Comparative Effectiveness and Durability of Biologics in Clinical Practice: Month 12 Outcomes from the International, Observational Psoriasis Study of Health Outcomes (PSoHO)

INTRODUCTION

Given the chronic nature of psoriasis (PsO), more studies are needed that directly compare the effectiveness of different biologics over long observation periods. This study compares the effectiveness and durability through 12 months of anti-interleukin (IL)-17A biologics relative to other approved biologics in patients with moderate-to-severe psoriasis in a real-world setting.

METHODS

The Psoriasis Study of Health Outcomes (PSoHO) is an ongoing 3-year, prospective, non-interventional cohort study of 1981 adults with chronic moderate-to-severe plaque psoriasis initiating or switching to a new biologic. The study compares the effectiveness of anti-IL-17A biologics with other approved biologics and provides pairwise comparisons of seven individual biologics versus ixekizumab. The primary outcome was defined as the proportion of patients who had at least a 90% improvement in Psoriasis Area and Severity Index score (PASI90) and/or a score of 0 or 1 in static Physician Global Assessment (sPGA). Secondary objective comparisons included the proportion of patients who achieved PASI90, PASI100, a Dermatology Life Quality Index (DLQI) score of 0 or 1, and three different actions of durability of treatment response. Unadjusted response rates are presented alongside the primary analysis, which uses frequentist model averaging (FMA) to evaluate the adjusted comparative effectiveness.

RESULTS

Compared to the other biologics cohort, the anti-IL-17A cohort had a higher response rate (68.0% vs. 65.1%) and significantly higher odds of achieving the primary outcome at month 12. The two cohorts had similar response rates for PASI100 (40.5% and 37.1%) and PASI90 (53.9% and 51.7%) at month 12, with no significant differences between the cohorts in the adjusted analyses. At month 12, the response rates across the individual biologics were 53.5-72.6% for the primary outcome, 27.6-48.3% for PASI100, and 41.7-61.4% for PASI90.

CONCLUSIONS

These results show the comparative effectiveness of biologics at 6 and 12 months in the real-world setting.

TRIAL REGISTRATION

ClinicalTrials.gov identifier EUPAS24207.

THU0564 PARTICIPANT ENGAGEMENT IN AN ARTHRITISPOWER REAL-WORLD STUDY TO CAPTURE SMARTWATCH AND PATIENT-REPORTED OUTCOME DATA AMONG RHEUMATOID ARTHRITIS PATIENTS

Background:

Clear characterization of how different types of patient-generated data reflect patient experience is needed to guide integration of electronic patient-reported outcome (ePRO) measures and biometrics in generating real-word evidence (RWE) related to rheumatoid arthritis (RA).

Objectives:

To characterize the level of participant (pt) engagement/adherence and data completeness in an ongoing study of 250 RA pts enrolled in the Digital Tracking of Arthritis Longitudinally (DIGITAL) study1of the ArthritisPower real-world registry.

Methods:

ArthritisPower pts with RA were invited to join a digital RWE study with 14-day lead-in and 12-week main study period. In the lead-in, pts were required to electronically complete: a) two daily single-item Pain and Fatigue numeric rating scales and b) longer weekly sets of ePROs. Successful completers of the lead-in were mailed a smartwatch (Fitbit Versa) and study materials. The smartwatch collected activity, heart rate, and sleep duration/quality biosensor data; a study-specific customization of the ArthritisPower mobile application collected ePROs. The main study period included automated and manual reminders/prompts about completing ePROs, wearing the smartwatch and regularly syncing it. Study coordinators monitored pt data and contacted pts via email, text and/or phone to resolve adherence issues during the conduct of the study based on pre-determined rules triggering pt contact. Rules were based chiefly on consecutive spans of missing data. Pts were considered adherent in giving complete data for each week if providing (1) daily ePROs for ≥5 of 7 days/week, (2) weekly ePROs and (3) ≥80% of synced activity data for ≥5 of 7 days/week. Composite adherence for the first month of the main study period required meeting >70% weekly adherence parameters during the first 30 days, ie completing daily ePROs for ≥5 of 7 days/week, weekly ePROs ≥3 of 4 weeks and ≥80% of synced activity data for ≥5 of 7 days/week.

