***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: In clinical measurement comparison of a new measurement technique with an established one is often needed to see whether they agree sufficiently for the new to replace the old . Such investigations are often analysed inappropriately , notably by using correlation coefficients . The use of correlation is misleading . An alternative approach , based on graphical techniques and simple calculations , is described , together with the relation between this analysis and the assessment of repeatability Key Summary Points When r and omized trials have been done for several different treatment options for the same condition , it is important to underst and the pattern of all these comparisons . This pattern of comparisons is called the geometry of the treatment network . Existing indices can be used to measure the diversity and co-occurrence of treatments . These measures are the probability of interspecific encounter index and the co-occurrence score , respectively . An evaluation of 18 treatment networks for different conditions with 4 to 16 treatments each reveals distinct geometries in clinical evidence . The geometry of a treatment network may reflect both rational choices and various preference biases . Many diseases have several competing treatments ; however , clinical research , systematic review s , and meta-analyses have tended to focus on assessing the merits of 1 treatment at a time or , at most , the relative merits of 2 different regimens . This approach provides only a partial view of the possible wealth of treatment options for a given condition , without addressing the relative merits of all available options . The comparisons between different treatments constitute a network . Appropriate methodology has been developed for combining the data from a network of trials ( 14 ) , allowing the incorporation of evidence from all direct and indirect comparisons toward estimating summary treatment effects . We do not address the techniques here ; rather , we focus on the patterns ( geometry ) of direct comparisons within each network and what those patterns might mean . When examining a treatment network , some key questions need to be addressed . First , are there few or many treatments and , if many , are all treatments represented equally in the network or have some been disproportionately studied ? Second , are some pairs preferentially studied , whereas evidence is systematic ally lacking for some other comparisons ? In the terminology of the ecological sciences , these issues pertain to diversity and co-occurrence , respectively . We cumulatively call these properties the geometry of the network . A network 's geometry may reflect the wider clinical context of the evidence and may be shaped by rational choices for treatment comparators or by specific biases . In another study ( 2 ) , we preliminarily adapted metrics from the ecological literature to describe the diversity and co-occurrence features of treatment networks . Here , we extend and further operationalize these metrics by demonstrating how exploration of network geometry may help with interpretation of the evidence on multiple competing treatments for the same indication . Methods Network Geometry The diagram of all comparisons between treatments in a network can take many different shapes . At one extreme , the same comparator is used in all trials . This network resembles a star , with the common reference st and ard ( often placebo or no treatment ) in its center . At the other extreme , all treatments are compared with all others and no particular treatment or comparison is studied more often than the others . A wide range of intermediate situations is possible between these 2 extremes . Comparator Preference Bias Network geometry is determined by many parameters . Networks may vary in complexity from the simple ( such as a network in which only 3 treatment options exist ) to the very complex ( such as a network involving several dozens of treatments ) . Networks may also be asymmetric , meaning that certain treatments or comparisons are represented more heavily than others . Investigators ' preference for specific treatments may be dictated by various factors , including how long a treatment has been available for testing , the treatment 's perceived effectiveness and safety , how actively its development agenda has been pushed by its sponsors , and whether regulatory m and ates force or discourage its use ( 5 ) . When a treatment has been the reference st and ard for a long time , it may become widely accepted that investigators should always consider it as one of the compared options . Finally , what is studied is not necessarily what is eventually published : Selective reporting biases , publication bias , time lag bias , and other selection forces further affect the amount of publicly visible evidence on specific treatments ( 6 , 7 ) . Similar forces may also shape the relative amount of evidence for specific comparisons within the network . In addition , head-to-head comparisons between effective agents may be avoided in some fields ; the agents ' sponsors may not wish to risk an agent being declared inferior and losing its market share . Selecting straw man comparators may result in exaggerated treatment effects , particularly in industry-sponsored trials ( 810 ) . The extent of these various selection forces are expected to vary among networks . We cumulatively call these selection forces comparator preference bias . Evaluation of network geometry and the accompanying asymmetry may help address such bias . Underst and ing the underlying reasons requires careful examination of each network and knowledge of the field and the circumstances under which its evidence was accumulated . Data Sources and Selection We search ed PubMed through April 2007 by using the following algorithms : network AND meta- analysis , multiple treatment AND meta- analysis , indirect comparison AND meta- analysis , and indirect AND meta- analysis . We also search ed Thompson ISI Web of Knowledge for studies that cited method ological articles on multiple treatments meta- analysis ( 1 , 3 , 4 , 11 ) . We limited our search to English- language articles . We included meta-analyses of r and omized , controlled trials that involved at least 4 different treatments . The term treatment is used here to denote any