Abstract:It has long been observed that defendants who are subject to pre-trial detention are more likely to be convicted than those who are free while they await trial. However, until recently, much of the literature in this area was only correlative and not causal. Using an instrumental variable that represents judge severity, we apply near-far matching–a statistical methodology designed to assess causal relationships using observational data–to a dataset of criminal cases that were handled by the New York Legal Aid Society in 2015. We find a strong causal relationship between bail–an obstacle that prevents many from pre-trial release–and case outcome. Specifically, we find setting bail results in a 34% increase in the likelihood of conviction for the cases in our analysis. To our knowledge, this marks the first time matching methodology from the observational studies tradition has been applied to understand the relationship between money bail and the likelihood of conviction.
摘要:长期以来，人们一直观察到，在候审期间被羁押的被告比被羁押的被告更容易被定罪。然而，直到最近，这一领域的许多文献都只是相关的，而不是因果的。我们使用代表法官严厉程度的工具变量，对纽约法律援助协会(New York Legal Aid Society) 2015年处理的刑事案件数据集应用了近距离匹配(near-far matching)——一种旨在利用观察数据评估因果关系的统计方法。我们发现保释(一个阻碍许多人审前释放的障碍)与案件结果之间存在很强的因果关系。具体来说，我们发现在我们的分析中，保释会使案件定罪的可能性增加34%。据我们所知，这标志着观察研究传统的匹配方法首次被应用于理解保释金与定罪可能性之间的关系。
{"title":"The causal impact of bail on case outcomes for indigent defendants in New York City","authors":"K. Lum, Erwin Ma, M. Baiocchi","doi":"10.1353/obs.2017.0007","DOIUrl":"https://doi.org/10.1353/obs.2017.0007","url":null,"abstract":"Abstract:It has long been observed that defendants who are subject to pre-trial detention are more likely to be convicted than those who are free while they await trial. However, until recently, much of the literature in this area was only correlative and not causal. Using an instrumental variable that represents judge severity, we apply near-far matching–a statistical methodology designed to assess causal relationships using observational data–to a dataset of criminal cases that were handled by the New York Legal Aid Society in 2015. We find a strong causal relationship between bail–an obstacle that prevents many from pre-trial release–and case outcome. Specifically, we find setting bail results in a 34% increase in the likelihood of conviction for the cases in our analysis. To our knowledge, this marks the first time matching methodology from the observational studies tradition has been applied to understand the relationship between money bail and the likelihood of conviction.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1353/obs.2017.0007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43742046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract:In the seminal paper from 1960, Thistlethwaite and Campbell (1960) introduce the key ideas underlying regression discontinuity (RD) designs, which, even if initially almost completely ignored, have then acted as a fuse of a blowing number of studies applying and extending RD designs starting from the late nineties. Building on the original idea by Thistlethwaite and Campbell (1960), RD designs have been often described as designs that lead to locally randomized experiments for units with a realized value of a so-called forcing variable falling around a pre-fixed threshold. We embrace this perspective, and in this discussion we offer our view on how the original proposal by Thistlethwaite and Campbell (1960) should be formalized. We introduce an explicit local overlap assumption for a subpopulation around the threshold, for which we re-formulate the Stable Unit Treatment Value Assumption (SUTVA), and provide a formal definition of the hypothetical experiment underlying RD designs, by invoking a local randomization assumption. A distinguishing feature of this approach is that it embeds RD designs in a framework that is fully consistent with the potential outcome approach to causal inference. We discuss how to select suitable subpopulation(s) around the threshold with adjustment for multiple comparisons, and how to draw inference for the causal estimands of interest in this framework. We illustrate our approach in a study concerning the effects of University grants on students’ dropout.
