Non-parametric inference on calibration of predicted risks.

Moderate calibration, the expected event probability among observations with predicted probability z being equal to z, is a desired property of risk prediction models. Current graphical and numerical techniques for evaluating moderate calibration of risk prediction models are mostly based on smoothing or grouping the data. As well, there is no widely accepted inferential method for the null hypothesis that a model is moderately calibrated. In this work, we discuss recently-developed, and propose novel, methods for the assessment of moderate calibration for binary responses. The methods are based on the limiting distributions of functions of standardized partial sums of prediction errors converging to the corresponding laws of Brownian motion. The novel method relies on well-known properties of the Brownian bridge which enables joint inference on mean and moderate calibration, leading to a unified "bridge" test for detecting miscalibration. Simulation studies indicate that the bridge test is more powerful, often substantially, than the alternative test. As a case study we consider a prediction model for short-term mortality after a heart attack, where we provide suggestions on graphical presentation and the interpretation of results. Moderate calibration can be assessed without requiring arbitrary grouping of data or using methods that require tuning of parameters.

A parametric model to jointly characterize rate, duration, and severity of exacerbations in episodic diseases.

BACKGROUND: The natural history of many chronic diseases is characterized by periods of increased disease activity, commonly referred to as flare-ups or exacerbations. Accurate characterization of the burden of these exacerbations is an important research objective. METHODS: The purpose of this work was to develop a statistical framework for nuanced characterization of the three main features of exacerbations: their rate, duration, and severity, with interrelationships among these features being a particular focus. We jointly specified a zero-inflated accelerated failure time regression model for the rate, an accelerated failure time regression model for the duration, and a logistic regression model for the severity of exacerbations. Random effects were incorporated into each component to capture heterogeneity beyond the variability attributable to observed characteristics, and to describe the interrelationships among these components. RESULTS: We used pooled data from two clinical trials in asthma as an exemplary application to illustrate the utility of the joint modeling approach. The model fit clearly indicated the presence of heterogeneity in all three components. A novel finding was that the new therapy reduced not just the rate but also the duration of exacerbations, but did not have a significant impact on their severity. After controlling for covariates, exacerbations among more frequent exacerbators tended to be shorter and less likely to be severe. CONCLUSIONS: We conclude that a joint modeling framework, programmable in available software, can provide novel insights about how the rate, duration, and severity of episodic events interrelate, and enables consistent inference on the effect of treatments on different disease outcomes. Trial registration Ethics approval was obtained from the University of British Columbia Human Ethics Board (H17-00938).

16-year trends in asthma hospital admissions in Canada.

BACKGROUND: Asthma hospitalizations declined rapidly in many parts of the world, including Canada, in the 1990s and early 2000s. OBJECTIVE: To examine whether the declining trend of asthma hospitalizations persisted in recent years in Canada. METHODS: Using the Canadian comprehensive nationwide hospitalization data (2002-2017), we identified hospital admissions with the main International Classification of Diseases codes for asthma. We analyzed sex-specific age-standardized trends in annual hospitalization rates among pediatric (< 19 years) and adult (19+ years) patients. We used change-point analysis to evaluate any substantial changes in the trends in the sex-age groups. RESULTS: There were 254,672 asthma-related hospital admissions (59% pediatric, 50% female) during the study period. Among children, age-adjusted annual rates per 100,000 decreased by 55% in females (152-69) and by 60% in males (270-108) from 2002 to 2017. Among adults, the rates decreased by 59% in both sexes (females: 61-25; males: 27-11). Change-point analysis indicated a substantial plateauing of the annual rate in both pediatric (from -15.3 [females] and -25.8 [males] before 2010 to -0.6 [females] and -0.8 [males] after 2010) and adult (from -5.4 [females] and -2.6 [males] before 2008 to -0.6 [females] and -0.2 [males] after 2008) groups. CONCLUSION: After a substantial decline in hospital admissions for acute asthma, there has been minimal further decline since 2010 for children and 2008 for adults. In addition to adhering to the contemporary standards of asthma care, novel, disruptive strategies are likely needed to further reduce the burden of asthma.

