Partial Identification of the Average Treatment Effect Using Instrumental Variables: Review of Methods for Binary Instruments, Treatments, and Outcomes

Several methods have been proposed for partially or point identifying the average treatment effect (ATE) using instrumental variable (IV) type assumptions. The descriptions of these methods are widespread across the statistical, economic, epidemiologic, and computer science literature, and the connections between the methods have not been readily apparent. In the setting of a binary instrument, treatment, and outcome, we review proposed methods for partial and point identification of the ATE under IV assumptions, express the identification results in a common notation and terminology, and propose a taxonomy that is based on sets of identifying assumptions. We further demonstrate and provide software for the application of these methods to estimate bounds. Supplementary materials for this article are available online

Electricity management in the production of leadaAcid batteries: the industrial case of a production plant in Colombia

Electricity stands as the main energy used for lead-acid battery (LAB) manufacturing. This study introduces an energy management methodology to address the electricity consumption in lead-acid battery plants, improving efficiency standards. The “equivalent battery production” is introduced to define the energy performance criteria to be met in the different production sections of the battery plant. The methodology combines the guidelines of the ISO 50001 standard with the energy management framework for manufacturing plants. The result is a structured approach for detecting inefficiencies and pinpointing their sources. The management methodology was implemented during 2016. In the formation area 222 MWh were saved during 2016. This saving accounts for 3.9% less electricity than forecasted by the energy baseline of the area. Additionally, the emission of some 40 tCO2.eq. associated with the generation of the electricity production were saved. Moreover, at plant level 424 MWh were saved, which account for 3.6% less electricity than forecasted by the energy baseline of the plant. In total, around 76 tCO2.eq. were saved as a result of the electricity savings in the plant

Causal null hypotheses of sustained treatment strategies: What can be tested with an instrumental variable?

Sometimes instrumental variable methods are used to test whether a causal effect is null rather than to estimate the magnitude of a causal effect. However, when instrumental variable methods are applied to time-varying exposures, as in many Mendelian randomization studies, it is unclear what causal null hypothesis is tested. Here, we consider different ver

Immediate versus deferred initiation of androgen deprivation therapy in prostate cancer patients with PSA-only relapse. An observational follow-up study

BackgroundThe optimal timing to start androgen deprivation therapy (ADT) in prostate cancer patients with rising prostate-specific antigen (PSA) as the only sign of relapse is unknown.MethodsWe identified men with prostate cancer in the Cancer of the Prostate Strategic Urologic Research Endeavour (CaPSURE) study who would have been eligible (⩽ cT3aN0M0, primary radical prostatectomy or radiotherapy, PSA relapse as the only evidence of recurrence) for a randomised trial comparing 'immediate' versus 'deferred' ADT initiation. We emulated such trial by assigning patients to the 'immediate' strategy if they initiated ADT within 3 months of PSA relapse and to the 'deferred' strategy if they initiated ADT when they presented with metastasis, symptoms or a short PSA doubling time. We censored patients when they deviated from the assigned strategy and adjusted for this censoring via inverse probability weighting.ResultsOf 2096 eligible patients (median age 69, interquartile range 63-75 years), 88% were white, 35% had a Gleason score ⩾ 7, 69% were treated with radical prostatectomy and 31% received radiotherapy only as primary treatment. The mean time from primary treatment to PSA relapse was 37.4 (standard deviation [SD] 34.2) months. Mean follow-up from primary treatment was 91.4 (SD 48.4) months. The adjusted mortality hazard ratio for immediate versus deferred ADT was 0.91 (95% confidence interval (CI), 0.52-1.60), which would be translated into a similar 5-year survival (difference between groups: -2.0% (95% CI: -10.0 to 5.9%).ConclusionOur analysis suggests that prostate cancer patients undergoing immediate ADT initiation within three months after PSA-only relapse had similar survival to those who deferred ADT initiation within 3 months after clinical progression

Primary prevention of cardiovascular disease with a Mediterranean diet supplemented with extra-virgin olive oil or nuts

BACKGROUND: Observational cohort studies and a secondary prevention trial have shown inverse associations between adherence to the Mediterranean diet and cardiovascular risk. METHODS: In a multicenter trial in Spain, we assigned 7447 participants (55 to 80 years of age, 57% women) who were at high cardiovascular risk, but with no cardiovascular disease at enrollment, to one of three diets: a Mediterranean diet supplemented with extra-virgin olive oil, a Mediterranean diet supplemented with mixed nuts, or a control diet (advice to reduce dietary fat). Participants received quarterly educational sessions and, depending on group assignment, free provision of extra-virgin olive oil, mixed nuts, or small nonfood gifts. The primary end point was a major cardiovascular event (myocardial infarction, stroke, or death from cardiovascular causes). After a median follow-up of 4.8 years, the trial was stopped on the basis of a prespecified interim analysis. In 2013, we reported the results for the primary end point in the Journal. We subsequently identified protocol deviations, including enrollment of household members without randomization, assignment to a study group without randomization of some participants at 1 of 11 study sites, and apparent inconsistent use of randomization tables at another site. We have withdrawn our previously published report and now report revised effect estimates based on analyses that do not rely exclusively on the assumption that all the participants were randomly assigned. RESULTS: A primary end-point event occurred in 288 participants; there were 96 events in the group assigned to a Mediterranean diet with extra-virgin olive oil (3.8%), 83 in the group assigned to a Mediterranean diet with nuts (3.4%), and 109 in the control group (4.4%). In the intention-to-treat analysis including all the participants and adjusting for baseline characteristics and propensity scores, the hazard ratio was 0.69 (95% confidence interval [CI], 0.53 to 0.91) for a Mediterranean diet with extra-virgin olive oil and 0.72 (95% CI, 0.54 to 0.95) for a Mediterranean diet with nuts, as compared with the control diet. Results were similar after the omission of 1588 participants whose study-group assignments were known or suspected to have departed from the protocol. CONCLUSIONS: In this study involving persons at high cardiovascular risk, the incidence of major cardiovascular events was lower among those assigned to a Mediterranean diet supplemented with extra-virgin olive oil or nuts than among those assigned to a reduced-fat diet. (Funded by Instituto de Salud Carlos III, Spanish Ministry of Health, and others; Current Controlled Trials number, ISRCTN35739639 .)

A review of spatial causal inference methods for environmental and epidemiological applications

The scientific rigor and computational methods of causal inference have had great impacts on many disciplines, but have only recently begun to take hold in spatial applications. Spatial casual inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality, and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies, and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID-19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided