scholarly journals Methodological Challenges When Studying Distance to Care as an Exposure in Health Research

2019 ◽  
Vol 188 (9) ◽  
pp. 1674-1681 ◽  
Author(s):  
Ellen C Caniglia ◽  
Rebecca Zash ◽  
Sonja A Swanson ◽  
Kathleen E Wirth ◽  
Modiegi Diseko ◽  
...  
Keyword(s):  
Measurement Error ◽  
Causal Inference ◽  
Selection Bias ◽  
Birth Outcomes ◽  
Health Research ◽  
Instrumental Variable ◽  
Sensitivity Analyses ◽  
Unmeasured Confounding ◽  
Instrument Selection ◽  
Distance To Care

Abstract Distance to care is a common exposure and proposed instrumental variable in health research, but it is vulnerable to violations of fundamental identifiability conditions for causal inference. We used data collected from the Botswana Birth Outcomes Surveillance study between 2014 and 2016 to outline 4 challenges and potential biases when using distance to care as an exposure and as a proposed instrument: selection bias, unmeasured confounding, lack of sufficiently well-defined interventions, and measurement error. We describe how these issues can arise, and we propose sensitivity analyses for estimating the degree of bias.

scholarly journals Mendelian randomisation for mediation analysis: current methods and challenges for implementation

2021 ◽  
Author(s):  
Alice R. Carter ◽  
Eleanor Sanderson ◽  
Gemma Hammerton ◽  
Rebecca C. Richmond ◽  
George Davey Smith ◽  
...  
Keyword(s):  
Measurement Error ◽  
Causal Inference ◽  
Mediation Analysis ◽  
Instrumental Variable ◽  
Real Data ◽  
Mendelian Randomisation ◽  
Differential Measurement ◽  
Individual Level ◽  
Differential Measurement Error ◽  
Summary Data

AbstractMediation analysis seeks to explain the pathway(s) through which an exposure affects an outcome. Traditional, non-instrumental variable methods for mediation analysis experience a number of methodological difficulties, including bias due to confounding between an exposure, mediator and outcome and measurement error. Mendelian randomisation (MR) can be used to improve causal inference for mediation analysis. We describe two approaches that can be used for estimating mediation analysis with MR: multivariable MR (MVMR) and two-step MR. We outline the approaches and provide code to demonstrate how they can be used in mediation analysis. We review issues that can affect analyses, including confounding, measurement error, weak instrument bias, interactions between exposures and mediators and analysis of multiple mediators. Description of the methods is supplemented by simulated and real data examples. Although MR relies on large sample sizes and strong assumptions, such as having strong instruments and no horizontally pleiotropic pathways, our simulations demonstrate that these methods are unaffected by confounders of the exposure or mediator and the outcome and non-differential measurement error of the exposure or mediator. Both MVMR and two-step MR can be implemented in both individual-level MR and summary data MR. MR mediation methods require different assumptions to be made, compared with non-instrumental variable mediation methods. Where these assumptions are more plausible, MR can be used to improve causal inference in mediation analysis.

scholarly journals Measurement errors in the binary instrumental variable model

Biometrika ◽  
2019 ◽  
Vol 107 (1) ◽  
pp. 238-245
Author(s):  
Zhichao Jiang ◽  
Peng Ding
Keyword(s):  
Measurement Error ◽  
Instrumental Variable ◽  
Measurement Errors ◽  
Causal Effects ◽  
Sensitivity Analyses ◽  
Variable Model ◽  
Differential Measurement ◽  
True Value ◽  
Instrumental Variable Model ◽  
Outcome Biases

Summary Instrumental variable methods can identify causal effects even when the treatment and outcome are confounded. We study the problem of imperfect measurements of the binary instrumental variable, treatment and outcome. We first consider nondifferential measurement errors, that is, the mismeasured variable does not depend on other variables given its true value. We show that the measurement error of the instrumental variable does not bias the estimate, that the measurement error of the treatment biases the estimate away from zero, and that the measurement error of the outcome biases the estimate toward zero. Moreover, we derive sharp bounds on the causal effects without additional assumptions. These bounds are informative because they exclude zero. We then consider differential measurement errors, and focus on sensitivity analyses in those settings.

scholarly journals Sensitivity Analysis for Causal Inference under Unmeasured Confounding and Measurement Error Problems

2013 ◽  
Vol 9 (2) ◽  
Author(s):  
Iván Díaz ◽  
Mark J. van der Laan
Keyword(s):  
Sensitivity Analysis ◽  
Measurement Error ◽  
Causal Inference ◽  
Parametric Model ◽  
Linear Estimator ◽  
Nonparametric Model ◽  
Asymptotically Linear ◽  
Full Data ◽  
Unmeasured Confounding ◽  
New Model

