Cluster randomised trials with a binary outcome and a small number of clusters: comparison of individual and cluster level analysis method.

BACKGROUND: Cluster randomised trials (CRTs) are often designed with a small number of clusters, but it is not clear which analysis methods are optimal when the outcome is binary. This simulation study aimed to determine (i) whether cluster-level analysis (CL), generalised linear mixed models (GLMM), and generalised estimating equations with sandwich variance (GEE) approaches maintain acceptable type-one error including the impact of non-normality of cluster effects and low prevalence, and if so (ii) which methods have the greatest power. We simulated CRTs with 8-30 clusters, altering the cluster-size, outcome prevalence, intracluster correlation coefficient, and cluster effect distribution. We analysed each dataset with weighted and unweighted CL; GLMM with adaptive quadrature and restricted pseudolikelihood; GEE with Kauermann-and-Carroll and Fay-and-Graubard sandwich variance using independent and exchangeable working correlation matrices. P-values were from a t-distribution with degrees of freedom (DoF) as clusters minus cluster-level parameters; GLMM pseudolikelihood also used Satterthwaite and Kenward-Roger DoF. RESULTS: Unweighted CL, GLMM pseudolikelihood, and Fay-and-Graubard GEE with independent or exchangeable working correlation matrix controlled type-one error in > 97% scenarios with clusters minus parameters DoF. Cluster-effect distribution and prevalence of outcome did not usually affect analysis method performance. GEE had the least power. With 20-30 clusters, GLMM had greater power than CL with varying cluster-size but similar power otherwise; with fewer clusters, GLMM had lower power with common cluster-size, similar power with medium variation, and greater power with large variation in cluster-size. CONCLUSION: We recommend that CRTs with ≤ 30 clusters and a binary outcome use an unweighted CL or restricted pseudolikelihood GLMM both with DoF clusters minus cluster-level parameters

Ivermectin treatment in humans for reducing malaria transmission.

BACKGROUND: Malaria is transmitted through the bite of Plasmodium-infected adult female Anopheles mosquitoes. Ivermectin, an anti-parasitic drug, acts by killing mosquitoes that are exposed to the drug while feeding on the blood of people (known as blood feeds) who have ingested the drug. This effect on mosquitoes has been demonstrated by individual randomized trials. This effect has generated interest in using ivermectin as a tool for malaria control. OBJECTIVES: To assess the effect of community administration of ivermectin on malaria transmission. SEARCH METHODS: We searched the Cochrane Infectious Diseases Group (CIDG) Specialized Register, CENTRAL, MEDLINE, Embase, LILACS, Science Citation index - expanded, the World Health Organization (WHO) International Clinical Trials Registry Platform, ClinicalTrials.gov, and the National Institutes of Health (NIH) RePORTER database to 14 January 2021. We checked the reference lists of included studies for other potentially relevant studies, and contacted researchers working in the field for unpublished and ongoing trials. SELECTION CRITERIA: We included cluster-randomized controlled trials (cRCTs) that compared ivermectin, as single or multiple doses, with a control treatment or placebo given to populations living in malaria-endemic areas, in the context of mass drug administration. Primary outcomes were prevalence of malaria parasite infection and incidence of clinical malaria in the community. DATA COLLECTION AND ANALYSIS: Two review authors independently extracted data on the number of events and the number of participants in each trial arm at the time of assessment. For rate data, we noted the total time at risk in each trial arm. To assess risk of bias, we used Cochrane's RoB 2 tool for cRCTs. We documented the method of data analysis, any adjustments for clustering or other covariates, and recorded the estimate of the intra-cluster correlation (ICC) coefficient. We re-analysed the trial data provided by the trial authors to adjust for cluster effects. We used a Poisson mixed-effect model with small sample size correction, and a cluster-level analysis using the linear weighted model to adequately adjust for clustering.  MAIN RESULTS: We included one cRCT and identified six ongoing trials.  The included cRCT examined the incidence of malaria in eight villages in Burkina Faso, randomized to two arms. Both trial arms received a single dose of ivermectin 150 µg/kg to 200 µg/kg, together with a dose of albendazole. The villages in the intervention arm received an additional five doses of ivermectin, once every three weeks. Children were enrolled into an active cohort, in which they were repeatedly screened for malaria infection.  The primary outcome was the cumulative incidence of uncomplicated malaria in a cohort of children aged five years and younger, over the 18-week study. We judged the study to be at high risk of bias, as the analysis did not account for clustering or correlation between participants in the same village. The study did not demonstrate an effect of Ivermectin on the cumulative incidence of uncomplicated malaria in the cohort of children over the 18-week study (risk ratio 0.86, 95% confidence interval (CI) 0.62 to 1.17; P = 0.2607; very low-certainty evidence). AUTHORS' CONCLUSIONS: We are uncertain whether community administration of ivermectin has an effect on malaria transmission, based on one trial published to date

Long-term oral antibiotic use in people with acne vulgaris in UK primary care: a drug utilization study

