aving grandly declared that he had “total authority” to direct the responses of the states to the virus, he opted instead to exhort states to hunt for whatever they needed within their borders or in private markets. When dispatching equipment in the hands of the federal government to imploring governors he warned that he expected them to express “appreciation” for his largesse and demeaned the performance of ones (Gretchen Whitmer of Michigan, Jay Inslee of Washington, JB Pritzker of Illinois, and Andrew Cuomo of New York, for instance) who were insufficiently unctuous. 17The federal government’s regulatory interventions likewise fell far short of what health experts sought. Whether and when to impose lockdowns that shut nonessential work sites and to issue work-from-home decrees was left to governors and local officials. As scientific consensus grew that wearing a mask when outside one's home was a vital protection against catching and spreading the virus, the president declared in early April that mask-wearing was a matter of personal choice and that he, for one, would not do so. (As noted above, he would briefly change his tune in mid and late July.) Attempts by the CDC to draft guidelines for safe conduct amid the pandemic were delayed, derailed, and diluted by White House reviews and edits, and emerged finally on April 16. Although Trump loudly demanded that the nation’s schools reopen as usual (parents stuck tutoring their kids at home could not easily rejoin and therefore help to stimulate the economy), practical decisions on the timing and terms of reopening and closure fell to states, localities, and school districts, which decried both the ensuing “patchwork” of rules and advisories and (in many areas) the lack of funds for the physical modifications needed to ensure the safety of students. 18Because combatting the pandemic entailed costly and disruptive interventions with which all citizens were asked or required to comply, communication that built legitimacy and won the public’s trust in these measures was critical to success. In the United States effective communication was crippled from the outset because Trump’s faith in the imminent miraculous disappearance of COVID-19 and conflation of its dangers with those of a flu or cold found an avid audience in extensive right wing media networks manned by Fox News, Rush Limbaugh, Breitbart news, and many still dimmer lights on television, radio, the internet, the blogosphere, Facebook, and Twitter. (On Fox see Stelter, 2020.) While it is hard to gauge precisely the share of the population that took advice on the pandemic primarily from these sources, an estimate of 20-30 percent would probably not be far afield. Hermetically sealed within a conservative communications bubble that has counterparts but no real “peers” in other Western democracies, millions of commentators and listeners embroidered interpretations of the virus as a hoax perpetrated and perpetuated by liberal Democrats, rejected masks and lockdowns as assaults on personal freedom and on the wellbeing of the economy, and touted the virtues of hydroxychloroquine and other unproven therapies. 19Denial of the gravity of the virus and disregard of expert counsel did not of course prevail across the whole population, nor even within a majority of it.

political commissions paralleled several conspicuous policy omissions. On the production front, many hospitals and state and local health departments

vidently fearful that a rising number of cases would undercut investor confidence,

president had chosen to minimize the risks in order, so he said, to avoid “panic.

Donald J. Trump was seeking reelection by a public with

Applied Epi is a nonprofit organisation and grassroots movement of frontline epis from around the world. We write in our spare time to offer this resource to the community. Your encouragement and feedback is most welcome:

R for applied epidemiology and public health

7  Modeling Longitudinal Responses using Generalized Least Squares

High Quality Forest Plots in R GGPLOT2

Package ‘riskRegression’

Why Test for Proportional Hazards?

The following R function calculates the average absolute correlation coefficient (AACC) among a continuous treatment and covariates after applying the inverse probability weights. The subsequent R codes demonstrate how to estimate the dose-response function using a real dataset.

Package ‘cmprskcoxmsm’

Machine learning methods have also been suggested for use in PS estimation, and boosted logistic regression, in particular, has been singled out as a potentially promising method 18-21. Boosted models are a type of ensemble model composed of many small, weak component models, typically small classification and regression trees using only a few variables 29, 30.

