Lysbeth Floden

Background: Rejoyn (CT-152) is a prescription digital therapeutic (DTx) adjunct to antidepressive medication authorized for patients with major depressive disorder. To better understand the patient benefit of DTx and other treatment modalities, current regulatory standards support the use of modern psychometric methods to interpret the clinical meaningfulness of treatment effects for patients. In the primary analysis of Rejoyn from the pivotal phase 3 Mirai trial (NCT04770285), Rejoyn showed a broad risk-to-benefit profile as demonstrated on multiple clinician- and patient-rated scales, including the primary efficacy outcome measure, the Montgomery-Åsberg Depression Rating Scale (MADRS). These findings supported US Food and Drug Administration authorization of Rejoyn as a prescription DTx. However, the clinical relevance of these changes in the MADRS score is not immediately interpretable in clinical practice. Here, we present the results from several post hoc analyses of the Mirai trial data to support the interpretation of clinically meaningful treatment differences on clinician- and patient-reported change in depressive symptoms. Objective: This study had two main objectives: (1) establish threshold parameters that allow for clinically meaningful interpretation of the Mirai results in clinical practice, based on the clinical trial end points of change from baseline in depressive symptoms, and (2) apply this threshold in a responder and sensitivity analysis in the intent-to-treat (ITT) population (which is more frequently reported in pharmacological trials) to further support the interpretation of change in unblinded analysis of the Mirai results. Methods: For the Mirai meaningful within-patient change (MWPC) analysis, anchor-based methods were used to define an MWPC threshold by exploring the associations between the MADRS and the clinician-rated Clinical Global Impression-Severity Scale (CGI-S) and the patient-reported Patient Health Questionnaire 9-Item Scale (PHQ-9). Additional post hoc efficacy analyses (including that of responders) are reported for the ITT population. Results: Using the MWPC thresholds of 8 and 10 points (derived with the CGI-S and PHQ-9 as anchor measures, respectively), the distribution of MADRS responders favored the Rejoyn group over the sham group, and the Rejoyn group had 24%‐47% higher odds of meaningful improvement on the MADRS. Post hoc ITT analyses also favored the Rejoyn group over the sham group for response rates and PHQ-9 and CGI-S score change from baseline. Conclusions: Results are consistent with the primary findings of the Mirai trial, supporting the efficacy of Rejoyn as an adjunctive treatment to antidepressive medication monotherapy for adults with major depressive disorder. The MWPC analyses offered a measure of meaningful change on the MADRS, providing a framework of clinical meaningfulness for the Mirai trial findings.

Analytical validation is a crucial step in the evaluation of algorithms that process data from sensor-based digital health technologies (sDHTs). Analytical validation of novel digital measures can be complicated when reference measures with directly comparable units are not available. To address this, we conducted a simulation study. Data was simulated assuming a latent physical ability trait, indirectly accessed through an sDHT-derived target measure collecting step count data, and the items of a clinical outcome assessment (COA) measuring self-reported physical activity. We quantified the ability of two methods to assess the latent relationship between reference and target measures: the Pearson Correlation Coefficient (PCC) and factor correlations from a two-factor confirmatory factor analysis (CFA) model. Additionally, three multiple linear regression models were used to evaluate if multiple COA reference measures can more completely represent a target measure of interest. Our findings show that PCC was more stable, easier to compute, and relatively robust with respect to violations of parametric assumptions than CFA, particularly with small sample sizes. However, CFA was less biased than PCC in all scenarios investigated. We demonstrate that using both PCC and CFA generates more confidence in the results of a target and reference measure comparison. Finally, regression results suggest that incorporating multiple reference measures with more frequent collection time points can provide a more complete presentation of the sDHT’s analytical validity. Novel digital measures are being developed at an accelerating pace and promise to revolutionize patient care and medical product development. Our findings provide investigators with crucial information for choosing appropriate methods to perform rigorous analytical validation of these novel measures, including an open-access simulation toolkit.

