Estimates Of Intervention Effects Research Articles

Combining meta-epidemiological study datasets on commercial funding of randomised clinical trials: Database, methods, and descriptive results of the COMFIT study.

Randomised trials are often funded by commercial companies and methodological studies support a widely held suspicion that commercial funding may influence trial results and conclusions. However, these studies often have a risk of confounding and reporting bias. The risk of confounding is markedly reduced in meta-epidemiological studies that compare fairly similar trials within meta-analyses, and risk of reporting bias is reduced with access to unpublished data. Therefore, we initiated the COMmercial Funding In Trials (COMFIT) study aimed at investigating the impact of commercial funding on estimated intervention effects in randomised clinical trials based on a consortium of researchers who agreed to share meta-epidemiological study datasets with information on meta-analyses and trials included in meta-epidemiological studies. Here, we describe the COMFIT study, its database, and descriptive results. We included meta-epidemiological studies with published or unpublished data on trial funding source and results or conclusions. We searched five bibliographic databases and other sources. We invited authors of eligible meta-epidemiological studies to join the COMFIT consortium and to share data. The final construction of the COMFIT database involves checking data quality, identifying trial references, harmonising variable categories, and removing non-informative meta-analyses as well as correlated meta-analyses and trial results. We included data from 17 meta-epidemiological studies, covering 728 meta-analyses and 6841 trials. Seven studies (405 meta-analyses, 3272 trials) had not published analyses on the impact of commercial funding, but shared unpublished data on funding source. On this basis, we initiated the construction of a combined database. Once completed, the database will enable comprehensive analyses of the impact of commercial funding on trial results and conclusions with increased statistical power and a markedly reduced risk of confounding and reporting bias.

Impact of scaling up prenatal nutrition interventions on human capital outcomes in low- and middle-income countries: a modeling analysis

ABSTRACTBackgroundPrenatal nutrition interventions can lead to improved birth outcomes, which in turn are associated with better education and human capital outcomes later in life.ObjectiveWe estimated the impact of scaling up iron–folic acid (IFA), calcium, multiple micronutrient (MMS), and balanced energy protein (BEP) supplementation for pregnant women, on human capital outcomes in low- and middle-income countries (LMIC).MethodsWe used mathematical modeling with proportional reductions in adverse birth outcomes to estimate the potential gains in school years and lifetime income due to scaling up each prenatal nutrition intervention. Estimates of intervention effects on birth outcomes were derived from meta-analyses of randomized trials. Estimates of the associations between birth outcomes and schooling and lifetime income were derived from de novo meta-analyses of observational studies.ResultsAcross 132 LMIC, scaling up prenatal nutrition interventions to 90% coverage was estimated to increase school years and lifetime income per birth cohort by: 2.28 million y (95% uncertainty intervals (UI): −0.44, 6.26) and $8.26 billion (95% UI: −1.60, 22.4) for IFA; 4.08 million y (95% UI: 0.12, 9.68) and $18.9 billion (95% UI: 0.59, 44.6) for calcium; 5.02 million y (95% UI: 1.07, 11.0) and $18.1 billion (95% UI: 3.88, 39.1) for MMS; and 0.53 million y (95% UI: −0.49, 1.70) and $1.34 billion (95% UI: −1.10, 3.10 billion) for BEP supplementation. South Asia and Sub-Saharan Africa tended to have the largest estimated regional gains in school years for scaling up each intervention due to the large population size and high burden of poor birth outcomes. Absolute income benefits for each intervention were estimated to be the largest in Latin America, where returns to education and incomes are higher relative to other regions.ConclusionIncreasing coverage of prenatal nutrition interventions in LMIC may lead to substantial gains in schooling and lifetime income. Decision makers should consider the potential long-term human capital returns of investments in maternal nutrition.

Portable HEPA filter air cleaner use during pregnancy and children's body mass index at two years of age: The UGAAR randomized controlled trial.

