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AbstractIs it ethical to select clients at random for a beneficial social service, then deny the benefits to a control group for the sake of science?

An avowed aim of post-occupancy housing evaluation is to assess the impact of housing programs or residential settings on occupants. Yet, research designs with control groups go unused in these evaluations, despite their direct relevance. To encourage such research designs, a field-tested model for the selection of respondents for treatment and control groups, under conditions that do not allow random assignment, is described. The model's adequacy is evaluated and its limitations are discussed.

Behavioral and social sciences randomized controlled trials (BSSTs) have a significant role in life sciences. Choosing an appropriate control or comparator group for BSSTs is critical, to provide true intervention effects. The objective of this study was to determine the types of control groups used in BSSTs, and the rationale provided to justify these choices. We conducted a systematic survey of BSST protocols published between January 2012 and October 2016 in the Cochrane Library and Medline databases. We randomly selected 200 protocols. The study selection and data extraction were performed independently in duplicate. The most frequent control groups were active concurrent (97/200, 48.5%), and no treatment concurrent controls (88/200, 44.0%). The majority of studies (71.5%) did not provide justification for comparators choice. We concluded that BSSTs trials compare interventions to active and no treatment controls however the majority of trials lacked rationale for the selection of the study comparator.

This article shows how new time series models can be used to track the progress of an epidemic, forecast key variables and evaluate the effects of policies. The univariate framework of Harvey and Kattuman (2020, Harvard Data Science Review, Special Issue 1—COVID-19, https://hdsr.mitpress.mit.edu/pub/ozgjx0yn) is extended to model the relationship between two or more series and the role of common trends is discussed. Data on daily deaths from COVID-19 in Italy and the UK provides an example of leading indicators when there is a balanced growth. When growth is not balanced, the model can be extended by including a non-stationary component in one of the series. The viability of this model is investigated by examining the relationship between new cases and deaths in the Florida second wave of summer 2020. The balanced growth framework is then used as the basis for policy evaluation by showing how some variables can serve as control groups for a target variable. This approach is used to investigate the consequences of Sweden's soft lockdown coronavirus policy in the spring of 2020.

Background: A large-scale online study completed by this research team found that brief psychological interventions were associated with high-intensity pleasant emotions and predicted performance. The present study extends this work using data from participants (n = 3376) who completed all self-report data and engaged in a performance task but who did not engage with an intervention or control condition and therefore present as an opportunistic no-treatment group. Methods: 41,720 participants were selected from the process and outcome focus goals intervention groups, which were the successful interventions (n = 30,096), active-control (n = 3039), and no-treatment (n = 8585). Participants completed a competitive task four times: first as practice, second to establish a baseline, third following an opportunity to complete a brief psychological skills intervention, and lastly following an opportunity to repeat the intervention. Repeated measures MANOVA indicated that over four performance rounds, the intensity of positive emotions increased, performance improved, and the amount of effort participants exerted increased; however, these increases were significantly smaller in the no-treatment group. Conclusions: Findings suggest that not engaging in active training conditions had negative effects. We suggest that these findings have implications for the development and deployment of online interventions.