Instrumental variable analysis as a complementary analysis in studies of adverse effects: venous thromboembolism and second‐generation versus third‐generation oral contraceptives

Boef, Anna G. C.; Souverein, Patrick C.; Vandenbroucke, Jan P.; Vlieg, Astrid van Hylckama; Boer, Anthonius de; Cessie, Saskia le; Dekkers, Olaf M.

doi:10.1002/pds.3956

Cited by 10 publications

(11 citation statements)

References 26 publications

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“…As mentioned above, an important assumption of the CMLE method is that independence between the genetic and environmental exposure in the population, that is, that the prescription of COC (and the different progestogen types in COC) does not differ between carriers of the genetic risk factor(s) and non‐carriers. A previous study used doctor’s preference for a certain COC type as an instrumental variable to assess the risk of VT associated with third versus second generation oral contraceptives and found similar risk estimates compared with conventional analysis 23 . This suggests that the doctor’s preference for a certain COC type is a valid instrumental variable and that prescription bias is unlikely.…”

Section: Methodsmentioning

confidence: 74%

The joint effect of genetic risk factors and different types of combined oral contraceptives on venous thrombosis risk

et al. 2020

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It is not known whether the synergistic effect of genetic markers, increasing the risk of venous thrombosis (VT), and combined oral contraceptives (COC) use varies between different types of progestogens in these preparations. We investigated the joint effect of genetic risk factor, that is, F5 rs6025, F2 rs1799963, and FGG rs2066865 mutations, and different progestogens on the risk of VT. The constrained maximum likelihood estimation (CMLE) method was used to calculate joint effects, expressed as odds ratio (OR) with 95% confidence intervals [CI]. As the dose of estrogen is known to be a risk factor for VT, analyses were restricted to COC with 30 µg estrogen and each progestogen. Overall, the joint effect of COC and genetic variants was lowest for COC containing the progestogen levonorgestrel, albeit CIs were wide. The OR (95% CI) of the four different analyses (i.e. joint effect with F5 rs6025, F2 rs1799963, F5 rs6025 or F2 rs1799963 and FGG rs2066865) ranged between 7Á4 (5Á4-10Á2) and 24Á8 (12Á3-50Á0) for levonorgestrel. For gestodene the joint effect ranged between 11Á7 (7Á2-19Á1) and 30Á9 (10Á6-89Á9). Desogestrel and cyproterone acetate had the highest risk estimates: 14Á6 (9Á7-21Á9) and 32Á6 (13Á2-80Á6) and 15Á5 (9Á7-24Á9) and 44Á4 (16Á9-116Á3) respectively. In women with inherited thrombophilia, COC containing levonorgestrel were associated with the lowest risk of VT, albeit the CIs were wide.

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Section: Methodsmentioning

confidence: 74%

The joint effect of genetic risk factors and different types of combined oral contraceptives on venous thrombosis risk

et al. 2020

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“…Researchers also use more conventional epidemiological designs, sometimes called observational, that exploit naturally occurring variation. Sometimes, the effects of interventions can be estimated in these cohorts using instrumental variables (prescribing preference; surgical volume; geographic variation, distance from health care facility), quantifying the effects of an intervention in a way that is considered to be unbiased [34] , [35] , [36] . Instrumental variable estimation using data from a randomized controlled trial to estimate the effect of treatment in the treated, when there is substantial nonadherence to the allocated intervention, is a particular instance of this approach [37] , [38] .…”

Section: Part 2: “Quasi-experimental” Designs Used By Health Care Evamentioning

confidence: 99%

Quasi-experimental study designs series—paper 5: a checklist for classifying studies evaluating the effects on health interventions—a taxonomy without labels

Reeves

Wells

Waddington

2017

Journal of Clinical Epidemiology

125

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ObjectivesThe aim of the study was to extend a previously published checklist of study design features to include study designs often used by health systems researchers and economists. Our intention is to help review authors in any field to set eligibility criteria for studies to include in a systematic review that relate directly to the intrinsic strength of the studies in inferring causality. We also seek to clarify key equivalences and differences in terminology used by different research communities.Study Design and SettingExpert consensus meeting.ResultsThe checklist comprises seven questions, each with a list of response items, addressing: clustering of an intervention as an aspect of allocation or due to the intrinsic nature of the delivery of the intervention; for whom, and when, outcome data are available; how the intervention effect was estimated; the principle underlying control for confounding; how groups were formed; the features of a study carried out after it was designed; and the variables measured before intervention.ConclusionThe checklist clarifies the basis of credible quasi-experimental studies, reconciling different terminology used in different fields of investigation and facilitating communications across research communities. By applying the checklist, review authors' attention is also directed to the assumptions underpinning the methods for inferring causality.

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“…To address the nature of the association between AA and long-term risk of DA, we conducted instrumental variable (IV) analyses 22,23 (Figure 1). Such an analysis requires the identification of an “instrument,” that is, a variable that influences the risk factor of interest—here AA—but has no direct influence on the key outcome—here DA.…”

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confidence: 99%

Academic Achievement and Drug Abuse Risk Assessed Using Instrumental Variable Analysis and Co-relative Designs

et al. 2018

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Instrumental variable analysis as a complementary analysis in studies of adverse effects: venous thromboembolism and second‐generation versus third‐generation oral contraceptives

Abstract: The similarity in direction of results from the IV analyses and conventional analyses suggests that major confounding is unlikely. IV analysis can be a useful complementary analysis to assess the presence of confounding in studies of adverse drug effects in very large databases.

Cited by 10 publications

References 26 publications

The joint effect of genetic risk factors and different types of combined oral contraceptives on venous thrombosis risk

The joint effect of genetic risk factors and different types of combined oral contraceptives on venous thrombosis risk

Quasi-experimental study designs series—paper 5: a checklist for classifying studies evaluating the effects on health interventions—a taxonomy without labels

Academic Achievement and Drug Abuse Risk Assessed Using Instrumental Variable Analysis and Co-relative Designs

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