Recommendations for the use of propensity score methods in multiple sclerosis research.

BACKGROUND: With many disease-modifying therapies currently approved for the management of multiple sclerosis, there is a growing need to evaluate the comparative effectiveness and safety of those therapies from real-world data sources. Propensity score methods have recently gained popularity in multiple sclerosis research to generate real-world evidence. Recent evidence suggests, however, that the conduct and reporting of propensity score analyses are often suboptimal in multiple sclerosis studies. OBJECTIVES: To provide practical guidance to clinicians and researchers on the use of propensity score methods within the context of multiple sclerosis research. METHODS: We summarize recommendations on the use of propensity score matching and weighting based on the current methodological literature, and provide examples of good practice. RESULTS: Step-by-step recommendations are presented, starting with covariate selection and propensity score estimation, followed by guidance on the assessment of covariate balance and implementation of propensity score matching and weighting. Finally, we focus on treatment effect estimation and sensitivity analyses. CONCLUSION: This comprehensive set of recommendations highlights key elements that require careful attention when using propensity score methods.

Serum neurofilament light chain correlates with myelin and axonal magnetic resonance imaging markers in multiple sclerosis.

BACKGROUND: Neurofilaments are cytoskeletal proteins that are detectable in the blood after neuroaxonal injury. Multiple sclerosis (MS) disease progression, greater lesion volume, and brain atrophy are associated with higher levels of serum neurofilament light chain (NfL), but few studies have examined the relationship between NfL and advanced magnetic resonance imaging (MRI) measures related to myelin and axons. We assessed the relationship between serum NfL and brain MRI measures in a diverse group of MS participants. METHODS AND MATERIALS: 103 participants (20 clinically isolated syndrome, 33 relapsing-remitting, 30 secondary progressive, 20 primary progressive) underwent 3T MRI to obtain myelin water fraction (MWF), geometric mean T2 (GMT2), water content, T1; high angular resolution diffusion imaging (HARDI)-derived axial diffusivity (AD), radial diffusivity (RD), fractional anisotropy (FA); diffusion basis spectrum imaging (DBSI)-derived AD, RD, FA; restricted, hindered, water and fiber fractions; and volume measurements of normalized brain, lesion, thalamic, deep gray matter (GM), and cortical thickness. Multiple linear regressions assessed the strength of association between serum NfL (dependent variable) and each MRI measure in whole brain (WB), normal appearing white matter (NAWM) and T2 lesions (independent variables), while controlling for age, expanded disability status scale, and disease duration. RESULTS: Serum NfL levels were significantly associated with metrics of axonal damage (FA: R2WB-HARDI = 0.29, R2NAWM-HARDI = 0.31, R2NAWM-DBSI = 0.30, R2Lesion-DBSI = 0.31; AD: R2WB-HARDI=0.31), myelin damage (MWF: R2WB = 0.29, R2NAWM = 0.30, RD: R2WB-HARDI = 0.32, R2NAWM-HARDI = 0.34, R2Lesion-DBSI = 0.30), edema and inflammation (T1: R2Lesion = 0.32; GMT2: R2WB = 0.31, R2Lesion = 0.31), and cellularity (restricted fraction R2WB = 0.30, R2NAWM = 0.32) across the entire MS cohort. Higher serum NfL levels were associated with significantly higher T2 lesion volume (R2 = 0.35), lower brain structure volumes (thalamus R2 = 0.31; deep GM R2 = 0.33; normalized brain R2 = 0.31), and smaller cortical thickness R2 = 0.31). CONCLUSION: The association between NfL and myelin MRI markers suggest that elevated serum NfL is a useful biomarker that reflects not only acute axonal damage, but also damage to myelin and inflammation, likely due to the known synergistic myelin-axon coupling relationship.

