Immunosuppression and cancer risk in kidney transplant recipients: A retrospective cohort study.

We assessed whether contemporary immunosuppression agents were associated with cancer among kidney transplant recipients (KTR), and if this association varied by age and sex. We studied a retrospective province-wide cohort of primary KTR (1997-2016). Employing multivariable Cox models, we estimated associations of cumulative doses of prednisone, mycophenolate and tacrolimus administered over the past 10 years, lagged by 2 years, with the incidence of primary malignant neoplasms (PMN). We assessed interactions with age and sex. To assess the impact of exposure recency, we used weighted cumulative exposure (WCE) modeling. Among 1064 KTR, 108 (10.2%) developed PMN over median follow-up of 73 months (interquartile range: 32-120). Adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) of 0.96 (0.64-1.43), 1.34 (0.96-1.86), and 1.06 (0.88-1.29) were estimated for cumulative daily doses of prednisone (5 mg), mycophenolate (1000 mg), and tacrolimus (2 mg) administered continuously over the past 10 years, respectively. PMN risk associated with cumulative tacrolimus exposure was modified by age (interaction p = .035) and was more pronounced in 15-year and 30-year-old KTR (aHRs of 1.57 [1.08-2.28] and 1.31 [1.03-1.66], respectively) in comparison to older KTR. PMN risk increase associated with higher cumulative mycophenolate dose was more pronounced in females (aHR = 1.86 [1.15-3.00]) than in males (aHR = 1.16 [0.74-1.81]; interaction p = .131). WCE analyses suggested increased PMN risk the higher the mycophenolate doses taken 5-10 years ago. A trend toward increased PMN risk with long-term mycophenolate exposure, particularly in females, and more pronounced risk with long-term tacrolimus exposure in younger KTR, identify opportunities for tailored immunosuppression to mitigate cancer risk.

Data-Driven Simulations to Assess the Impact of Study Imperfections in Time-to-Event Analyses.

Quantitative bias analysis (QBA) permits assessment of the expected impact of various imperfections of the available data on the results and conclusions of a particular real-world study. This article extends QBA methodology to multivariable time-to-event analyses with right-censored endpoints, possibly including time-varying exposures or covariates. The proposed approach employs data-driven simulations, which preserve important features of the data at hand while offering flexibility in controlling the parameters and assumptions that may affect the results. First, the steps required to perform data-driven simulations are described, and then two examples of real-world time-to-event analyses illustrate their implementation and the insights they may offer. The first example focuses on the omission of an important time-invariant predictor of the outcome in a prognostic study of cancer mortality, and permits separating the expected impact of confounding bias from non-collapsibility. The second example assesses how imprecise timing of an interval-censored event - ascertained only at sparse times of clinic visits - affects its estimated association with a time-varying drug exposure. The simulation results also provide a basis for comparing the performance of two alternative strategies for imputing the unknown event times in this setting. The R scripts that permit the reproduction of our examples are provided.

Comparison of Different Modeling Approaches for Prescription Opioid Use and Its Association With Adverse Events.

Previous research linking opioid prescribing to adverse drug events has failed to properly account for the time-varying nature of opioid exposure. This study aimed to explore how the risk of opioid-related emergency department visits, readmissions, or deaths (composite outcome) varies with opioid dose and duration, comparing different novel modeling techniques. A prospective cohort of 1,511 hospitalized patients discharged from 2 McGill-affiliated hospitals in Montreal, 2014-2016, was followed from the first postdischarge opioid dispensation until 1 year after discharge. Marginal structural Cox proportional hazards models and their flexible extensions were used to explore the association between time-varying opioid use and the composite outcome. Weighted cumulative exposure models assessed cumulative effects of past use and explored how its impact depends on the recency of exposure. The patient mean age was 69.6 (standard deviation = 14.9) years; 57.7% were male. In marginal structural model analyses, current opioid use was associated with a 71% increase in the hazard of opioid-related adverse events (adjusted hazard ratio = 1.71, 95% confidence interval: 1.21, 2.43). The weighted cumulative exposure results suggested that the risk cumulates over the previous 50 days of opioid consumption. Flexible modeling techniques helped assess how the risk of opioid-related adverse events may be associated with time-varying opioid exposures while accounting for nonlinear relationships and the recency of past use.

