LONG-TERM BENEFITS OF EARLY TREATMENT IN MULTIPLE SCLEROSIS: AN INVESTIGATION UTILIZING A NOVEL DATA COLLECTION TECHNIQUE DEVON S. CONWAY, M.D.

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1 LONG-TERM BENEFITS OF EARLY TREATMENT IN MULTIPLE SCLEROSIS: AN INVESTIGATION UTILIZING A NOVEL DATA COLLECTION TECHNIQUE by DEVON S. CONWAY, M.D. Submitted in partial fulfillment of the requirements For the degree of Master of Science Thesis Adviser: Jeffrey Cohen, M.D. Clinical Research Scholars Program School of Graduate Studies CASE WESTERN RESERVE UNIVERSITY August 2011

2 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/dissertation of Devon Conway, M.D. candidate for the _ Master of Science degree *. (signed) Jeffrey A. Cohen, M.D. (chair of the committee) Ralph O Brien, Ph.D. Deborah Miller, Ph.D. (date) 6 June 2011 *We also certify that written approval has been obtained for any proprietary material contained therein. 1

3 TABLE OF CONTENTS List of Tables...3 List of Figures...4 Acknowledgements...6 Abstract...7 Introduction...8 Methods...11 Results...15 Discussion...18 Tables...24 Figures...30 References

4 List of Tables Table 1: Error Rates Observed for Each Variable and for Data Entries Overall...24 Table 2: Basic Characteristics for the Entire Sample...25 Table 3: Basic Characteristics by Propensity Score Quintile...26 Table 4: Correlations of Propensity Model Variables and the Propensity Scores...27 Table 5: Results of ANOVA Model Comparison Tests...28 Table 6: Treatment Effect Sizes and 99% Confidence Intervals for each Outcome by Quintile

5 List of Figures Figure 1: Early Treatment Effect by Quintile on Mobility as Measured by the EQ5D...30 Figure 2: Early Treatment Effect by Quintile on Self Care as Measured by the EQ5D...31 Figure 3: Early Treatment Effect by Quintile on Ability to Perform Usual Activities as Measured by the EQ5D 32 Figure 4: Early Treatment Effect by Quintile on Pain as Measured by the EQ5D...33 Figure 5: Early Treatment Effect by Quintile on Depression as Measured by the EQ5D.34 Figure 6: Early Treatment Effect by Quintile on Overall Quality of Life as Measured by the EQ5D Index...35 Figure 7: Early Treatment Effect by Quintile on Mobility as Measured by the Multiple Sclerosis Performance Scales...36 Figure 8: Early Treatment Effect by Quintile on Hand Function as Measured by the Multiple Sclerosis Performance Scales...37 Figure 9: Early Treatment Effect by Quintile on Vision as Measured by the Multiple Sclerosis Performance Scales...38 Figure 10: Early Treatment Effect by Quintile on Fatigue as Measured by the Multiple Sclerosis Performance Scales...39 Figure 11: Early Treatment Effect by Quintile on Cognition as Measured by the Multiple Sclerosis Performance Scales...40 Figure 12: Early Treatment Effect by Quintile on Bladder/Bowel Function as Measured by the Multiple Sclerosis Performance Scales

6 Figure 13: Early Treatment Effect by Quintile on Sensation as Measured by the Multiple Sclerosis Performance Scales...42 Figure 14: Early Treatment Effect by Quintile on Spasticity as Measured by the Multiple Sclerosis Performance Scales...43 Figure 15: Early Treatment Effect by Quintile on Pain as Measured by the Multiple Sclerosis Performance Scales...44 Figure 16: Early Treatment Effect by Quintile on Depression as Measured by the Multiple Sclerosis Performance Scales...45 Figure 17: Early Treatment Effect by Quintile on Coordination as Measured by the Multiple Sclerosis Performance Scales...46 Figure 18: Early Treatment Effect by Quintile on Overall Performance as Measured by the Multiple Sclerosis Performance Scales...47 Figure 19: Early Treatment Effect by Quintile on Depression as Measured by the Patient Health Questionnaire Figure 20: Early Treatment Effect by Quintile on Walking Speed as Measured by the Timed 25 Foot Walk

7 Acknowledgements I would like to thank the following people for their support and encouragement: My thesis committee: o Dr. Jeffrey Cohen for helping me to formulate, carry out, and write up this project and for serving as my fellowship director and mentor for the last two years. o Dr. Ralph O Brien for serving as my Clinical Research Scholars Program adviser and for his great patience and assistance with the statistical methodology in this project. o Dr. Deborah Miller for helping me to develop this project and for her tireless work in bringing the Knowledge Project to the Mellen Center. My family for their continuous love and support. 6

