Potential Biases in Test-Negative Design Studies of COVID-19 Vaccine Effectiveness Arising from the Inclusion of Asymptomatic Individuals.

The test-negative design (TND) is a popular method for evaluating vaccine effectiveness (VE). A "classical" TND study includes symptomatic individuals tested for the disease targeted by the vaccine to estimate VE against symptomatic infection. However, recent applications of the TND have attempted to estimate VE against infection by including all tested individuals, regardless of their symptoms.

Structured learning in time-dependent Cox models.

Cox models with time-dependent coefficients and covariates are widely used in survival analysis. In high-dimensional settings, sparse regularization techniques are employed for variable selection, but existing methods for time-dependent Cox models lack flexibility in enforcing specific sparsity patterns (ie, covariate structures). We propose a flexible framework for variable selection in time-dependent Cox models, accommodating complex selection rules.

Effect of statin use for the primary prevention of cardiovascular disease among older adults: a cautionary tale concerning target trials emulation.

Objectives: Evidence concerning the effect of statins in primary prevention of cardiovascular disease (CVD) among older adults is lacking. Using Quebec population-wide administrative data, we emulated a hypothetical randomized trial including older adults >65 years on April 1, 2013, with no CVD history and no statin use in the previous year.

Study Design And Setting: We included individuals who initiated statins and classified them as exposed if they were using statin at least 3 months after initiation and nonexposed otherwise.

Test negative design for vaccine effectiveness estimation in the context of the COVID-19 pandemic: A systematic methodology review.

Background: During the height of the global COVID-19 pandemic, the test-negative design (TND) was extensively used in many countries to evaluate COVID-19 vaccine effectiveness (VE). Typically, the TND involves the recruitment of care-seeking individuals who meet a common clinical case definition. All participants are then tested for an infection of interest.

The application of target trials with longitudinal targeted maximum likelihood estimation to assess the effect of alcohol consumption in adolescence on depressive symptoms in adulthood.

Time-varying confounding is a common challenge for causal inference in observational studies with time-varying treatments, long follow-up periods, and participant dropout. Confounder adjustment using traditional approaches can be limited by data sparsity, weight instability, and computational issues. The Nicotine Dependence in Teens Study is a prospective cohort study, and we used data from 21 data collection cycles carried out from 1999 to 2008 among 1294 students recruited from 10 high schools in Montreal, Quebec, Canada, including follow-up into adulthood.

Two-stage targeted maximum likelihood estimation for mixed aggregate and individual participant data analysis with an application to multidrug resistant tuberculosis.

In this study, we develop a new method for the meta-analysis of mixed aggregate data (AD) and individual participant data (IPD). The method is an adaptation of inverse probability weighted targeted maximum likelihood estimation (IPW-TMLE), which was initially proposed for two-stage sampled data. Our methods are motivated by a systematic review investigating treatment effectiveness for multidrug resistant tuberculosis (MDR-TB) where the available data include IPD from some studies but only AD from others.

Identifying patients with multidrug-resistant tuberculosis who may benefit from shorter durations of treatment.

Objective: Studying treatment duration for rifampicin-resistant and multidrug-resistant tuberculosis (MDR/RR-TB) using observational data is methodologically challenging. We aim to present a hypothesis generating approach to identify factors associated with shorter duration of treatment.

Study Design And Setting: We conducted an individual patient data meta-analysis among MDR/RR-TB patients restricted to only those with successful treatment outcomes.

Marginal structural models with latent class growth analysis of treatment trajectories: Statins for primary prevention among older adults.

Latent class growth analysis is increasingly proposed as a solution to summarize the observed longitudinal treatment into a few distinct groups. When latent class growth analysis is combined with standard approaches like Cox proportional hazards models, confounding bias is not properly addressed because of time-varying covariates that have a double role of confounders and mediators. We propose to use latent class growth analysis to classify individuals into a few latent classes based on their medication adherence pattern, then choose a working marginal structural model that relates the outcome to these groups.

Polypharmacy and risk of mortality among patients with heart failure following hospitalization: a nested case-control study.

Heart failure (HF) is associated with morbidity, rehospitalization and polypharmacy. The incidence rate of mortality in HF patients with polypharmacy is poorly studied. We examine the association of polypharmacy with mortality risk in incident hospitalized HF patients with a primary diagnosis after discharge from the hospital using Quebec administrative databases, Canada from 1999 to 2015.

Identifying asthma patients at high risk of exacerbation in a routine visit: A machine learning model.

Background: Tools capable of predicting the risk of asthma exacerbations can facilitate asthma management in clinical practice. However, existing tools require additional data from patients beyond electronic medical records.

Objective: To predict asthma exacerbation in an upcoming year using electronically accessible data conditional on past adherence to asthma medications.

