Rational and design of an individual participant data meta-analysis of spinal manipulative therapy for chronic low back pain—a protocol

Abstract

BackgroundChronic low back pain (LBP) is the leading cause of pain and disability, resulting in a major socioeconomic impact. The Cochrane Review which examined the effect of spinal manipulative therapy (SMT) for chronic LBP concluded that SMT is moderately effective, but was based on conventional meta-analysis of aggregate data. The use of individual participant data (IPD) from trials allows for a more precise estimate of the treatment effect and has the potential to identify moderators and/or mediators. The aim is (1) to assess the overall treatment effect of SMT for primary and secondary outcomes in adults with chronic LBP, (2) to determine possible moderation of baseline characteristics on treatment effect, (3) to identify characteristics of intervention (e.g., manipulation/mobilization) that influence the treatment effect, and (4) to identify mediators of treatment effects.MethodsAll trials included in the Cochrane Review on SMT for chronic LBP will be included which were published after the year 2000, and the search will be updated. No restrictions will be placed on the type of comparison or size of the study. Primary outcomes are pain intensity and physical functioning. A dataset will be compiled consisting of individual trials and variables included according to a predefined coding scheme. Variables to be included are descriptive of characteristics of the study, treatment, comparison, participant characteristics, and outcomes at all follow-up periods. A one-stage approach with a mixed model technique based on the intention-to-treat principle will be used for the analysis. Subsequent analyses will focus on treatment effect moderators and mediators.DiscussionWe will analyze IPD for LBP trials in which SMT is one of the interventions. IPD meta-analysis has been shown to be more reliable and valid than aggregate data meta-analysis, although this difference might also be attributed to the number of studies that can be used or the amount of data that can be utilized. Therefore, this project may identify important gaps in our knowledge with respect to prognostic factors of treatment effects.Systematic review registration:PROSPERO CRD42015025714 Electronic supplementary materialThe online version of this article (doi:10.1186/s13643-017-0413-y) contains supplementary material, which is available to authorized users.

A Procedure and Guidelines for Analyzing Groups of Software Engineering Replications

“…that we used to drive a subsequent literature review. Throughout the literature review, we came across numerous references to the statistical techniques that can be used to analyze MCTs [15], [25], [47]- [49], meta-analysis [19], [22], [50], hierarchical linear models [18], [51]- [53], and study protocols [54].…”

“…We designed an analysis procedure that is identical to the steps followed in medicine and pharmacology to analyze and report MCTs [42]- [46], [54]. We adapted this procedure to groups of SE replications taking into account their typical characteristics in order to overcome common limitations with regard to joint data analysis.…”

“…• As in MCTs [44]- [46], [54], we propose to provide summary statistics and visualizations (e.g., box plots or violin plots) to describe the data of each replication and use consistent pre-processing steps to remove outliers or replace missing data [79], [80].…”

“…We analyze each replication with consistent analyses (e.g., t-test, ANOVA, etc. [44], [46], [54]). This ensures consistency of results across the replications and eases the integration of results in later phases.…”

“…Conduct exploratory analyses. As in MCTs [45], [46], [54], [86], [89], exploratory analyses should be conducted after providing joint conclusions. The objective of this step is to identify experiment-level and participant-level moderators that may be behind the statistical heterogeneity commonly present in groups of SE replications.…”