Treatment decisions in multiple sclerosis — insights from real-world observational studies

[1]  M. Tsolaki,et al.  Data quality evaluation for observational multiple sclerosis registries , 2017, Multiple sclerosis.

[2]  P. Soelberg Sørensen,et al.  A comparison of multiple sclerosis clinical disease activity between patients treated with natalizumab and fingolimod , 2017, Multiple sclerosis.

[3]  Pierre Grammond,et al.  Contribution of different relapse phenotypes to disability in multiple sclerosis , 2017, Multiple sclerosis.

[4]  Robert Zivadinov,et al.  Combining clinical and magnetic resonance imaging markers enhances prediction of 12-year disability in multiple sclerosis , 2017, Multiple sclerosis.

[5]  M. Trojano,et al.  Immunomodulatory therapies delay disease progression in multiple sclerosis , 2016, Multiple sclerosis.

[6]  R. Linker,et al.  Cardiac Safety Profile of First Dose of Fingolimod for Relapsing-Remitting Multiple Sclerosis in Real-World Settings: Data from a German Prospective Multi-Center Observational Study , 2016, Neurology and Therapy.

[7]  P. Vermersch,et al.  Comparative efficacy of fingolimod vs natalizumab , 2016, Neurology.

[8]  Pierre Grammond,et al.  Defining secondary progressive multiple sclerosis. , 2016, Brain : a journal of neurology.

[9]  M. Filippi,et al.  Assessing response to interferon-β in a multicenter dataset of patients with MS , 2016, Neurology.

[10]  Pierre Grammond,et al.  Predictors of long‐term disability accrual in relapse‐onset multiple sclerosis , 2016, Annals of neurology.

[11]  I. Scott,et al.  Cautionary tales in the interpretation of observational studies of effects of clinical interventions , 2016, Internal medicine journal.

[12]  H. Butzkueven,et al.  Observational data: Understanding the real MS world , 2016, Multiple sclerosis.

[13]  H. Hartung,et al.  Safety and efficacy of dimethyl fumarate in multiple sclerosis: a multi-center observational study , 2016, Journal of Neurology.

[14]  Philip W Lavori,et al.  Integrating Randomized Comparative Effectiveness Research with Patient Care. , 2016, The New England journal of medicine.

[15]  F. Piehl,et al.  Rituximab versus fingolimod after natalizumab in multiple sclerosis patients , 2016, Annals of neurology.

[16]  V. Martinelli,et al.  Natalizumab versus fingolimod in patients with relapsing-remitting multiple sclerosis non-responding to first-line injectable therapies , 2016, Multiple sclerosis.

[17]  C. Ohmann,et al.  Bayesian evidence synthesis for exploring generalizability of treatment effects: a case study of combining randomized and non‐randomized results in diabetes , 2016, Statistics in medicine.

[18]  Jeffrey A. Cohen,et al.  Recommendations for observational studies of comorbidity in multiple sclerosis , 2016, Neurology.

[19]  R. Marrie,et al.  Examining the effects of comorbidities on disease-modifying therapy use in multiple sclerosis , 2016, Neurology.

[20]  J. Lechner-Scott,et al.  Risk of early relapse following the switch from injectables to oral agents for multiple sclerosis , 2016, European journal of neurology.

[21]  H. Hartung,et al.  Disease-modifying therapies and infectious risks in multiple sclerosis , 2016, Nature Reviews Neurology.

[22]  L. Kappos,et al.  Comparative efficacy of first-line natalizumab vs IFN-&bgr; or glatiramer acetate in relapsing MS , 2016, Neurology. Clinical practice.

[23]  P. Vermersch,et al.  Comparative efficacy of fingolimod vs natalizumab , 2016, Neurology.

[24]  M. Porter,et al.  Standardizing Patient Outcomes Measurement. , 2016, The New England journal of medicine.

[25]  L. Alfredsson,et al.  Comparative analysis of first-year fingolimod and natalizumab drug discontinuation among Swedish patients with multiple sclerosis , 2016, Multiple sclerosis.

[26]  M. Sormani,et al.  Inclusion of brain volume loss in a revised measure of ‘no evidence of disease activity’ (NEDA-4) in relapsing–remitting multiple sclerosis , 2015, Multiple sclerosis.

[27]  Pierre Grammond,et al.  Defining reliable disability outcomes in multiple sclerosis. , 2015, Brain : a journal of neurology.