Investigating the Shape and Strength of the Relationship Between Maternal Weight Gain and Gestational Age at Delivery in Twin and Singleton Pregnancies.

Both inadequate and excessive maternal weight gain are correlated with preterm delivery in singleton pregnancies, yet this relationship has not been adequately studied in twins. We investigated the relationship between time-varying maternal weight gain and gestational age at delivery in twin pregnancies and compared it with that in singletons delivered in the same study population. We used serial weight measurements abstracted from charts for twin and singleton pregnancies delivered during 1998-2013 in Pittsburgh, Pennsylvania. Our exposure was time-varying weight gain z score, calculated using gestational age-standardized and prepregnancy body mass index-stratified twin- and singleton-specific charts, and our outcome was gestational age at delivery. Our analyses used a flexible extension of the Cox proportional hazards model that allowed for nonlinear and time-dependent effects. We found a U-shaped relationship between weight gain z score and gestational age at delivery among twin pregnancies (lowest hazard of delivery observed at z score = 1.2), which we attributed to increased hazard of early preterm spontaneous delivery among pregnancies with low weight gain and increased hazard of late preterm delivery without labor among pregnancies with high weight gain. Our findings may be useful for updating provisional guidelines for maternal weight gain in twin pregnancies.

Adapting SIMEX to correct for bias due to interval-censored outcomes in survival analysis with time-varying exposure.

Many clinical and epidemiological applications of survival analysis focus on interval-censored events that can be ascertained only at discrete times of clinic visits. This implies that the values of time-varying covariates are not correctly aligned with the true, unknown event times, inducing a bias in the estimated associations. To address this issue, we adapted the simulation-extrapolation (SIMEX) methodology, based on assessing how the estimates change with the artificially increased time between clinic visits. We propose diagnostics to choose the extrapolating function. In simulations, the SIMEX-corrected estimates reduced considerably the bias to the null and generally yielded a better bias/variance trade-off than conventional estimates. In a real-life pharmacoepidemiological application, the proposed method increased by 27% the excess hazard of the estimated association between a time-varying exposure, representing the 2-year cumulative duration of past use of a hypertensive medication, and the hazard of nonmelanoma skin cancer (interval-censored events). These simulation-based and real-life results suggest that the proposed SIMEX-based correction may help improve the accuracy of estimated associations between time-varying exposures and the hazard of interval-censored events in large cohort studies where the events are recorded only at relatively sparse times of clinic visits/assessments. However, these advantages may be less certain for smaller studies and/or weak associations.

Determination of the time-dependent association between ciprofloxacin consumption and ciprofloxacin resistance using a weighted cumulative exposure model compared with standard models.

OBJECTIVES: To obtain comprehensive insight into the association of ciprofloxacin use at different times in the past with the current risk of detecting resistance. METHODS: This retrospective nested case-control study of ciprofloxacin users used Dutch data from the PHARMO Database Network and one laboratory for the period 2003-14. Cases and controls were selected as patients with an antibiotic susceptibility test (AST) indicating ciprofloxacin resistance or susceptibility, respectively. We performed univariable and multivariable conditional logistic regression analyses, defining time-dependent exposure using standard definitions (current ciprofloxacin use, used 0-30, 31-90, 91-180 and 181-360 days ago) and a flexible weighted cumulative effect (WCE) model with four alternative time windows of past doses (0-30, 0-90, 0-180 and 0-360 days). RESULTS: The study population consisted of 230 cases and 909 controls. Under the standard exposure definitions, the association of ciprofloxacin use with resistance decreased with time [current use: adjusted OR 6.8 (95% CI 3.6-12.4); used 181-360 days ago: 1.3 (0.8-1.9)]. Under the 90 day WCE model (best-fitting model), more recent doses were more strongly associated with resistance than past doses, as was longer or repeated treatment. The 180 day WCE model, which fitted the data equally well, suggested that doses taken 91-180 days ago were also significantly associated with resistance. CONCLUSIONS: The estimates for the association between ciprofloxacin use at different times and resistance show that ciprofloxacin prescribers should consider ciprofloxacin use 0-180 days ago to ensure that patients receive suitable treatment. The OR of ciprofloxacin resistance could be reduced by eliminating repeated ciprofloxacin prescription within 180 days and by treating for no longer than necessary.