8 Long-Term Benefits of Early Treatment in Multiple Sclerosis: An Investigation Utilizing a Novel Data Collection Technique Abstract By DEVON S. CONWAY, M.D. Introduction: The Knowledge Project (KP) is an initiative to collect self-reported patient data and objective clinician assessments electronically at each appointment. The long-term benefits of early treatment (ET) in multiple sclerosis (MS) are unclear but could be evaluated with the outcomes available in the KP data set. Methods: The KP was queried for patients with relapsing-remitting MS or secondary progressive MS who were 5 years from symptom onset. Propensity scores for ET were calculated based on early clinical characteristics. Patients were divided into quintiles based on their propensity scores and linear regression models were constructed to determine the treatment effect sizes and confidence intervals. Results: A total of 1,082 records were analyzed. Those in higher quintiles who received ET had significantly better predicted scores for most outcomes. Discussion: The benefits of ET were modest but support its use in patients similar to those in the higher quintiles. 7

9 Introduction The Knowledge Project (KP) is a Cleveland Clinic initiative to collect data from patients and clinicians electronically at each clinical encounter. The type of data collected is individualized according to the center the patient is visiting. Within the Mellen Center for Multiple Sclerosis (MS), patients complete several validated questionnaires by computer prior to their appointment, including the EuroQol-5D (a standardized assessment of quality of life), 1 the Patient Health Questionnaire-9 (a standardized depression scale), 2 and the MS Performance Scales (assessing mobility, hand function, vision, fatigue, cognition, bowel and bladder control, sensation, and spasticity). 3,4 Following each encounter, Mellen Center clinicians enter objective information into the database. This includes the patient s date of symptom onset, date of MS diagnosis, and the MS clinical course. Two components of the multiple sclerosis functional composite (MSFC) 5,6 are also recorded, the nine hole peg test and the timed 25 foot walk (T25FW). The database has recently been updated so that clinicians can record what MS disease modifying therapy the patient is on, how many times they have fallen in the last month, and whether or not they are receiving disability payments. The KP represents a novel method of data collection that can be applied to answer important clinical questions. Its strength lies in the fact that it allows rapid and longitudinal data collection from both patients and clinicians. We elected to utilize the KP to address a pressing issue in MS care. Specifically, the long-term benefits of initiating disease modifying therapy early in the course of MS are unclear and the KP offers a data set to investigate this question. 8

10 MS is a chronic, inflammatory, demyelinating, neurodegenerative disorder of the central nervous system of unclear etiology. It is the leading non-traumatic cause of neurologic disability among young people in the Western world. The clinical course of MS is heterogeneous and most patients are classified into one of four subtypes. 7 Patients with relapsing-remitting multiple sclerosis (RRMS) experience episodic worsening of disability (relapses) followed by full or partial recovery. On average, after years, RRMS patients tend to develop relapse-free progression, with or without superimposed relapses, termed secondary progressive multiple sclerosis (SPMS). Alternatively, patients may have gradual disability progression from disease onset, and are termed primary progressive multiple sclerosis (PPMS). Finally, a rare subtype of MS in which patients have relapse-free progression from onset, but also experience relapses is termed progressive relapsing multiple sclerosis (PRMS). Eight agents have been approved by European and North American regulatory agencies for the treatment of MS. These therapies have been shown to reduce relapserate and MS activity on magnetic resonance imaging (MRI) in the short-term Several of these agents have also been shown to delay confirmation of clinically definite MS after an initial demyelinating event This has led some to strongly advocate for the importance of early disease modifying treatment in MS. 23 Traditionally, demyelination has been considered the primary pathologic feature of MS. This model suggests that clinical relapses result from acute episodes of demyelination and post-relapse recovery occurs with remyelination of the affected axons. An eventual failure of remyelination was thought to underlie SPMS, and axonal loss was felt to result from a loss of trophic support for neurons due to the loss of 9

