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10.1097/EDE.0000000000001369 http://scihub22266oqcxt.onion/10.1097/EDE.0000000000001369 34001754!8162442!34001754     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Epidemiology 2021 ; 32 (4): 533-540 Nephropedia Template TP {{tp|p=34001754|t=2021. A Trial Emulation Approach for Policy Evaluations with Group-level Longitudinal Data |pdf=|usr=}}{{34001754}} gab.com Text A Trial Emulation Approach for Policy Evaluations with Group-level Longitudinal Data JO: Epidemiology http://www.kidney.de/pget.php?&tw=1&pmid=34001754 #FOAvip 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. Although these methods are less common in epidemiology, epidemiologic researchers are well accustomed to handling similar complexities in studies of individual-level interventions. Target trial emulation emphasizes the need to carefully design a nonexperimental study in terms of inclusion and exclusion criteria, covariates, exposure definition, and outcome measurement-and the timing of those variables. We argue that policy evaluations using group-level longitudinal ("panel") data need to take a similar careful approach to study design that we refer to as policy trial emulation. This approach is especially important when intervention timing varies across jurisdictions; the main idea is to construct target trials separately for each treatment cohort (states that implement the policy at the same time) and then aggregate. We present a stylized analysis of the impact of state-level stay-at-home orders on total coronavirus cases. We argue that estimates from panel methods-with the right data and careful modeling and diagnostics-can help add to our understanding of many policies, though doing so is often challenging. Twit Text FOAVip A Trial Emulation Approach for Policy Evaluations with Group-level Longitudinal Data ????Epidemiology http://www.kidney.de/pget.php?&tw=1&pmid=34001754 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34001754 #FOAvip English Wikipedia <ref>{{Cite pmid|34001754}}</ref> A Trial Emulation Approach for Policy Evaluations with Group-level Longitudinal Data #MMPMID34001754 Ben-Michael E; Feller A; Stuart EA Epidemiology 2021[Jul]; 32 (4): 533-540 PMID34001754show ga 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. Although these methods are less common in epidemiology, epidemiologic researchers are well accustomed to handling similar complexities in studies of individual-level interventions. Target trial emulation emphasizes the need to carefully design a nonexperimental study in terms of inclusion and exclusion criteria, covariates, exposure definition, and outcome measurement-and the timing of those variables. We argue that policy evaluations using group-level longitudinal ("panel") data need to take a similar careful approach to study design that we refer to as policy trial emulation. This approach is especially important when intervention timing varies across jurisdictions; the main idea is to construct target trials separately for each treatment cohort (states that implement the policy at the same time) and then aggregate. We present a stylized analysis of the impact of state-level stay-at-home orders on total coronavirus cases. We argue that estimates from panel methods-with the right data and careful modeling and diagnostics-can help add to our understanding of many policies, though doing so is often challenging. |*COVID-19[MESH] |Humans[MESH] |Physical Distancing[MESH] |Policy[MESH]A Trial Emulation Approach for Policy Evaluations with Group-level Lon(epmc).pdf DeepDyve Pubget Overpricing