Associations of 4 Nurse Staffing Practices With Hospital Mortality.

BACKGROUND: Cross-sectional studies of hospital-level administrative data have suggested that 4 nurse staffing practices-using adequate staffing levels, higher proportions of registered nurses (RNs) (skill mix), and more educated and experienced RNs-are each associated with reduced hospital mortality. To increase the validity of this evidence, patient-level longitudinal studies assessing the simultaneous associations of these staffing practices with mortality are required. METHODS: A dynamic cohort of 146,349 adult medical, surgical, and intensive care patients admitted to a Canadian University Health Center was followed for 7 years (2010-2017). We used a multivariable Cox proportional hazards model to estimate the associations between patients' time-varying cumulative exposure to measures of RN understaffing, skill mix, education, and experience, each relative to nursing unit and shift means, and the hazard of in-hospital mortality, while adjusting for patient and nursing unit characteristics, and modeling the current nursing unit of hospitalization as a random effect. RESULTS: Overall, 4854 in-hospital deaths occurred during 3,478,603 patient-shifts of follow-up (13.95 deaths/10,000 patient-shifts). In multivariable analyses, every 5% increase in the cumulative proportion of understaffed shifts was associated with a 1.0% increase in mortality (hazard ratio: 1.010; 95% confidence interval: 1.002-1.017; P=0.009). Moreover, every 5% increase in the cumulative proportion of worked hours by baccalaureate-prepared RNs was associated with a 2.0% reduction of mortality (hazard ratio: 0.980; 95% confidence interval: 0.965-0.995, P=0.008). RN experience and skill mix were not significantly associated with mortality. CONCLUSION: Reducing the frequency of understaffed shifts and increasing the proportion of baccalaureate-prepared RNs are associated with reduced hospital mortality.

Long-term effectiveness of catheter ablation in patients with atrial fibrillation and heart failure.

AIMS: Randomized trials suggest reductions in all-cause mortality and heart failure (HF) rehospitalizations with catheter ablation (CA) in patients with atrial fibrillation (AF) and HF. Whether these results can be replicated in a real-world population with long-term follow-up or varies over time is unknown. We sought to evaluate the long-term effectiveness of CA in reducing the incidence of all-cause mortality, HF hospitalizations, stroke, and major bleeding in AF-HF patients. METHODS AND RESULTS: In a cohort of patients newly diagnosed with AF-HF in Quebec, Canada (2000-2017), CA patients were matched 1:2 to controls on time and frequency of hospitalizations. Confounders were controlled for using inverse probability of treatment weighting. Multivariable Cox models adjusted for the presence of cardiac electronic implantable devices and medication use during follow-up, and the effect of time since CA was modelled with B-splines. For non-fatal outcomes, the Lunn-McNeil approach was used to account for the competing risk of death. Among 101 933 AF-HF patients, 451 underwent CA and were matched to 899 controls. Over a median follow-up of 3.8 years, CA was associated with a statistically significant reduction in all-cause mortality [hazard ratio 0.4 (95% confidence interval 0.2-0.7)], but no difference in stroke or major bleeding. The hazard of HF rehospitalization for CA patients, relative to non-CA patients, varied with time since CA (P = 0.01), with a reduction in HF rehospitalizations until approximately 3 years post-CA. CONCLUSION: Compared with matched non-CA patients, CA was associated with a long-term reduction in all-cause mortality and a reduction in HF rehospitalizations until 3 years post-CA.