11 oligodendrocytes. 24,25 The traditional theory holds that acute inflammatory demyelination can cause relapses with incomplete recovery due to persistent demyelination and axonal damage. The presence of significant axonal damage within acute inflammatory lesions was elucidated in a key study by Trapp et al. 26 With chronic demyelination, and possibly through other mechanisms, there appears to be chronic deterioration of neurons independent of ongoing outside inflammation. 27,28 This neurodegeneration is poorly targeted by existing therapies. The approved agents appear to have strong anti-inflammatory activity as evidenced by their ability to reduce both relapses and the accumulation of new lesions on MRI. However, their ability to delay time to confirmed disability progression, as measured by the Kurtzke Expanded Disability Status Scale (EDSS), 29 in SPMS is minimal. For instance, only one study of interferon-β in SPMS demonstrated an effect on time to confirmed disability progression, 10 whereas a number of studies failed to show any such effect The positive study enrolled younger patients in earlier stages of SPMS, suggesting they likely still had a significant inflammatory component to their disease. 33 Glatiramer acetate was similarly ineffective in progressive forms of MS. 34 The lack of efficacy in progressive forms of MS is presumably because the underlying mechanism of disease at the progressive stage is not inflammation dependent. Based on this, it has been suggested that the inflammatory phase represents a window of opportunity for treatment and that treatment should be initiated as early as possible. 35 However, there is a paucity of evidence supporting the long-term benefits of early disease modifying therapy. 10

12 Some natural history studies have shown that participants with fewer early relapses and less MRI activity have a better long-term prognosis Thus, theoretically, early treatment (ET) in RRMS patients should minimize relapses and thereby reduce later disability. However, one study of 1,844 patients with MS by Confavreux et al. found that, although the time from onset to moderate disability was related to relapses, the time to progression from moderate disability to severe disability was similar regardless of the initial disease course. 39 There were similar findings in another study by Confavreux et al. 40 and in a study by Leray et al. 41 Overall, these studies support the notion of two stages of MS disability progression and suggest that these stages have some independence from each other. It is uncertain if an intervention during the earlier inflammatory stage of RRMS will be of benefit once the patient reaches the later neurodegenerative stage. Prior to analysis of the KP contents, it is important to verify the accuracy of the entered data. Once validated, the KP offers a wide array of potential outcomes that reflect numerous aspects of the patient s life. These outcomes offer an ideal means of comparison for MS patients who did and did not receive ET. We hypothesized that those patients receiving ET would have better outcomes at five years from MS symptom onset and beyond. Methods Source Data Validation To validate the contents of the KP, a pool of database entries with complete data was created. From this pool, 100 entries by different clinicians were randomly selected. Each 11

13 database entry corresponds to a specific clinical encounter, and the progress note for that encounter was located using the electronic medical record (EMR). Eight clinician assessed variables were selected and their values from both the KP and the progress note were recorded. The variables included diagnosis, date of symptom onset, date of diagnosis, MS clinical course, T25FW, assistive device requirements, right hand nine hole peg test time, and left hand nine hole peg test time. The information in the progress note was assumed to be correct because it is collected during the patient s visit, whereas the database entries are usually made at a later time. Whenever possible, the contents of the progress note in question were verified against other documentation in the EMR to assure accuracy. Any discrepancy between the KP entry and the EMR was considered an error. Using the two sources, it was possible to estimate a data entry error rate for each variable and an overall data entry error rate for the KP database. Data Collection The KP database was queried for patients with a diagnosis of RRMS or SPMS who were 5 years from symptom onset. Data from the resulting patients were extracted including the patient s age, gender, race, date of symptom onset, MS clinical course, responses to each of the five EuroQol-5D (EQ5D) questions, EQ5D index, Patient Health Questionnaire-9 (PHQ9) score, answers to each of the 8 Multiple Sclerosis Performance Scales (MSPS) questions, MSPS summary score, and T25FW. Three questions were added to the MSPS pertaining to pain, depression, and coordination, but these were not included in calculating the MSPS summary score. The EMR was reviewed to determine the number of relapses the patient had in the first year after symptom onset and their ET 12

14 status. ET was defined as receiving treatment with an approved MS disease modifying therapy for 3 of the first 5 years after symptom onset. Five years was chosen as a cutoff because it is roughly half the time most patients require to begin experiencing relapse free progression, the hallmark of SPMS. 42 As most patients received ET with either interferon-β or glatiramer acetate, and these drugs have similar efficacy, 43,44 it was felt appropriate to regard the treatment groups as a whole. Propensity Analysis Propensity scores are a useful method for reducing bias in non-randomized and retrospective studies. 45,46 A logistic regression model was used to determine the probability (propensity score) that a patient would receive ET. The covariates included in the logistic regression model were the patient s age at symptom onset, gender, race, time from symptom onset at outcome assessment, and the number of clinical relapses in the first year. Correlation coefficients were calculated between the propensity model variables and the final propensity scores. Patients were divided into equally sized quintiles (Q1, Q2, Q3, Q4, Q5) based on their propensity scores. Quintiles were chosen because it was felt that five groups would be sufficient to distinguish the patients based on their likelihood for receiving treatment. Q1 contained those patients who were least likely to receive ET and Q5 contained those who were most likely to receive ET. Intraquintile comparisons are, therefore, between patients who were similar at the time the treatment decision was made. This reduces bias inherent in this type of retrospective observational study and makes the comparisons more like what would be observed in a randomized, controlled trial. 13

15 Linear regression models were subsequently constructed in order to assess the treatment effect for each outcome. The treatment effect was the difference in the model s predicted outcome scores between those who did and did not receive ET. Model Selection Several models were assessed for each outcome. The most comprehensive (model A) was designed to account for non-linearity and included the following as covariates: presence or absence of ET, the quintile, a linear variable representing the quintile and centered on the quintile of interest, an interaction term between the quintile and the ET variable, and an interaction term between the linear quintile variable and the ET variable. Two other models were also constructed for each outcome. The first (model B) omitted the interaction term between the ET variable and the quintile variable, and the second (model C) omitted both the quintile variable and the interaction term of the quintile variable and the ET variable. An analysis of variance was conducted to compare the simpler models to the comprehensive model. In generating the results, the simplest model that did not significantly differ from the comprehensive model was used. The linear quintile variable was centered at 0 for each quintile in order to determine that quintile s effect size. Confidence intervals for the effect size were generated from the linear models. As five assessments were made for each model (one for each quintile), we required a significance level of 0.05 / 5 = This adjustment is according to the Bonferonni method for multiplicity adjustment

16 Results Source Data Validation The results of the data validation analysis are summarized in Table 1. The maximum observed error rate was 6%, which was found with the date of diagnosis variable. Of the 800 variable entries that were reviewed, 31 total errors were observed, for an overall data entry error rate of 3.9%. Sample Characteristics A total of 1,082 patients were included in the ET analysis. Of these, 453 (41.9%) received ET and 629 (58.1%) did not. The mean age was 45.7 years and the sample was 77.6% female. The basic characteristics of the entire sample, broken down by ET status, are summarized in Table 2. Basic characteristics by quintile and ET status are summarized in Table 3. The first two quintiles consisted of 217 patients each and the remaining three quintiles each contained 216 patients. Propensity Scores The propensity scores ranged from 0.42 to The correlation coefficients of the propensity model variables with the final propensity scores are shown in Table 4. The propensity scores were largely driven by the number of relapses in the first year, which had a positive correlation of 0.80 with the final propensity score. Another important variable was the time between symptom onset and outcome assessment, which was negatively correlated with the final propensity score at a value of

17 Model Selection The results of the ANOVA model comparison tests are shown in Table 5. Nonlinearity in the response was not an issue as model B did not significantly differ from model A for any of the outcomes. For most outcomes, model C was used because there was no significant difference between models B and C. However, for five outcomes it was necessary to use model B. These included the EQ5Da (mobility), MSPS1 (mobility), MSPS5 (cognition), MSPS8 (spasticity), and the MSPS summary score. Effect of Early Treatment The treatment effect for each outcome was calculated by subtracting the linear model s predicted score for those not receiving ET from the predicted scores for those who did receive ET. The treatment effect sizes and 99% confidence intervals are summarized in Table 6 and graphically in the figures. Figures 1-6 correspond to the five individual EQ5D outcomes (range 1-3, lower scores are better, a more negative difference suggests superiority of ET) and the EQ5D index (range 0-1, higher scores are better, a more positive difference suggests superiority of ET). Figures 7-18 represent the 11 individual MSPS outcomes (range 0-6, lower scores are better, a more negative difference suggests superiority of ET) and the MSPS summary score (range 0-66, lower scores are better, a more negative difference suggests superiority of ET). Figures 19 and 20 correspond to the PHQ9 (range 0-27, lower scores are better, a more negative difference suggests superiority of ET) and the T25FW (lower scores are better, a more negative difference suggests superiority of ET) respectively. 16

