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1.  Investigating clinical heterogeneity in systematic reviews: a methodologic review of guidance in the literature 
Background
While there is some consensus on methods for investigating statistical and methodological heterogeneity, little attention has been paid to clinical aspects of heterogeneity. The objective of this study is to summarize and collate suggested methods for investigating clinical heterogeneity in systematic reviews.
Methods
We searched databases (Medline, EMBASE, CINAHL, Cochrane Library, and CONSORT, to December 2010) and reference lists and contacted experts to identify resources providing suggestions for investigating clinical heterogeneity between controlled clinical trials included in systematic reviews. We extracted recommendations, assessed resources for risk of bias, and collated the recommendations.
Results
One hundred and one resources were collected, including narrative reviews, methodological reviews, statistical methods papers, and textbooks. These resources generally had a low risk of bias, but there was minimal consensus among them. Resources suggested that planned investigations of clinical heterogeneity should be made explicit in the protocol of the review; clinical experts should be included on the review team; a set of clinical covariates should be chosen considering variables from the participant level, intervention level, outcome level, research setting, or others unique to the research question; covariates should have a clear scientific rationale; there should be a sufficient number of trials per covariate; and results of any such investigations should be interpreted with caution.
Conclusions
Though the consensus was minimal, there were many recommendations in the literature for investigating clinical heterogeneity in systematic reviews. Formal recommendations for investigating clinical heterogeneity in systematic reviews of controlled trials are required.
doi:10.1186/1471-2288-12-111
PMCID: PMC3564789  PMID: 22846171
2.  Measurement Properties of Questionnaires Assessing Complementary and Alternative Medicine Use in Pediatrics: A Systematic Review 
PLoS ONE  2012;7(6):e39611.
Objective
Complementary and alternative medicine (CAM) is commonly used by children, but estimates of that use vary widely partly due to the range of questionnaires used to assess CAM use. However, no studies have attempted to appraise measurement properties of these questionnaires. The aim of this systematic review was to critically appraise and summarize measurement properties of questionnaires of CAM use in pediatrics.
Study design
A search strategy was implemented in major electronic databases in March 2011 and conference websites, scientific journals and experts were consulted. Studies were included if they mentioned a questionnaire assessing the prevalence of CAM use in pediatrics. Members of the team independently rated the methodological quality of the studies (using the COSMIN checklist) and measurement properties of the questionnaires (using the Terwee and Cohen criteria).
Results
A total of 96 CAM questionnaires were found in 104 publications. The COSMIN checklist showed that no studies reported adequate methodological quality. The Terwee criteria showed that all included CAM questionnaires had indeterminate measurement properties. According to the Cohen score, none were considered to be a well-established assessment, two approached the level of a well-established assessment, seven were promising assessments and the remainder (n = 87) did not reach the score’s minimum standards.
Conclusion
None of the identified CAM questionnaires have been thoroughly validated. This systematic review highlights the need for proper validation of CAM questionnaires in pediatrics, which may in turn lead to improved research and knowledge translation about CAM in clinical practice.
doi:10.1371/journal.pone.0039611
PMCID: PMC3387262  PMID: 22768098
3.  An empirical study using permutation-based resampling in meta-regression 
Systematic Reviews  2012;1:18.
Background
In meta-regression, as the number of trials in the analyses decreases, the risk of false positives or false negatives increases. This is partly due to the assumption of normality that may not hold in small samples. Creation of a distribution from the observed trials using permutation methods to calculate P values may allow for less spurious findings. Permutation has not been empirically tested in meta-regression. The objective of this study was to perform an empirical investigation to explore the differences in results for meta-analyses on a small number of trials using standard large sample approaches verses permutation-based methods for meta-regression.
Methods
We isolated a sample of randomized controlled clinical trials (RCTs) for interventions that have a small number of trials (herbal medicine trials). Trials were then grouped by herbal species and condition and assessed for methodological quality using the Jadad scale, and data were extracted for each outcome. Finally, we performed meta-analyses on the primary outcome of each group of trials and meta-regression for methodological quality subgroups within each meta-analysis. We used large sample methods and permutation methods in our meta-regression modeling. We then compared final models and final P values between methods.
Results
We collected 110 trials across 5 intervention/outcome pairings and 5 to 10 trials per covariate. When applying large sample methods and permutation-based methods in our backwards stepwise regression the covariates in the final models were identical in all cases. The P values for the covariates in the final model were larger in 78% (7/9) of the cases for permutation and identical for 22% (2/9) of the cases.
Conclusions
We present empirical evidence that permutation-based resampling may not change final models when using backwards stepwise regression, but may increase P values in meta-regression of multiple covariates for relatively small amount of trials.
doi:10.1186/2046-4053-1-18
PMCID: PMC3351721  PMID: 22587815
4.  Practitioners' validation of framework of team-oriented practice models in integrative health care: a mixed methods study 
Background
Biomedical and Complementary and Alternative Medicine (CAM) academic and clinical communities have yet to arrive at a common understanding of what Integrative healthcare (IHC) is and how it is practiced. The Models of Team Health Care Practice (MTHP) framework is a conceptual representation of seven possible practice models of health care within which teams of practitioners could elect to practice IHC, from an organizational perspective. The models range from parallel practice at one end to integrative practice at the other end. Models differ theoretically, based on a series of hypotheses. To date, this framework has not been empirically validated. This paper aims to test nine hypotheses in an attempt to validate the MTHP framework.
Methods
Secondary analysis of two studies carried out by the same research team was conducted, using a mixed methods approach. Data were collected from both biomedical and CAM practitioners working in Canadian IHC clinics. The secondary analysis is based on 21 participants in the qualitative study and 87 in the quantitative study.
Results
We identified three groups among the initial seven models in the MTHP framework. Differences between practitioners working in different practice models were found chiefly between those who thought that their clinics represented an integrative model, versus those who perceived their clinics to represent a parallel or consultative model. Of the scales used in the analysis, only the process of information sharing varied significantly across all three groups of models.
Conclusions
The MTHP framework should be used with caution to guide the evaluation of the impact of team-oriented practice models on both subjective and objective outcomes of IHC. Groups of models may be more useful, because clinics may not "fit" under a single model when more than one model of collaboration occurs at a single site. The addition of a hypothesis regarding power relationships between practitioners should be considered. Further validation is required so that integrative practice models are well described with appropriate terminology, thus facilitating the work of health care practitioners, managers, policy makers and researchers.
doi:10.1186/1472-6963-10-289
PMCID: PMC2974681  PMID: 20942973

Results 1-4 (4)