{"title":"Regression Discontinuity Designs as Local Randomized Experiments","authors":"Alessandra Mattei, F. Mealli","doi":"10.1353/obs.2017.0004","DOIUrl":"https://doi.org/10.1353/obs.2017.0004","url":null,"abstract":"Abstract:In the seminal paper from 1960, Thistlethwaite and Campbell (1960) introduce the key ideas underlying regression discontinuity (RD) designs, which, even if initially almost completely ignored, have then acted as a fuse of a blowing number of studies applying and extending RD designs starting from the late nineties. Building on the original idea by Thistlethwaite and Campbell (1960), RD designs have been often described as designs that lead to locally randomized experiments for units with a realized value of a so-called forcing variable falling around a pre-fixed threshold. We embrace this perspective, and in this discussion we offer our view on how the original proposal by Thistlethwaite and Campbell (1960) should be formalized. We introduce an explicit local overlap assumption for a subpopulation around the threshold, for which we re-formulate the Stable Unit Treatment Value Assumption (SUTVA), and provide a formal definition of the hypothetical experiment underlying RD designs, by invoking a local randomization assumption. A distinguishing feature of this approach is that it embeds RD designs in a framework that is fully consistent with the potential outcome approach to causal inference. We discuss how to select suitable subpopulation(s) around the threshold with adjustment for multiple comparisons, and how to draw inference for the causal estimands of interest in this framework. We illustrate our approach in a study concerning the effects of University grants on students’ dropout.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1353/obs.2017.0004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66461026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract:Many decades after being introduced by Thistlewaite and Campbell (1960), regression discontinuity designs have become an important tool for causal inference in social sciences. Researchers have found the methods to be widely applicable in settings where eligibility or incentives for participation in programs is at least partially regulated. Alongside, and motivated by, the many studies applying regression discontinuity methods there have been a number of methodological studies improving our understanding, and implementation, of, these methods. Here I report on some of the recent advances in the econometrics literature.
{"title":"Regression Discontinuity Designs in the Econometrics Literature","authors":"G. Imbens","doi":"10.1353/obs.2017.0003","DOIUrl":"https://doi.org/10.1353/obs.2017.0003","url":null,"abstract":"Abstract:Many decades after being introduced by Thistlewaite and Campbell (1960), regression discontinuity designs have become an important tool for causal inference in social sciences. Researchers have found the methods to be widely applicable in settings where eligibility or incentives for participation in programs is at least partially regulated. Alongside, and motivated by, the many studies applying regression discontinuity methods there have been a number of methodological studies improving our understanding, and implementation, of, these methods. Here I report on some of the recent advances in the econometrics literature.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1353/obs.2017.0003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42421574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As the introduction of Guanglei Hong’s Causality in a Social World makes clear, this book would not be necessary if all treatments we wished to study had constant effects through simple mechanisms on independent individuals who were randomly assigned to treatments. While, such conditions may hold in some idealized agricultural settings, this is not the phenomenon we encounter in a social policy oriented world with human agency. In response, Hong presents a coherent theoretical and empirical framework for estimating causality when people choose their own treatments, when they encounter mediating and moderating effects of treatments and when they influence others’ choices and outcomes. The book is presented in four large sections: overview, moderation, mediation and spillover, with a chapter introducing the core ideas in each section (chapters 4, 7, 11 and 14 respectively). Beyond merely consolidating her own foundational work, the book is steeped in deep and historical statistical principles of sampling, propensity score analysis, mediation and moderation, and spill-over mechanisms. Ultimately, the book will mark a passageway from underlying statistical principles to a framework that may endure and expand beyond even what Hong anticipates.
