Randomized controlled trials (RCTs) are cited as the highest level of evidence that can inform clinical and policy decisions about the efficacy and/or effectiveness of an intervention [1–3]. However, RCTs are generally costly, with many stringent inclusion and exclusion criteria which limit generalizability of results and relevance to routine clinical practice. Consequently, there is increased interest in designing RCTs that show real-world effectiveness of an intervention in broad patient populations [4–7]. Schwartz and Lellouch [8] proposed a distinction between explanatory trials, which confirm a physiological or clinical hypothesis, and pragmatic trials, which inform a clinical or policy decision by providing evidence for adoption of the intervention into real-world clinical practice. Since their seminal paper, several papers have investigated the strengths and limitations of pragmatic trials [4–12]. Thorpe et al. [13, 14] proposed the original PRECIS (pragmatic–explanatory continuum indicator summary) tool that further clarified the concept and features of pragmatism and a scoring system and visual representation of the graphical representation of the pragmatic features of a trial. Loudon et al. [15, 16] later proposed a revision of the PRECIS, called PRECIS-2, a 9-item tool to assess the characteristics of a pragmatic design. Features of the PRECIS-2 tools include the recruitment of investigators and participants, the intervention and its delivery, follow-up, and the determination and analysis of outcomes. Many trials could be deemed to be pragmatic with regard to at least one of these dimensions, but few are truly pragmatic on all dimensions.

A number of studies have explored the use of PRECIS instruments when synthesizing evidence from published trials. For example, Patsopoulos [17] suggests that “systematic reviews and meta-analyses could incorporate a PRECIS score for synthesized trials and help the systematic mapping of the pragmatism in published research”. Yoong et al. [18] investigated the impact of pragmatic–explanatory study design characterization on conclusions of systematic reviews of public health interventions of obesity trials. They observed that there were no differences among the intervention effects across classifications of the synthesized studies based on PRECIS ratings. Koppenaal et al. [19] applied a modified version of PRECIS, called PRECIS review tool, to judge the applicability of studies in systematic reviews for daily clinical practice in two systematic reviews [19]. Tosh et al. [20] proposed the pragmascope, an adapted version of the PRECIS tool that uses a 5-point scale to assess the degree of pragmatism when designing RCTs in mental health. Witt et al. [21] conducted a systematic analysis in trials of acupuncture for lower back pain with the intention of applying the PRECIS tool. Glasgow et al. [22] also used the PRECIS tool to describe the design features of three effectiveness trials investigating weight loss in obese patients with comorbid conditions. More recently, Jordan et al. [23] also demonstrated the potential benefit of using the PRECIS-2 instrument for aiding systematic review and meta-analysis of studies in hepatitis C virus care. Louma et al. [24] used PRECIS-2 to identify interventions that effectively increased physical activity and glycemic controls among patients with type 2 diabetes and assess the potential use of PRECIS-2 for implementing physical activity interventions in clinical practice settings.

While the uptake of PRECIS instruments (i.e., PRECIS and PRECIS-2) in systematic reviews is increasing, these instruments are mostly used descriptively but their impact in explaining heterogeneity in meta-analytic investigations has not been investigated. Given the variations in study designs, differences in study characteristics of explanatory and pragmatic trials are likely to influence both statistical heterogeneity and intervention effect estimates in meta-analyses. Aves et al. [25] argues that “….If heterogeneity is substantial, due to 66the degree of pragmatism, it might not be appropriate to pool data from pragmatic and explanatory trials….” Although modern meta-analytic methods such as mixture meta-regression and robust meta-analytic methods have been developed to pool evidence from heterogeneous populations [26–28], there is limited application of these methods and incorporation of PRECIS ratings in synthesizing evidence from explanatory and pragmatic trials.

This study aimed to assess whether the incorporation of PRECIS classification could improve the modeling of heterogeneity among published trials in meta-analytic investigations. Using data from a Cochrane systematic review of 31 trials of community-based obesity intervention in children [29], we compared the performance of random-effects, stratified random-effects, and mixture random-effects meta-regression techniques that accounted for differences between explanatory and pragmatic trials for synthesizing evidence from published trials.