[28]  C. Pozzilli,et al.  Fingolimod versus interferon beta/glatiramer acetate after natalizumab suspension in multiple sclerosis. , 2015, Brain : a journal of neurology.

[29]  F. Barkhof,et al.  Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis—establishing disease prognosis and monitoring patients , 2015, Nature Reviews Neurology.

[30]  J. Lechner-Scott,et al.  Comparative effectiveness of glatiramer acetate and interferon beta formulations in relapsing–remitting multiple sclerosis , 2015, Multiple sclerosis.

[31]  À. Rovira,et al.  Defining high, medium and low impact prognostic factors for developing multiple sclerosis. , 2015, Brain : a journal of neurology.

[32]  K. Schmierer,et al.  Is it time to target no evident disease activity (NEDA) in multiple sclerosis? , 2015, Multiple sclerosis and related disorders.

[33]  D. Ramasamy,et al.  Early magnetic resonance imaging predictors of clinical progression after 48 months in clinically isolated syndrome patients treated with intramuscular interferon β‐1a , 2015, European journal of neurology.

[34]  Raimar Kern,et al.  The PANGAEA study design – a prospective, multicenter, non-interventional, long-term study on fingolimod for the treatment of multiple sclerosis in daily practice , 2015, BMC Neurology.

[35]  W. Carlo,et al.  Data quality monitoring and performance metrics of a prospective, population-based observational study of maternal and newborn health in low resource settings , 2015, Reproductive Health.

[36]  J. Hillert,et al.  The Swedish MS registry – clinical support tool and scientific resource , 2015, Acta neurologica Scandinavica.

[37]  B. Laursen,et al.  Registers of multiple sclerosis in Denmark , 2015, Acta neurologica Scandinavica.

[38]  M. Duddy,et al.  Effectiveness and cost-effectiveness of interferon beta and glatiramer acetate in the UK Multiple Sclerosis Risk Sharing Scheme at 6 years: a clinical cohort study with natural history comparator , 2015, The Lancet Neurology.

[39]  L. Kappos,et al.  Fingolimod and CSF neurofilament light chain levels in relapsing-remitting multiple sclerosis , 2015, Neurology.

[40]  T. Sprenger,et al.  Disease activity in the first year predicts longer-term clinical outcomes in the pooled population of the phase III FREEDOMS and FREEDOMS II studies (P7.239) , 2015 .

[41]  G. Kitsios.  Propensity score studies are unlikely to underestimate treatment effects in critical care medicine: a critical reanalysis. , 2015, Journal of clinical epidemiology.

[42]  J. Lechner-Scott,et al.  Comparison of switch to fingolimod or interferon beta/glatiramer acetate in active multiple sclerosis. , 2015, JAMA neurology.

[43]  J. Lechner-Scott,et al.  Predictors of disability worsening in clinically isolated syndrome , 2015, Annals of clinical and translational neurology.

[44]  M. Sormani,et al.  Can we measure long-term treatment effects in multiple sclerosis? , 2015, Nature Reviews Neurology.

[45]  J. Lechner-Scott,et al.  Switch to natalizumab versus fingolimod in active relapsing–remitting multiple sclerosis , 2015, Annals of neurology.

[46]  L. Kappos,et al.  Comparative efficacy of switching to natalizumab in active multiple sclerosis , 2015, Annals of clinical and translational neurology.

[47]  H. Weiner,et al.  Evaluation of no evidence of disease activity in a 7-year longitudinal multiple sclerosis cohort. , 2015, JAMA neurology.

[48]  Carsten Lukas,et al.  Towards the implementation of ‘no evidence of disease activity’ in multiple sclerosis treatment: the multiple sclerosis decision model , 2015, Therapeutic advances in neurological disorders.

[49]  J A Cook,et al.  The rise of big clinical databases , 2015, The British journal of surgery.

[50]  Ludwig Kappos,et al.  Oral teriflunomide for patients with a first clinical episode suggestive of multiple sclerosis (TOPIC): a randomised, double-blind, placebo-controlled, phase 3 trial , 2014, The Lancet Neurology.

[51]  M. Sormani,et al.  Treatment of relapsing-remitting multiple sclerosis after 24 doses of natalizumab: evidence from an Italian spontaneous, prospective, and observational study (the TY-STOP Study). , 2014, JAMA neurology.