Long-Term Natural History of Severe Asthma Exacerbations and Their Impact on the Disease Course.

Rationale: The long-term natural history of asthma in terms of successive severe exacerbations and the influence of each exacerbation on the course of the disease is not well studied. Objectives: To investigate the long-term natural history of asthma among patients who are hospitalized for asthma for the first time in terms of the risk of future severe exacerbations and heterogeneity in this risk across patients. Methods: Using the administrative health databases of British Columbia, Canada (January 1, 1997 to March 31, 2016), we created an incident cohort of patients with at least one asthma exacerbation that required inpatient care. We estimated the 5-year cumulative incidence of severe exacerbations after successive numbers of previous events. We used a joint frailty model to investigate the extent of between-individual variability in exacerbation risk and the associations of each exacerbation with the rate of subsequent events. Analyses were conducted separately for pediatric (<14 years old) and adult (⩾14 years old) patients. Results: Analyses were based on 3,039 pediatric (mean age at baseline, 6.4; 35% female) and 5,442 (mean age at baseline, 50.8; 68% female) adult patients. The 5-year rates of severe exacerbations after the first three events were 0.16, 0.29, and 0.35 for the pediatric group, and 0.14, 0.33, and 0.49 for the adult group. Both groups exhibited substantial variability in patient-specific risks of exacerbation: the mid-95% interval of 5-year risk of experiencing a severe exacerbation ranged from 11% to 24% in pediatric patients and from 8% to 40% in adult patients. After controlling for potential confounders, the first follow-up exacerbation was associated with an increase of 79% (95% confidence interval [CI], 11-189%) in the rate of subsequent events in the pediatric group, whereas this increase was 188% (95% CI, 35-515%) for the adult group. The effects of subsequent exacerbations were not statistically significant. Conclusions: After the first severe exacerbation, the risk of subsequent events is substantially different among patients. The number of previous severe exacerbations carries nuanced prognostic information about future risk. Our results suggest that severe exacerbations in the early course of asthma detrimentally affect the course of the disease and risk of subsequent exacerbations.

Model-Based ROC Curve: Examining the Effect of Case Mix and Model Calibration on the ROC Plot.

BACKGROUND: The performance of risk prediction models is often characterized in terms of discrimination and calibration. The receiver-operating characteristic (ROC) curve is widely used for evaluating model discrimination. However, when comparing ROC curves across different samples, the effect of case mix makes the interpretation of discrepancies difficult. Further, compared with model discrimination, evaluating model calibration has not received the same level of attention. Current methods for examining model calibration require specification of smoothing or grouping factors. METHODS: We introduce the "model-based" ROC curve (mROC) to assess model calibration and the effect of case mix during external validation. The mROC curve is the ROC curve that should be observed if the prediction model is calibrated in the external population. We show that calibration-in-the-large and the equivalence of mROC and ROC curves are together sufficient conditions for the model to be calibrated. Based on this, we propose a novel statistical test for calibration that, unlike current methods, does not require any subjective specification of smoothing or grouping factors. RESULTS: Through a stylized example, we demonstrate how mROC separates the effect of case mix and model miscalibration when externally validating a risk prediction model. We present the results of simulation studies that confirm the properties of the new calibration test. A case study on predicting the risk of acute exacerbations of chronic obstructive pulmonary disease puts the developments in a practical context. R code for the implementation of this method is provided. CONCLUSION: mROC can easily be constructed and used to interpret the effect of case mix and calibration on the ROC plot. Given the popularity of ROC curves among applied investigators, this framework can further promote assessment of model calibration. HIGHLIGHTS: Compared with examining model discrimination, examining model calibration has not received the same level of attention among investigators who develop or examine risk prediction models.This article introduces the model-based ROC (mROC) curve as the basis for graphical and statistical examination of model calibration on the ROC plot.This article introduces a formal statistical test based on mROC for examining model calibration that does not require arbitrary smoothing or grouping factors.Investigators who develop or validate risk prediction models can now also use the popular ROC plot for examining model calibration, as a critical but often neglected component in predictive analytics.