18 In Q1 and Q2, no significant difference was found for any outcome with the exception of the T25FW. Those receiving ET in Q1 were predicted to have T25FW times that were 2.9 seconds faster than those not receiving ET. Similarly, ET patients in Q2 were predicted to have T25FW times of 1.8 seconds faster than those who did not receive ET. However, the upper 99% confidence interval approached zero for both of these predictions (-0.27 for Q1 and for Q2). No significant difference in T25FW times was predicted for any of the upper three quintiles. In Q3, none of the individual EQ5D questions showed a significant difference between the ET and no ET groups. However, the EQ5D index favored those receiving ET, with a predicted score of 0.04 points better (99% CI: 0.01 to 0.07) than the no ET group. Those receiving ET also fared better on the PHQ9 with a 1.5 point lower predicted score than their no ET counterparts (99% CI: -2.5 to -0.5). Sensation, as measured by MSPS question 7, and the MSPS summary score significantly favored the ET group, although the upper 99% confidence intervals for both approached zero. There was no significant difference in any of the other outcomes in Q3. In Q4, all of the EQ5D questions significantly favored the ET group except for self care. The predicted score for the EQ5D index was 0.06 points better for the ET group (99% CI: 0.02 to 0.10). The ET group was significantly favored in the MSPS summary score with a score predicted to be better by 2.4 points (99% CI: -4.0 to -0.9). The ET group also did better on all MSPS questions except those pertaining to vision (question 3), fatigue (question 4), and bladder/bowel function (question 6). Finally, the predicted scores for the PHQ9 were significantly less for the ET group, with a predicted difference of 2.1 points less (99% CI: -3.3 to -0.9). 17

19 In Q5, which contained those patients most likely to receive ET, all EQ5D questions were predicted to be better for the ET group except for the self-care question. The predicted score for the EQ5D index was 0.08 points better for the ET group (99% CI: 0.02 to 0.13). The ET group was significantly favored for the MSPS summary score with a score predicted to be better by 3.5 points (99% CI: -5.7 to -1.3). The ET group also did better on all MSPS questions except those pertaining to hand function (question 2), vision (question 3), fatigue (question 4), and depression (question 10). Finally, the predicted scores for the PHQ9 were significantly less for the ET group, with a predicted difference of 2.7 points less (99% CI: -4.4 to -1.1). Discussion The calculated data error rate of 3.9% is fairly small. This suggests that the contents of the KP database are valid and can be reasonably used to answer important clinical questions such as ours. To our knowledge, the KP model for electronic data collection from patients and clinicians at each visit is unique. It offers the advantage of rapid data collection with minimal effort on the part of participants. As the database continues to expand, it will also contain significant longitudinal data on included patients, which will increase its utility as a tool for clinical investigation. The propensity scores in our study are estimates of each patient s probability of receiving ET. However, there is reason to believe the propensity scores also reflect the patients initial disease severity and activity. This notion is based on the fact that neurologists are typically more inclined to treat patients early who have more active disease at presentation. Also, the propensity scores were largely driven by the number of 18

20 relapses in the first year (correlation coefficient of +0.80), a clear marker of initial disease severity. In addition, male gender was positively correlated with the propensity scores. Male gender has been identified in some studies as a predictor of a worse prognosis in MS. 42,48,49 There are several possible explanations for why the time between symptom onset and outcome assessment was negatively correlated with the propensity score. The most likely is that a number of patients in the study experienced symptom onset shortly after the first disease modifying therapy became available in They may not have been able to receive ET due to shortages and lack of physician familiarity with the new disease modifying therapies. Our data showed long-term benefits of early disease modifying therapy in patients with more active MS at presentation. Most outcomes favored the ET group in Q4 and Q5. These quintiles contained those patients with more than one relapse in the first year, suggesting that this may be an important biomarker in the decision to treat early. Based on our data, it may be reasonable to delay treatment in patients who are less severely affected at presentation, particularly those who do not have a second relapse in the first year, without concern for a detrimental effect on long-term outcomes. It should be noted that although there was a significant benefit of ET for most outcomes in Q4 and Q5, the effect was relatively modest. For instance, with regards to the EQ5D quality of life index, the Q5 ET group was predicted to have only a 0.08 point advantage over the no ET group. Similarly, the ET group was only predicted to have a 3.5 point advantage on the MSPS summary score, which ranges from 0-66, and a 2.7 point advantage on the PHQ9, which ranges from This suggests that the benefits of early treatment, even in the most active patients, is not substantial for the outcomes we 19