Package ‘MatchThem

escription This function produces a CIF plots for cif_est objects. Usage plot_est_cif(cif.data, color = color, ci.cif = FALSE)

Foreword

When interested in ATT: To illustrate what happens when estimating ATT weights, webegin by assuming that it is of interest to estimate ATT for youth like those who were in theMET/CBT-5 condition. In this case, there are two ATTs of potential interest (MET/CBT-5vs. Community,μ2,2 −μ2,1, and MET/CBT-5 vs. SCYμ2,2 −μ2,3 for youth like thosereceiving MET/CBT-5). Therefore, the analysis begins by creating two subsamples from theoverall dataset: one that contains youth in the MET/CBT-5 and community groups and theother which contains youth in the MET/CBT-5 and SCY groups. To estimate the weights forestimating the ATT of MET/CBT-5 relative to community among youth like those whoreceived MET/CBT-5 (μ2,2 −μ2,1), we fit a binary GBM propensity score model for theMET/CBT-5 treatment indicator to the pooled sample with youth from the MET/CBT-5 andcommunity samples. We use an ATT stopping rule that selects the iteration of GBM which

Several authors[5, 17] have found that among a variety of propensity score estimation methods, GBM usedin this fashion provides estimated weights that yield the best balance of the pretreatmentvariables and estimated treatment effects with the smallest mean square error in the binarytreatment case.

1. Fit a simple model with only main effects for all the proposed covariates2. ForT = 1, ...M find the areas of common support for among all treatmentgroups, retain observations that in the area of common support for allT3. Test for balance on the covariates4. Add to the model polynomial and cross product terms for covariates that do notbalance5. Fit the new model6. Repeat steps 3 – 5 until all the covariates are balanced.

GBM estimates the propensity scorefor the binary treatment indicator using a flexible estimation method that can adjust for alarge number of pretreatment covariates. GBM estimation involves an iterative process withmultiple regression trees to capture complex and nonlinear relationships between treatmentassignment and the pretreatment covariates without over-fitting the data [5, 21, 22, 23, 24].

outliers that were outside of 6 SDs from the mean were excluded.

Our hope is that increased use of these diagnostics will improve the practice of IPTW for estimating average treatment effects using observational data. Adherence to the methods described in this paper may contribute towards the evolvement of what is considered ‘best practice’ when using IPTW to estimate causal treatment effects.

conclusion, our results show that weight trimming can help reduce bias and standard error associated with logistic regression-estimated propensity score weights. However, weight trimming is of little to no utility for boosted CART and random forests-estimated propensity score weights, possibly because those methods perform so well already. We suggest that analysts should focus attention on improving propensity score model specification and rely less on weight trimming to optimize propensity score weighting.

However, here, one could reasonably argue in favor of reporting the result with the weights truncated at the first and 99th percentiles, on the basis of the centering of the weights at one and the order of magnitude reduction in the 1/minimum and maximum weights.

primal analysis is and how toimplement it with an R package called rbounds.

Other models may also be used to estimate treatment effects.If 1:1 matching without replacement is implemented, theweights of each sample =1, and thus, the model does notrequire adjustment. However, the weighted versions of aspecified model should be used, if the sample weights are≠1. This is an example of weighted logistic regression

as matching, stratification, regressionadjustment, and so on (3,8,38). In other words,we balance our data successively by PSM andconventional method

PSM with an exactmatching method, by setting exact=c('x.Gender')in matchit. That is to say, when matching patientswith similar PSs, some covariates (gender) mustbe equal

. The command summary(m.out, interactions = TRUE, addlvariables = TRUE,standardize = TRUE) provides balanced interactioninformation and high-order terms of all covariate

Then, we addeda quadratic term to our model, but unfortunately,the result was still poor.

CreateTableOnefunction to calculate SMD. However, we do not recommendusing it for matched data, because this function only takesmatched data into account, and the mean difference isstandardized (divided) by the SD in the matched da

We suggest that standardize = TRUE should

The Economics of Policing and Public Safety