Gestational exposure to particulate matter (PM) air pollution may increase the risk of childhood obesity, but the impact of reducing air pollution during pregnancy on obesity-related outcomes in childhood has not been examined. To assess the impact of reducing gestational PM exposure on body mass index (BMI) at two years of age. In this single-blind, parallel group randomized controlled trial in Ulaanbaatar Mongolia, we randomly assigned 540 pregnant women to receive 1-2 portable high efficiency particulate air (HEPA) filter air cleaners or no air cleaners. We measured height and weight when children were a mean age of 23.8months. Our primary outcome was age- and sex-specific BMI z-score based on the World Health Organization 2007 Growth Charts. Secondary outcomes included age- and sex-specific weight z score, overweight/obesity (defined as BMI z-score>2.00), and catch-up growth (defined using various cut-offs to identify children with relatively low birth weight for sex and gestational age and relatively high age- and sex-specific weight in childhood). We imputed missing outcome data using multiple imputation with chained equations and our primary analysis was by intention to treat (ITT). We estimated intervention effects on continuous and binary outcomes using linear and logistic regression, respectively. After excluding known miscarriages, still births, and neonatal deaths our analysis included 480 children (235 control and 245 intervention). The mean (SD) child BMI z score was 0.79 (1.0); 9.8% of children were overweight or obese. The mean BMI z score of children who were randomly assigned to the intervention group was 0.16-units lower (95% CI: -0.35, 0.04) than children in the control group. The intervention was also associated with reductions in overweight/obesity (odds ratio=0.59; 95% CI: 0.31, 1.12). Catch-up growth occurred less frequently in the intervention group, but effect estimates varied depending on the specific definition of catch-up growth and confidence intervals consistently spanned no effect. We found that the use of portable air cleaners during pregnancy was associated with improvements in obesity-related outcomes, although some effect estimates lacked precision. Reducing PM exposure during pregnancy may lead to improvements in cardiometabolic health in childhood.

Benchmarking the effectiveness of interventions to promote physical activity: A metasynthesis.

The effectiveness of physical activity interventions is typically evaluated using null hypothesis significance testing or conventional interpretations of effect size (i.e., "small," "medium," or "large"). As these criteria have recently attracted criticism, we conducted a quantitative integration of meta-analyses (a metasynthesis) in order to provide precise, numerical estimates of intervention effectiveness. The research aimed to specify the percentile distribution of effect sizes in meta-analyses of physical activity trials, and their corresponding values for different types of activity (i.e., steps per day, minutes of moderate/vigorous physical activity [MVPA], meeting World Health Organization (WHO) physical activity guidelines). Computerized searches identified 104 meta-analyses incorporating findings from 2,762 trials that met the inclusion criteria for the metasynthesis. The median effect size across all meta-analyses was d+ = .21, equivalent to an increase of 1,320 steps per day, 15.6 additional minutes of daily MVPA, and a 4.3% increase in the proportion of participants meeting WHO guidelines. Separate percentile distributions were computed for different samples (e.g., children, older adults, cancer survivors), measures of physical activity (i.e., objective vs. self-report), settings (e.g., schools, workplace), and follow-up periods (3+, 6+, and 12+ months). Conventional interpretations of effect size gravely misrepresent the effectiveness of interventions to promote physical activity. The percentile values for effect sizes, steps per days, minutes of MVPA, and percentage meeting guidelines reported here can be used to benchmark the effectiveness of future trials and should enable more informed judgments about trade-offs between effectiveness and considerations such as reach, burden, and cost. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Clinical interventions for adults with comorbid alcohol use and depressive disorders: A systematic review and network meta-analysis.