Should the number of acute exacerbations in the previous year be used to guide treatments in COPD?

BACKGROUND: In contemporary management of chronic obstructive pulmonary disease (COPD), the frequent exacerbator phenotype, based on a 12-month history of acute exacerbation of COPD (AECOPD), is a major determinant of therapeutic recommendations. However, there is considerable debate as to the stability of this phenotype over time. METHODS: We used fundamental principles in time-to-event analysis to demonstrate that variation in the frequent exacerbator phenotype has two major sources: variability in the underlying AECOPD rate and randomness in the occurrence of individual AECOPDs. We re-analysed data from two large cohorts, the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) study and the SubPopulations and InteRmediate OutcoMes In COPD Study (SPIROMICS), using a Bayesian model that separated these sources of variability. We then evaluated the stability of the frequent exacerbator phenotype based on these results. RESULTS: In both cohorts, the pattern of AECOPDs strongly supported the presence of an individual-specific underlying AECOPD rate which is stable over time (Bayes Factor less than 0.001). Despite this, the observed AECOPD rate can vary markedly year-to-year within individual patients. For those with an underlying rate of 0.8-3.1 events·year-1, the frequent exacerbator classification, based on the observed rate, changes more than 30% of the time over two consecutive years due to chance alone. This value increases to more than 45% for those with an underlying rate of 1.2-2.2 events·year-1. CONCLUSIONS: While the underlying AECOPD rate is a stable trait, the frequent exacerbator phenotype based on observed AECOPD patterns is unstable, so much so that its suitability for informing treatment decisions should be questioned. Whether evaluating AECOPD history over longer durations or using multivariate prediction models can result in more stable phenotyping needs to be evaluated.

Dealing With Treatment-Confounder Feedback and Sparse Follow-up in Longitudinal Studies: Application of a Marginal Structural Model in a Multiple Sclerosis Cohort.

The beta-interferons are widely prescribed platform therapies for patients with multiple sclerosis (MS). We accessed a cohort of patients with relapsing-onset MS from British Columbia, Canada (1995-2013), to examine the potential survival advantage associated with beta-interferon exposure using a marginal structural model. Accounting for potential treatment-confounder feedback between comorbidity, MS disease progression, and beta-interferon exposure, we found an association between beta-interferon exposure of at least 6 contiguous months and improved survival (hazard ratio (HR) = 0.63, 95% confidence interval 0.47, 0.86). We also assessed potential effect modifications by sex, baseline age, or baseline disease duration, and found these factors to be important effect modifiers. Sparse follow-up due to variability in patient contact with the health system is one of the biggest challenges in longitudinal analyses. We considered several single-level and multilevel multiple imputation approaches to deal with sparse follow-up and disease progression information; both types of approach produced similar estimates. Compared to ad hoc imputation approaches, such as linear interpolation (HR = 0.63), and last observation carried forward (HR = 0.65), all multiple imputation approaches produced a smaller hazard ratio (HR = 0.53), although the direction of effect and conclusions drawn concerning the survival advantage remained the same.

Adjusting for differential misclassification in matched case-control studies utilizing health administrative data.

In epidemiological studies of secondary data sources, lack of accurate disease classifications often requires investigators to rely on diagnostic codes generated by physicians or hospital systems to identify case and control groups, resulting in a less-than-perfect assessment of the disease under investigation. Moreover, because of differences in coding practices by physicians, it is hard to determine the factors that affect the chance of an incorrectly assigned disease status. What results is a dilemma where assumptions of non-differential misclassification are questionable but, at the same time, necessary to proceed with statistical analyses. This paper develops an approach to adjust exposure-disease association estimates for disease misclassification, without the need of simplifying non-differentiality assumptions, or prior information about a complicated classification mechanism. We propose to leverage rich temporal information on disease-specific healthcare utilization to estimate each participant's probability of being a true case and to use these estimates as weights in a Bayesian analysis of matched case-control data. The approach is applied to data from a recent observational study into the early symptoms of multiple sclerosis (MS), where MS cases were identified from Canadian health administrative databases and matched to population controls that are assumed to be correctly classified. A comparison of our results with those from non-differentially adjusted analyses reveals conflicting inferences and highlights that ill-suited assumptions of non-differential misclassification can exacerbate biases in association estimates.

