Preference-based instrumental variables in health research rely on important and underreported assumptions: a systematic review

OBJECTIVE

Preference-based instrumental variables (PP IV) designs can identify causal effects when patients receive treatment due to variation in providers' treatment preference. We offer a systematic review and methodological assessment of PP IV applications in health research.

STUDY DESIGN AND SETTING

We included studies that applied PP IV for evaluation of any treatment in any population in health research (PROSPERO: CRD42020165014). We searched within four databases (Medline, Web of Science, ScienceDirect, SpringerLink) and four journals (including full-text and title and abstract sources) between January 1, 1998, and March 5, 2020. We extracted data on areas of applications and methodology, including assumptions using Swanson and Hernan's (2013) guideline.

RESULTS

We included 185 of 1087 identified studies. The use of PP IV has increased, being predominantly used for treatment effects in cancer, cardiovascular disease, and mental health. The most common PP IV was treatment variation at the facility-level, followed by physician- and regional-level. Only 12 percent of applications report the four main assumptions for PP IV. Selection on treatment may be a potential issue in 46 percent of studies.

CONCLUSION

The assumptions of PP IV are not sufficiently reported in existing work. PP IV-studies should use reporting guidelines.

Alliance between selective serotonin reuptake inhibitors and fracture risk: an updated systematic review and meta-analysis

PURPOSE

In the past few years, several fracture-related events have been reported with chronic use of selective serotonin reuptake inhibitors (SSRIs) throughout the globe. Hence, an updated systematic review and meta-analysis was necessary to ascertain the risk involved. The present work evaluated the association of SSRIs with the risk of fracture in adults.

METHODS

We systematically searched PubMed, Cochrane library, and Google Scholar for observational studies on the same from inception to April 2019. Screening, data extraction, and risk of bias assessment were conducted independently by 2 authors.

RESULTS

We assessed 69 studies out of which 37 (14 case-control, 23 cohorts) were included. Our results showed that SSRIs were significantly associated with an increased fracture risk (relative risk of 1.62, 95% CI 1.52-1.73; P < 0.000; I² = 90.8%). The relative risk values for case-control and cohort studies were found to be 1.80 (95% CI 1.58-2.03; P < 0.000; I² = 93.2%) and 1.51 (95% CI 1.39-1.64; P < 0.000; I² = 88.0%) respectively. Subgroup analysis showed that association of risk of fracture persisted regardless of geographical location, study design, risk factors, defined daily dose, SSRI use duration, site of the fracture, period of study and after adjusting for depression, physical activity, gender, and age group. The sensitivity analysis data shows that the studies adjusted for bone mineral density and osteoporosis show lesser fracture risk.

CONCLUSION

Our findings suggests that SSRIs may be associated with an increased fracture risk; hence, bone health should be taken into consideration while prescribing this class of drugs.

Instrumental variable analysis in the context of dichotomous outcome and exposure with a numerical experiment in pharmacoepidemiology

Background

In pharmacoepidemiology, the prescription preference-based instrumental variables (IV) are often used with linear models to solve the endogeneity due to unobserved confounders even when the outcome and the endogenous treatment are dichotomous variables. Using this instrumental variable, we proceed by Monte-Carlo simulations to compare the IV-based generalized method of moment (IV-GMM) and the two-stage residual inclusion (2SRI) method in this context.

Methods

We established the formula allowing us to compute the instrument’s strength and the confounding level in the context of logistic regression models. We then varied the instrument’s strength and the confounding level to cover a large range of scenarios in the simulation study. We also explore two prescription preference-based instruments.

Results

We found that the 2SRI is less biased than the other methods and yields satisfactory confidence intervals. The proportion of previous patients of the same physician who were prescribed the treatment of interest displayed a good performance as a proxy of the physician’s preference instrument.

Conclusions

This work shows that when analysing real data with dichotomous outcome and exposure, appropriate 2SRI estimation could be used in presence of unmeasured confounding.

Identification of causal effects with latent confounding and classical additive errors in treatment

In this paper, we discuss the identifiability and estimation of causal effects of a continuous treatment on a binary response when the treatment is measured with errors and there exists a latent categorical confounder associated with both treatment and response. Under some widely used parametric models, we first discuss the identifiability of the causal effects and then propose an approach for estimation and inference. Our approach can eliminate the biases induced by latent confounding and measurement errors by using only a single instrumental variable. Based on the identification results, we give guidelines for determining the existence of a latent categorical confounder and for selecting the number of levels of the latent confounder. We apply the proposed approach to a data set from the Framingham Heart Study to evaluate the effect of the systolic blood pressure on the coronary heart disease.

Multi-centre, multi-database studies with common protocols: lessons learnt from the IMI PROTECT project

PURPOSE

To assess the impact of a variety of methodological parameters on the association between six drug classes and five key adverse events in multiple databases.

METHODS

The selection of Drug-Adverse Event pairs was based on public health impact, regulatory relevance, and the possibility to study a broad range of methodological issues. Common protocols and data analytical specifications were jointly developed and independently and blindly executed in different databases in Europe with replications in the same and different databases.

RESULTS

The association between antibiotics and acute liver injury, benzodiazepines and hip fracture, antidepressants and hip fracture, inhaled long-acting beta2-agonists and acute myocardial infarction was consistent in direction across multiple designs, databases and methods to control for confounding. Some variation in magnitude of the associations was observed depending on design, exposure and outcome definitions, but none of the differences were statistically significant. The association between anti-epileptics and suicidality was inconsistent across the UK CPRD, Danish National registries and the French PGRx system. Calcium channel blockers were not associated with the risk of cancer in the UK CPRD, and this was consistent across different classes of calcium channel blockers, cumulative durations of use up to >10 years and different types of cancer.

CONCLUSIONS

A network for observational drug effect studies allowing the execution of common protocols in multiple databases was created. Increased consistency of findings across multiple designs and databases in different countries will increase confidence in findings from observational drug research and benefit/risk assessment of medicines.