[52]  P. Gustafson,et al.  Practice of Epidemiology Marginal Structural Cox Models for Estimating the Association Between β-Interferon Exposure and Disease Progression in a Multiple Sclerosis Cohort , 2014 .

[53]  Joy Adamson,et al.  The opportunities and challenges of pragmatic point-of-care randomised trials using routinely collected electronic records: evaluations of two exemplar trials. , 2014, Health technology assessment.

[54]  T. Friede,et al.  Multiple sclerosis registries in Europe – results of a systematic survey , 2014, Multiple sclerosis.

[55]  Sivanesan Dakshanamurthy,et al.  Big data: the next frontier for innovation in therapeutics and healthcare , 2014, Expert review of clinical pharmacology.

[56]  P. Sørensen,et al.  Recurrence or rebound of clinical relapses after discontinuation of natalizumab therapy in highly active MS patients , 2014, Journal of Neurology.

[57]  J. Lechner-Scott,et al.  Fingolimod after natalizumab and the risk of short-term relapse , 2014, Neurology.

[58]  S. Vukusic,et al.  Switching from natalizumab to fingolimod in multiple sclerosis: a French prospective study. , 2014, JAMA neurology.

[59]  Carlo Pozzilli,et al.  Interferon beta failure predicted by EMA criteria or isolated MRI activity in multiple sclerosis , 2014, Multiple sclerosis.

[60]  À. Rovira,et al.  Evaluating the response to glatiramer acetate in relapsing–remitting multiple sclerosis (RRMS) patients , 2014, Multiple sclerosis.

[61]  B. Cree,et al.  Disease activity free status: a new end point for a new era in multiple sclerosis clinical research? , 2014, JAMA neurology.

[62]  Olivier Gout,et al.  Treatment effect on brain atrophy correlates with treatment effect on disability in multiple sclerosis , 2014, Annals of neurology.

[63]  L. Kappos,et al.  Efficacy and safety of natalizumab in multiple sclerosis: interim observational programme results , 2014, Journal of Neurology, Neurosurgery & Psychiatry.

[64]  K. Schmierer,et al.  Assessing treatment response to interferon-β , 2014, Neurology.

[65]  Richard D Riley,et al.  Developing and validating risk prediction models in an individual participant data meta-analysis , 2014, BMC Medical Research Methodology.

[66]  D. Arnold,et al.  Treatment effect on brain atrophy correlates with treatment effect on disability in multiple sclerosis , 2014, Annals of neurology.

[67]  Karel G M Moons,et al.  A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta‐analysis , 2013, Statistics in medicine.

[68]  C. Di Pietrantonj,et al.  Immunomodulators and immunosuppressants for multiple sclerosis: a network meta-analysis. , 2013, The Cochrane database of systematic reviews.

[69]  O. Nerman,et al.  Time to secondary progression in patients with multiple sclerosis who were treated with first generation immunomodulating drugs , 2013, Multiple sclerosis.

[70]  B Stubinski,et al.  Scoring treatment response in patients with relapsing multiple sclerosis , 2013, Multiple sclerosis.

[71]  Elizabeth Fisher,et al.  Reliability of classifying multiple sclerosis disease activity using magnetic resonance imaging in a multiple sclerosis clinic. , 2013, JAMA neurology.

[72]  Lu Tian,et al.  Effectively Selecting a Target Population for a Future Comparative Study , 2013, Journal of the American Statistical Association.

[73]  Chunhua Weng,et al.  Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research , 2013, J. Am. Medical Informatics Assoc..

[74]  R. Rudick,et al.  Predictors of long‐term outcome in multiple sclerosis patients treated with interferon beta , 2013, Annals of neurology.

[75]  W G Havercroft,et al.  Simulating from marginal structural models with time‐dependent confounding , 2012, Statistics in medicine.

[76]  J. Aarseth,et al.  The Norwegian Multiple Sclerosis Registry and Biobank , 2012, Acta neurologica Scandinavica. Supplementum.

[77]  E. Havrdová,et al.  Early predictors of non‐response to interferon in multiple sclerosis , 2012, Acta neurologica Scandinavica.

[78]  Jeffrey A. Cohen,et al.  Alemtuzumab for patients with relapsing multiple sclerosis after disease-modifying therapy: a randomised controlled phase 3 trial , 2012, The Lancet.

[79]  S. Cummings,et al.  Improving the efficiency and effectiveness of pragmatic clinical trials in older adults in the United States. , 2012, Contemporary clinical trials.

