scholarly journals Direct and Indirect Treatment Effects: Causal Chains and Mediation Analysis with Instrumental Variables

2014 ◽  
Author(s):  
Markus Frölich ◽  
Martin Huber
Keyword(s):  
Instrumental Variables ◽  
Mediation Analysis ◽  
Treatment Effects ◽  
Indirect Treatment ◽  
Causal Chains

scholarly journals Causal inference and adjustment for reference-arm risk in indirect treatment comparison meta-analysis

2020 ◽  
Vol 9 (10) ◽  
pp. 737-750
Author(s):  
Elyse Swallow ◽  
Oscar Patterson-Lomba ◽  
Rajeev Ayyagari ◽  
Corey Pelletier ◽  
Rina Mehta ◽  
...  
Keyword(s):  
Causal Inference ◽  
Treatment Effects ◽  
Meta Analysis ◽  
Real Data ◽  
Indirect Treatment Comparison ◽  
Common Reference ◽  
Treatment Comparison ◽  
Improve Model ◽  
Indirect Treatment ◽  
Meta Analyses

Aim: To illustrate that bias associated with indirect treatment comparison and network meta-analyses can be reduced by adjusting for outcomes on common reference arms. Materials & methods: Approaches to adjusting for reference-arm effects are presented within a causal inference framework. Bayesian and Frequentist approaches are applied to three real data examples. Results: Reference-arm adjustment can significantly impact estimated treatment differences, improve model fit and align indirectly estimated treatment effects with those observed in randomized trials. Reference-arm adjustment can possibly reverse the direction of estimated treatment effects. Conclusion: Accumulating theoretical and empirical evidence underscores the importance of adjusting for reference-arm outcomes in indirect treatment comparison and network meta-analyses to make full use of data and reduce the risk of bias in estimated treatments effects.

Use of instrumental variables in electronic health record-driven models

2016 ◽  
Vol 27 (2) ◽  
pp. 608-621 ◽  
Author(s):  
Luca Salmasi ◽  
Enrico Capobianco
Keyword(s):  
Electronic Health Records ◽  
Electronic Health Record ◽  
Instrumental Variables ◽  
Treatment Effects ◽  
Ad Hoc ◽  
Health Record ◽  
Health Records ◽  
Negative Effect ◽  
Electronic Health ◽  
High Degree

Precision medicine presents various methodological challenges whose assessment requires the consideration of multiple factors. In particular, the data multitude in the Electronic Health Records poses interoperability issues and requires novel inference strategies. A problem, though apparently a paradox, is that highly specific treatments and a variety of outcomes may hardly match with consistent observations (i.e., large samples). Why is it the case? Owing to the heterogeneity of Electronic Health Records, models for the evaluation of treatment effects need to be selected, and in some cases, the use of instrumental variables might be necessary. We studied the recently defined person-centered treatment effects in cancer and C-section contexts from Electronic Health Record sources and identified as an instrument the distance of patients from hospitals. We present first the rationale for using such instrument and then its model implementation. While for cancer patients consideration of distance turns out to be a penalty, implying a negative effect on the probability of receiving surgery, a positive effect is instead found in C-section due to higher propensity of scheduling delivery. Overall, the estimated person-centered treatment effects reveal a high degree of heterogeneity, whose interpretation remains context-dependent. With regard to the use of instruments in light of our two case studies, our suggestion is that this process requires ad hoc variable selection for both covariates and instruments and additional testing to ensure validity.

scholarly journals Using Instrumental Variables for Inference about Policy Relevant Treatment Effects

2017 ◽  
Author(s):  
Magne Mogstad ◽  
Andres Santos ◽  
Alexander Torgovitsky
Keyword(s):  
Instrumental Variables ◽  
Treatment Effects

Instrument-based estimation with binarised treatments: issues and tests for the exclusion restriction

2021 ◽  
Author(s):  
Martin E Andresen ◽  
Martin Huber
Keyword(s):  
Instrumental Variables ◽  
Treatment Effects ◽  
College Graduation ◽  
Average Effect ◽  
Exclusion Restriction ◽  
Unit Level ◽  
Policy Relevance ◽  
Induced Changes ◽  
Local Average

Summary When estimating local average and marginal treatment effects using instrumental variables (IVs), multivalued endogenous treatments are frequently converted to binary measures, supposedly to improve interpretability or policy relevance. Such binarisation introduces a violation of the IV exclusion if (a) the IV affects the multivalued treatment within support areas below and/or above the threshold and (b) such IV-induced changes in the multivalued treatment affect the outcome. We discuss assumptions that satisfy the IV exclusion restriction with a binarised treatment and permit identifying the average effect of (a) the binarised treatment and (b) unit-level increases in the original multivalued treatment among specific compliers. We derive testable implications of these assumptions and propose tests which we apply to the estimation of the returns to college graduation instrumented by college proximity.

Local Average and Quantile Treatment Effects Under Endogeneity: A Review

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Martin Huber ◽  
Kaspar Wüthrich
Keyword(s):  
Instrumental Variable ◽  
Treatment Effects ◽  
Randomized Experiments ◽  
Quantile Treatment Effects ◽  
Indirect Treatment ◽  
Treatment Effect Models ◽  
Multiple Treatments ◽  
The Relationship ◽  
Local Average ◽  
Heterogeneous Treatment Effect

Abstract This paper provides a review of methodological advancements in the evaluation of heterogeneous treatment effect models based on instrumental variable (IV) methods. We focus on models that achieve identification by assuming monotonicity of the treatment in the IV and analyze local average and quantile treatment effects for the subpopulation of compliers. We start with a comprehensive discussion of the binary treatment and binary IV case as for instance relevant in randomized experiments with imperfect compliance. We then review extensions to identification and estimation with covariates, multi-valued and multiple treatments and instruments, outcome attrition and measurement error, and the identification of direct and indirect treatment effects, among others. We also discuss testable implications and possible relaxations of the IV assumptions, approaches to extrapolate from local to global treatment effects, and the relationship to other IV approaches.

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