Nonlinear and time-dependent effects of sparsely measured continuous time-varying covariates in time-to-event analysis.

Many flexible extensions of the Cox proportional hazards model incorporate time-dependent (TD) and/or nonlinear (NL) effects of time-invariant covariates. In contrast, little attention has been given to the assessment of such effects for continuous time-varying covariates (TVCs). We propose a flexible regression B-spline-based model for TD and NL effects of a TVC. To account for sparse TVC measurements, we added to this model the effect of time elapsed since last observation (TEL), which acts as an effect modifier. TD, NL, and TEL effects are estimated with the iterative alternative conditional estimation algorithm. Furthermore, a simulation extrapolation (SIMEX)-like procedure was adapted to correct the estimated effects for random measurement errors in the observed TVC values. In simulations, TD and NL estimates were unbiased if the TVC was measured with a high frequency. With sparse measurements, the strength of the effects was underestimated but the TEL estimate helped reduce the bias, whereas SIMEX helped further to correct for bias toward the null due to "white noise" measurement errors. We reassessed the effects of systolic blood pressure (SBP) and total cholesterol, measured at two-year intervals, on cardiovascular risks in women participating in the Framingham Heart Study. Accounting for TD effects of SBP, cholesterol and age, the NL effect of cholesterol, and the TEL effect of SBP improved substantially the model's fit to data. Flexible estimates yielded clinically important insights regarding the role of these risk factors. These results illustrate the advantages of flexible modeling of TVC effects.

Flexible Modeling of the Association Between Cumulative Exposure to Low-Dose Ionizing Radiation From Cardiac Procedures and Risk of Cancer in Adults With Congenital Heart Disease.

Adults with congenital heart disease are increasingly being exposed to low-dose ionizing radiation (LDIR) from cardiac procedures. In a recent study, Cohen et al. (Circulation. 2018;137(13):1334-1345) reported an association between increased LDIR exposure and cancer incidence but did not explore temporal relationships. Yet, the impact of past exposures probably accumulates over years, and its strength may depend on the amount of time elapsed since exposure. Furthermore, LDIR procedures performed shortly before a cancer diagnosis may have been ordered because of early symptoms of cancer, raising concerns about reversal causality bias. To address these challenges, we combined flexible modeling of cumulative exposures with competing-risks methodology to estimate separate associations of time-varying LDIR exposure with cancer incidence and all-cause mortality. Among 24,833 patients from the Quebec Congenital Heart Disease Database, 602 had incident cancer and 500 died during a follow-up period of up to 15 years (1995-2010). Initial results suggested a strong association of cancer incidence with very recent LDIR exposures, likely reflecting reverse causality bias. When exposure was lagged by 2 years, an increased cumulative LDIR dose from the previous 2-6 years was associated with increased cancer incidence, with a stronger association for women. These results illustrate the importance of accurate modeling of temporal relationships between time-varying exposures and health outcomes.

Acute vs cumulative benefits of metformin use in patients with type 2 diabetes and heart failure.

AIMS: To evaluate the association between metformin use and heart failure (HF) exacerbation in people with type 2 diabetes (T2D) and pre-existing HF using alternative exposure models. MATERIALS AND METHODS: We analysed data for patients with T2D and incident HF from a national US insurance claims database. We compared the results of several multivariable Cox models where time-varying use of metformin was modelled as: (1) current use; (2) total duration of past use; and (3) use within the past 30 days or 10 days. The outcome was defined as time to HF-related hospitalization. We then re-analysed the data using flexible weighted cumulative exposure (WCE) models. RESULTS: A total of 7620 patients with diabetes and incident HF were analysed. The mean (SD) patient age was 54 (8) years, and 58% (n = 4440) were men. In all, 3799 individuals (50%) were exposed to metformin, and 837 HF hospitalizations (11%) occurred (mean follow-up 1.7 years). Results of conventional models suggested potential acute benefits in reducing HF exacerbation with metformin use in the past 10 days (adjusted hazard ratio [aHR] 0.76, 95% confidence interval [CI] 0.60-0.97), while WCE models, which provided a better fit for the data, suggested lack of a systematic effect (aHR 0.91, 95% CI 0.69-1.20). CONCLUSIONS: Our results suggest that cumulative metformin exposure does not decrease the risk of HF-related exacerbation. Use of other anti-hyperglycaemic agents with proven efficacy in patients with HF should also be considered as treatment options in this population.