Multiple sclerosis: effect of beta interferon treatment on survival.

Worldwide, the beta interferons remain the most commonly prescribed disease-modifying drugs for multiple sclerosis. However, it is unclear if they alter survival. We investigated the association between beta interferon and mortality in the 'real-world' setting. This was a multi-centre population-based observational study of patients with relapsing-onset multiple sclerosis who were initially registered at a clinic in British Columbia, Canada (1980-2004) or Rennes, France (1976-2013). Data on this cohort were accessed from the clinical multiple sclerosis databases and from individually linked health administrative data; all data were collected prospectively. Participants were followed from the latter of their first multiple sclerosis clinic visit, 18th birthday or 1 January 1996; until death, emigration or 31 December 2013. Only those who were naïve to disease-modifying therapy and immunosuppressant treatment of multiple sclerosis at the start of their follow-up were included in the analysis. A nested case-control approach was used. Up to 20 controls, matched to cases (deaths) by country, sex, age ± 5 years, year and disability level at study entry, were randomly selected from the cohort by incidence density sampling. The associations between all-cause mortality and at least 6 months beta interferon exposure, and also cumulative exposure ('low', 6 months to 3 years; and 'high', >3 years), were estimated by conditional logistic regression adjusting for treatment with other disease-modifying therapies and age in years. Further analyses included separate analyses by sex and country, additional adjustment for comorbidity burden in the Canadian cohort, and estimation of the association between beta interferon and multiple sclerosis-related death in both countries. Among 5989 participants (75% female) with a mean age of 42 (standard deviation, SD 11) years at study entry, there were 742 deaths (70% female) and the mean age at death was 61 (SD 13) years. Of these cases, 649 were matched to between one and 20 controls. Results of the conditional logistic regression analyses are expressed as adjusted odds ratios with 95% confidence intervals. The odds of beta interferon exposure were 32% lower among cases than controls (0.68; 0.53-0.89). Increased survival was associated with >3 years beta interferon exposure (0.44; 0.30-0.66), but not between 6 months and 3 years exposure (1.00; 0.73-1.38). Findings were similar within sex and country, and for multiple sclerosis-related death. Beta interferon treatment was associated with a lower mortality risk among people with relapsing-onset multiple sclerosis. Findings were consistent between two geographically distinct regions in North America and Europe.

Comparison of statistical approaches dealing with time-dependent confounding in drug effectiveness studies.

In longitudinal studies, if the time-dependent covariates are affected by the past treatment, time-dependent confounding may be present. For a time-to-event response, marginal structural Cox models are frequently used to deal with such confounding. To avoid some of the problems of fitting marginal structural Cox model, the sequential Cox approach has been suggested as an alternative. Although the estimation mechanisms are different, both approaches claim to estimate the causal effect of treatment by appropriately adjusting for time-dependent confounding. We carry out simulation studies to assess the suitability of the sequential Cox approach for analyzing time-to-event data in the presence of a time-dependent covariate that may or may not be a time-dependent confounder. Results from these simulations revealed that the sequential Cox approach is not as effective as marginal structural Cox model in addressing the time-dependent confounding. The sequential Cox approach was also found to be inadequate in the presence of a time-dependent covariate. We propose a modified version of the sequential Cox approach that correctly estimates the treatment effect in both of the above scenarios. All approaches are applied to investigate the impact of beta-interferon treatment in delaying disability progression in the British Columbia Multiple Sclerosis cohort (1995-2008).

