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Background: Randomised controlled trials (RCTs) are used to evaluate social and psychological interventions and inform policy decisions about them. Accurate, complete, and transparent reports of social and psychological intervention RCTs are essential for understanding their design, conduct, results, and the implications of the findings. However, the reporting of RCTs of social and psychological interventions remains suboptimal. The CONSORT Statement has improved the reporting of RCTs in biomedicine. A similar high-quality guideline is needed for the behavioural and social sciences. Our objective was to develop an official extension of the Consolidated Standards of Reporting Trials 2010 Statement (CONSORT 2010) for reporting RCTs of social and psychological interventions: CONSORT-SPI 2018. Methods: We followed best practices in developing the reporting guideline extension. First, we conducted a systematic review of existing reporting guidelines. We then conducted an online Delphi process including 384 international participants. In March 2014, we held a 3-day consensus meeting of 31 experts to determine the content of a checklist specifically targeting social and psychological intervention RCTs. Experts discussed previous research and methodological issues of particular relevance to social and psychological intervention RCTs. They then voted on proposed modifications or extensions of items from CONSORT 2010. Results: The CONSORT-SPI 2018 checklist extends 9 of the 25 items from CONSORT 2010: background and objectives, trial design, participants, interventions, statistical methods, participant flow, baseline data, outcomes and estimation, and funding. In addition, participants added a new item related to stakeholder involvement, and they modified aspects of the flow diagram related to participant recruitment and retention. Conclusions: Authors should use CONSORT-SPI 2018 to improve reporting of their social and psychological intervention RCTs. Journals should revise editorial policies and procedures to require use of reporting guidelines by authors and peer reviewers to produce manuscripts that allow readers to appraise study quality, evaluate the applicability of findings to their contexts, and replicate effective interventions.

The density, biomass, species richness and composition of birds in plots in two Mountain Fynbos plant-species assemblages (Tall Mixed Fynbos and Restionaceous Tussock Marsh), infested with alien woody plants (mainly Australian Acacia spp.) at the Cape of Good Hope Nature Reserve, South Africa, were compared with those of uninfested plots. The relative density of frugivorous and granivorous birds tended to increase, but total bird density did not change significantly with increasing density of Acacia cyclops in Tall Mixed Fynbos. However, the density of nectarivorous birds decreased with increasing A. cyclops infestation as a result of the elimination of their food plants (proteaceous and ericaceous shrubs). Thus, alien woody plants have the capacity to disrupt the putative avian nectarivore – indigenous plant pollination relationship. Dense infestations of mixed alien species in Restionaceous Tussock Marsh supported fewer nectarivorous birds, but more granivores and insectivores than did uninfested vegetation. Moreover, bird species more typical of woodland and thicket (and absent from uninfested fynbos vegetation), notably large granivores, were found in dense mixed-alien infestations of this plant-species assemblage.

Objective: This article introduces Bayesian assurance as an alternative to traditional power analysis in intervention research. Bayesian assurance is defined as the unconditional probability of identifying an intervention effect. Method: Assurance can be calculated as the expected statistical power based on a prior distribution of the unknown parameters related to the effect size. Using Monte Carlo simulation methods, we demonstrate Bayesian assurance in 2 small-scale randomized trials: a trial of motivational interviewing for patients with behavioral health disorders and a trial of a specialty mental health probation. Results: The findings suggest that traditional statistical power is highly sensitive to misspecification. Because assurance can be calculated across all possible effect sizes, it controls the uncertainty associated with the selection of a point effect size in traditional power estimation. Assurance usually produces larger sample-size estimates, and thus cutoff values for assurance may be lower than those typically used in classical power estimation. Conclusions: Compared to traditional power estimation, assurance appears to be more robust against inaccurate prior information. Assurance may be a preferred method for estimating sample sizes when prior information is poor and the costs of underpowering a study are great.

Objective: Behavioral and physical health services are increasingly being integrated, with care provided by interprofessional teams of physicians, nurses, social workers, and other professionals. The objective of this study was to describe the functions of social workers on interprofessional teams in primary care and to assess the impact of interprofessional teams that include social workers in integrated care settings. Method: We undertook a systematic review of randomized controlled trials (RCTs) of routine vs. integrated primary care where social workers served on interprofessional teams. A 5-phase search process to identify RCTs from 9 electronic databases and the gray literature published between 2000 and 2016 was used. We calculated effect sizes across identified studies and conducted 2 subsample meta-analyses for behavioral health outcomes. Results: The searches recovered 502 citations. After screening, 107 reports were retained for a full-text review, and 32 of those (from 26 RCTs) met study criteria. In the 26 RCTs, social workers engaged in 3 patient-centered activities: behavioral health treatment, care management, and referral for social services. Conclusion: Although mixed, the findings suggest that, compared to routine services, integrated primary care provided by interprofessional teams that include social workers significantly improves the behavioral health and care of patients.

Objective: In intervention research, the decision to continue developing a new program or treatment is dependent on both the change-inducing potential of a new strategy (i.e., its effect size) and the methods used to measure change, including the size of samples. This article describes a Bayesian approach to determining sample sizes in the sequential development of interventions. Description: Because sample sizes are related to the likelihood of detecting program effects, large samples are preferred. But in the design and development process that characterizes intervention research, smaller scale studies are usually required to justify more costly, larger scale studies. We present 4 scenarios designed to address common but complex questions regarding sample-size determination and the risk of observing misleading (e.g., false-positive) findings. From a Bayesian perspective, this article describes the use of decision rules composed of different target probabilities and prespecified effect sizes. Monte-Carlo simulations are used to demonstrate a Bayesian approach—which tends to require smaller samples than the classical frequentist approach—in the development of interventions from one study to the next.

Objective: By presenting a simulation study that compares Bayesian and classical frequentist approaches to research design, this paper describes and demonstrates a Bayesian perspective on intervention research. Method: Using hypothetical pilot-study data where an effect size of 0.2 had been observed, we designed a 2-arm trial intended to compare an intervention with a control condition (e.g., usual services). We determined the trial sample size by a power analysis with a Type I error probability of 2.5% (1-sided) at 80% power. Following a Monte-Carlo computational algorithm, we simulated 1 million outcomes for this study and then compared the performance of the Bayesian perspective with the performance of the frequentist analytic perspective. Treatment effectiveness was assessed using a frequentist t-test and an empirical Bayesian t-test. Statistical power was calculated as the criterion for comparison of the 2 approaches to analysis. Results: In the simulations, the classical frequentist t-test yielded 80% power as designed. However, the Bayesian approach yielded 92% power. Conclusion: Holding sample size constant, a Bayesian analytic approach can improve power in intervention research. A Bayesian approach may also permit smaller samples holding power constant. Using a Bayesian analytic perspective could reduce design demands in the developmental experimentation that typifies intervention research.