Background: Sensor-based digital health technologies (sDHTs) are increasingly used to support scientific and clinical decision-making. The digital measures (DMs) they generate offer significant potential to accelerate the drug development timeline, decrease clinical trial costs, and improve access to care. However, choosing an appropriate statistical methodology when conducting analytical validation (AV) of a DM is complicated, particularly for novel DMs, for which appropriate, established reference measures (RMs) may not exist. More understanding of, and a standardized approach to, AV in these scenarios is needed. Objective: In a prior simulation study, 3 statistical methods were tested for their ability to estimate a simulated relationship between a sDHT-derived DM and several clinical outcome assessment (COA) RMs. The aim of this work was to assess the feasibility of implementation of these methods in real data and to examine the impact of AV study design factors on the relationships estimated. Methods: Four real-world datasets, captured using sDHTs, were used to prepare hypothetical AV studies representing a range of scenarios with respect to 3 key study design properties: temporal coherence, construct coherence, and data completeness. The datasets analyzed were as follows: Urban Poor (comparing nighttime awakenings to measures of psychological well-being), STAGES (comparing daily step count to psychological and fatigue measures), mPower (comparing daily smartphone screen taps to measures of function in Parkinson’s disease), and Brighten (comparing smartphone communication activity to measures of psychological well-being). For each hypothetical AV study, 3 statistical methods were leveraged: the Pearson correlation coefficient (PCC) between DM and RM, simple linear regression (SLR) between DM and RM, multiple linear regression (MLR) between DMs and combinations of RMs, and 2-factor, correlated-factor confirmatory factor analysis (CFA) models. Performance measures were the PCC magnitudes (for PCC), R ² and adjusted R ² statistics (for SLR and MLR, respectively), and factor correlations (for CFA). Results: Most of the CFA models exhibited an acceptable fit according to the majority of the fit statistics employed, and each model was able to estimate a factor correlation. For each model, these correlations were greater than or equal to the corresponding PCC in magnitude. Correlations were the strongest in the hypothetical studies with strong temporal and construct coherence. Conclusions: The performance of the selected statistical methods shown in this work supports their feasibility when implemented in real-world data. Our findings, in particular, support the use of CFA to assess the relationship between a novel DM and a COA RM. The observed impact of AV study design factors on the relationships estimated allowed the authors to determine practical recommendations for study design in AV of novel DMs. By using a standardized methodology for evaluating novel DMs, sDHT developers, biostatisticians, and clinical researchers can navigate the complex validation landscape more easily, with more certainty, and with more tools at their disposal.

BACKGROUND Sensor-based digital health technologies (sDHTs) are increasingly used to support scientific and clinical decision-making. The digital measures (DMs) they generate offer significant potential to accelerate the drug development timeline, decrease clinical trial costs, and improve access to care. However, choosing appropriate statistical methodology when conducting analytical validation (AV) of a DM is complicated, particularly for novel DMs, for which appropriate, established reference measures may not exist. More understanding of, and a standardization of approach to, AV in these scenarios is needed. OBJECTIVE In a prior simulation study, three statistical methods were tested for their ability to estimate a simulated relationship between a sDHT-derived DM and several clinical outcome assessment (COA) reference measures. The aim of this work was to assess the feasibility of these methods’ implementation in real data, and to examine the impact of AV study design factors on the relationships estimated. METHODS Four real-world datasets, captured using sDHTs, were used to prepare hypothetical AV studies that represented a range of scenarios with respect to three key study design properties: temporal coherence, construct coherence and data completeness. For each hypothetical study, two-factor, correlated-factors confirmatory factor analysis (CFA) models, and a combination of simple and multiple linear regression models, were built using the DM and reference measure data. The factor correlation, R2 and adjusted R2 statistics were calculated, in addition to the Pearson Correlation Coefficients (PCCs). RESULTS The majority of CFA models exhibited an acceptable fit according to the majority of the fit statistics employed, and each model was able to estimate a factor correlation. For each model, these correlations were greater than or equal to the corresponding PCC in magnitude. Correlations were strongest in the hypothetical studies with strong temporal and construct coherence. CONCLUSIONS The performance of the measures shown in this work supports the feasibility of the selected statistical methods when implemented in real-world data. Our findings in particular support the use of CFA to assess the relationship between a novel digital measure and a COA reference measure. The observed impact of AV study design factors on the relationships estimated allowed the authors to determine practical recommendations to aid in appropriate study design for AV of novel digital measures. By utilizing a standardized methodology for evaluating novel digital measures, sDHT developers, biostatisticians and clinical researchers will be able to navigate the complex validation landscape more easily, with more certainty, and with more tools at their disposal, expediting the pathway to validation and regulatory review.