Effect of adherence to the first-generation injectable immunomodulatory drugs on disability accumulation in multiple sclerosis: a longitudinal cohort study.

OBJECTIVE: To examine the association between optimal adherence to the first-generation injectable immunomodulatory drugs (IMDs) for multiple sclerosis (MS) and subsequent disability accumulation. METHODS: We accessed prospectively collected linked clinical and administrative health data from British Columbia, Canada. Subjects with MS treated with a first-generation injectable IMD at an MS clinic (1996-2004) were followed until their last clinic visit before 2009. Adherence was estimated using the proportion of days covered (PDC). The primary outcome was disability accumulation, defined as an increase in the Expanded Disability Status Scale (EDSS) score as recorded during each year of follow-up. Generalised estimating equation models, adjusted for baseline sex, age, EDSS and time between scores, were used to measure associations between optimal adherence (≥80% PDC) during the first year of treatment and subsequent disability accumulation. The relationship between early IMD adherence and the secondary outcome, time to sustained EDSS 6, was examined using Cox proportional hazards regression. RESULTS: Among 801 subjects, 598 (74.7%) had optimal adherence over the first year of IMD treatment and 487 (39.0%) demonstrated one or more instances of disability accumulation. Early optimal adherence was not associated with disability accumulation (adjusted OR 0.94; 95% CI 0.78 to 1.15), nor with time to sustained EDSS 6 (adjusted HR 0.91; 95% CI 0.57 to 1.44). CONCLUSION: Almost three-quarters of subjects with MS had optimal early adherence to their first-line injectable IMD. There was no evidence that this was associated with disability accumulation in the following years.

Bayesian analysis of pair-matched case-control studies subject to outcome misclassification.

We examine the impact of nondifferential outcome misclassification on odds ratios estimated from pair-matched case-control studies and propose a Bayesian model to adjust these estimates for misclassification bias. The model relies on access to a validation subgroup with confirmed outcome status for all case-control pairs as well as prior knowledge about the positive and negative predictive value of the classification mechanism. We illustrate the model's performance on simulated data and apply it to a database study examining the presence of ten morbidities in the prodromal phase of multiple sclerosis.

Evaluating the safety of ß-interferons in MS: A series of nested case-control studies.

OBJECTIVE: To examine the association between interferon-ß (IFN-ß) and potential adverse events using population-based health administrative data in British Columbia, Canada. METHODS: Patients with relapsing-remitting multiple sclerosis (RRMS) who were registered at a British Columbia Multiple Sclerosis Clinic (1995-2004) were eligible for inclusion and were followed up until death, absence from British Columbia, exposure to a non-IFN-ß disease-modifying drug, or December 31, 2008. Incidence rates were estimated for each potential adverse event (selected a priori and defined with ICD-9/10 diagnosis codes from physician and hospital claims). A nested case-control study was conducted to assess the odds of previous IFN-ß exposure for each potential adverse event with at least 30 cases. Cases were matched by age (±5 years), sex, and year of cohort entry, with up to 20 randomly selected (by incidence density sampling) controls. Odds ratios (ORs) with 95% confidence intervals (95% CIs) were estimated with conditional logistic regression adjusted for age at cohort entry. RESULTS: Of the 2,485 eligible patients, 77.9% were women, and 1,031 were treated with IFN-ß during follow-up. From the incidence analyses, 27 of the 47 potential adverse events had at least 30 cases. Patients with incident stroke (ORadj 1.83, 95% CI 1.16-2.89), migraine (ORadj 1.55, 95% CI 1.18-2.04), depression (ORadj 1.33, 95% CI 1.13-1.56), and hematologic abnormalities (ORadj 1.32, 95% CI 1.01-1.72) were more likely to have previous exposure to IFN-ß than controls. CONCLUSIONS: Among patients with RRMS, IFN-ß was associated with a 1.8- and 1.6-fold increase in the risk of stroke and migraine and 1.3-fold increases in depression and hematologic abnormalities.

