Observational studies最新文献

{"title":"Comment on Cochran’s “Observational Studies”","authors":"Herbert L. Smith","doi":"10.1353/obs.2015.0023","DOIUrl":"https://doi.org/10.1353/obs.2015.0023","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.2015.0023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45072302","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":"Comparing logistic and log-binomial models for causal mediation analyses of binary mediators and rare binary outcomes: evidence to support cross-checking of mediation results in practice","authors":"Mariia Samoilenko, L. Blais, Geneviève Lefebvre","doi":"10.1353/OBS.2018.0013","DOIUrl":"https://doi.org/10.1353/OBS.2018.0013","url":null,"abstract":"Abstract:BackgroundIn the binary outcome framework to causal mediation, closed-form expressions introduced by Valeri and VanderWeele for the natural direct and indirect effect odds ratios (ORs) are established from a logistic outcome model by invoking several approximations that hold under the rare-disease assumption. Such ORs are expected to be close to corresponding effects on the risk ratio (RR) scale based on a log-binomial outcome model, however new insight indicates that this is not always verified. The objective was to report on mediation results from these two models when the incidence of the outcome was <10%.MethodsStandard (approximate) ORs and RRs were estimated using data on a cohort of asthmatic pregnant women from Québec (Canada) and their babies. Prematurity and low birthweight were the mediator and outcome variables, respectively, and two binary exposure variables were considered: treatment to inhaled corticosteroids and placental abruption. Exact closed-form effects expressed on the OR scale were also derived and estimated using a SAS code we provide. A study based on two simulation scenarios was subsequently devised to supplement on the substantive findings.ResultsMany approximate ORs and RRs estimated from our cohort analyses did not closely agree. Approximate ORs were systematically observed farther from RRs in comparison with exact ORs, possibly leading to different conclusions regarding the null hypothesis. Exact OR estimates were very close to RR estimates for exposure to inhaled corticosteroids, but less so for placental abruption. The approximate OR estimator was found to exhibit important bias and undercoverage in the simulation scenario which featured a strong mediator-outcome relationship.ConclusionsLogistic and log-binomial outcome models can yield dissimilar binary-binary mediation effects even if the outcome incidence is small marginally. Large discrepancies between approximate ORs and RRs may indicate invalid inference for these ORs. Exact OR estimates can be obtained for validation or to replace RRs if the log-binomial model exhibits convergence problems.","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.2018.0013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42473431","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":"Book Review of Explanation in Causal Inference: Methods of Mediation and Interaction (author: T.J. Vanderweele)","authors":"L. Keele","doi":"10.1353/obs.2016.0007","DOIUrl":"https://doi.org/10.1353/obs.2016.0007","url":null,"abstract":"Explanation in Causal Inference: Methods of Mediation and Interaction is an introductory text on two widely used methods in statistical analysis: mediation and interaction. The book is both meant to serve as an introduction to these two topics, but also provides considerable mathematical detail in a lengthy appendix. Importantly, the treatment of these two topics is entirely grounded in a counterfactual framework. The counterfactual framework, often referred to as the potential outcomes framework, has been hailed as a revolution in how we think about causality and statistical analysis. I would agree with that sentiment, but the impact of the counterfactual framework is varied. On some topics, the insights have been less revolutionary, but in other areas this framework has I think completely revised how we think. The topics of mediation and interaction analysis are two that I would say have been seriously changed by the counterfactual framework. I think there is already a fairly widespread understanding of how mediation analysis has changed, and this book will only help further spread that awareness. On the topic of interaction analysis, I think there is less appreciation for how the counterfactual framework has changed thinking. This book serves as the remedy.","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.2016.0007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43857358","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":"Review of the book “Causal Inference for Statistics, Social, and Biomedical Sciences” by G.W. Imbens and D.B. Rubin","authors":"F. Mealli","doi":"10.1353/obs.2015.0006","DOIUrl":"https://doi.org/10.1353/obs.2015.0006","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.2015.0006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43872273","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":"Evidence of False Positives in Research Clearinghouses and Influential Journals: An Application of P-Curve to Policy Research","authors":"S. Tanner","doi":"10.1353/obs.2015.0001","DOIUrl":"https://doi.org/10.1353/obs.2015.0001","url":null,"abstract":"Abstract:This article presents a pre-analysis plan for analyzing the evidential value in a selection of policy research taken from scholarly journals and two research clearinghouses run by the federal government. The analysis will collect p-values from selected studies and estimate the evidential value that they represent using the newly introduced p-curve. This article