Risk of acute myocardial infarction with real-world NSAIDs depends on dose and timing of exposure.

PURPOSE: Real-life use of nonsteroidal anti-inflammatory drugs (NSAIDs) is dynamic. This study aimed to characterize the temporal association between time-varying NSAID exposure and acute myocardial infarction (MI). METHODS: Nested case-control analyses were conducted on a Quebec administrative health cohort. NSAID dose, confounders, and outcome status were determined for each day of follow-up. To better account for dose and timing of past exposures, flexible weighted cumulative exposure models were also fitted. RESULTS: The cohort consisted of 233 816 older adults including 21 256 acute MI cases. Dose-related increased risks of MI were found with current use of all NSAIDs. In models not accounting for duration of use, ORs (95%CI) for the most common current daily dose vs. no current exposure were: celecoxib 200 mg: 1.16 (1.10, 1.22), diclofenac 150 mg: 1.59 (1.38, 1.84), ibuprofen 1200 mg: 1.42 (1.17, 1.74), naproxen 750 mg: 1.38 (1.21, 1.58), and rofecoxib 25 mg: 1.54 (1.43, 1.66). Weighted cumulative exposure models confirmed that all NSAIDs-including naproxen-are associated with an increased risk of MI and suggested that doses taken up to 3 weeks ago for rofecoxib, ibuprofen, and naproxen and up to 75 days ago for diclofenac and celecoxib may contribute to the current MI risk. However, the celecoxib risk seems to require continuous use for more than 30 days, whereas for other NSAIDs, a heightened MI risk occurs within 7 days. CONCLUSIONS: Weighted cumulative exposure analysis uncovered NSAID-specific differences in the immediate MI risk and how this risk seems to accumulate. KEY POINTS Accurate assessment of drug safety requires an etiologically correct model encompassing all relevant aspects of exposure. Weighted cumulative exposure models suggest that the relative importance of past doses on the risk of MI differs among NSAIDs. All common NSAIDs are associated with an increased MI risk. Celecoxib MI risk seems to depend on continuously using the drug for more than 30 days, whereas for ibuprofen, rofecoxib, diclofenac, and naproxen, a heightened MI risk occurs within 7 days of use.

The missing cause approach to unmeasured confounding in pharmacoepidemiology.

Unmeasured confounding is a major threat to the validity of pharmacoepidemiological studies of medication safety and effectiveness. We propose a new method for detecting and reducing the impact of unobserved confounding in large observational database studies. The method uses assumptions similar to the prescribing preference-based instrumental variable (IV) approach. Our method relies on the new 'missing cause' principle, according to which the impact of unmeasured confounding by (contra-)indication may be detected by assessing discrepancies between the following: (i) treatment actually received by individual patients and (ii) treatment that they would be expected to receive based on the observed data. Specifically, we use the treatment-by-discrepancy interaction to test for the presence of unmeasured confounding and correct the treatment effect estimate for the resulting bias. Under standard IV assumptions, we first proved that unmeasured confounding induces a spurious treatment-by-discrepancy interaction in risk difference models for binary outcomes and then simulated large pharmacoepidemiological studies with unmeasured confounding. In simulations, our estimates had four to six times smaller bias than conventional treatment effect estimates, adjusted only for measured confounders, and much smaller variance inflation than unbiased but very unstable IV estimates, resulting in uniformly lowest root mean square errors. The much lower variance of our estimates, relative to IV estimates, was also observed in an application comparing gastrointestinal safety of two classes of anti-inflammatory drugs. In conclusion, our missing cause-based method may complement other methods and enhance accuracy of analyses of large pharmacoepidemiological studies.

