Outcome of beta-interferon treatment in relapsing-remitting multiple sclerosis: a Bayesian analysis

Borrowing strength from clinical trials in analysing longitudinal data from a treated cohort: investigating the effectiveness of acetylcholinesterase inhibitors in the management of dementia

BACKGROUND

Health care professionals seek information about effectiveness of treatments in patients who would be offered them in routine clinical practice. Electronic medical records (EMRs) and randomized controlled trials (RCTs) can both provide data on treatment effects; however, each data source has limitations when considered in isolation.

METHODS

A novel modelling methodology which incorporates RCT estimates in the analysis of EMR data via informative prior distributions is proposed. A Bayesian mixed modelling approach is used to model outcome trajectories among patients in the EMR dataset receiving the treatment of interest. This model incorporates an estimate of treatment effect based on a meta-analysis of RCTs as an informative prior distribution. This provides a combined estimate of treatment effect based on both data sources.

RESULTS

The superior performance of the novel combined estimator is demonstrated via a simulation study. The new approach is applied to estimate the effectiveness at 12 months after treatment initiation of acetylcholinesterase inhibitors in the management of the cognitive symptoms of dementia in terms of Mini-Mental State Examination scores. This demonstrated that estimates based on either trials data only (1.10, SE = 0.316) or cohort data only (1.56, SE = 0.240) overestimated this compared with the estimate using data from both sources (0.86, SE = 0.327).

CONCLUSIONS

It is possible to combine data from EMRs and RCTs in order to provide better estimates of treatment effectiveness.

Precision medicine in myasthenia graves: begin from the data precision

Myasthenia gravis (MG) is a prototypic autoimmune disease with overt clinical and immunological heterogeneity. The data of MG is far from individually precise now, partially due to the rarity and heterogeneity of this disease. In this review, we provide the basic insights of MG data precision, including onset age, presenting symptoms, generalization, thymus status, pathogenic autoantibodies, muscle involvement, severity and response to treatment based on references and our previous studies. Subgroups and quantitative traits of MG are discussed in the sense of data precision. The role of disease registries and scientific bases of precise analysis are also discussed to ensure better collection and analysis of MG data.

Incorporating data from various trial designs into a mixed treatment comparison model

Estimates of relative efficacy between alternative treatments are crucial for decision making in health care. Bayesian mixed treatment comparison models provide a powerful methodology to obtain such estimates when head-to-head evidence is not available or insufficient. In recent years, this methodology has become widely accepted and applied in economic modelling of healthcare interventions. Most evaluations only consider evidence from randomized controlled trials, while information from other trial designs is ignored. In this paper, we propose three alternative methods of combining data from different trial designs in a mixed treatment comparison model. Naive pooling is the simplest approach and does not differentiate between-trial designs. Utilizing observational data as prior information allows adjusting for bias due to trial design. The most flexible technique is a three-level hierarchical model. Such a model allows for bias adjustment while also accounting for heterogeneity between-trial designs. These techniques are illustrated using an application in rheumatoid arthritis.

The use of continuous data versus binary data in MTC models: a case study in rheumatoid arthritis

Background

Estimates of relative efficacy between alternative treatments are crucial for decision making in health care. When sufficient head to head evidence is not available Bayesian mixed treatment comparison models provide a powerful methodology to obtain such estimates. While models can be fit to a broad range of efficacy measures, this paper illustrates the advantages of using continuous outcome measures compared to binary outcome measures.

Methods

Using a case study in rheumatoid arthritis a Bayesian mixed treatment comparison model is fit to estimate the relative efficacy of five anti-TNF agents currently licensed in Europe. The model is fit for the continuous HAQ improvement outcome measure and a binary version thereof as well as for the binary ACR response measure and the underlying continuous effect. Results are compared regarding their power to detect differences between treatments.

Results

Sixteen randomized controlled trials were included for the analysis. For both analyses, based on the HAQ improvement as well as based on the ACR response, differences between treatments detected by the binary outcome measures are subsets of the differences detected by the underlying continuous effects.