21 measured. However, it should be recognized that our study only accounted for disease modifying therapy in 3 of the first 5 years after symptom onset. It is possible that more continuous exposure to therapy would expand the difference between those receiving and those not receiving treatment. For example, a recent analysis of data from the Prevention of Relapses and disability by Interferon β-1a Subcutaneously in MS (PRISMS) long-term follow-up study compared those receiving the highest cumulative doses of interferon β-1a to those receiving the lowest cumulative doses at 7-8 years after initial enrollment. 50 The higher group showed less disability progression than the lower group, and were also favored in several relapse and MRI based outcomes. Of note, this difference in cumulative dose was typically between those who were initially randomized to interferon β-1a and those who were initially assigned to placebo. 51,52 Although participants were not required to start treatment within a specific time from symptom onset, those patients initially randomized to interferon β-1a received treatment two years earlier than their placebo group counterparts. A similar long-term study of clinical trials participants found that individuals from the placebo groups reached EDSS 6 sooner than would have been expected from on-study progression rates. 53 It was speculated that this finding might be because they started treatment later than those initially assigned to active treatment arms. An interesting finding in our study was the increasing percentage of males in the higher quintiles. Q1 was only 5% male whereas Q5 was 42% male. Some studies have suggested that males with MS have a worse prognosis than females, 42,48,49 while other studies have not found a gender difference Our data suggested that males had a 20

22 higher propensity for ET, and would indirectly support the notion that males are more affected at the time of presentation. The results pertaining to the T25FW were also intriguing. Unlike most other outcomes, significant benefit of ET was seen only in the lower quintiles. However, the 99% confidence intervals for ET effect in Q1 and Q2 closely approach zero, suggesting a very small difference overall. One possible explanation is that permanent deterioration in walking speeds may be mostly secondary to neurodegeneration rather than due to the inflammation that current treatments reduce. This is supported by the fact that gait impairment is a hallmark feature of SPMS and PPMS. A similar analysis to ours was published by Trojano et al. assessing disability progression in MS patients who received ET with interferon-β (within one year of symptom onset) to those who received delayed treatment (greater than one year from symptom onset). They assessed 2,570 patients for up to seven years. Survival analysis was used to calculate time from treatment initiation to three endpoints: an irreversible 1- point progression in the EDSS score, EDSS 4 and EDSS 6. Propensity score quintiles were used to directly adjust for likelihood of early treatment in the Cox proportional hazards regression models. The propensity score model was constructed based on age, sex, number of relapses in the previous year, EDSS scores and a number of interaction variables. The authors found that early treatment significantly lengthened the time required to reach disability milestones. Several key differences exist between the study from Trojano et al and ours. Their study utilized the EDSS as a disability measure, while ours used the self-reported MSPS and the T25FW. The MSPS summary score has been validated against the EDSS, 3 21

23 and the individual subscales have been validated against the EDSS and MSFC. 4 Of note, we added three additional performance scale questions pertaining to pain, depression, and coordination that have not yet been validated. The Trojano et al. study also had less follow-up than ours with a maximum follow-up time of seven years. Our patients were tested at a mean of 10.4 years from symptom onset. The longer follow-up in our sample may explain our less striking results, as our patients are more likely to have been in the progressive phase of the disease. Finally, while Trojano et al. s study was limited to treatment with interferon-β, ours allowed treatment with any therapy approved for MS. One potential criticism of our study is that burden of disease on the MRI was not incorporated into our propensity score model. MRI is often used as the primary endpoint in phase II trials of emerging MS therapeutics and it was recently shown that active MRI lesions can account for up to 80% of the variance of a treatment s effect on relapse rate. 57 Burden of disease on MRI also typically plays an important role in clinical decision making about initiating disease modifying therapy. Unfortunately, due to the lack of a standardized system for documentation of MRI characteristics in either the KP or the electronic medical record, we were unable to include it in our analysis. However, as the characteristics of early relapses have been shown to affect the prognosis of MS, and because relapse rate has been the primary endpoint in all pivotal trials of MS therapeutics, we believe our methods are sound. A number of other prognostic factors may have been helpful to distinguish patients when calculating the propensity scores. For instance, the severity of the initial relapse and the degree of recovery may predict future disability progression, making them good candidate variables. 41,59 Also, it has been shown that non-ms comorbidities 22

24 may affect progression of the disease. 61 While these characteristics were not easily available to us through the KP, it would be valuable to account for them in future propensity score research. Our study also does not account for factors beyond the early stages of the disease. However, the hope is that through the pseudo-randomization process of propensity scores in a large sample, such factors will balance out between treatment groups. The principal finding of our study was a modest benefit of ET in most outcomes for patients with more active MS at presentation. In addition, we demonstrated the validity of the KP database and its value as a research tool. Finally, it should be noted that propensity scores proved to be a useful technique for creating appropriate comparisons in our sample. The technique is useful for reducing bias in retrospective database studies such as this one. However, to utilize propensity scores it is necessary to have significant information about each patient at the time the treatment decision is made. In our case, this required extensive chart review. 23