Early consultation with a rheumatologist for RA: does it reduce subsequent use of orthopaedic surgery?

OBJECTIVE: Optimal care in RA includes early use of DMARDs to prevent joint damage and hopefully decrease the need for costly surgical interventions. Our objective was to determine whether a reduced rate of orthopaedic surgery was evident for persons with RA who saw a rheumatologist early in the disease course. METHODS: We studied persons who had a diagnosis of RA based on billing code data in the province of Quebec in 1995, and for whom the initial date of RA diagnosis by a non-rheumatologist could be established before the confirmatory diagnosis by the rheumatologist. We followed these patients until 2007. Patients were classified as early consulters or late consulters depending on whether they were seen by a rheumatologist within or beyond 3 months of being diagnosed with RA by their referring physician. The outcome, orthopaedic surgery, was defined using International Classification of Diseases (ICD) procedure codes ICD9 and ICD10. Multivariate Cox regression with time-dependent covariates estimated the effect of early consultation on the time to orthopaedic surgery. RESULTS: Our cohort consisted of 1051 persons; mean age at diagnosis was 55.7 years, 68.2% were female and 50.7% were early consulters. Among all patients, 20.5% (215) had an orthopaedic surgery during the observation interval. Early consulters were less likely to undergo orthopaedic surgery during the 12-year follow-up period (adjusted hazard ratio 0.60, 95% CI 0.44, 0.82). CONCLUSION: Persons with RA who consult a rheumatologist later in the disease course have a worse outcome in terms of eventual requirement for orthopaedic surgery.

Evidence of subgroup-specific treatment effect in the absence of an overall effect: is there really a contradiction?

PURPOSE: Interaction and subgroup analyses remain controversial topics in epidemiology. A recent theoretical paper suggested that a combination of no overall treatment-outcome association and treatment effect limited to a single subgroup would imply a clinically implausible interaction, with opposite treatment effects in the two subgroups. However, this argument was based entirely on point estimates and ignored sampling error and statistical inference. METHODS: We simulated hypothetical studies in which treatment truly affected the outcome in only one subgroup, with no effect in the other subgroup. We generated 1000 random samples for three study designs (small clinical study, case-control, and large cohort), and different values of total sample size (N), relative size of the affected subgroup, and treatment effect. We estimated the frequency of significant results for tests of overall and subgroup-specific treatment effects, and treatment-by-subgroup interaction. RESULTS: Combination of statistically non-significant overall treatment effect and significant treatment-by-subgroup interaction occurred frequently, especially if the affected subgroup was proportionally smaller, even in studies with high power to detect the overall effect (e.g. in 37.1% of samples with N = 20 000, with 600 outcomes, and an effect (odds ratio of 1.5) limited to 30% of subjects). Furthermore, in most samples with a significant interaction, subgroup analyses correctly indicated that the significant effect was limited to one subgroup. CONCLUSION: In studies where the treatment truly affects the risks in only one subgroup, a non-significant overall effect will often coincide with a statistically significant treatment-by-subgroup interaction. Thus, a non-significant overall effect should not prevent testing plausible interactions.

Immediate and delayed impact of oral glucocorticoid therapy on risk of serious infection in older patients with rheumatoid arthritis: a nested case-control analysis.