[80]  S. Cole,et al.  A simulation study of finite‐sample properties of marginal structural Cox proportional hazards models , 2012, Statistics in medicine.

[81]  P. Gustafson,et al.  Association between use of interferon beta and progression of disability in patients with relapsing-remitting multiple sclerosis. , 2012, JAMA.

[82]  Meena Subramanyam,et al.  Risk of natalizumab-associated progressive multifocal leukoencephalopathy. , 2012, The New England journal of medicine.

[83]  Abhi Shelat,et al.  One‐to‐many propensity score matching in cohort studies , 2012, Pharmacoepidemiology and drug safety.

[84]  R. Kinkel,et al.  Association between immediate initiation of intramuscular interferon beta-1a at the time of a clinically isolated syndrome and long-term outcomes: a 10-year follow-up of the Controlled High-Risk Avonex Multiple Sclerosis Prevention Study in Ongoing Neurological Surveillance. , 2012, Archives of neurology.

[85]  N. Patsopoulos.  A pragmatic view on pragmatic trials , 2011, Dialogues in clinical neuroscience.

[86]  Jeffrey A. Cohen,et al.  Comparison of fingolimod with interferon beta-1a in relapsing-remitting multiple sclerosis: a randomised extension of the TRANSFORMS study , 2011, The Lancet Neurology.

[87]  L. Kappos,et al.  Disease activity return during natalizumab treatment interruption in patients with multiple sclerosis , 2011, Neurology.

[88]  J P Vandenbroucke,et al.  How to assess the external validity of therapeutic trials: a conceptual approach. , 2010, International journal of epidemiology.

[89]  David H. Miller,et al.  Long-term effect of early treatment with interferon beta-1b after a first clinical event suggestive of multiple sclerosis: 5-year active treatment extension of the phase 3 BENEFIT trial , 2009, The Lancet Neurology.

[90]  C. Pozzilli,et al.  Real‐life impact of early interferonβ therapy in relapsing multiple sclerosis , 2009, Annals of neurology.

[91]  M. Trojano,et al.  observational studies: propensity score analysis of non-randomized data. , 2009, International MS journal.

[92]  Peter C. Austin,et al.  The Relative Ability of Different Propensity Score Methods to Balance Measured Covariates Between Treated and Untreated Subjects in Observational Studies , 2009, Medical decision making : an international journal of the Society for Medical Decision Making.

[93]  J. Avorn,et al.  High-dimensional Propensity Score Adjustment in Studies of Treatment Effects Using Health Care Claims Data , 2009, Epidemiology.

[94]  J Sastre-Garriga,et al.  Measures in the first year of therapy predict the response to interferon β in MS , 2009, Multiple sclerosis.

[95]  Ian Harvey,et al.  A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. , 2009, Journal of clinical epidemiology.

[96]  Jasjeet S. Sekhon,et al.  Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching Package for R , 2008 .

[97]  F. Barkhof,et al.  Subgroups of the BENEFIT study: Risk of developing MS and treatment effect of interferon beta-1b , 2008, Journal of Neurology.

[98]  O. Pearson,et al.  Contribution of relapses to disability in multiple sclerosis , 2008, Journal of neurology.

[99]  À. Rovira,et al.  Relationship between MRI lesion activity and response to IFN-β in relapsing–remitting multiple sclerosis patients , 2008, Multiple sclerosis.

[100]  Peter C Austin,et al.  Propensity-score matching in the cardiovascular surgery literature from 2004 to 2006: a systematic review and suggestions for improvement. , 2007, The Journal of thoracic and cardiovascular surgery.

[101]  M. Trojano,et al.  New natural history of interferon‐β–treated relapsing multiple sclerosis , 2007 .

[102]  H. Leufkens,et al.  Availability of comparative trials for the assessment of new medicines in the European Union at the moment of market authorization. , 2007, British journal of clinical pharmacology.

[103]  L. Kappos,et al.  MSBase: an international, online registry and platform for collaborative outcomes research in multiple sclerosis , 2006, Multiple sclerosis.

[104]  J. Ioannidis,et al.  Indirect comparisons: the mesh and mess of clinical trials , 2006, The Lancet.

[105]  L. Kappos,et al.  Long-term subcutaneous interferon beta-1a therapy in patients with relapsing-remitting MS , 2006, Neurology.