Uncertainty remains regarding the effectiveness of treatments for patients diagnosed with both an alcohol use disorder (AUD) and depressive disorder. This study aimed to compare the effectiveness of clinical interventions for improving symptoms of adults with co-occurring AUDs and depressive disorders. We searched CINAHL, ClinicalTrials.gov, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Excerpta Medica Database, International Clinical Trials Registry Platform (ICTRP), PubMed, PsycINFO, and Web of Science from inception to December 2020. We included randomized controlled trials (RCTs) evaluating clinical interventions for adults with co-occurring AUDs and depressive disorders. Two independent reviewers extracted study-level information and outcome data. We assessed risk of bias using the Cochrane Risk of Bias tool, used frequentist random effects models for network meta-analyses, and rated our confidence in effect estimates using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. Primary outcomes were remission from depression and alcohol use. Secondary outcomes were depressive symptoms, alcohol use, heavy drinking, health-related quality of life, functional status, and adverse events. We used standardized mean differences (SMDs) for continuous outcomes and odds ratios (ORs) for dichotomous outcomes to estimate intervention effects. Overall, 36 RCTs with 2,729 participants evaluated 14 pharmacological and 4 psychological interventions adjunctive to treatment as usual (TAU). Studies were published from 1971 to 2019, conducted in 13 countries, and had a median sample size of 50 participants (range: 14 to 350 participants). We have very low confidence in all estimates of intervention effects on our primary outcomes (i.e., remission from depression and remission from alcohol use). We have moderate confidence that cognitive behavioral therapies (CBTs) demonstrated greater benefit than no additional treatment (SMD = -0.84; 95% confidence interval [CI], -1.05 to -0.63; p < 0.001) for depressive symptoms and low confidence (SMD = -0.25; 95% CI, -0.47 to -0.04; p = 0.021) for alcohol use. We have low confidence that tricyclic antidepressants (TCAs) demonstrated greater benefit than placebo (SMD = -0.37; 95% CI, -0.72 to -0.02, p = 0.038) for depressive symptoms. Compared with placebo, we have moderate confidence that selective serotonin reuptake inhibitors (SSRIs) demonstrated greater benefit for functional status (SMD = -0.92; 95% CI, -1.36 to -0.47, p < 0.001) and low confidence for alcohol use (SMD = -0.30; 95% CI, -0.59 to -0.02, p = 0.039). However, we have moderate confidence that patients receiving SSRIs also were more likely to experience an adverse event (OR = 2.20; 95% CI, 0.94 to 5.16, p = 0.07). We have very low confidence in all other effect estimates, and we did not have high confidence in any effect estimates. Limitations include the sparsity of evidence on intervention effects over the long term, risks of attrition bias, and heterogeneous definitions of adverse events in the evidence base. We are very uncertain about the existence (or not) of any non-null effects for our primary outcomes of remission from depression and remission from alcohol use. The available evidence does suggest that CBTs likely reduced, and TCAs may have resulted in a slight reduction of depressive symptoms. SSRIs likely increased functional status, and SSRIs and CBTs may have resulted in a slight reduction of alcohol use. However, patients receiving SSRIs also likely had an increased risk of experiencing an adverse event. In addition, these conclusions only apply to postintervention and are not against active comparators, limiting the understanding of the efficacy of interventions in the long term as well as the comparative effectiveness of active treatments. As we did not have high confidence in any outcomes, additional studies are warranted to provide more conclusive evidence.

Developing a synthetic control group using electronic health records: Application to a single-arm lifestyle intervention study

The electronic health records (EHR) infrastructure offers a tremendous resource for identifying controls who match the characteristics of study participants in a single-arm trial. The objectives are to (1) demonstrate the feasibility of curating a synthetic control group for an existing study cohort through EHR data extraction and (2) evaluate the effect of a lifestyle intervention on selected cardiovascular health metrics. A total of 711 university employees were recruited between 2008 and 2012 to participate in a health partner intervention to improve cardiovascular health and were followed for five years. Data of nearly 8000 eligible subjects were extracted from the EHR to create a synthetic control cohort during the same study period. To minimize confounding, crude comparison, exact matching, propensity score matching, and doubly robust estimation were used to compare the selected cardiovascular health metrics at 1 and 5 years of follow-up. Blood pressure and body mass index improved in the intervention group compared to the EHR synthetic controls. The findings of changes in lipid measurements were somewhat unexpected. When analyzing the subgroup without lipid-lowering medications, the intervention group exhibited better control of cholesterol levels over time than did our synthetic controls. Some measurements in the EHR system may be more robust for synthetic selection than others. EHR synthetic controls can provide an alternative to estimate intervention effects appropriately in single-arm studies for these measurements.