OBJECTIVES: To explore the relationship of serious infection risk with current and prior oral glucocorticoid (GC) therapy in elderly patients with rheumatoid arthritis (RA). METHODS: A case-control analysis matched 1947 serious infection cases to five controls, selected from 16207 RA patients aged ≥ 65 between 1985-2003 in Quebec, Canada. Adjusted odds ratios for infection associated with different GC patterns were estimated using conventional models and a weighted cumulative dose (WCD) model. RESULTS: The WCD model predicted risks better than conventional models. Current and recent GC doses had highest impact on current risk. Doses taken up to 2.5 years ago were also associated with increased risk, albeit to a lesser extent. A current user of 5mg prednisolone had a 30%, 46% or 100% increased risk of serious infection when used continuously for the last 3 months, 6 months or 3 years, respectively, compared to a non-user. The risk associated with 5mg prednisolone taken for the last 3 years was similar to that associated with 30 mg taken for the last month. Discontinuing a two-year course of 10mg prednisolone six months ago halved the risk compared to ongoing use. CONCLUSIONS: GC therapy is associated with infection risk in older patients with RA. The WCD model provided more accurate risk estimates than conventional models. Current and recent doses have greatest impact on infection risk, but the cumulative impact of doses taken in the last 2-3 years still affects risk. Knowing how risk depends on pattern of GC use will contribute to an improved benefit/harm assessment.

Comparison of alternative models for linking drug exposure with adverse effects.

Pharmacoepidemiology investigates associations between time-varying medication use/dose and risk of adverse events. Applied research typically relies on a priori chosen simple conventional models, such as current dose or any use in the past 3 months. However, different models imply different risk predictions, and only one model can be etiologically correct in any specific applications. We first formally defined several candidate models mapping the time vector of past drug doses (X (t), t = 1, … ,u) into the value of a time-varying exposure metric M(u) at current time u. In addition to conventional one-parameter models, we considered two-parameter models accounting for recent dose increase or withdrawal and a flexible spline-based weighted cumulative exposure (WCE) model that defines M(u) as the weighted sum of past doses. In simulations, we generated event times assuming one of the models was correct and then analyzed the data with all candidate models. We demonstrated that the minimum AIC criterion is able to identify the correct model as the best-fitting model or one of the equivalent (within 4 AIC points of the minimum) models in a vast majority of simulated samples, especially with 500 or more events. We also showed how relying on an incorrect a priori chosen model may largely reduce the power to test for an association. Finally, we demonstrated how the flexible WCE estimates may help with model diagnostics even if the correct model is not WCE. We illustrated the practical advantages of AIC-based a posteriori model selection and WCE modeling in a real-life pharmacoepidemiology example.

Reducing the variance of the prescribing preference-based instrumental variable estimates of the treatment effect.

Instrumental variable (IV) methods based on the physician's prescribing preference may remove bias due to unobserved confounding in pharmacoepidemiologic studies. However, IV estimates, originally defined as the treatment prescribed for a single previous patient of a given physician, show important variance inflation. The authors proposed and validated in simulations a new method to reduce the variance of IV estimates even when physicians' preferences change over time. First, a potential "change-time," after which the physician's preference has changed, was estimated for each physician. Next, all patients of a given physician were divided into 2 homogeneous subsets: those treated before the change-time versus those treated after the change-time. The new IV was defined as the proportion of all previous patients in a corresponding homogeneous subset who were prescribed a specific drug. In simulations, all alternative IV estimators avoided strong bias of the conventional estimates. The change-time method reduced the standard deviation of the estimates by approximately 30% relative to the original previous patient-based IV. In an empirical example, the proposed IV correlated better with the actual treatment and yielded smaller standard errors than alternative IV estimators. Therefore, the new method improved the overall accuracy of IV estimates in studies with unobserved confounding and time-varying prescribing preferences.

Associations Between Methotrexate Use and the Risk of Cardiovascular Events in Patients with Elderly-onset Rheumatoid Arthritis.