Results:

As of December 2019, 170 ArthritisPower members enrolled and completed at least 30 days of the main study period; 92.9% female with mean (SD) age 52.5 (10.7) and 10.5 (10.4) years since diagnosis. The overall conversion rate from initial interest to successful completion of the lead-in period was 49.0%. Pts who advanced to the main study were significantly more likely than those who did not to be currently employed (52.9% vs. 41.8%, p=0.038) and be on biologic DMARD monotherapy (64.7% vs. 47.5%, p=0.001). Overall, daily ePRO data had the lowest adherence with 70.0% of pts providing >70% of the requested data consistently across the first 30 days of the main study period (Figure 1). Composite adherence was met by 66.5% of pts. The most common time of day to provide ePRO data was morning, in the hours around scheduled app and email notifications at 10 a.m. in pt’s local time zone. Activity data had the highest adherence and persistence, with 92.9% of pts providing 80% or more of activity data for each 24-hour period in the first 30 days (Figures 1 & 2). Observed weekly adherence did not decline over time. Of 5100 possible person days in the study at day 30, we observed 643 days (91.0% of actual to maximum possible total patient days) where activity data was provided for at least 80% of the 24-hour period.

Conclusion:

RWE studies involving passive data collection in RA require pt-centric implementation and design to minimize pt burden, promote longitudinal engagement and maximize adherence. Passive data capture via activity trackers such as smartwatches, along with regular contact such as automated reminders, may facilitate greater pt adherence in providing longitudinal data for clinical trials.

References:

[1]Nowell WB, et al. JMIR Res Protoc. 2019;8(9):e14665.

Disclosure of Interests:

W. Benjamin Nowell: None declared, Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Fenglong Xie: None declared, Hong Zhao: None declared, David Curtis: None declared, Kelly Gavigan: None declared, Shilpa Venkatachalam: None declared, Laura Stradford: None declared, Jessica Boles: None declared, Justin Owensby: None declared, Cassie Clinton: None declared, Ilya Lipkovich Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Amy Calvin Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Virginia S. Haynes Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company

FRI0018 USING SELF-REPORTED OUTCOMES TO DETECT NEW-ONSET FLARE IN A REAL-WORLD STUDY OF PARTICIPANTS WITH RHEUMATOID ARTHRITIS - INTERIM RESULTS FROM THE DIGITAL TRACKING OF ARTHRITIS LONGITUDINALLY (DIGITAL) STUDY

Background:

Patients with rheumatoid arthritis (RA) experience fluctuating symptoms, increased pain, decreased function and variable quality of life; such changes often occur between visits to clinicians. Digital Tracking of Arthritis Longitudinally (DIGITAL) study2is evaluating the use of electronically captured patient-reported outcomes (ePRO) and passive data collection from a Fitbit device to identify disease worsening in a real-world study of participants (pts) with RA.

Objectives:

Evaluate agreement between self-reported new-onset flare and ePROs in an interim analysis from DIGITAL using a classification model.

Methods:

Members of the ArthritisPower registry with RA were invited to participate in DIGITAL. Pts who successfully completed a two-week Lead-in period entered the Main Study in which they wore a smartwatch and provided daily (pain and fatigue numeric rating scales (NRS)) and weekly ePROs, including the OMERACT RA Flare Questionnaire (FLARE) and PROMIS measures. This interim analysis is of ePRO data from pts who completed at least 30 days of the Main Study. A “Yes” response to the FLARE item, “Are you having a flare now?” identified flare. For modeling association between new-onset flare and ePRO, the dataset was split into training (the first 30 days of the Main Study) and test data (Day 31 and following). Within each dataset, repeated binary outcomes (Flare/No Flare) per pt were defined each week. To focus on new-onset flare, within each dataset, outcomes for patient weeks for which flare was present in the previous week were excluded.

Candidate variables for the model included baseline and current FLARE score (0-50 scale) and each of its 5 items, daily pain, daily fatigue, and several PROMIS weekly instruments and their lagged values (last week or last 6 days for daily). ‘Baseline’ was calculated in non-flare weeks. Training data was used for logistic regression model selection combining clinical expertise with backward elimination. Performance of the final model was evaluated using test data.