25 Table 1 Variable Charts Reviewed Errors Error Rate Diagnosis % Symptom Onset % Date Diagnosis Date % Clinical Course % Timed 25 Foot Walk % Assistive Device % Right 9 Hole Peg % Test Left 9 Hole Peg Test % Overall Data Entries % Error rates observed for each variable and for data entries overall. 24

26 Table 2 Characteristic ET (N=453) No ET (N=629) Age at testing yr Mean ± sd 44.7 ± ± 10.3 Median (range) 45.1 ( ) 45.4 ( ) Age at Sx Onset yr Mean ± sd 35.3 ± ± 10.1 Median (range) 34.7 ( ) 34.6 ( ) Female Sex no. (%) 319 (70.4) 488 (77.6) White Race no. (%) 410 (90.5) 553 (87.9) Time From Sx Onset Mean ± sd 9.5 ± ± 3.4 Median (range) 9.3 ( ) 11.1 RRMS no. (%) 389 (85.9) 492 (78.2) SPMS no. (%) 64 (14.1) 137 (21.8) Relapses in the 1st Year Mean ± sd 1.4 ± ± 0.3 Median (range) 1 (1 4) 1 (1 3) Basic Characteristics for the entire sample, ET = early treatment, sd = standard deviation, Sx= symptom, RRMS = relapsing remitting MS, SPMS = secondary progressive MS. 25

27 Table 3 Characteristic Q1 ET (N = 42) Q1 No ET (N = 175) Q2 ET (N = 66) Q2 No ET (N= 151) Q3 ET (N = 101) Q3 No ET (N= 115) Q4 ET (N = 93) Q4 No ET (N = 123) Q5 ET (N = 151) Q5 No ET (N = 65) 26 Age at testing yr Mean ± sd Median (range) Age at Sx Onset yr Mean ± sd Median (range) 45.8 ± ( ) 31.2 ± ( ) Female Sex no. (%) 39 (92.9) White Race no. (%) 33 (78.6) Time From Sx Onset Mean ± sd Median (range) 14.6 ± ( ) RRMS no. (%) 29 (69) 47.1 ± ( ) 46.5 ± ( ) 32.8 ± ± (10.9 ( ) 51.8) 167 (95.4) 58 (87.9) 142 (81.1) 58 (87.9) 14.4 ± ( ( ) 14.3) 125 (71.4) 53 (80.3) 46.6 ± ( ) 46.5 ± ( ) 45.3 ± ± (24.1 ( ) 66.1) 45.2 ± ( ) 42.8 ± ( ) 41.6 ± ( ) 34.6 ± ± ± ± ± ± ± (10.3 (18.9 (15.6 ( ( (8.6 ( ) 56.8) 61.7) 59.9) 64.7) 59.3) 64.7) 116 (76.8) 77 (76.2) 87 (75.7) 59 (63.4) 79 (64.2) 86 (57) 39 (60) (86.1) 24 (23.8) (89.6) 90 (96.8) (94.3) (91.4) 57 (87.7) 11.6 ± ± ± ± ± ± ± ± ( ) 9.7 ( ) 9.3 ( ) 7.3 ( ) 114 (75.5) 84 (83.2) 92 (80) 89 (95.7) 7.8 ( ) 105 (85.4) 7.5 ( ( ) 15.0) 134 (88.7) 56 (86.2) SPMS no. (%) 13 (31) 50 (28.6) 13 (19.7) 37 (24.5) 17 (16.8) 23 (20) 4 (4.3) 18 (14.6) 17 (11.3) 9 (13.8) Relapses in the 1st Year Mean ± sd 1 ± 0 1 ± 0 1 ± 0 1 ± 0 1 ± 0 1 ± 0 1 ± ± ± ± 0.5 Median (range) 1 (1 1) 1 (1 1) 1 (1 1) 1 (1 1) 1 (1 1) 1 (1 1) 1 (1 2) 1 (1 2) 2 (1 4) 2 (1 3) Basic characteristics by propensity score quintile. ET=Early Treatment, sd = standard deviation, Sx = symptom, RRMS = relapsing-remitting multiple sclerosis, SPMS = secondary progressive multiple sclerosis 26