Conclusions

The information lost when transforming continuous data into a binary response measure translates into a loss of power to detect differences between treatments in mixed treatment comparison models. Binary outcome measures are therefore less sensitive to change than continuous measures. Furthermore the choice of cut-off point to construct the binary measure also impacts the relative efficacy estimates.

Management of breakthrough disease in patients with multiple sclerosis: when an increasing of Interferon beta dose should be effective?

Background

In daily clinical setting, some patients affected by relapsing-remitting Multiple Sclerosis (RRMS) are switched from the low-dose to the high-dose Interferon beta (IFNB) in order to achieve a better control of the disease.

Purpose

In this observational, post-marketing study we reported the 2-year clinical outcomes of patients switched to the high-dose IFNB; we also evaluated whether different criteria adopted to switch patients had an influence on the clinical outcomes.

Methods

Patients affected by RRMS and switched from the low-dose to the high-dose IFNB due to the occurrence of relapses, or contrast-enhancing lesions (CELs) as detected by yearly scheduled MRI scans, were followed for two years. Expanded Disability Status Scale (EDSS) scores, as well as clinical relapses, were evaluated during the follow-up period.

Results

We identified 121 patients switched to the high-dose IFNB. One hundred patients increased the IFNB dose because of the occurrence of one or more relapses, and 21 because of the presence of one or more CELs, even in absence of clinical relapses. At the end of the 2-year follow-up, 72 (59.5%) patients had a relapse, and 51 (42.1%) reached a sustained progression on EDSS score. Overall, 85 (70.3%) patients showed some clinical disease activity (i.e. relapses or disability progression) after the switch.

Relapse risk after increasing the IFNB dose was greater in patients who switched because of relapses than those switched only for MRI activity (HR: 5.55, p = 0.001). A high EDSS score (HR: 1.77, p < 0.001) and the combination of clinical and MRI activity at switch raised the risk of sustained disability progression after increasing the IFNB dose (HR: 2.14, p = 0.01).

Conclusion

In the majority of MS patients, switching from the low-dose to the high-dose IFNB did not reduce the risk of further relapses or increased disability in the 2-year follow period.

Although we observed that patients who switched only on the basis on MRI activity (even in absence of clinical attacks) had a lower risk of further relapses, larger studies are warranted before to recommend a switch algorithm based on MRI findings.

One-year MRI scan predicts clinical response to interferon beta in multiple sclerosis

BACKGROUND AND PURPOSE

To define the predictive value of clinical and magnetic resonance imaging (MRI) characteristics in identifying relapsing-remitting multiple sclerosis (RR-MS) patients with sustained disability progression during interferon beta (IFNB) treatment.

METHODS

All patients receiving treatment with one of the available IFNB formulations for at least 1 year were included in this single-centre, prospective and post-marketing study. Demographic, clinical and MRI data were collected at IFNB start and at 1 year of therapy; patients were followed-up at least yearly. Poor clinical response was defined as the occurrence of a sustained disability progression of > or =1 point in the Expanded Disability Status Scale (EDSS) during the follow-up period.

RESULTS

Out of 454 RR-MS patients starting IFNB therapy, data coming from 394 patients with a mean follow-up of 4.8 (2.4) years were analysed. Sixty patients were excluded because of too short follow-up. Less than 1/3 (30.4%) of the patients satisfied the criterion of 'poor responders'. Patients presenting new lesions on T2-weighted MRI scan after 1 year of therapy (compared with baseline) had a higher risk of being poor responder to treatment with IFNB during the follow-up period (HR 16.8, 95% CI 7.6-37.1, P < 0.001). An augmented risk increasing the number of lesions was observed, with a 10-fold increase for each new lesion.

CONCLUSIONS

Developing new T2-hyperintense lesions during IFNB treatment was the best predictor of long-term poor response to therapy. MRI scans performed after 1 year of IFNB treatment may be useful in contributing to early identification of poor responders.