Mohammad Ehsanul Karim

Association Between Use of Interferon Beta and Progression of Disability in Patients With Relapsing-Remitting Multiple Sclerosis

CONTEXT Interferon beta is widely prescribed to treat multiple sclerosis (MS); however, its relationship with disability progression has yet to be established. OBJECTIVE To investigate the association between interferon beta exposure and disability progression in patients with relapsing-remitting MS. DESIGN, SETTING, AND PATIENTS Retrospective cohort study based on prospectively collected data (1985-2008) from British Columbia, Canada. Patients with relapsing-remitting MS treated with interferon beta (n = 868) were compared with untreated contemporary (n = 829) and historical (n = 959) cohorts. MAIN OUTCOME MEASURES The main outcome measure was time from interferon beta treatment eligibility (baseline) to a confirmed and sustained score of 6 (requiring a cane to walk 100 m; confirmed at >150 days with no measurable improvement) on the Expanded Disability Status Scale (EDSS) (range, 0-10, with higher scores indicating higher disability). A multivariable Cox regression model with interferon beta treatment included as a time-varying covariate was used to assess the hazard of disease progression associated with interferon beta treatment. Analyses also included propensity score adjustment to address confounding by indication. RESULTS The median active follow-up times (first to last EDSS measurement) were as follows: for the interferon beta-treated cohort, 5.1 years (interquartile range [IQR], 3.0-7.0 years); for the contemporary control cohort, 4.0 years (IQR, 2.1-6.4 years); and for the historical control cohort, 10.8 years (IQR, 6.3-14.7 years). The observed outcome rates for reaching a sustained EDSS score of 6 were 10.8%, 5.3%, and 23.1% in the 3 cohorts, respectively. After adjustment for potential baseline confounders (sex, age, disease duration, and EDSS score), exposure to interferon beta was not associated with a statistically significant difference in the hazard of reaching an EDSS score of 6 when either the contemporary control cohort (hazard ratio, 1.30; 95% CI, 0.92-1.83; P = .14) or the historical control cohort (hazard ratio, 0.77; 95% CI, 0.58-1.02; P = .07) were considered. Further adjustment for comorbidities and socioeconomic status, where possible, did not change interpretations, and propensity score adjustment did not substantially change the results. CONCLUSION Among patients with relapsing-remitting MS, administration of interferon beta was not associated with a reduction in progression of disability.

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 β-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).

Beta-interferon exposure and onset of secondary progressive multiple sclerosis.

Beta‐interferons (IFNβ) are the most widely prescribed drugs for patients with multiple sclerosis (MS). However, whether or not treatment with IFNβ can delay secondary progressive MS (SPMS) onset remains unknown. Our aim was to examine the association between IFNβ exposure and SPMS onset in patients with relapsing−remitting MS (RRMS).

Multiple Sclerosis in Older Adults: The Clinical Profile and Impact of Interferon Beta Treatment

Background. We examined (1) patient characteristics and disease-modifying drug (DMD) exposure in late-onset (LOMS, ≥50 years at symptom onset) versus adult-onset (AOMS, 18–<50 years) MS and (2) the association between interferon-beta (IFNβ) and disability progression in older relapsing-onset MS adults (≥50 years). Methods. This retrospective study (1980–2004, British Columbia, Canada) included 358 LOMS and 5627 AOMS patients. IFNβ-treated relapsing-onset MS patients aged ≥50 (regardless of onset age, 90) were compared with 171 contemporary and 106 historical controls. Times to EDSS 6 from onset and from IFNβ eligibility were examined using survival analyses. Results. LOMS patients (6%) were more likely to be male, with motor onset and a primary-progressive course, and exhibit faster progression and were less likely to take DMDs. Nonetheless, 57% were relapsing-onset, of which 31% were prescribed DMDs, most commonly IFNβ. Among older relapsing-onset MS adults, no significant association between IFNβ exposure and disability progression was found when either the contemporary (hazard ratio [HR]: 0.46; 95% CI: 0.18–1.22) or historical controls (HR: 0.54; 95% CI: 0.20–1.42) were considered. Conclusion. LOMS differed clinically from AOMS. One-third of older relapsing-onset MS patients were prescribed a DMD. IFNβ exposure was not significantly associated with reduced disability in older MS patients.

