Literature DB >> 29187059

Bridging observational studies and randomized experiments by embedding the former in the latter.

Marie-Abele C Bind1, Donald B Rubin1.   

Abstract

Consider a statistical analysis that draws causal inferences from an observational dataset, inferences that are presented as being valid in the standard frequentist senses; i.e. the analysis produces: (1) consistent point estimates, (2) valid p-values, valid in the sense of rejecting true null hypotheses at the nominal level or less often, and/or (3) confidence intervals, which are presented as having at least their nominal coverage for their estimands. For the hypothetical validity of these statements, the analysis must embed the observational study in a hypothetical randomized experiment that created the observed data, or a subset of that hypothetical randomized data set. This multistage effort with thought-provoking tasks involves: (1) a purely conceptual stage that precisely formulate the causal question in terms of a hypothetical randomized experiment where the exposure is assigned to units; (2) a design stage that approximates a randomized experiment before any outcome data are observed, (3) a statistical analysis stage comparing the outcomes of interest in the exposed and non-exposed units of the hypothetical randomized experiment, and (4) a summary stage providing conclusions about statistical evidence for the sizes of possible causal effects. Stages 2 and 3 may rely on modern computing to implement the effort, whereas Stage 1 demands careful scientific argumentation to make the embedding plausible to scientific readers of the proffered statistical analysis. Otherwise, the resulting analysis is vulnerable to criticism for being simply a presentation of scientifically meaningless arithmetic calculations. The conceptually most demanding tasks are often the most scientifically interesting to the dedicated researcher and readers of the resulting statistical analyses. This perspective is rarely implemented with any rigor, for example, completely eschewing the first stage. We illustrate our approach using an example examining the effect of parental smoking on children's lung function collected in families living in East Boston in the 1970s.

Entities:  

Keywords:  Experimental design; Rubin Causal Model (RCM); causal inference; environmental epidemiology; lung function; observational studies; parental smoking

Mesh:

Year:  2017        PMID: 29187059      PMCID: PMC5902671          DOI: 10.1177/0962280217740609

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  14 in total

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7.  A longitudinal study of the effects of parental smoking on pulmonary function in children 6-18 years.

Authors:  X Wang; D Wypij; D R Gold; F E Speizer; J H Ware; B G Ferris; D W Dockery
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9.  Joint Bayesian weight and height postnatal growth model to study the effects of maternal smoking during pregnancy.

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Authors:  Michelle L Bell; Roger D Peng; Francesca Dominici
Journal:  Environ Health Perspect       Date:  2006-04       Impact factor: 9.031

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8.  Framework for the synthesis of non-randomised studies and randomised controlled trials: a guidance on conducting a systematic review and meta-analysis for healthcare decision making.

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9.  The Role of Ambient Particle Radioactivity in Inflammation and Endothelial Function in an Elderly Cohort.

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