Background: Myelofibrosis (MF)‐associated constitutional symptoms can severely impact health‐related quality of life. Clinical trials in MF traditionally measure symptom response to treatment as a landmark endpoint of total symptom score (TSS) reduction ≥50% from baseline. However, this dichotomous assessment provides a limited view of clinically relevant symptomatic changes. Herein we evaluated longitudinal change from baseline in TSS over the continuous 24‐week period and individual symptom scores to obtain a more comprehensive understanding of symptom benefits experienced by patients with MF receiving therapy.Methods: Longitudinal symptom change was evaluated using mixed‐effect model repeated measure (MMRM) methodology with individual item‐level analyses to complement the interpretation of the landmark symptom results in the completed phase III SIMPLIFY studies of momelotinib in MF. MMRM compared mean change in TSS from baseline with Week 24 using data from all patient visits. Generalized estimating equations were used to estimate item‐level odds ratios using multiple predictive imputations for missing data.Results: Momelotinib and ruxolitinib groups reported similar overall symptom improvements, with a TSS difference of <1.5 points between groups for each post‐baseline visit in SIMPLIFY‐1. In SIMPLIFY‐2, the improvement in TSS observed in momelotinib‐treated patients was consistent with that observed in SIMPLIFY‐1, whereas progressive TSS deterioration was observed with control. Item‐level scores were heterogeneous in both studies. A similar and greater proportion of momelotinib‐treated patients were categorized as “improved” or “stable” compared with control in SIMPLIFY‐1 and SIMPLIFY‐2, respectively. Odds ratios for between‐group comparison ranged from 0.75 to 1.21 in SIMPLIFY‐1, demonstrating similarity in likelihood of symptom improvement. In SIMPLIFY‐2, the likelihood of symptom improvement in each item was higher in the momelotinib arm.Conclusions: These findings suggest that momelotinib provides clinically relevant symptom benefits in the JAK inhibitor‐naïve and JAK inhibitor‐exposed settings.

There are several methodological challenges when conducting randomised controlled trials in palliative care. These include worsening function and high mortality, leading to treatment discontinuation, some of which will be unrelated to the intervention being evaluated.Recently, a new framework for handling postrandomisation events, such as attrition, has been released. This framework aims to align trial objectives, design, conduct and analysis by clarifying what and how to estimate treatment effects in the presence of data affected by postrandomisation events.The purpose of this paper is to introduce palliative care researchers to this framework and how it can guide trial design, and efficacy and safety analysis in a palliative care context where individual withdrawal from the trial is common.In this paper, we describe the estimand framework and the background for it. We also consider postrandomisation events that are frequently encountered in palliative care trials and how these might affect objectives of interest. We then construct efficacy and safety estimands for a trial in palliative care. Better trial design and alignment of objectives with analysis can improve our understanding of what treatments do and do not work in palliative care.

Objective The objective of this study was to determine which symptoms measured by the Patient Health Questionnaire (PHQ-9) and Montgomery-Asberg Depression Rating Scale (MADRS) improve in those treated with esketamine nasal spray in combination with oral antidepressant (AD) compared with those treated with placebo plus AD for adult patients with treatment-resistant depression (TRD). These results complement the interpretation of PHQ-9 and MADRS total scores. Methods The TRANSFORM 2 study evaluated the efficacy and safety of esketamine nasal spray in combination with AD. This post-hoc analysis used PHQ-9 and MADRS data to evaluate symptom changes. The total scores change and proportions of individual item change scores on the PHQ-9 and MADRS were evaluated at days 15 and 28; analysis of variance was used to test differences on total scores. Generalized estimation equations of logistic regression models were used to estimate the likelihood of improvement on instrument items. Results The mean total score reduction of the PHQ-9, indicating improvement, was greater in the esketamine plus AD arm compared with placebo plus AD at day 15 (− 1.8; p = 0.045) and day 28 (− 2.8; p = 0.006). Proportions of those who improved (≥ 1 point on a 4-point scale and ≥ 2 points on a 7-point scale for the PHQ-9 and MADRS, respectively) was greater in the esketamine plus AD group compared with the placebo plus AD group across all items. The odds of improving for those in the esketamine plus AD group compared with the placebo plus AD group were over two times greater on the PHQ-9 items: “Little interest/pleasure in things” (OR 2.252, 95% CI 1.165–4.355); “Feeling down, depressed, or hopeless” (OR 2.767, 95% CI 1.400–5.470); and “Feeling tired or having little energy” (OR 2.171, 95% CI 1.153–4.087). The mean reduction in total scores on the MADRS, indicating improvement, was numerically greater at day 15 (− 2.0; p = 0.189) and statistically significantly greater at day 28 (− 4.4; p = 0.017) in the esketamine plus AD arm compared with placebo plus AD. The odds of improving for those in the esketamine plus AD group compared with the placebo plus AD group were over two times greater on the MADRS items measuring “Apparent sadness” (OR 2.007, 95% CI 1.096–3.674); and “Inability to feel” (OR 2.099, 95% CI 1.180–3.735). Conclusion Improvement in mean total scores in those treated with esketamine plus AD compared with placebo plus AD are important results to confirm efficacy. The odds of improving in those treated with esketamine plus AD was at least two times greater than with placebo plus AD on three patient- and two clinician-reported individual symptoms of TRD. These findings provide patient-relevant quantification of the esketamine plus AD treatment benefit, adding understanding as to which symptoms are most improved with treatment. Trial Registration ClinicalTrials.gov identifier: NCT02418585; first posted 16 April 2015.