Deliverable D 1.3 RESULTS OF META-ANALYSIS ... - cast-eu.org

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Campaigns and awareness raising strategies in traffic safety — <strong>Deliverable</strong> D-1.1complex, real-world issues such as what factors determine the effect of roadsafety campaigns.The meta-regression analyses carried out on the CAST database are simplystandard multiple regression analyses in which each effect is given a weightaccording to its importance in terms of the number of counts or events onwhich it is based. This is achieved using the fixed effects weights methodexemplified in 7.3. The meta-regression used here, then, is in fact a ‘metamultiple-regression’analysis.Multiple regression analyses provide a description of how a set of givenvariables influence an outcome variable, which in our case is either accidentcounts or seatbelt use. This set of given variables is called a model. In CASTmodels were developed using a number of criteria. The variable must:1. Make theoretical sense i.e. there must be a reason to believe that thevariable may influence campaign effect2. Together with other variables in the model capture a full range ofpossible influences on campaign effects3. Be fully described by most of the campaign evaluation studies in theCAST databaseThe variable may summarize other variables and can be included based onthe results of previous meta-analyses or the subgroup analyses presented inchapter 7 before and after adjusting for publication bias.The results of analysis of a model can only be properly understood afterconsidering certain theoretical issues, which we shall now present.8.1.1 IMPORTANT THEORETICAL CONSIDERATIONSRegression analyses tell us about relationships among a selected given set ofvariables. This statement reveals two important limitations of regression,namely (i) that it tells us nothing about cause and effect between variables,either between the explanatory and outcome variable or between twoexplanatory variables; and (ii) that the output of regression depends entirelyon the set of variables given by the researcher.These two points are often overlooked by the reader who tries to interpret theresults of meta-regression without fully appreciating its limitations. It mustalways be remembered that regression only provides information about theway variables vary together, regardless of causal direction, and can onlydescribe the variables under consideration. In other words it relies heavily onthe theoretical input of the researcher, which in turn is limited by existingknowledge and the information available in campaign evaluation studies.Meta-regression is also very sensitive to missing cases, which means thatinformation about a variable needs to be widely reported if it is to be includedin a meta-regression model.Meta-regression assumes that the variables used in the model are measuredwithout error and the data describing a variable represents that variablevalidly. Let us consider the outcome variables, for example. In the case of100
Campaigns and awareness raising strategies in traffic safety — <strong>Deliverable</strong> D-1.1accidents, while it is reasonable to assert that accident counts over a definedperiod are a good representation of accident levels, when interpretingregression models we must remember that often only officially reportedaccident figures are used (a source of error) and that in CAST differentaccident types are lumped together. Similarly, with seatbelt usemeasurements we should remember that the variable comprises bothobserved and self-reported measures, which will also contain error. Weshould also consider how each of the explanatory variables in the models hasbeen measured.It should further be considered that the final regression solution is sensitive tothe combination of variables included in it. The importance of a particularvariable can depend highly on the other variables in the set. In CAST weattempt to deal with this by testing various combinations of variables aroundthe final model.In the final model an important variable will be the only one included in themodel that assesses some important facet of the outcome variable. Forvariables that appear less important there are two possibilities: (i) that it is notas important with regards to the outcome variable; or (ii) it assesses someimportant facet of the outcome variable but shares this property with two ormore other variables included in the model.It should also be noted that meta-regression does not account for publicationbias. This would involve generation of artificial data for each of the variablesand we do not know of a method that is available to do this. Although methodsare available for carrying out meta-regression using a random-effects model(see chapter 6 for discussion), we have employed a fixed-effects model in ouranalysis out of a wish to maintain the transparency of the generated data.8.2 FACTORS ASSOCIATED WITH CAMPAIGN EFFECT ONACCIDENTSA preliminary model of factors for meta-analysis was evolved through variousconsiderations by the project team. Available variables were considered withrespect to overall effects of subgroups based on them (both before and afteradjustment for publication bias). Results from past meta-analyses were alsoconsidered, along with theoretical reasoning. In this way the variables belowwere chosen as candidates for the explanation of variance in accident counteffects. The number of variables that could be used from this model wasprimarily limited by the information available in the study evaluations, asexplained below.101
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