Results:

The training data was composed of outcomes from 128 pts who reported 388 weekly flare assessments as no flare or onset flare over 2800 days during the first month of the Main Study. Of pts in the training dataset, 92.2% were female, 87.5% white, with mean age (SD) 52.7 (11.0) and years since RA diagnosis 10.4 (10.3); 62.5% were on a biologic. Among those in the training dataset, 58 flare outcomes occurred in 50 (39.1%) unique pts.

The test data comprised outcomes from 123 pts who reported 442 weekly flare assessments as no flare or onset flare over 3366 days in which 64 flare outcomes occurred, and primarily included continued observations from pts who contributed to the training dataset.

The best-performing model to classify flare in training data included the current and baseline FLARE instrument activity question (i.e. “Considering how active your rheumatoid arthritis has been, how much difficulty have you had when taking part in activities such as work, family life, social events that are typical for you during the last week”), current daily pain, and baseline daily pain average and standard deviation. In test data, this model had an area under the receiver operator curve of 0.81 (Figure). At a cut point requiring specificity to be ≥0.80, sensitivity to detect flare was 0.62 and overall accuracy was 0.78.

Conclusion:

New-onset flare is common among RA patients, and the FLARE instrument and daily pain scores appear effective to classify it. Evaluation of passive data as a proxy for self-reported new-onset flare is ongoing.

References:

[1]Bartlett SJ, et al. JRheumatol, 2017;44:1536-43.

[2]Nowell WB, et al. JMIR Res Protoc, 2019;8:e14665.

Disclosure of Interests:

Virginia S. Haynes Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Fenglong Xie: None declared, Ilya Lipkovich Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Hong Zhao: None declared, Carol L. Kannowski Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Jiat-Ling Poon Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Kelly Gavigan: None declared, David Curtis: None declared, Sandra K. Nolot Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, W. Benjamin Nowell: None declared

Aligning Estimators With Estimands in Clinical Trials: Putting the ICH E9(R1) Guidelines Into Practice

The draft ICH E9(R1) addendum stipulates that an estimator should align with its associated estimand and yield an estimate that facilitates reliable interpretations. The addendum further stipulates that assumptions should be justifiable and plausible, and that the extent of assumptions is an important consideration for whether an estimate will be robust because assumptions are often unverifiable. The draft addendum specifies 5 strategies for dealing with intercurrent events. The intent of this paper is to provide conceptual considerations and technical details for various estimators that align with these strategies. We include focus on how the nature and extent of assumptions influences the potential robustness of the various estimators. The content reflects the knowledge, experience, and opinions of the Drug Information Association's Scientific Working Group on Missing Data. This group includes experienced statisticians from across industry and academia, primarily in the US and European Union.

Predictors of risk for relapse in patients with schizophrenia or schizoaffective disorder during olanzapine drug therapy

PURPOSE

To evaluate the relationship of dose decrease, symptom worsening, and baseline covariates on subsequent relapse during olanzapine treatment in patients with schizophrenia or schizoaffective disorder.

METHODS

In two 28-week, randomized, double-blind clinical trials, a Cox proportional hazards model was used to determine potential correlates of relapse (defined as > or =20% worsening on PANSS total and CGI-Severity 3) among patients (N=271) who responded to 8 weeks of olanzapine treatment (10-20mg/day). Variables examined included: demographics, illness characteristics, baseline symptoms, symptom change, dose, adverse events, and functioning.

RESULTS

Patients with a lower last dose relative to the preceding visit interval were 4 times more likely to relapse during that visit interval than other patients (p<.001). A similar finding was observed for a decrease in interval modal dose, although this variable was more predictive of relapse in the visit interval immediately following dose decrease (p=.027). In a subgroup analysis by gender, there was a significantly greater incidence of relapse in men with a dose decrease, whereas a dose decrease in women did not correlate with relapse. Relapse was also correlated with the emergence or worsening of a psychiatric adverse event during the same (p<.001) and preceding (p=.007) visit intervals, and with increased rating scale measures of psychopathology. The occurrence of a non-psychiatric adverse event was not associated with relapse.

CONCLUSION

Dose decrease is a significant predictor of relapse in male but not female patients. Psychiatric adverse events also predicted relapse. Patients should be periodically reassessed to determine the need for maintenance treatment with appropriate dose.