Clinical Correlates Identify ProBDNF and Thrombo-Inflammatory Markers as Key Predictors of Circulating p75 Extracellular Domain Levels in Older Adults.

The p75 receptor binds all neurotrophins and is mostly known for its role in neuronal survival and apoptosis. Recently, the extracellular domain (ECD) of p75 has been reported in plasma, its levels being dysregulated in numerous neurological diseases. However, the factors associated with p75 ECD levels remain unknown.

Estimands and Estimation of COVID-19 Vaccine Effectiveness Under the Test-Negative Design: Connections to Causal Inference.

The test-negative design is routinely used for the monitoring of seasonal flu vaccine effectiveness. More recently, it has become integral to the estimation of COVID-19 vaccine effectiveness, in particular for more severe disease outcomes. Because the design has many important advantages and is becoming a mainstay for monitoring postlicensure vaccine effectiveness, epidemiologists and biostatisticians may be interested in further understanding the effect measures being estimated in these studies and connections to causal effects.

Comparative Effectiveness and Safety of Low-Dose Oral Anticoagulants in Patients With Atrial Fibrillation.

Observational studies of various dose levels of direct oral anticoagulants (DOACs) for patients with atrial fibrillation (AF) found that a high proportion of patients received a dose lower than the target dose tested in randomized controlled trials. There is a need to compare low-dose DOACs with warfarin or other DOACs on effectiveness and safety. Using administrative data from Quebec province, Canada, we built a cohort of new warfarin or DOAC users discharged from hospital between 2011 and 2017.

A potential outcomes approach to defining and estimating gestational age-specific exposure effects during pregnancy.

Many studies seek to evaluate the effects of potentially harmful pregnancy exposures during specific gestational periods. We consider an observational pregnancy cohort where pregnant individuals can initiate medication usage or become exposed to a drug at various times during their pregnancy. An important statistical challenge involves how to define and estimate exposure effects when pregnancy loss or delivery can occur over time.

Doubly robust adaptive LASSO for effect modifier discovery.

Effect modification occurs when the effect of a treatment on an outcome differsaccording to the level of some pre-treatment variable (the effect modifier). Assessing an effect modifier is not a straight-forward task even for a subject matter expert. In this paper, we propose a two-stageprocedure to automatically selecteffect modifying variables in a Marginal Structural Model (MSM) with a single time point exposure based on the two nuisance quantities (the conditionaloutcome expectation and propensity score).

A comparison of confounder selection and adjustment methods for estimating causal effects using large healthcare databases.

Purpose: Confounding adjustment is required to estimate the effect of an exposure on an outcome in observational studies. However, variable selection and unmeasured confounding are particularly challenging when analyzing large healthcare data. Machine learning methods may help address these challenges.

Modeling treatment effect modification in multidrug-resistant tuberculosis in an individual patientdata meta-analysis.

Effect modification occurs while the effect of the treatment is not homogeneous across the different strata of patient characteristics. When the effect of treatment may vary from individual to individual, precision medicine can be improved by identifying patient covariates to estimate the size and direction of the effect at the individual level. However, this task is statistically challenging and typically requires large amounts of data.

Proton pump inhibitors and risk of gastric cancer: population-based cohort study.

Objective: To determine whether new users of proton pump inhibitors (PPIs) are at an increased risk of gastric cancer compared with new users of histamine-2 receptor antagonists (H2RAs).

Design: Using the UK Clinical Practice Research Datalink, we conducted a population-based cohort study using a new-user active comparator design. From 1 January 1990 to 30 April 2018, we identified 973 281 new users of PPIs and 193 306 new users of H2RAs.

Proton pump inhibitors and risk of colorectal cancer.

Objective: To determine whether proton pump inhibitors (PPIs) are associated with an increased risk of colorectal cancer, compared with histamine-2 receptor antagonists (H2RAs).

Design: The United Kingdom Clinical Practice Research Datalink was used to identify initiators of PPIs and H2RA from 1990 to 2018, with follow-up until 2019. Cox proportional hazards models were fit to estimate marginal HRs and 95% CIs of colorectal cancer.

Immunosuppression does not prevent severe gastrointestinal tract involvement in systemic sclerosis.

Objectives: We aimed to test the hypothesis that exposure to immunosuppression in early systemic sclerosis (SSc) could modify the risk of developing new onset severe gastrointestinal (GIT) involvement.

Methods: A total of 762 subjects with <3 years of disease duration and without severe GIT disease at baseline study visit were identified from combined longitudinal cohort data from the Canadian Scleroderma Research Group (CSRG) and Australian Scleroderma Interest Group (ASIG). The primary exposure was ever use of methotrexate, cyclophosphamide, mycophenolate mofetil and/or azathioprine during the study period.