Analysis of contamination in cluster randomized trials of malaria interventions

BackgroundIn cluster randomized trials (CRTs) of interventions against malaria, mosquito movement between households ultimately leads to contamination between intervention and control arms, unless they are separated by wide buffer zones.MethodsThis paper proposes a method for adjusting estimates of intervention effectiveness for contamination and for estimating a contamination range between intervention arms, the distance over which contamination measurably biases the estimate of effectiveness. A sigmoid function is fitted to malaria prevalence or incidence data as a function of the distance of households to the intervention boundary, stratified by intervention status and including a random effect for the clustering. The method is evaluated in a simulation study, corresponding to a range of rural settings with varying intervention effectiveness and contamination range, and applied to a CRT of insecticide treated nets in Ghana.ResultsThe simulations indicate that the method leads to approximately unbiased estimates of effectiveness. Precision decreases with increasing mosquito movement, but the contamination range is much smaller than the maximum distance traveled by mosquitoes. For the method to provide precise and approximately unbiased estimates, at least 50% of the households should be at distances greater than the estimated contamination range from the discordant intervention arm.ConclusionsA sigmoid approach provides an appropriate analysis for a CRT in the presence of contamination. Outcome data from boundary zones should not be discarded but used to provide estimates of the contamination range. This gives an alternative to “fried egg” designs, which use large clusters (increasing costs) and exclude buffer zones to avoid bias.

A bias correction method in meta-analysis of randomized clinical trials with no adjustments for zero-inflated outcomes.

SummaryMany clinical endpoint measures, such as the number of standard drinks consumed per week or the number of days that patients stayed in the hospital, are count data with excessive zeros. However, the zero‐inflated nature of such outcomes is sometimes ignored in analyses of clinical trials. This leads to biased estimates of study‐level intervention effect and, consequently, a biased estimate of the overall intervention effect in a meta‐analysis. The current study proposes a novel statistical approach, the Zero‐inflation Bias Correction (ZIBC) method, that can account for the bias introduced when using the Poisson regression model, despite a high rate of inflated zeros in the outcome distribution of a randomized clinical trial. This correction method only requires summary information from individual studies to correct intervention effect estimates as if they were appropriately estimated using the zero‐inflated Poisson regression model, thus it is attractive for meta‐analysis when individual participant‐level data are not available in some studies. Simulation studies and real data analyses showed that the ZIBC method performed well in correcting zero‐inflation bias in most situations.

Impact of missing outcome data in meta-analyses of lifestyle interventions during pregnancy to reduce postpartum weight retention: An overview of systematic reviews with meta-analyses and additional sensitivity analyses.

High risk of bias associated with missing outcome data (MOD) in meta-analyses (MAs) of the effects of lifestyle interventions during pregnancy on postpartum weight retention (PPWR) casts doubt on whether such interventions can be relied upon as truly effective. This systematic overview of three MAs (19 RCTs), each with high MOD rates in the subset of RCTs included, examined how MOD were addressed in the estimation of summary intervention effects. All MAs reported beneficial and statistically significant intervention effects estimated based on complete case analyses, deemed valid if MOD was missing at random (MAR). Therefore, we conducted sensitivity analyses using pattern mixture models and informative missingness parameters (describing how the outcome in the missing participants may be related to the outcome in the completers), to ascertain the robustness of the estimates to reasonable deviations from the MAR assumption. In plausible scenarios where the response in intervention group participants with versus without MOD was worse (by just 0.5kg), effect estimates were attenuated in all MAs and no longer statistically significant in two MAs. Statistical significance was retained when all 19 RCTs identified across MAs were examined together in a broader meta-analysis: -0.63 kg (95%CI -0.17, -0.08), but the clinical relevancy of effects of this magnitude remains unclear.

Estimating the Impact of Statewide Policies to Reduce Spread of Severe Acute Respiratory Syndrome Coronavirus 2 in Real Time, Colorado, USA.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic necessitated rapid local public health response, but studies examining the impact of social distancing policies on SARS-CoV-2 transmission have struggled to capture regional-level dynamics. We developed a susceptible-exposed-infected-recovered transmission model, parameterized to Colorado, USA‒specific data, to estimate the impact of coronavirus disease‒related policy measures on mobility and SARS-CoV-2 transmission in real time. During March‒June 2020, we estimated unknown parameter values and generated scenario-based projections of future clinical care needs. Early coronavirus disease policy measures, including a stay-at-home order, were accompanied by substantial decreases in mobility and reduced the effective reproductive number well below 1. When some restrictions were eased in late April, mobility increased to near baseline levels, but transmission remained low (effective reproductive number <1) through early June. Over time, our model parameters were adjusted to more closely reflect reality in Colorado, leading to modest changes in estimates of intervention effects and more conservative long-term projections.

1463Impact of missing outcome data in meta-analyses of lifestyle interventions during pregnancy to reduce postpartum-weight-retention

Abstract Background High risk of bias associated with missing outcome data (MOD) in meta-analyses (MAs) of the effects of lifestyle interventions during pregnancy on postpartum weight retention (PPWR), casts doubt on whether such interventions can be relied upon as truly effective, since estimates are deemed valid only if MOD was missing at random (MAR). Methods We conducted a systematic overview of MAs to examine the impact of MOD on the estimation of meta-analytic summary intervention effects and conducted sensitivity analyses using pattern mixture models and informative missingness parameters (describing how the outcome in the missing participants may be related to the outcome in the completers), to ascertain the robustness of the estimates to reasonable deviations from the MAR assumption. Results Three relevant MAs were identified, all with high MOD rates in the RCTs included (median&amp;gt;30%), and all reporting beneficial intervention effects on PPWR (in kg) estimated based on complete case analyses: [-0.78 (95%CI: -1.39,-0.16), -0.81 (-1.57,-0.06), and -0.94 (-1.52,-0.37)] in MAs of any lifestyle, exercise, or diet + exercise interventions, respectively. In plausible scenarios where the outcome for the intervention group in participants with vs without MOD was worse (by 0.5kg), effect estimates were attenuated in all and no longer significant in 2 of the MAs [-0.58 (-1.29,0.13), -0.70 (-1.50,0.10) and -0.88 (-1.73,-0.02)]. Conclusions Statistical significance was retained when all 19 RCTs identified across MAs were meta-analysed: -0.63 (-0.17,-0.08). Key messages The clinical relevancy of effects of this magnitude remains unclear.

Reducing bias in trials from reactions to measurement: the MERIT study including developmental work and expert workshop.

Measurement can affect the people being measured; for example, asking people to complete a questionnaire can result in changes in behaviour (the 'question-behaviour effect'). The usual methods of conduct and analysis of randomised controlled trials implicitly assume that the taking of measurements has no effect on research participants. Changes in measured behaviour and other outcomes due to measurement reactivity may therefore introduce bias in otherwise well-conducted randomised controlled trials, yielding incorrect estimates of intervention effects, including underestimates. The main objectives were (1) to promote awareness of how and where taking measurements can lead to bias and (2) to provide recommendations on how best to avoid or minimise bias due to measurement reactivity in randomised controlled trials of interventions to improve health. We conducted (1) a series of systematic and rapid reviews, (2) a Delphi study and (3) an expert workshop. A protocol paper was published [Miles LM, Elbourne D, Farmer A, Gulliford M, Locock L, McCambridge J, et al. Bias due to MEasurement Reactions In Trials to improve health (MERIT): protocol for research to develop MRC guidance. Trials 2018;19:653]. An updated systematic review examined whether or not measuring participants had an effect on participants' health-related behaviours relative to no-measurement controls. Three new rapid systematic reviews were conducted to identify (1) existing guidance on measurement reactivity, (2) existing systematic reviews of studies that have quantified the effects of measurement on outcomes relating to behaviour and affective outcomes and (3) experimental studies that have investigated the effects of exposure to objective measurements of behaviour on health-related behaviour. The views of 40 experts defined the scope of the recommendations in two waves of data collection during the Delphi procedure. A workshop aimed to produce a set of recommendations that were formed in discussion in groups. Systematic reviews - we identified a total of 43 studies that compared interview or questionnaire measurement with no measurement and these had an overall small effect (standardised mean difference 0.06, 95% confidence interval 0.02 to 0.09; n = 104,096, I2 = 54%). The three rapid systematic reviews identified no existing guidance on measurement reactivity, but we did identify five systematic reviews that quantified the effects of measurement on outcomes (all focused on the question-behaviour effect, with all standardised mean differences in the range of 0.09-0.28) and 16 studies that examined reactive effects of objective measurement of behaviour, with most evidence of reactivity of small effect and short duration. Delphi procedure - substantial agreement was reached on the scope of the present recommendations. Workshop - 14 recommendations and three main aims were produced. The aims were to identify whether or not bias is likely to be a problem for a trial, to decide whether or not to collect further quantitative or qualitative data to inform decisions about if bias is likely to be a problem, and to identify how to design trials to minimise the likelihood of this bias. The main limitation was the shortage of high-quality evidence regarding the extent of measurement reactivity, with some notable exceptions, and the circumstances that are likely to bring it about. We hope that these recommendations will be used to develop new trials that are less likely to be at risk of bias. The greatest need is to increase the number of high-quality primary studies regarding the extent of measurement reactivity. The first systematic review in this study is registered as PROSPERO CRD42018102511. Funded by the Medical Research Council UK and the National Institute for Health Research as part of the Medical Research Council-National Institute for Health Research Methodology Research Programme.