Identification of treatment responders based on multiple longitudinal outcomes with applications to multiple sclerosis patients.

Identification of treatment responders is a challenge in comparative studies where treatment efficacy is measured by multiple longitudinally collected continuous and count outcomes. Existing procedures often identify responders on the basis of only a single outcome. We propose a novel multiple longitudinal outcome mixture model that assumes that, conditionally on a cluster label, each longitudinal outcome is from a generalized linear mixed effect model. We utilize a Monte Carlo expectation-maximization algorithm to obtain the maximum likelihood estimates of our high-dimensional model and classify patients according to their estimated posterior probability of being a responder. We demonstrate the flexibility of our novel procedure on two multiple sclerosis clinical trial datasets with distinct data structures. Our simulation study shows that incorporating multiple outcomes improves the responder identification performance; this can occur even if some of the outcomes are ineffective. Our general procedure facilitates the identification of responders who are comprehensively defined by multiple outcomes from various distributions. Copyright © 2017 John Wiley & Sons, Ltd.

Comparison of Statistical Approaches for Dealing With Immortal Time Bias in Drug Effectiveness Studies.

In time-to-event analyses of observational studies of drug effectiveness, incorrect handling of the period between cohort entry and first treatment exposure during follow-up may result in immortal time bias. This bias can be eliminated by acknowledging a change in treatment exposure status with time-dependent analyses, such as fitting a time-dependent Cox model. The prescription time-distribution matching (PTDM) method has been proposed as a simpler approach for controlling immortal time bias. Using simulation studies and theoretical quantification of bias, we compared the performance of the PTDM approach with that of the time-dependent Cox model in the presence of immortal time. Both assessments revealed that the PTDM approach did not adequately address immortal time bias. Based on our simulation results, another recently proposed observational data analysis technique, the sequential Cox approach, was found to be more useful than the PTDM approach (Cox: bias = -0.002, mean squared error = 0.025; PTDM: bias = -1.411, mean squared error = 2.011). We applied these approaches to investigate the association of ß-interferon treatment with delaying disability progression in a multiple sclerosis cohort in British Columbia, Canada (Long-Term Benefits and Adverse Effects of Beta-Interferon for Multiple Sclerosis (BeAMS) Study, 1995-2008).

Association between the use of selective serotonin reuptake inhibitors and multiple sclerosis disability progression.

BACKGROUND: Benefits of selective serotonin reuptake inhibitors (SSRIs) in modifying the multiple sclerosis (MS) disease course have been suggested, but their ability to delay disability progression remains unknown. We examined the association between SSRI exposure and MS disability progression. METHODS: A nested case-control study was conducted using the British Columbia (Canada) Multiple Sclerosis clinical data linked to health administrative data. The primary outcome was a sustained score of 6 (requires a cane to walk) on the Expanded Disability Status Scale (EDSS), and the secondary outcome was the onset of secondary progressive MS (SPMS, an advanced stage of MS). The cases were those who reached a study outcome and were matched with up to four randomly selected controls by sex, age, EDSS and calendar year at study entry using incidence density sampling. The associations between disability worsening and SSRI exposure were assessed with conditional logistic regression models, adjusted for confounders. RESULTS: A total of 3920 patients were included in the main analyses, of which 272 reached sustained EDSS 6 and 187 reached SPMS. SSRI exposure was significantly different between patients who reached sustained EDSS 6 and controls [adjusted odds ratio (adjOR):1.44; 95% confidence interval (CI):1.03-2.01]. However, SSRI exposure was not significantly different between those who reached SPMS and their controls (adjOR:1.35; 95%CI:0.89-2.04). CONCLUSION: We found no evidence to suggest that SSRI exposure was associated with a delay in MS disability accumulation or progression. Copyright © 2016 John Wiley & Sons, Ltd.