{"title":"Book review of “Causality in a Social World” by Guanglei Hong","authors":"K. Frank, G. Saw, Ran Xu","doi":"10.1353/obs.2016.0001","DOIUrl":"https://doi.org/10.1353/obs.2016.0001","url":null,"abstract":"As the introduction of Guanglei Hong’s Causality in a Social World makes clear, this book would not be necessary if all treatments we wished to study had constant effects through simple mechanisms on independent individuals who were randomly assigned to treatments. While, such conditions may hold in some idealized agricultural settings, this is not the phenomenon we encounter in a social policy oriented world with human agency. In response, Hong presents a coherent theoretical and empirical framework for estimating causality when people choose their own treatments, when they encounter mediating and moderating effects of treatments and when they influence others’ choices and outcomes. The book is presented in four large sections: overview, moderation, mediation and spillover, with a chapter introducing the core ideas in each section (chapters 4, 7, 11 and 14 respectively). Beyond merely consolidating her own foundational work, the book is steeped in deep and historical statistical principles of sampling, propensity score analysis, mediation and moderation, and spill-over mechanisms. Ultimately, the book will mark a passageway from underlying statistical principles to a framework that may endure and expand beyond even what Hong anticipates.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1353/obs.2016.0001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48217783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract:We are concerned with the unbiased estimation of a treatment effect in the context of non-experimental studies with grouped or multilevel data. When analyzing such data with this goal, practitioners typically include as many predictors (controls) as possible, in an attempt to satisfy ignorability of the treatment assignment. In the multilevel setting with two levels, there are two classes of potential confounders that one must consider, and attempts to satisfy ignorability conditional on just one set would lead to a different treatment effect estimator than attempts to satisfy the other (or both). The three estimators considered in this paper are so-called “within,” “between” and OLS estimators. We generate bounds on the potential differences in bias for these competing estimators to inform model selection. Our approach relies on a parametric model for grouped data and omitted confounders and establishes a framework for sensitivity analysis in the two-level modeling context. The method relies on information obtained from parameters estimated under a variety of multilevel model specifications. We characterize the strength of the confounding and corresponding bias using easily interpretable parameters and graphical displays. We apply this approach to data from a multinational educational evaluation study. We demonstrate the extent to which different treatment effect estimators may be robust to potential unobserved individual- and group-level confounding.
{"title":"Potential for Bias Inflation with Grouped Data: A Comparison of Estimators and a Sensitivity Analysis Strategy","authors":"M. Scott, Ronli Diakow, J. Hill, J. Middleton","doi":"10.1353/obs.2018.0016","DOIUrl":"https://doi.org/10.1353/obs.2018.0016","url":null,"abstract":"Abstract:We are concerned with the unbiased estimation of a treatment effect in the context of non-experimental studies with grouped or multilevel data. When analyzing such data with this goal, practitioners typically include as many predictors (controls) as possible, in an attempt to satisfy ignorability of the treatment assignment. In the multilevel setting with two levels, there are two classes of potential confounders that one must consider, and attempts to satisfy ignorability conditional on just one set would lead to a different treatment effect estimator than attempts to satisfy the other (or both). The three estimators considered in this paper are so-called “within,” “between” and OLS estimators. We generate bounds on the potential differences in bias for these competing estimators to inform model selection. Our approach relies on a parametric model for grouped data and omitted confounders and establishes a framework for sensitivity analysis in the two-level modeling context. The method relies on information obtained from parameters estimated under a variety of multilevel model specifications. We characterize the strength of the confounding and corresponding bias using easily interpretable parameters and graphical displays. We apply this approach to data from a multinational educational evaluation study. We demonstrate the extent to which different treatment effect estimators may be robust to potential unobserved individual- and group-level confounding.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1353/obs.2018.0016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45391755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The seminal paper of Thistlethwaite and Campbell (1960) is one of the greatest breakthroughs in program evaluation and causal inference for observational studies. The originally coined Regression-Discontinuity Analysis, and nowadays widely known as the Regression Discontinuity (RD) design, is likely the most credible and internally valid quantitative approach for the analysis and interpretation of non-experimental data. Early reviews and perspectives on RD designs include Cook (2008), Imbens and Lemieux (2008) and Lee and Lemieux (2010); see also Cattaneo and Escanciano (2017) for a contemporaneous edited volume with more recent overviews, discussions, and references. The key design feature in RD is that units have an observable running variable, score or index, and are assigned to treatment whenever this variable exceeds a known cutoff. Empirical work in RD designs seeks to compare the response of units just below the cutoff (control group) to the response of units just above (treatment group) to learn about the treatment