28 Table 4 Age Sx Onset Year 1 Relapses Female Time From Sx Onset Propensity Score Age Sx Onset X Year 1 Relapses 0.09 X Female X Time From Sx Onset X 0.68 Propensity Score X Correlations of propensity model variables and the propensity scores. Sx = symptom, Time From Sx Onset = Time from symptom onset until outcomes were assessed. 27

29 ANOVA p value comparing model A to model B Table 5 ANOVA p value comparing model B to model C Outcome EQ5Da B EQ5Db C EQ5Dc C EQ5Dd C EQ5De C EQ5D_Index C PHQ C MSPS B MSPS C MSPS C MSPS C MSPS B MSPS C MSPS C MSPS B MSPS C MSPS C MSPS C MSPS B T25FW C Chosen Model Results of ANOVA model comparison tests. Model A: Outcome ~ ET + Quintile.lin + Quintile + Quintile.lin*ET + Quintile*ET Model B: Outcome ~ ET + Quintile.lin + Quintile + Quintile.lin*ET Model C: Outcome ~ ET + Quintile.lin + Quintile.lin*ET ET = Early Treatment, Quintile.lin = linear variable representing the quintile and centered on the quintile of interest, * = interaction term between the two adjacent variables. 28

30 Table 6 Outcome Q1 Q2 Q3 Q4 Q5 EQ5Da % CI 0.1 to to to to to 0.04 EQ5Db % CI 0.11 to to to to to 0.02 EQ5Dc % CI 0.18 to to to to to 0.01 EQ5Dd % CI 0.16 to to to to to 0.05 EQ5De % CI 0.19 to to to to to 0.01 EQ5D_Index % CI 0.06 to to to to to 0.13 PHQ % CI 2.06 to to to to to 1.06 MSPS % CI 0.26 to to to to to 0.13 MSPS % CI 0.4 to to to to to 0 MSPS % CI 0.3 to to to to to 0.15 MSPS % CI 0.44 to to to to to 0.04 MSPS % CI 0.28 to to to to to 0.12 MSPS % CI 0.37 to to to to to 0.17 MSPS % CI 0.25 to to to to to 0.23 MSPS % CI 0.27 to to to to to 0.14 MSPS % CI 0.33 to to to to to 0.14 MSPS % CI 0.44 to to to to to 0.04 MSPS % CI 0.38 to to to to to 0.03 MSPS % CI 1.6 to to to to to 1.32 T25FW % CI 5.46 to to to to to 3.31 Treatment effect sizes and 99% confidence intervals for each outcome by quintile. 29

31 Figure 1 Early treatment effect by quintile on mobility as measured by the EQ5D. 30

32 Figure 2 Early treatment effect by quintile on self care as measured by the EQ5D. 31

33 Figure 3 Early treatment effect by quintile on ability to perform usual activities as measured by the EQ5D. 32

34 Figure 4 Early treatment effect by quintile on pain as measured by the EQ5D. 33

35 Figure 5 Early treatment effect by quintile on depression as measured by the EQ5D. 34

36 Figure 6 Early treatment effect by quintile on overall quality of life as measured by the EQ5D index. 35

37 Figure 7 Early treatment effect by quintile on mobility as measured by the multiple sclerosis performance scales. 36

38 Figure 8 Early treatment effect by quintile on hand function as measured by the multiple sclerosis performance scales. 37

39 Figure 9 Early treatment effect by quintile on vision as measured by the multiple sclerosis performance scales. 38

40 Figure 10 Early treatment effect by quintile on fatigue as measured by the multiple sclerosis performance scales. 39

41 Figure 11 Early treatment effect by quintile on cognition as measured by the multiple sclerosis performance scales. 40

42 Figure 12 Early treatment effect by quintile on bladder/bowel function as measured by the multiple sclerosis performance scales. 41

43 Figure 13 Early treatment effect by quintile on sensation as measured by the multiple sclerosis performance scales. 42

44 Figure 14 Early treatment effect by quintile on spasticity as measured by the multiple sclerosis performance scales. 43

45 Figure 15 Early treatment effect by quintile on pain as measured by the multiple sclerosis performance scales. 44

46 Figure 16 Early treatment effect by quintile on depression as measured by the multiple sclerosis performance scales. 45

47 Figure 17 Early treatment effect by quintile on coordination as measured by the multiple sclerosis performance scales. 46

48 Figure 18 Early treatment effect by quintile on overall performance as measured by the multiple sclerosis performance scales. 47

49 Figure 19 Early treatment effect by quintile on depression as measured by the patient health questionnaire 9. 48

50 Figure 20 Early treatment effect by quintile on walking speed as measured by the timed 25 foot walk. 49

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