Quantitative research is philosophically grounded in the positivist tradition which proposes scientific truths exist and can be studied (Gerrish & Lathlean 2015), and denotes research designs and methods that yield numerical data (Gerrish & Lacey 2010) referred to as statistical evidence (Clair et al. 2014). Quantitative research tests objective theories by examining the relationship among variables (Polit et al. 2002). Quantitative research falls into four main designs, namely, descriptive, correlational, experimental and quasi-experimental (Grove et al. 2013, Borbasi & Jackson 2015).
In a systematic review of quantitative research, a meta-analysis of quantitative data can be conducted where appropriate (White & Schmidt 2005). The term ‘meta- analysis’ was originally coined in 1976 by an American social scientist and statistician named Gene V. Glass, (O'Rourke 2007). Glass (1976) defined meta-analysis formally as the statistical analysis of a large compilation of analysis data from separate studies with the intention of integrating the results. Meta-analysis has more recently been described as a quantitative statistical exercise which involves drawing together the results of several independent studies that engage a set of connected research
hypotheses (Moore 2012). The data are then reanalysed to calculate a pooled estimate of effect, an estimate of the strength of the relationship between two variables, and a confidence interval (CI), an indication of the reliability of the estimate of effect (Higgins & Green 2011, Moore 2012). A meta-analysis can increase power, improve precision, answer questions not posed by single studies, and resolve disagreements between conflicting study findings (Higgins & Green 2011). Power is defined as the probability of a statistical study to reliably reject a false null hypothesis (Moore 2010) and
precision is defined as the extent to which repeated tests produce unchanged findings in conditions that remain unchanged (Moore 2012).
In a systematic review, it may not always be appropriate to conduct a meta-analysis, such as in situations where the differences between studies have not occurred only by chance but due to other factors (Moore 2012). This necessitates that specific
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differences across studies, and reporting biases, in order to reduce the potential for findings that are significantly misleading (Higgins & Green 2011). By carrying out an accurate and detailed quality appraisal, data can be generated to examine
heterogeneity and inform decisions concerning suitability of meta-analysis (Khan et al. 2003).
Heterogeneity is defined as ‘the differences in study populations or in methodologies used to study them that may have the effect of reaching differing conclusions’ (Moore 2012)(p. 2799). Moore (2012) advises that a meta-analysis is performed only where the included studies are relatively alike, or homogenous, in order that the findings of the analysis will yield beneficial information. The chi-square test is a typically used test to determine heterogeneity between studies with results reported as I2, where I2
describes the degree of variability in effect estimates in studies that is as a result of heterogeneity rather than chance (Moore 2012). If a meta-analysis is conducted which comprises of studies that are too diverse, no meaningful data may be extracted from the analysis (Deeks et al. 2011). Essentially, combining poor quality or diverse studies will inevitably yield poor quality review results (Higgins & Green 2011, Moore 2012, Sambunjak & Franic 2012). In such cases where meta-analysis is deemed
inappropriate, a thorough narrative summary with a thoughtful discussion of primary study results can provide a more meaningful approach (Sambunjak & Franic 2012).
Moore (2012) describes three types of data most typically seen as relevant for conducting meta-analysis, dichotomous or binary data, continuous data, and survival or time to event data. For outcomes measured on a dichotomous scale, common approaches to summarise data are the use of the odds ratio (OR), relative risk (RR) or risk difference (RD), whereas, for outcomes measured on a continuous scale, the weighted mean difference (WMD) is commonly used (Moore 2012). Dichotomous or binary scales measure data that can only take two possible values, such as healed or not healed, whereas continuous scales measure data that can take any value within a range such as percentage reduction in wound size (Moore 2012). In studies where the outcome of interest is to determine the time duration before an event occurs, data are
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measured using survival or time to event scales and may be summarised using hazard ratios (HRs) (Moore 2012).
The results of a meta-analysis can be displayed graphically, easing the interpretation and comparison of the findings of individual studies for the review reader (Evans 2000), such as in the form of forest plots (Sambunjak & Franic 2012). In a typical forest plot, the results of component studies are presented as squares centred on the point estimate of the result of each study with horizontal lines that run through the squares typically indicating 95% confidence intervals (CI) of the estimates (Lewis & Clarke 2001, Sambunjak & Franic 2012). The area of each square indicates the weight and relative contribution of the study to the overall meta-analysis (Moore 2012). The overall estimate and confidence interval from the meta-analysis is represented as a diamond at the bottom of the graph (Lewis & Clarke 2001, Sambunjak & Franic 2012). The centre of the diamond represents the pooled point estimate, and its horizontal tips indicate the confidence interval (Lewis & Clarke 2001, Sambunjak & Franic 2012). When using risk difference (RD), the vertical line of no effect at zero indicates that there is no statistically significant difference between the study groups, whereas when using odds ratio (OR), risk ratio (RR) or relative risk, a vertical line at one indicates no statistically significant difference (Moore 2012). Where the points of the diamond are clear of the line of no effect, the overall meta-analysed outcome suggests that there is statistical difference between the control and experimental groups (Lewis & Clarke 2001). Conversely, where the vertical line of no effect cuts through the diamond, the overall result suggests that there is no statistically significant difference (Moore 2012, Sambunjak & Franic 2012).
The advantage of displaying results using a forest plot is that it provides a clear view of the information from the meta-analysis in the form of a simple visual representation depicting the amount of variation between the study results as well as an estimate of the overall result of the studies combined (Lewis & Clarke 2001).
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