[106]  G. Comi,et al.  Italian Multiple Sclerosis Database Network , 2006, Neurological Sciences.

[107]  Til Stürmer,et al.  Indications for propensity scores and review of their use in pharmacoepidemiology. , 2006, Basic & clinical pharmacology & toxicology.

[108]  X. Montalban,et al.  Defining the response to interferon‐β in relapsing‐remitting multiple sclerosis patients , 2006, Annals of neurology.

[109]  N. Powe,et al.  Association between Screening for Osteoporosis and the Incidence of Hip Fracture , 2005, Annals of Internal Medicine.

[110]  P. Rothwell,et al.  External validity of randomised controlled trials: “To whom do the results of this trial apply?” , 2005, The Lancet.

[111]  R. Rudick,et al.  Defining interferon β response status in multiple sclerosis patients , 2004 .

[112]  F. Lublin,et al.  Effect of relapses on development of residual deficit in multiple sclerosis , 2003, Neurology.

[113]  Simon G. Thompson,et al.  Multistate Markov models for disease progression with classification error , 2003 .

[114]  Nicolette de Keizer,et al.  Model Formulation: Defining and Improving Data Quality in Medical Registries: A Literature Review, Case Study, and Generic Framework , 2002, J. Am. Medical Informatics Assoc..

[115]  M. Lauer,et al.  Aspirin use and all-cause mortality among patients being evaluated for known or suspected coronary artery disease: A propensity analysis. , 2001, JAMA.

[116]  Marco Rovaris,et al.  Effect of early interferon treatment on conversion to definite multiple sclerosis: a randomised study , 2001, The Lancet.

[117]  L. Wallentin,et al.  Early statin treatment following acute myocardial infarction and 1-year survival. , 2001, JAMA.

[118]  J H Simon,et al.  Intramuscular interferon beta-1a therapy initiated during a first demyelinating event in multiple sclerosis. CHAMPS Study Group. , 2000, The New England journal of medicine.

[119]  J. Concato,et al.  Randomized, controlled trials, observational studies, and the hierarchy of research designs. , 2000, The New England journal of medicine.

[120]  A. Hartz,et al.  A comparison of observational studies and randomized, controlled trials , 2000, American journal of ophthalmology.

[121]  Jeffrey A. Cohen,et al.  Multiple Sclerosis Therapeutics , 1999 .

[122]  R. Kronmal,et al.  Assessing the sensitivity of regression results to unmeasured confounders in observational studies. , 1998, Biometrics.

[123]  C Confavreux,et al.  EDMUS, a European database for multiple sclerosis. , 1992, Journal of neurology, neurosurgery, and psychiatry.

[124]  P. Rosenbaum.  Discussing hidden bias in observational studies. , 1991, Annals of internal medicine.

[125]  D H Christiansen,et al.  Computer-assisted data collection in multicenter epidemiologic research. The Atherosclerosis Risk in Communities Study. , 1990, Controlled clinical trials.

[126]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[127]  Jared K Lunceford,et al.  Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. , 2017, Statistics in medicine.

[128]  Saurabh Jain,et al.  Automatic multiple sclerosis brain lesion localization and volumetry , 2014 .

[129]  J. Stockman.  Effect of glatiramer acetate on conversion to clinically definite multiple sclerosis in patients with clinically isolated syndrome (PreCISe study): a randomised, double-blind, placebo-controlled trial , 2011 .

[130]  M. Trojano.  Multiple Sclerosis Therapeutics: The challenge of demonstrating long-term benefit of disease-modifying therapies in multiple sclerosis , 2011 .

[131]  M. Trojano,et al.  New natural history of interferon-beta-treated relapsing multiple sclerosis. , 2007, Annals of neurology.

[132]  R. Rudick,et al.  Defining interferon beta response status in multiple sclerosis patients. , 2004, Annals of neurology.

[133]  B. Craig,et al.  Estimation of the transition matrix of a discrete-time Markov chain. , 2002, Health economics.

[134]  Lori S. Parsons.  Reducing Bias in a Propensity Score Matched-Pair Sample Using Greedy Matching Techniques , 2001 .

[135]  W. Gilks,et al.  Following a moving target—Monte Carlo inference for dynamic Bayesian models , 2001 .

[136]  David A. Schoenfeld,et al.  Chi-squared goodness-of-fit tests for the proportional hazards regression model , 1980 .