ArticleThe international journal of biostatistics2007
Preference-based instrumental variable methods for the estimation of treatment effects: assessing validity and interpreting results.
Article in The international journal of biostatistics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
89 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Instrumental variable meta-analysis of individual patient data: application to adjust for treatment non-compliance.BMC medical research methodology · 2011Pooled it
- Acupuncture and chiropractic care for chronic pain in an integrated health plan: a mixed methods study.BMC complementary and alternative medicine · 2011Trial
- Overall survival of patients with localized soft tissue sarcoma treated with or without adjuvant chemotherapy (DEEPSARC): a real world nationwide retrospective cohort study in France.EClinicalMedicine · 2026Article
- PERsonalised Medicine for Intensification of Treatment (PERMIT) in type 2 diabetes mellitus: a target trial emulation from routine data.Health technology assessment (Winchester, England) · 2026Article
- Diagnostic Timing and Ovarian Cancer Survival.JAMA network open · 2026Article
- Nonparametric Bayesian Adjustment of Unmeasured Confounders in Cox Proportional Hazards Models.Statistics in medicine · 2026Article
- Antidepressant treatment and risk of subsequent bipolar disorder in adolescents with unipolar depression.BMJ mental health · 2025Article
- Using double-negative controls to adjust for healthy-user bias in a recombinant zoster vaccine safety study.American journal of epidemiology · 2025Article
- Instrumental variable approaches for estimating time-varying treatment effects in comparative effectiveness research.BMC medical research methodology · 2025Article
- Comparative effectiveness of alternative second-line oral glucose-lowering therapies for type 2 diabetes: a precision medicine approach applied to routine data.Diabetologia · 2025Article
- The impacts of Liverpool Citizen's Advice on Prescription (CAP) on mental health outcomes- an Instrumental Variable (IV) approach.SSM - population health · 2025Article
- Instrumental Variable Methods to Target Hypothetical Estimands With Longitudinal Repeated Measures Data: Application to the STEP 1 Trial.Statistics in medicine · 2025Article
- Developing an individualized treatment rule for Veterans with major depressive disorder using electronic health records.Molecular psychiatry · 2024Article
- Comparative effectiveness of second line oral antidiabetic treatments among people with type 2 diabetes mellitus: emulation of a target trial using routinely collected health data.BMJ (Clinical research ed.) · 2024Article
- Assessing the performance of physician's prescribing preference as an instrumental variable in comparative effectiveness research with moderate and small sample sizes: a simulation study.Journal of comparative effectiveness research · 2024Article
- Opportunities and challenges in using instrumental variables to study causal effects in nonrandomized stress and trauma research.Psychological trauma : theory, research, practice and policy · 2023Review
- Long term outcomes and causal modelling of compulsory inpatient and outpatient mental health care using Norwegian registry data: Protocol for a controversies in psychiatry research project.International journal of methods in psychiatric research · 2023Article
- Strategies to Assess the Effect of Continuous Positive Airway Pressure on Long-Term Clinically Important Outcomes among Patients with Symptomatic Obstructive Sleep Apnea: An Official American Thoracic Society Workshop Report.Annals of the American Thoracic Society · 2023Article
- Application of Instrumental Variable Analysis in Pediatric End-of-Life Research: A Case Study.Western journal of nursing research · 2023Article
- Frameworks for estimating causal effects in observational settings: comparing confounder adjustment and instrumental variables.BMC medical research methodology · 2023Review
29 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Observational studies of drugs and medical procedures based on administrative data are increasingly used to inform regulatory and clinical decisions. However, the validity of such studies is often questioned because available data may not contain measurements of many important prognostic variables that guide treatment decisions. Recently, approaches to this problem have been proposed that use instrumental variables (IV) defined at the level of an individual health care provider or aggregation of providers. Implicitly, these approaches attempt to estimate causal effects by using differences in medical practice patterns as a quasi-experiment. Although preference-based IV methods may usefully complement standard statistical approaches, they make assumptions that are unfamiliar to most biomedical researchers and therefore the validity of such analyses can be hard to evaluate. Here, we propose a simple framework based on a single unobserved dichotomous variable that can be used to explore how violations of IV assumptions and treatment effect heterogeneity may bias the standard IV estimator with respect to the average treatment effect in the population. This framework suggests various ways to anticipate the likely direction of bias using both empirical data and commonly available subject matter knowledge, such as whether medications or medical procedures tend to be overused, underused, or often misused. This approach is described in the context of a study comparing the gastrointestinal bleeding risk attributable to different non-steroidal anti-inflammatory drugs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.