BACKGROUND: The inappropriate use of antibiotics is understood to contribute to antimicrobial resistance. Oral antibiotics are regularly used to treat moderate-to-severe acne vulgaris. In practice, we do not know the typical length of oral antibiotic treatment courses for acne in routine primary care and what proportion of people receive more than one course of treatment following a new acne diagnosis. OBJECTIVES: To describe how oral antibiotics are prescribed for acne over time in UK primary care. METHODS: We conducted a descriptive longitudinal drug utilization study using routinely collected primary care data from the Clinical Practice Research Datalink GOLD (2004-2019). We included individuals (8-50 years) with a new acne diagnosis recorded between 1 January 2004 and 31 July 2019. RESULTS: We identified 217 410 people with a new acne diagnosis. The median age was 17 years [interquartile range (IQR) 15-25] and median follow-up was 4.3 years (IQR 1.9-7.6). Among people with a new acne diagnosis, 96 703 (44.5%) received 248 560 prescriptions for long-term oral antibiotics during a median follow-up of 5.3 years (IQR 2.8-8.5). The median number of continuous courses of antibiotic therapy (≥ 28 days) per person was four (IQR 2-6). The majority (n = 59 010, 61.0%) of first oral antibiotic prescriptions in those with a recorded acne diagnosis were between the ages of 12 and 18. Most (n = 71 544, 74.0%) first courses for oral antibiotics were for between 28 and 90 days. The median duration of the first course of treatment was 56 days (IQR 50-93 days) and 18 127 (18.7%) of prescriptions of ≥ 28 days were for < 6 weeks. Among people who received a first course of oral antibiotic for ≥ 28 days, 56 261 (58.2%) received a second course after a treatment gap of ≥ 28 days. The median time between first and second courses was 135 days (IQR 67-302). The cumulative duration of exposure to oral antibiotics during follow-up was 255 days (8.5 months). CONCLUSIONS: Further work is needed to understand the consequences of using antibiotics for shorter periods than recommended. Suboptimal treatment duration may result in reduced clinical effectiveness or repeated exposures, potentially contributing to antimicrobial resistance

The effect of initiation of renin-angiotensin system inhibitors on haemoglobin: A national cohort study

Aims: To determine whether initiation of treatment with angiotensin converting enzyme inhibitors or angiotensin II receptor blockers (ACEI/ARBs) is associated with a subsequent reduction in haemoglobin in the general population. Methods : We undertook a national cohort study over a 13‐year period (2004–2016), using routine primary healthcare data from the UK Clinical Practice Research Datalink. We compared ACEI/ARB initiation with calcium channel blocker (CCB) initiation, to minimise confounding by indication. We included all first ACEI/ARB or CCB prescriptions in adults with at least 1 haemoglobin result in the 12 months before and 6 months after drug initiation. Our primary outcome was a ≥1 g/dL haemoglobin reduction in the 6 months after drug initiation. Results: We examined 146 610 drug initiation events in 136 655 patients. Haemoglobin fell by ≥1 g/dL after drug initiation in 19.5% (16 936/86 652) of ACEI/ARB initiators and 15.9% (9521/59 958) of CCB initiators. The adjusted odds ratio of a ≥1 g/dL haemoglobin reduction in ACEI/ARB initiators vs CCB initiators was 1.15 (95% confidence interval 1.12–1.19). Conclusion: ACEI/ARBs are associated with a modest increase in the risk of a haemoglobin reduction. For every 100 patients in our study that initiated a CCB, 16 experienced a ≥1 g/dL haemoglobin decline. If the effect is causal, 3 additional patients would have experienced this outcome if they had received an ACEI/ARB. This may have implications for drug choice and monitoring for many patients in primary care. Further research could identify patients at higher risk of this outcome, who may benefit from closer monitoring

Letter to the Editor: Pulling Unmeasured Confounding Out by your Bootstraps: Too Good to be True?

Inverse probability of treatment weighting can account for confounding under a number of assumptions, including that of no unmeasured confounding. A recent simulation study proposed a bootstrap bias correction, apparently demonstrating good performance in removing bias due to unmeasured confounding. We revisited the simulations, finding no evidence of bias reduction. Journal of Statistical Research 2021, Vol. 55, No. 2, pp. 293-29

MatchThem: Matching and Weighting Multiply Imputed Datasets

Provides the necessary tools for the pre-processing techniques of matching and weighting multiply imputed datasets to control for effects of confounders and to reduce the degree of dependence on certain modeling assumptions in studying the causal associations between an exposure and an outcome. This package includes functions to perform matching within and across the multiply imputed datasets using several matching methods, to estimate weights of units in the imputed datasets using several weighting methods, to calculate the causal effect estimate in each matched or weighted dataset using parametric or non-parametric statistical models, and to pool the obtained estimates from these models according to Rubin's rules

MatchThem:: Matching and Weighting after Multiple Imputation

Balancing the distributions of the confounders across the exposure levels in an observational study through matching or weighting is an accepted method to control for confounding due to these variables when estimating the association between an exposure and outcome and to reduce the degree of dependence on certain modeling assumptions. Despite the increasing popularity in practice, these procedures cannot be immediately applied to datasets with missing values. Multiple imputation of the missing data is a popular approach to account for missing values while preserving the number of units in the dataset and accounting for the uncertainty in the missing values. However, to the best of our knowledge, there is no comprehensive matching and weighting software that can be easily implemented with multiply imputed datasets. In this paper, we review this problem and suggest a framework to map out the matching and weighting multiply imputed datasets to 5 actions as well as the best practices to assess balance in these datasets after matching and weighting. We also illustrate these approaches using a companion package for R, MatchThem

Additional file 2 of Cluster randomised trials with a binary outcome and a small number of clusters: comparison of individual and cluster level analysis method

Code to replicate illustrative example outlined in "Cluster randomised trials with a binary outcome and a small number of clusters: comparison of individual and cluster level analysis method"

Open Science during the pandemic

A set of scripts and data files used to perform an analysis of publication practices during the COVID-19 pandemic. These are made available to support the manuscript, "Open Science Saves Lives: Lessons from the COVID-19 Pandemic"