488Trial design and statistical considerations for a trial comparing group and individual treatments

Abstract Focus of Presentation A randomized clinical trial is considered the best experimental study design for comparing the effect of an intervention against a control. Selecting a trial design depends on the trial objective, a better choice would be a design that provides optimal estimation of the intervention effect, i.e. a design that yields smaller variance for the estimated intervention effect and giving stable estimates. The choice of analysis method depends on trial design aspects, reasonable assumption on the underlying probabilistic model generating the data and the credibility of findings depend on the appropriateness of the analysis method used. Here, we describe a planned randomized standard of care-controlled trial on interactive weekly mobile text messaging added to a motivational interviewing intervention aimed at sustaining continued breastfeeding among women living with HIV in South Africa. Under the “findings” heading, we highlight some of the trial design and statistical issues for discussion at the early career workshop, to gain insights on possible approaches to address the methods issues. Methods and trial design: Women from peri-urban informal settlements and a rural setting will be invited to participate within 24 hours of giving birth at selected primary healthcare facilities. Eligible women will be individually randomized to intervention or control arm after providing written informed consent. The intervention will consist of a weekly text message encouraging women to exclusively breastfeed and inquiring if they have any problems breastfeeding their infants. In addition to text messaging, women assigned to the intervention arm must visit the research site where a research nurse or counsellor will conduct an individual motivational interview, at weeks 2, 6, and 10 post-delivery. Women assigned to the control arm will be counselled by the standard of care service, i.e. primary healthcare nurses and trained counsellors will counsel women to exclusively breastfeed for the first six months through group educational infant feeding counselling sessions. Study participation will not change standard of care of participants, so women assigned to the intervention arm will receive the standard of care service, in line with provincial guidelines applicable in the sector during the study period. The primary outcomes are 1) number of women who are exclusively breastfeeding at week 24 post-delivery and 2) number of women reporting any breastfeeding at week 24 post-delivery. Findings or trial design and statistical issues Although women are individually randomly assigned to intervention and control arms, the standard of care at the primary health care facility may induce a dependency between these participants, also called the group therapy effect. For example, promotion of exclusive breastfeeding in HIV-infected women may be enhanced through positive feedback from women who follow this practice or vice versa. The standard of care will be clustered within group infant feeding counselling session, where participants (both assigned to the control and intervention arm) will receive the standard of care in groups. Women assigned to the intervention arm will receive individual motivational interview and the text messages will be send separately, to each woman’s mobile phone. Implications The trial objective will be to determine whether at week 24 following delivery, weekly text message added to motivational interview leads to better adherence to exclusive breastfeeding and leads to extended breastfeeding than standard of care. Trial design and statistical issues to discuss during the workshop will include a discussion around: 1) the appropriateness of invoking the standard experimental design to address the study objectives, and possible alternatives 2) Sample size estimation for a trial comparing group and individual treatments? 3) Reasonable assumption on the underlying probabilistic model generating the outcome data and 4) Candidate statistical models. Key messages Use of sound statistical principles of experimental design established for clinical trials allow objective and unbiased comparisons.

G-Computation and Agent-Based Modeling for Social Epidemiology: Can Population Interventions Prevent Posttraumatic Stress Disorder?