effects of interest. It is by now generally recognized that the most important task in practice is to select the appropriate neighborhood near the cutoff, that is, to correctly determine which observations near the cutoff will be used. Localizing near the cutoff is crucial because empirical findings can be quite sensitive to which observations are included in the analysis. Several neighborhood selection methods have been developed in the literature depending on the goal (e.g., estimation, inference, falsification, graphical presentation), the underlying assumptions invoked (e.g., parametric specification, continuity/nonparametric specification, local randomization), the parameter of interest (e.g., sharp, fuzzy, kink), and even the specific design (e.g., single-cutoff, multi-cutoff, geographic). We offer a comprehensive discussion of both deprecated and modern neighborhood selection approaches available in the literature, following their historical as well as methodological evolution over the last decades. We focus on the prototypical case of a continuously distributed running variable for the most part, though we also discuss the discrete-valued case towards the end of the discussion. The bulk of the presentation focuses on neighborhood selection for estimation and inference, outlining different methods and approaches according to, roughly speaking, the size of a typical selected neighborhood in each case, going from the largest to smallest neighborhood. Figure 1 provides a heuristic summary, which we
{"title":"The Choice of Neighborhood in Regression Discontinuity Designs","authors":"M. D. Cattaneo, Cattaneo","doi":"10.1353/obs.2017.0002","DOIUrl":"https://doi.org/10.1353/obs.2017.0002","url":null,"abstract":"The seminal paper of Thistlethwaite and Campbell (1960) is one of the greatest breakthroughs in program evaluation and causal inference for observational studies. The originally coined Regression-Discontinuity Analysis, and nowadays widely known as the Regression Discontinuity (RD) design, is likely the most credible and internally valid quantitative approach for the analysis and interpretation of non-experimental data. Early reviews and perspectives on RD designs include Cook (2008), Imbens and Lemieux (2008) and Lee and Lemieux (2010); see also Cattaneo and Escanciano (2017) for a contemporaneous edited volume with more recent overviews, discussions, and references. The key design feature in RD is that units have an observable running variable, score or index, and are assigned to treatment whenever this variable exceeds a known cutoff. Empirical work in RD designs seeks to compare the response of units just below the cutoff (control group) to the response of units just above (treatment group) to learn about the treatment effects of interest. It is by now generally recognized that the most important task in practice is to select the appropriate neighborhood near the cutoff, that is, to correctly determine which observations near the cutoff will be used. Localizing near the cutoff is crucial because empirical findings can be quite sensitive to which observations are included in the analysis. Several neighborhood selection methods have been developed in the literature depending on the goal (e.g., estimation, inference, falsification, graphical presentation), the underlying assumptions invoked (e.g., parametric specification, continuity/nonparametric specification, local randomization), the parameter of interest (e.g., sharp, fuzzy, kink), and even the specific design (e.g., single-cutoff, multi-cutoff, geographic). We offer a comprehensive discussion of both deprecated and modern neighborhood selection approaches available in the literature, following their historical as well as methodological evolution over the last decades. We focus on the prototypical case of a continuously distributed running variable for the most part, though we also discuss the discrete-valued case towards the end of the discussion. The bulk of the presentation focuses on neighborhood selection for estimation and inference, outlining different methods and approaches according to, roughly speaking, the size of a typical selected neighborhood in each case, going from the largest to smallest neighborhood. Figure 1 provides a heuristic summary, which we","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1353/obs.2017.0002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44027642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Statistical Criticism, Self-Criticism and the Scientific Method","authors":"D. Rindskopf","doi":"10.1353/obs.2018.0007","DOIUrl":"https://doi.org/10.1353/obs.2018.0007","url":null,"abstract":"","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1353/obs.2018.0007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43207789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thistlethwaite and Campbell (1960) proposed to use a “regression-discontinuity analysis” in settings where exposure to a treatment or intervention is determined by an observable score and a fixed cutoff. The type of setting they described, now widely known as the regression discontinuity (RD) design, is one where units receive a score, and a binary treatment is assigned according to a very specific rule. In the simplest case, all units whose score is above a known cutoff are assigned to the treatment condition, and all units whose score is below the cutoff are assigned to the control (i.e., absence of treatment) condition. Thistlethwaite and Campbell insightfully noted that, under appropriate assumptions, the discontinuity in the probability of treatment status induced by such an assignment rule could be leveraged to learn about the effect of the treatment at the cutoff. Their seminal contribution led to what is now one of the most rigorous non-experimental research designs across the social and biomedical sciences. See Cook (2008), Imbens and Lemieux (2008) and Lee and Lemieux (2010) for reviews, and the recent volume edited by Cattaneo and Escanciano (2017) for recent specific applications and methodological developments.