OBJECTIVE: We evaluated the associations between time-varying methotrexate (MTX) use and risk of cardiovascular events (CVE) in patients with rheumatoid arthritis (RA). METHODS: We studied an inception cohort of 23,994 patients with RA diagnosed after their 65th birthday. Multivariable Cox regression models were fit to evaluate the associations between time-varying MTX use, controlling for other risk factors, and time to CVE. Alternative models assessed the cumulative duration of MTX use over the (1) first year, (2) previous year (recent use), and (3) entire duration of followup. We also assessed whether the strength of the association varied over time. RESULTS: Over 115,453 patient-years (PY), 3294 (13.7%) patients experienced a CVE (28.5 events per 1000 PY; 95% CI 27.6-29.5). In the multivariable analyses, the model assessing time-varying continuous use in the most recent year yielded the best fit. Increasing recent MTX use was associated with lower CVE risks (HR 0.79 for continuous use vs no use in past 12 months, 95% CI 0.70-0.88; p < 0.0001). Greater MTX use in the first year after cohort entry was also protective (HR 0.84, 95% CI 0.72-0.96; p = 0.0048), but this effect decreased with increasing followup. In contrast, longer MTX use during the entire followup was not clearly associated with CVE risk (HR 0.98, 95% CI 0.95-1.01; p = 0.1441). CONCLUSION: We observed about a 20% decrease in CVE associated with recent continuous MTX use. Greater MTX use in the first year of cohort entry also appeared to be important in the association between MTX and CVE risk.

Direct-Acting Antivirals for Hepatitis C: Predictors of Early Discontinuation in the Real World.

BACKGROUND: The treatment for hepatitis C virus (HCV) infection has evolved over time, and direct-acting antivirals (DAA) have revolutionized HCV therapy. OBJECTIVES: To (a) assess early treatment discontinuation and (b) identify predictors of early discontinuation in a cohort of patients receiving second-generation DAAs. METHODS: We identified HCV patients newly prescribed simeprevir/sofosbuvir (SIM/SOF), ledipasvir/sofosbuvir (LDV/SOF), ombitasvir/paritaprevir/ritonavir + dasabuvir (OPrD), sofosbuvir/velpatasvir (SOF/VEL), elbasvir/grazoprevir (EBR/GZR), and glecaprevir/pibrentasvir (GLE/PIB) between 2014 and 2017. Early discontinuation was defined as duration of therapy less than 8 weeks. Multivariable logistic regression was performed to evaluate the association of drug regimens and potential predictive factors to early discontinuation. RESULTS: We identified 26,098 DAA-treated patients: 67.8% with LDV/SOF, 9.9% with OPrD, 8.5% with SIM/SOF, 7.8% with SOF/VEL, 5.2% with EBR/GZR, and 0.8% with GLE/PIB. With approval of new therapies in 2016 and 2017, use of OPrD, LDV/SOF, and SIM/SOF declined substantially. At baseline, there was some heterogeneity of past HCV drug use and comorbidity across groups; patients on SIM/SOF had the highest frequency of previous interferon, cirrhosis, and decompensated cirrhosis. Most HCV patients received therapy for 8-12 weeks; fewer patients went through 16-week and 24-week therapy courses. Early discontinuation rates (95% CI) were 7.1% (6.0-8.2) for SIM/SOF, 3.2% (2.9-3.5) for LDV/SOF, 9.6% (8.5-10.7) for OPrD, 3.1% (2.3-3.8) for SOF/VEL, 4.2% (3.1-5.3) for EBR/GZR, and 2.5% (0.3-4.7) for GLE/PIB. In multivariable analyses, versus OPrD, patients starting other drug regimens were less likely to discontinue therapy early. Early discontinuation was more common in women, patients with baseline anemia, and Medicare and Medicaid patients. CONCLUSIONS: These real-world data confirm low rates of early discontinuation in users of second-generation DAAs. Future research focusing on socio-economic and sex/gender issues may help further optimize care for patients with HCV. DISCLOSURES: This study was funded by the Canadian Institutes of Health Research. Klein has received grants for investigator-initiated trials from ViiV Healthcare, Janssen, Gilead, and Merck, as well as consulting fees from ViiV Healthcare, Merck, and AbbVie. Feld has received research support and/or scientific consulting fees from AbbVie, Contravir, Enanta, Gilead, Janssen, Merck, and Wako. All other authors have no conflicts of interest to declare. Results from this study were presented as a poster at the 34th International Conference of Phamacoepidemiology and Therapeutic Risk Management; August 22-26, 2018; Prague, Czech Republic.