outlines a precise data collection routine, a set of decision rules for including p-values in the analysis sample, and exact hypothesis tests to be used.","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.2015.0001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46784855","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":"Pre-analysis Plan for a Comparison of Matching and Black Box-based Covariate Adjustment","authors":"L. Keele, Dylan S. Small","doi":"10.1353/obs.2018.0017","DOIUrl":"https://doi.org/10.1353/obs.2018.0017","url":null,"abstract":"Abstract:This article presents a pre-analysis plan for a comparison of methods for the statistical adjustment of observed confounders. In the planned analysis, we intend to replicate five existing studies that used customized form of matching and substantive input from subject matter experts. We will replicate the treatment effect estimates from these studies using machine learning methods that need little user input. In this article, we outline the five studies we will use for replication and discuss the methods we use for replication.","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.2018.0017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45824703","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":"Mitigating Reporting Bias in Observational Studies Using Covariate Balancing Methods","authors":"G. Cafri, E. Paxton","doi":"10.1353/obs.2018.0009","DOIUrl":"https://doi.org/10.1353/obs.2018.0009","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.2018.0009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45997818","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":"Assessing the dose-response relationship between maternal use of inhaled corticosteroids therapy and birth weight: a generalized propensity score approach","authors":"Mariia Samoilenko, L. Blais, B. Cossette, A. Forget, Geneviève Lefebvre","doi":"10.1353/obs.2016.0000","DOIUrl":"https://doi.org/10.1353/obs.2016.0000","url":null,"abstract":"Abstract:PurposeInhaled corticosteroids (ICS) are the first-line controller therapy for asthma. The objective was to assess the impact of different ICS doses during pregnancy on birth weight (BW) using generalized propensity scores (GPS).MethodsA cohort of 7374 pregnancies from 6197 asthmatic women giving birth in Quebec (Canada) in 1998-2008 was constructed. The average treatment effects (ATE) of ICS daily doses (0, >0-125, >125-250, >250 μg/day) during pregnancy on BW were estimated using multilevel GPS and a conventional multivariable approach. Additional analyses were done to evaluate the robustness of the results.ResultsUsing GPS, we found no significant associations between ICS doses and BW (ATE for >0-125 vs 0 μg/day: 27.62 g, 95% confidence interval (CI): -8.68, 64.10; ATE for >125-250 vs 0 μg/day: 17.07 g, 95% CI: -55.85, 92.16; ATE for >250 vs 0 μg/day: -37.83 g, 95% CI: -117.74, 41.53). Similar results were obtained using the multivariable approach.ConclusionsWhile, in our primary analyses, no significant differences were found between the BW of babies exposed to the higher ICS doses, as opposed to no use of ICS, our sensitivity analyses, which adjusted for gestational age in the models, suggest the possibility of a small detrimental effect of the higher ICS doses on BW.","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.2016.0000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45170747","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":"Learning from and Responding to Statistical Criticism","authors":"A. Gelman","doi":"10.1353/obs.2018.0003","DOIUrl":"https://doi.org/10.1353/obs.2018.0003","url":null,"abstract":"Irwin Bross’s article, “Statistical Criticism,” gives advice that is surprisingly current, given that it appeared in the journal Cancer nearly sixty years ago. Indeed, the only obviously dated aspects of this paper are the use of the generic male pronoun and the sense that it was still an open question whether cigarette smoking caused lung cancer. In his article, Bross acts a critic of criticism, expressing support for the general form but recommending that critics go beyond hit-and-run, dogmatism, speculation, and tunnel vision. This all seems reasonable to me, but I think criticisms can also be taken at face value. If I publish a paper and someone replies with a flawed criticism, I still should be able to respond to its specifics. Indeed, there have been times when my own work has been much improved by criticism that was itself blinkered but which still revealed important and fixable flaws in my published work. I would go further and argue that nearly all criticism has value. Again, I’ll place myself in the position of the researcher whose work is being slammed. Consider the following sorts of statistical criticism, aligned in roughly decreasing order of quality:","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.2018.0003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42157120","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":"Beyond Statistical Criticism","authors":"P. Rosenbaum, Dylan S. Small","doi":"10.1353/obs.2018.0008","DOIUrl":"https://doi.org/10.1353/obs.2018.0008","url":null,"abstract":"Abstract:In an admirable essay, Bross makes many useful observations. The goal, however, should be to take a step beyond statistical criticism, arriving at an objective statement about what the (research design + data) say and fail to say. Often this entails saying a bit less than one might like in exchange for saying something definite and objective.","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.2018.0008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45312970","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}