Approximate balancing weights for clustered observational study designs.

In a clustered observational study, a treatment is assigned to groups and all units within the group are exposed to the treatment. We develop a new method for statistical adjustment in clustered observational studies using approximate balancing weights, a generalization of inverse propensity score weights that solve a convex optimization problem to find a set of weights that directly minimize a measure of covariate imbalance, subject to an additional penalty on the variance of the weights. We tailor the approximate balancing weights optimization problem to the clustered observational study setting by deriving an upper bound on the mean square error and finding weights that minimize this upper bound, linking the level of covariate balance to a bound on the bias.

Using Balancing Weights to Target the Treatment Effect on the Treated when Overlap is Poor.

Inverse probability weights are commonly used in epidemiology to estimate causal effects in observational studies. Researchers often focus on either the average treatment effect or the average treatment effect on the treated with inverse probability weighting estimators. However, poor overlap in the baseline covariates between the treated and control groups can produce extreme weights that can result in biased treatment effect estimates.

Problems with evidence assessment in COVID-19 health policy impact evaluation: a systematic review of study design and evidence strength.

Introduction: Assessing the impact of COVID-19 policy is critical for informing future policies. However, there are concerns about the overall strength of COVID-19 impact evaluation studies given the circumstances for evaluation and concerns about the publication environment.

Methods: We included studies that were primarily designed to estimate the quantitative impact of one or more implemented COVID-19 policies on direct SARS-CoV-2 and COVID-19 outcomes.

A Trial Emulation Approach for Policy Evaluations with Group-level Longitudinal Data.

To limit the spread of the novel coronavirus, governments across the world implemented extraordinary physical distancing policies, such as stay-at-home orders. Numerous studies aim to estimate the effects of these policies. Many statistical and econometric methods, such as difference-in-differences, leverage repeated measurements, and variation in timing to estimate policy effects, including in the COVID-19 context.

Problems with Evidence Assessment in COVID-19 Health Policy Impact Evaluation: A systematic review of study design and evidence strength.

Introduction: Assessing the impact of COVID-19 policy is critical for informing future policies. However, there are concerns about the overall strength of COVID-19 impact evaluation studies given the circumstances for evaluation and concerns about the publication environment. This study systematically reviewed the strength of evidence in the published COVID-19 policy impact evaluation literature.

A trial emulation approach for policy evaluations with group-level longitudinal data.

To limit the spread of the novel coronavirus, governments across the world implemented extraordinary physical distancing policies, such as stay-at-home orders, and numerous studies aim to estimate their effects. Many statistical and econometric methods, such as difference-in-differences, leverage repeated measurements and variation in timing to estimate policy effects, including in the COVID-19 context. While these methods are less common in epidemiology, epidemiologic researchers are well accustomed to handling similar complexities in studies of individual-level interventions.

Layoffs and the mental health and safety of remaining workers: a difference-in-differences analysis of the US aluminium industry.

Background: Relatively few studies have examined the effects of layoffs on remaining workers, although the effects of layoffs and downsizing events may extend beyond those employees who lose their jobs.

Methods: We examined the effects of layoffs on mental healthcare utilisation and injury risk among workers at 30 US plants between 2003 and 2013. We defined layoffs as reductions in the hourly workforce of 20% or more at each plant.