Agent-based modeling and g-computation can both be used to estimate impacts of intervening on complex systems. We explored each modeling approach within an applied example: interventions to reduce posttraumatic stress disorder (PTSD). We used data from a cohort of 2,282 adults representative of the adult population of the New York City metropolitan area from 2002-2006, of whom 16.3% developed PTSD over their lifetimes. We built 4 models: g-computation, an agent-based model (ABM) with no between-agent interactions, an ABM with violent-interaction dynamics, and an ABM with neighborhood dynamics. Three interventions were tested: 1) reducing violent victimization by 37.2% (real-world reduction); 2) reducing violent victimization by100%; and 3) supplementing the income of 20% of lower-income participants. The g-computation model estimated population-level PTSD risk reductions of 0.12% (95% confidence interval (CI): -0.16, 0.29), 0.28% (95% CI: -0.30, 0.70), and 1.55% (95% CI: 0.40, 2.12), respectively. The ABM with no interactions replicated the findings from g-computation. Introduction of interaction dynamics modestly decreased estimated intervention effects (income-supplement risk reduction dropped to 1.47%), whereas introduction of neighborhood dynamics modestly increased effectiveness (income-supplement risk reduction increased to 1.58%). Compared with g-computation, agent-based modeling permitted deeper exploration of complex systems dynamics at the cost of further assumptions.

When should matching be used in the design of cluster randomized trials?

For cluster randomized trials (CRTs) with a small number of clusters, the matched-pair (MP) design, where clusters are paired before randomizing one to each trial arm, is often recommended to minimize imbalance on known prognostic factors, add face-validity to the study, and increase efficiency, provided the analysis recognizes the matching. Little evidence exists to guide decisions on when to use matching. We used simulation to compare the efficiency of the MP design with the stratified and simple designs, based on the mean confidence interval width of the estimated intervention effect. Matched and unmatched analyses were used for the MP design; a stratified analysis was used for the stratified design; and analyses without and with post-stratification adjustment for factors that would otherwise have been used for restricted allocation were used for the simple design. Results showed the MP design was generally the most efficient for CRTs with 10 or more pairs when the correlation between cluster-level outcomes within pairs (matching correlation) was moderate to strong (0.3-0.5). There was little gain in efficiency for the MP or stratified designs compared to simple randomization when the matching correlation was weak (0.05-0.1). For trials with four pairs of clusters, the simple and stratified designs were more efficient than the MP design because greater degrees of freedom were available for the analysis, although an unmatched analysis of the MP design recovered precision for weak matching correlations. Practical guidance on choosing between the MP, stratified, and simple designs is provided.

Impacts of an Urban Sanitation Intervention on Fecal Indicators and the Prevalence of Human Fecal Contamination in Mozambique.

Fecal source tracking (FST) may be useful to assess pathways of fecal contamination in domestic environments and to estimate the impacts of water, sanitation, and hygiene (WASH) interventions in low-income settings. We measured two nonspecific and two human-associated fecal indicators in water, soil, and surfaces before and after a shared latrine intervention from low-income households in Maputo, Mozambique, participating in the Maputo Sanitation (MapSan) trial. Up to a quarter of households were impacted by human fecal contamination, but trends were unaffected by improvements to shared sanitation facilities. The intervention reduced Escherichia coli gene concentrations in soil but did not impact culturable E. coli or the prevalence of human FST markers in a difference-in-differences analysis. Using a novel Bayesian hierarchical modeling approach to account for human marker diagnostic sensitivity and specificity, we revealed a high amount of uncertainty associated with human FST measurements and intervention effect estimates. The field of microbial source tracking would benefit from adding measures of diagnostic accuracy to better interpret findings, particularly when FST analyses convey insufficient information for robust inference. With improved measures, FST could help identify dominant pathways of human and animal fecal contamination in communities and guide the implementation of effective interventions to safeguard health.

Comparison of Within- and Between-Series Effect Estimates in the Meta-Analysis of Multiple Baseline Studies

Multiple baseline (MB) designs are becoming more prevalent in educational and behavioral research, and as they do, there is growing interest in combining effect size estimates across studies. To further refine the meta-analytic methods of estimating the effect, this study developed and compared eight alternative methods of estimating intervention effects from a set of MB studies. The methods differed in the assumptions made and varied in whether they relied on within- or between-series comparisons, modeled raw data or effect sizes, and did or did not standardize. Small sample functioning was examined through two simulation studies, which showed that when data were consistent with assumptions the bias was consistently less than 5% of the effect size for each method, whereas root mean squared error varied substantially across methods. When assumptions were violated, substantial biases were found. Implications and limitations are discussed.