AbstractIn this article, we present a sensitivity analysis for drawing inferences about parameters that are not estimable from observed data without additional assumptions. We present the methodology using two different examples: a causal parameter that is not identifiable due to violations of the randomization assumption, and a parameter that is not estimable in the nonparametric model due to measurement error. Existing methods for tackling these problems assume a parametric model for the type of violation to the identifiability assumption and require the development of new estimators and inference for every new model. The method we present can be used in conjunction with any existing asymptotically linear estimator of an observed data parameter that approximates the unidentifiable full data parameter and does not require the study of additional models.

scholarly journals Stuck in the middle: a systematic review of authorship in collaborative health research in Africa, 2014–2016

2019 ◽  
Vol 4 (5) ◽  
pp. e001853 ◽  
Author(s):  
Bethany L Hedt-Gauthier ◽  
Herve Momo Jeufack ◽  
Nicholas H Neufeld ◽  
Atalay Alem ◽  
Sara Sauer ◽  
...  
Keyword(s):  
Global Health ◽  
Health Research ◽  
African Country ◽  
High Income ◽  
Sub Saharan Africa ◽  
Income Country ◽  
High Income Country ◽  
Sensitivity Analyses ◽  
Global Health Research ◽  
The Usa

BackgroundCollaborations are often a cornerstone of global health research. Power dynamics can shape if and how local researchers are included in manuscripts. This article investigates how international collaborations affect the representation of local authors, overall and in first and last author positions, in African health research.MethodsWe extracted papers on ‘health’ in sub-Saharan Africa indexed in PubMed and published between 2014 and 2016. The author’s affiliation was used to classify the individual as from the country of the paper’s focus, from another African country, from Europe, from the USA/Canada or from another locale. Authors classified as from the USA/Canada were further subclassified if the author was from a top US university. In primary analyses, individuals with multiple affiliations were presumed to be from a high-income country if they contained any affiliation from a high-income country. In sensitivity analyses, these individuals were presumed to be from an African country if they contained any affiliation an African country. Differences in paper characteristics and representation of local coauthors are compared by collaborative type using χ² tests.ResultsOf the 7100 articles identified, 68.3% included collaborators from the USA, Canada, Europe and/or another African country. 54.0% of all 43 429 authors and 52.9% of 7100 first authors were from the country of the paper’s focus. Representation dropped if any collaborators were from USA, Canada or Europe with the lowest representation for collaborators from top US universities—for these papers, 41.3% of all authors and 23.0% of first authors were from country of paper’s focus. Local representation was highest with collaborators from another African country. 13.5% of all papers had no local coauthors.DiscussionIndividuals, institutions and funders from high-income countries should challenge persistent power differentials in global health research. South-South collaborations can help African researchers expand technical expertise while maintaining presence on the resulting research.

Pseudorandomization using an instrumental variable: a strong tool to break through selection bias

2012 ◽  
Vol 76 (5) ◽  
pp. 1079-1080 ◽  
Author(s):  
Tsuyoshi Hamada ◽  
Takeshi Tsujino ◽  
Hiroyuki Isayama ◽  
Kazuhiko Koike
Keyword(s):  
Selection Bias ◽  
Instrumental Variable

scholarly journals Estimation of Natural Indirect Effects Robust to Unmeasured Confounding and Mediator Measurement Error

Epidemiology ◽  
2019 ◽  
Vol 30 (6) ◽  
pp. 825-834 ◽  
Author(s):  
Isabel R. Fulcher ◽  
Xu Shi ◽  
Eric J. Tchetgen Tchetgen
Keyword(s):  
Measurement Error ◽  
Indirect Effects ◽  
Unmeasured Confounding

scholarly journals To what extent is social inequality in school-age achievement determined before and during schooling? A longitudinal analysis in three European countries

2020 ◽  
Author(s):  
Giampiero Passaretta ◽  
Jan Skopek ◽  
Thomas van Huizen
Keyword(s):  
Measurement Error ◽  
Social Inequality ◽  
Instrumental Variable ◽  
School Age ◽  
Formal Schooling ◽  
School Life ◽  
Primary Schooling ◽  
The United Kingdom ◽  
Before School

We estimate the degree to which socioeconomic status (SES) gaps in children’s language skills observed in primary schooling are already determined before children enter school in Germany, the Netherlands, and the United Kingdom. We use representative and longitudinal cohort data and apply instrumental variable estimation to deal with measurement error in test scores. Around 60–80% of SES gaps in language at the end of primary school are attributable to gaps settled before formal schooling, while at most 20–40% is attributable to SES operating during the school years. We also show that ignoring measurement error results in a major overestimation of the role of SES during schooling. Our findings suggest that the most effective strategy for reducing social inequality in school-age achievement is reducing inequality before school life starts.

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