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

Color Doppler imaging in glaucoma patients with asymmetric visual field loss

WOS: 000088656400016With color Doppler imaging, we attempted to determine whether glaucoma patients with asymmetric visual field losses had evidence of asymmetric blood flow velocities in the central retinal artery despite similar intraocular pressure (IOP) curves in both eyes. We found that eyes with more severe visual field damage had an increased local resistance to blood flow in the central retinal artery. Thus vascular factors might have important roles in the pathogenesis of primary open-angle glaucoma

Observer interpretation variability of peripapillary flow using the Heidelberg Retina Flowmeter.

To evaluate the intraobserver and interobserver reproducibility of the automatic full field perfusion image analysis (AFFPIA) program on Heidelberg Retina Flowmeter (HRF) derived perfusion images in a multicentre study group.A total of 10 subjects were consecutively recruited in the study. One eye was randomly selected for each patient. Blood flow was assessed by HRF and flow measurements were analyzed by using the AFFPIA program. AFFPIA calculates the Doppler frequency shift and the haemodynamic variables: flow for each pixel. Intraobserver and interobserver reproducibility was calculated for AFFPIA program. The retinal blood flow was calculated in the superior and inferior section, furthermore, each section was divided into three parts: the temporal area, the nasal, and the rim area, as for software, but only the temporal and nasal areas were considered in this study. The blood flow and the area considered were evaluated for each part.When the intraobserver and intraimage reproducibility was studied, the coefficient of variation ranged from 0.4 to 1.9\%. When the interobserver and intraimage reproducibility was studied, the retinal blood flow coefficient of variation ranged from 0.52 to 3.30\% for the supero-temporal area, from 0.13 to 2.67\% for the inferotemporal area, from 0.15 to 2.75\% for the supero-nasal area, and from 0.04 to 5.65\% for the infero-nasal area.Our results with AFFPIA showed an interobserver coefficient of variation of retinal blood flow measurements always less than 6\% in both temporal and nasal areas. No significant difference was found among the four observers for the flow measurements