{"title":"Understanding Regression Discontinuity Designs As Observational Studies","authors":"J. Sekhon, R. Titiunik","doi":"10.1353/obs.2017.0005","DOIUrl":"https://doi.org/10.1353/obs.2017.0005","url":null,"abstract":"Thistlethwaite and Campbell (1960) proposed to use a “regression-discontinuity analysis” in settings where exposure to a treatment or intervention is determined by an observable score and a fixed cutoff. The type of setting they described, now widely known as the regression discontinuity (RD) design, is one where units receive a score, and a binary treatment is assigned according to a very specific rule. In the simplest case, all units whose score is above a known cutoff are assigned to the treatment condition, and all units whose score is below the cutoff are assigned to the control (i.e., absence of treatment) condition. Thistlethwaite and Campbell insightfully noted that, under appropriate assumptions, the discontinuity in the probability of treatment status induced by such an assignment rule could be leveraged to learn about the effect of the treatment at the cutoff. Their seminal contribution led to what is now one of the most rigorous non-experimental research designs across the social and biomedical sciences. See Cook (2008), Imbens and Lemieux (2008) and Lee and Lemieux (2010) for reviews, and the recent volume edited by Cattaneo and Escanciano (2017) for recent specific applications and methodological developments.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1353/obs.2017.0005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49355673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract:The simulation extrapolation method developed by Cook and Stefanski (1995) is a simulation based technique for estimating and reducing bias due to additive measurement error armed only with knowledge of the variance of the measurement error distribution. However there are many instances in which validation data are not available, and measurement error is known not to have mean zero. For example, in assessing phylogenetic cluster size of HIV viruses, cluster size is systematically underestimated since clustering can only be performed on the viruses of those individuals who have presented for testing. In this setting, it is not possible to obtain validation data; however, using knowledge gleaned from the literature, the distribution of the errors may be estimated. In this work, we extend the simulation extrapolation procedure to accommodate errors with non-zero means, motivated by an interest in determining behavioural correlates of HIV phylogenetic cluster size. We provide theoretical justification for the generalization to the non-zero mean measurement error case, proving its consistency and demonstrating its performance via simulation. We then apply the result to data from a the province of Quebec in Canada to show that findings from a naïve analysis are robust to a substantial range of possible measurement error distributions.
{"title":"The non-zero mean SIMEX: Improving estimation in the face of measurement error","authors":"Nabila Parveen, E. Moodie, B. Brenner","doi":"10.1353/obs.2015.0005","DOIUrl":"https://doi.org/10.1353/obs.2015.0005","url":null,"abstract":"Abstract:The simulation extrapolation method developed by Cook and Stefanski (1995) is a simulation based technique for estimating and reducing bias due to additive measurement error armed only with knowledge of the variance of the measurement error distribution. However there are many instances in which validation data are not available, and measurement error is known not to have mean zero. For example, in assessing phylogenetic cluster size of HIV viruses, cluster size is systematically underestimated since clustering can only be performed on the viruses of those individuals who have presented for testing. In this setting, it is not possible to obtain validation data; however, using knowledge gleaned from the literature, the distribution of the errors may be estimated. In this work, we extend the simulation extrapolation procedure to accommodate errors with non-zero means, motivated by an interest in determining behavioural correlates of HIV phylogenetic cluster size. We provide theoretical justification for the generalization to the non-zero mean measurement error case, proving its consistency and demonstrating its performance via simulation. We then apply the result to data from a the province of Quebec in Canada to show that findings from a naïve analysis are robust to a substantial range of possible measurement error distributions.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1353/obs.2015.0005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49458199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Observational Studies and Study Designs: An Epidemiologic Perspective","authors":"T. J. Vander Weele","doi":"10.1353/obs.2015.0025","DOIUrl":"https://doi.org/10.1353/obs.2015.0025","url":null,"abstract":"","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1353/obs.2015.0025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48496744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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