is given, performing meta-analysis is straightforward. However, when some of the sampling distributions only have mean values, a challenging issue is to decide how to use such distributions in meta-analysis. Currently, the most common approaches are either ignoring such trials or for each trial with a missing SEM, finding a similar trial and taking its SEM value as the missing SEM. Both approaches have drawbacks. As an alternative, this paper develops and tests two new methods, the first being the prognostic method and the second being the interval method, to estimate any missing SEMs from a set of sampling distributions with full information. A merging method is also proposed to handle clinical trials with partial information to simulate meta-analysis.</p> <p>Methods</p> <p>Both of our methods use the assumption that the samples for which the sampling distributions will be merged are randomly selected from the same population. In the prognostic method, we predict the missing SEMs from the given SEMs. In the interval method, we define intervals that we believe will contain the missing SEMs and then we use these intervals in the merging process.</p> <p>Results</p> <p>Two sets of clinical trials are used to verify our methods. One family of trials is on comparing different drugs for reduction of low density lipprotein cholesterol (LDL) for Type-2 diabetes, and the other is about the effectiveness of drugs for lowering intraocular pressure (IOP). Both methods are shown to be useful for approximating the conventional meta-analysis including trials with incomplete information. For example, the meta-analysis result of Latanoprost versus Timolol on IOP reduction for six months provided in <abbrgrp><abbr bid="B1">1</abbr></abbrgrp> was 5.05 ± 1.15 (Mean ± SEM) with full information. If the last trial in this study is assumed to be with partial information, the traditional analysis method for dealing with incomplete information that ignores this trial would give 6.49 ± 1.36 while our prognostic method gives 5.02 ± 1.15, and our interval method provides two intervals as Mean ∈ [4.25, 5.63] and SEM ∈ [1.01, 1.24].</p> <p>Conclusion</p> <p>Both the prognostic and the interval methods are useful alternatives for dealing with missing data in meta-analysis. We recommend clinicians to use the prognostic method to predict the missing SEMs in order to perform meta-analysis and the interval method for obtaining a more cautious result.</p

An XML based framework for merging incomplete and inconsistent statistical information from clinical trials.

Meta-analysis is a vital task for systematically summarizing statistical results from clinical trials that are carried out to compare the effect of one medication (or other treatment) against another. Currently, most meta-analysis activities are done by manually pooling data. This is a very time consuming and expensive task. An automated or even semi-automated tool that can support some of the processes underlying meta-analysis is greatly needed. Furthermore, statistical results from clinical trials are usually represented as sampling distributions (i.e., with the mean value and the SEM). When collecting statistical information from reports on clinical trials, not all reports contain full statistical information (i.e., some do not provide SEMs) whilst traditional meta-analysis excludes trials reports that contain incomplete information,which inevitably ignores many trials that could be valuable. Furthermore, some trials results can be significantly inconsistent with the rest of trials that address the same problem. Therefore, highlighting (resp. removing) such inconsistencies is also very important to reveal (resp. reduce) any potential flaws in some of the trials results. In this paper, we aim to design and develop a framework that tackles the above three issues. We first present an XML-based merging framework that aims to merge statistical information automatically with the potential to add a component to extract clinical trials information automatically. This framework shall consider any valid clinical trial including trials with partial information. We then develop a method to analyze inconsistencies among a collection of clinical trials and if necessary to exclude any trials that are deemed to be illegible. Finally, we use two sets of clinical trials, trials on Type 2 diabetes and on neurocognitive outcomes after off-pump versus on-pump coronary revascularisation, to illustrate our framework. © 2010 Springer-Verlag Berlin Heidelberg