Antibody dissociation rates are predictive of neutralizing antibody (NAb) course: A comparison of interferon beta-1b-treated Multiple Sclerosis (MS) patients with transient versus sustained NAbs

A proportion of multiple sclerosis (MS) patients treated with interferon-β (IFNβ) develop neutralizing antibodies (NAbs), which can reduce therapeutic efficacy. In the Betaseron/Betaferon in Newly Emerging MS for Initial Treatment (BENEFIT) study, 88/277 patients developed NAbs, 48 having transient positivity and 29 having sustained positivity. This study aimed to investigate the antibody binding characteristics of serial sera in a subset of these two patient groups. Using Biacore™, a surface plasmon resonance-based technology that monitors biomolecular interactions in real time, we immobilized pure IFNβ-1b and analyzed antibody binding responses and dissociation rates of these sera. NAb titers correlated directly with binding responses and inversely with dissociation rates, and sera from sustained NAb patients demonstrated significantly higher binding responses and slower dissociation rates than sera from transient NAb patients. Thus, transient and sustained NAbs are quantitatively and qualitatively different, and interestingly, binding responses and dissociation rates at month 12 could predict the NAb course.

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).

Estimating inverse probability weights using super learner when weight-model specification is unknown in a marginal structural Cox model context: Estimating inverse probability weights using super learner when weight-model specification is unknown in a marginal structural Cox model context

Correct specification of the inverse probability weighting (IPW) model is necessary for consistent inference from a marginal structural Cox model (MSCM). In practical applications, researchers are typically unaware of the true specification of the weight model. Nonetheless, IPWs are commonly estimated using parametric models, such as the main-effects logistic regression model. In practice, assumptions underlying such models may not hold and data-adaptive statistical learning methods may provide an alternative. Many candidate statistical learning approaches are available in the literature. However, the optimal approach for a given dataset is impossible to predict. Super learner (SL) has been proposed as a tool for selecting an optimal learner from a set of candidates using cross-validation. In this study, we evaluate the usefulness of a SL in estimating IPW in four different MSCM simulation scenarios, in which we varied the specification of the true weight model specification (linear and/or additive). Our simulations show that, in the presence of weight model misspecification, with a rich and diverse set of candidate algorithms, SL can generally offer a better alternative to the commonly used statistical learning approaches in terms of MSE as well as the coverage probabilities of the estimated effect in an MSCM. The findings from the simulation studies guided the application of the MSCM in a multiple sclerosis cohort from British Columbia, Canada (1995-2008), to estimate the impact of beta-interferon treatment in delaying disability progression. Copyright

On the application of statistical learning approaches to construct inverse probability weights in marginal structural Cox models: Hedging against weight-model misspecification

ABSTRACT The marginal structural Cox model (MSCM) estimates can be highly sensitive to weight-model misspecification. We assess the performance of various popular statistical learners, such as LASSO, support vector machines, CART, bagged CART, and boosted CART, in estimating MSCM weights. When weight-models are misspecified, we find that the weights computed from boosted CART generally lead to less MSE and better coverage for the MSCM estimates. This study is motivated by the investigation of the impact of beta-interferon treatment on disability progression in subjects with multiple sclerosis from British Columbia, Canada (1995–2008).

Can joint replacement reduce cardiovascular risk

The role of propensity score matching and landmark analysis in interpreting observational studies of treatments

The Effect of Serious Offending on Health: A Marginal Structural Model:

In this study, we contribute to the emerging scholarship at the intersection of crime and health by estimating the effect of serious offending on offenders’ health. By building on sociological stress research, we identify and adjust for the key life course processes that may intervene on the pathway from offending to health using a rich set of measures available in the panel data from the National Longitudinal Study of Adolescent to Adult Health. Because offending and health share many causes and consequences, a critical challenge is accounting for confounding and mediation that unfold over time. We adjust for these time-varying processes by estimating repeated measures marginal structural models with inverse probability of treatment weights. The results show that offending over the life course is adversely linked to health but not uniformly across race and gender.