The incorporation of PRECIS-2 classification of the trials as covariate in meta-regression models results in larger estimates of pooled intervention effects in published trials than previously reported pooled effect sizes [19, 29]. Meta-regression methods, such as mixture regression models that controls for PRECIS-2 rating as a covariate, are particularly advantageous in that they can account for heterogeneity among the studies by modeling the heterogeneity attributable to pragmatism using mixture distribution. This finding supports results from previous research that recognize the importance of accounting for between-study heterogeneity that is attributable to the degree of pragmatism in systematic review of published trials [18, 27]. It highlights the utility of PRECIS-2 tool for aiding synthesis of evidence from published trials and the impact of information that design characteristics can have in explaining the between-study heterogeneity when conducting meta-analysis of published studies. We recommend that researchers should not only use PRECIS-2 rating information descriptively in meta-analysis of published studies but also for inferential purposes for modeling heterogeneity when estimating pooled intervention effects.

Also, we found that stratified meta-analytic methods, in which explanatory and pragmatic trials are defined with explicit criteria and independently synthesized, supported the notion that there was an overall significant effect of the community-based obesity intervention in children in pragmatic trials but not in explanatory trials. The estimated pooled effect size obtained from synthesis of pragmatic trials was significantly smaller than the estimated pooled effect size obtained from synthesis of explanatory trials. This finding is in line with previous research that shows explanatory trials often report larger effect sizes than pragmatic trials [18]. One main advantage of stratified meta-regression methodology is that it can help researchers and policy makers understand the strength of evidence for efficacy and/or effectiveness of an intervention in a population. It can also aid policy decision making about an intervention when the direction of pooled effect size in both pragmatic and explanatory trials are in the same direction. However, policy decision making about an intervention based on this stratified meta-analysis approach may not always be straightforward especially when the estimated pooled intervention effect sizes in both explanatory and pragmatic trials are in opposite directions. Few studies have recommended that policy decision making should be based on evidence from pragmatic trials only since they confirm the real-world effectiveness of an intervention [23, 24, 32]. But this recommendation may not be valid when there is limited number of pragmatic trials included in the systematic review due to low statistical power.

Our comparisons of the meta-analytic methods for synthesizing evidence from published trials rely on observed data only. Future research will explore the use of Monte Carlo methods to examine the statistical properties of these methods including their statistical power, bias, mean square error, and coverage under a variety of data analytic conditions. Importantly, the accuracy of the pooled intervention effects obtained from meta-regression analysis hinges on the accuracy of the PRECIS-2 ratings to assess the degree of pragmatism in each trial. While the ratings obtained from the three reviewers in our study had good overall inter-rater agreement, flexibility delivery and follow-up domains of PRECIS-2 exhibited only moderate agreement. This is consistent with previous studies that report high variability or poor agreement on flexibility and/or follow-up domains [18, 19, 23, 24]. Additionally, we had missing scores on some of the PRECIS domains for some studies because the original articles lack this information. Although Yoong et al. [18] recommend that investigators should endeavor to contact primary authors of each published trial with incomplete information when using PRECIS-2, we did not contact authors of the original articles but derived the mean scores on PRECIS domains and overall score based on all the available scores [18, 19]. Future research will use sensitivity analysis to assess the impact of missing data on estimates of pooled effect sizes from meta-analytic investigations. Moreover, while we have analyzed the overall PRECIS-2 scores for these published trials, the component domains that constitute this overall score may have ratings that vary on the explanatory–pragmatic continuum. The PRECIS domain-specific information about these trials might provide policy makers with relevant information (e.g., about implementation of interventions).

This study shows that the incorporation of information about the type of trials (explanatory or pragmatic), assessed using PRECIS-2 tool, can influence the estimation of pooled intervention effects in meta-analysis of published trials when there is substantial heterogeneity attributable to pragmatism. Secondly, this study also reveals the need for meta-regression methods that, adjusts for PRECIS information as covariate, for estimating pooled intervention effects in meta-analytic investigations. This ensures that valid conclusions are derived from systematic reviews and meta-analysis of published trials. We recommend that meta-analytic investigations in systematic reviews should incorporate information about design characteristics using PRECIS-2 when synthesizing evidence from published studies.