Small numbers are an opportunity, not a problem

Aims: outcomes of research in education and training are partly a function of the context in which that study takes place, the questions we ask, and what is feasible. Many questions are about learning, which involves repeated measurements in a particular time window, and the practical context is usually such that offering an intervention to some but not to all learners does not make sense or is unethical. For quality assurance and other purposes, education and training centers may have very locally oriented questions that they seek to answer, such as whether an intervention can be considered effective in their context of small numbers of learners. While the rationale behind the design and outcomes of this kind of studies may be of interest to a much wider community, for example to study the transferability of findings to other contexts, people are often discouraged to report on the outcomes of such studies at conferences or in educational research journals. The aim of this paper is to counter that discouragement and instead encourage people to see small numbers as an opportunity instead of as a problem.Method: a worked example of a parametric and a non-parametric method for this type of situation, using simulated data in the zero-cost Open Source statistical program R version 4.0.5.Results: contrary to the non-parametric method, the parametric method can provide estimates of intervention effectiveness for the individual participant, account for trends in different phases of a study. However, the non-parametric method provides a solution in several situations where the parametric method should be used.Conclusion: Given the costs of research, the lessons to be learned from research, and statistical methods available, small numbers should be considered an opportunity, not a problem.

Feasibility and acceptability of a group-mediated exercise intervention for gynecological cancer survivors

Purpose Examine feasibility and acceptability of a group-mediated cognitive-behavioral (GMCB) intervention targeting planned, self-managed physical activity (PA). Design Sequential mixed methods, single arm pre-/post-test design with a 4-week follow-up. Participants Post-treatment gynecologic cancer survivors. Methods Participants attended 8 weekly facilitator-led group sessions and completed assessments at baseline, post-intervention and follow-up. Feasibility was assessed by recruitment rate, retention rate, capture of outcomes, intervention usability and intervention fidelity. Acceptability was examined via qualitative interviews. Preliminary estimates of intervention effectiveness (PA, PA social cognitions and sleep) were collected. Findings 355 participants were approached and 38 consented. Twenty took part in the study and 17 (85%) completed the intervention. Thematic content analysis revealed positive group experiences. Cognitive-behavioral strategies were beneficial. Goal-setting and shared cancer recovery experience facilitated connection among group members. Implications Program acceptability was high among a diverse sample of gynecologic cancer survivors and delivery of the program is feasible to this group of gynecologic cancer survivors. Recruitment challenges were present but study retention was high.

Two decades of digital interventions for anxiety disorders: a systematic review and meta-analysis of treatment effectiveness.

Digital interventions for anxiety disorders are a promising solution to address barriers to evidence-based treatment access. Precise and powerful estimates of digital intervention effectiveness for anxiety disorders are necessary for further adoption in practice. The present systematic review and meta-analysis examined the effectiveness of digital interventions across all anxiety disorders and specific to each disorder v. wait-list and care-as-usual controls. A systematic search of bibliographic databases identified 15 030 abstracts from inception to 1 January 2020. Forty-seven randomized controlled trials (53 comparisons; 4958 participants) contributed to the meta-analysis. Subgroup analyses were conducted by an anxiety disorder, risk of bias, treatment support, recruitment, location and treatment adherence. A large, pooled effect size of g = 0.80 [95% Confidence Interval: 0.68-0.93] was found in favor of digital interventions. Moderate to large pooled effect sizes favoring digital interventions were found for generalized anxiety disorder (g = 0.62), mixed anxiety samples (g = 0.68), panic disorder with or without agoraphobia (g = 1.08) and social anxiety disorder (g = 0.76) subgroups. No subgroups were significantly different or related to the pooled effect size. Notably, the effects of guided interventions (g = 0.84) and unguided interventions (g = 0.64) were not significantly different. Supplemental analysis comparing digital and face-to-face interventions (9 comparisons; 683 participants) found no significant difference in effect [g = 0.14 favoring digital interventions; Confidence Interval: -0.01 to 0.30]. The precise and powerful estimates found further justify the application of digital interventions for anxiety disorders in place of wait-list or usual care.