kind of intervention ( medical , surgical , or other ) under any circumstances ( therapeutic or preventive ) , as well as no treatment or placebo . Where meta-analysts merged different schedules or doses of the same regimen or similar agents , we respected this merging . We excluded meta-analyses when their eligibility criteria excluded comparisons among treatments otherwise considered in the network . We did not consider the simplest networks of only 3 different treatments , because those have been extensively studied elsewhere ( 12 ) . Data Extraction One author extracted data about the main outcome and the condition studied ; the number of studies per comparison ; and , when available , the number of individuals per r and omized group . A second review er checked the extracted information . Statistical Analysis Diversity A network has less diversity when it contains fewer treatments . Among networks that contain the same number of treatments , a network has less diversity when the treatments are not equally represented ( some treatments are used more often than others ) . We use the probability of interspecific encounter ( PIE ) index , which is the probability that 2 r and omly sample d treatment groups from the network ( without replacement ) have been allocated to 2 different treatments . The PIE index is a continuous variable that decreases with increasing unevenness ; for operational purpose s , values of 0.75 or less can be considered to reflect limited diversity ( 13 ) . We also use the PIE index , which is the value of PIE divided by the maximum value that PIE can take for the given number of studies . The Appendix further discusses some properties of PIE and PIE . Co-occurrence Co-occurrence reflects whether particular pairwise comparisons of specific treatments are preferred or avoided . The number of checkerboard units shows how many times 2 treatments could have been compared , given their relative frequencies . The C-score reflects the overall average tendency of the treatments not to occur together . The statistical significance of the C-score is measured by using a permutations procedure . A P value less than 0.05 is considered to represent significant co-occurrence ; however , a more liberal threshold of less than 0.10 may also be used because the test tends to be underpowered with few trials ( 13 ) . We plotted the number of checkerboard units for each comparison in networks that showed significant co-occurrence . Visual inspection of the plot can reveal whether the apparent co-occurrence is due to a particular comparison that is highly avoided or to a small degree of selectiveness in many of the comparisons . All calculations were performed by using EcoSim software , version 7 ( 14 ) . Results Eligible Treatment Networks We retrieved 41 potentially eligible articles . Of those , 12 included fewer than 4 treatments , 8 excluded specific comparisons among otherwise eligible treatments , 1 did not form a network , 1 used some data for a method ological project but the network could not be reconstructed , and 1 used a subset of a network presented elsewhere . We included 18 eligible networks ( Figure 1 and Appendix Table 1 ) ( 3 , 1531 ) . Most of these networks were published very recently ( 2 in 1997 to 2000 , 5 in 2001 to 2004 , and 11 in 2005 to 2007 ) . With 1 exception ( 25 ) , all networks addressed effectiveness . Figure 1 . Eligible treatment networks . Shaded nodes indicate placebo or no active treatment . The thickness of the lines is proportional to the number of trials addressing each specific comparison . A. Star-shaped networks . B. Nonstar-shaped networks with limited diversity and significant co-occurrence . C. Nonstar-shaped networks with limited diversity and nonsignificant co-occurrence . D. Networks with considerable diversity and significant co-occurrence . E. Networks with considerable diversity and nonsignificant co-occurrence . ACE-i = angiotensin-converting enzyme inhibitor ; AES = actinomycin Deluting stents ; ARB = angiotensin-receptor blocker ; BMS = bare-metal stents ; CCB = calcium-channel blockers ; EES = everolimus-eluting stents ; GART = genotypic antiretroviral resistance testing ; ip = intraperitoneal ; MES = mycophenolate-eluting stents ; PART = phenotypic antiretroviral resistance testing ; PES = paclitaxel-eluting stents ; SES = OBJECTIVES To compare the use of  pair-wise meta- analysis methods to multiple treatment comparison ( MTC ) methods for evidence -based health-care evaluation to estimate the effectiveness and cost-effectiveness of alternative health-care interventions based on the available evidence . METHODS Pair-wise meta- analysis and more complex evidence syntheses , incorporating an MTC component , are applied to three examples : 1 ) clinical effectiveness of interventions for preventing strokes in people with atrial fibrillation ; 2 ) clinical and cost-effectiveness of using drug-eluting stents in percutaneous coronary intervention in patients with coronary artery disease ; and 3 ) clinical and cost-effectiveness of using neuraminidase inhibitors in the treatment of influenza . We compare the two synthesis approaches with respect to the assumptions made , empirical estimates produced , and conclusions drawn . RESULTS The difference between point estimates of effectiveness produced by the pair-wise and MTC approaches was generally unpredictable-sometimes agreeing closely whereas in other instances differing considerably . In all three examples , the MTC approach allowed the inclusion of r and omized controlled trial evidence ignored in the pair-wise meta- analysis approach . This generally increased the precision of the effectiveness estimates from the MTC model . CONCLUSIONS The MTC approach to synthesis allows the evidence base on clinical effectiveness to be treated as a coherent whole , include more data , and sometimes relax the assumptions made in the pair-wise approaches . However , MTC models are necessarily more complex than those developed for pair-wise meta- analysis and thus could be seen as less transparent . Therefore , it is important that model details and the assumptions made are carefully reported alongside the results 
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Mean thickness , average weighted degree and average path length produced similar results than other metrics , but they can lead to misleading conclusions .