Treatment Effects In Clinical Trials Research Articles

Confidence distributions for treatment effects in clinical trials: Posteriors without priors.

An attractive feature of using a Bayesian analysis for a clinical trial is that knowledge and uncertainty about the treatment effect is summarized in a posterior probability distribution. Researchers often find probability statements about treatment effects highly intuitive and the fact that this is not accommodated in frequentist inference is a disadvantage. At the same time, the requirement to specify a prior distribution in order to obtain a posterior distribution is sometimes an artificial process that may introduce subjectivity or complexity into the analysis. This paper considers a compromise involving confidence distributions, which are probability distributions that summarize uncertainty about the treatment effect without the need for a prior distribution and in a way that is fully compatible with frequentist inference. The concept of a confidence distribution provides a posterior-like probability distribution that is distinct from, but exists in tandem with, the relative frequency interpretation of probability used in frequentist inference. Although they have been discussed for decades, confidence distributions are not well known among clinical trial statisticians and the goal of this paper is to discuss their use in analyzing treatment effects from randomized trials. As well as providing an introduction to confidence distributions, some illustrative examples relevant to clinical trials are presented, along with various case studies based on real clinical trials. It is recommended that trial statisticians consider presenting confidence distributions for treatment effects when reporting analyses of clinical trials.

Evaluating a shrinkage estimator for the treatment effect in clinical trials.

The main objective of most clinical trials is to estimate the effect of some treatment compared to a control condition. We define the signal-to-noise ratio (SNR) as the ratio of the true treatment effect to the SE of its estimate. In a previous publication in this journal, we estimated the distribution of the SNR among the clinical trials in the Cochrane Database of Systematic Reviews (CDSR). We found that the SNR is often low, which implies that the power against the true effect is also low in many trials. Here we use the fact that the CDSR is a collection of meta-analyses to quantitatively assess the consequences. Among trials that have reached statistical significance we find considerable overoptimism of the usual unbiased estimator and under-coverage of the associated confidence interval. Previously, we have proposed a novel shrinkage estimator to address this "winner's curse." We compare the performance of our shrinkage estimator to the usual unbiased estimator in terms of the root mean squared error, the coverage and the bias of the magnitude. We find superior performance of the shrinkage estimator both conditionally and unconditionally on statistical significance.

Routes to standardisation for tau protein measurement in CSF

AbstractBackgroundCSF biomarkers are the target of several commercially available in vitro diagnostic (IVD) kits allowing an early diagnosis of Alzheimer’s disease (AD) and eventually the monitoring of treatment effects in clinical trials. Tau hyper‐phosphorylation is one of the main hallmarks of AD and its measurement provides higher accuracy to stratify patients at an early‐stage.Due to the rapid advancements in proteomics, standardisation of measurements can be achieved through the development of reference measurement procedures (RMPs) and certified reference materials (CRMs).MethodSI‐traceable protein and peptide primary calibrators were developed for t‐tau and p‐tau: mass fraction was determined by amino acid analysis and purity was assessed by LC‐MS at high resolution. CSF pools at different tau concentration were used for the development and validation of LC‐IDMS candidate RMP and their commutability was assessed.ResultAn LC‐IDMS candidate reference method was develop and validated in CSF for t‐tau using a SI‐traceable candidate protein primary calibrator. Three different CSF pools (low, medium, high) were successfully quantified with a relative expanded uncertainty below 10%. The commutability of 13 matrix‐based CRMs was assessed involving 8 immunoassays from 5 IVD providers by measuring 40 single CSF donations.According to the AT(N) classification, high levels of p‐tau(181) are highly correlated to tau pathology and commercially available immunoassays are able to target this specific epitope. We conducted a feasibility study on the development of a candidate reference method for p‐tau(181) in CSF relying on a SI‐traceable peptide primary calibrator and an antibody‐free sample preparation workflow.New assays targeting p‐tau(217) in CSF and plasma were also developed, making it necessary the organisation of standardisation initiatives. We sourced and characterised multiple phosphopeptide primary calibrators as well as a GSK3beta‐phosphorylated protein primary calibrator for the development of an LC‐MS RMP including an immunoprecipitation step in the sample preparation workflow.Conclusiontau is considered as a priority for the IFCC WG on NDD and the development of SI traceable RMPs and CRMs is essential to underpin a worldwide standardisation of results. This will help IVD providers to address the traceability requirements of the IVDR thus facilitating regulatory approvals of the kits.

Patient-Reported Outcome Measures for Severe Recurrent Bilateral Nasal Polyps: Psychometric Evaluation and Content Validity.

To date, no patient-reported outcome measures have been specifically developed to assess pharmacological treatment effect in participants with severe chronic rhinosinusitis (CRS) with recurrent bilateral nasal polyps (NP). These studies aimed to assess (1) the psychometric properties and (2) content validity of Visual Analogue Scales (VAS) assessing NP symptom severity. (1) Retrospective psychometric validation study using clinical trial data and (2) cross-sectional qualitative patient interview study. (1) Multicentre trial; (2) real-world. (1) Psychometric validation was performed using data from a randomized, double-blind, placebo-controlled, Phase II study (NCT01362244) investigating the effect of mepolizumab in 105 participants with severe, recurrent bilateral NP currently needing polypectomy surgery. (2) Content validity was explored through cognitive debriefing interviews in 27 adults with severe CRS with recurrent bilateral NP who had received NP surgery in the past 10 years (NCT03221192). (1) Acceptable reliability, validity, and responsiveness were shown for individual VAS items, although the loss of smell VAS item performed poorly in several analyses, suggesting further evaluation of this item is needed. (2) All individual VAS items were well understood, considered relevant and were consistently interpreted by most participants, providing evidence for their content validity. These findings support the use of symptom VAS measures to evaluate disease experience and treatment effect in clinical trials of participants with severe CRS with recurrent bilateral NP.

Adherence, per-protocol effects, and the estimands framework.

In the statistical literature, treatment effects in clinical trials are frequently described as either ITT or per-protocol effects. The estimand given for the per-protocol effect is the effect in adherers, where adherers are typically defined as adhering to the intervention as specified in the trial protocol. This dichotomy of treatment effects is unhelpful when there are in reality multiple treatment effects that can be of clinical interest and relevance. The terms "per-protocol" and "adherence" are confusing to non-statisticians. Protocols always allow for discontinuation of randomized treatment so participants discontinuing have actually followed the protocol. When rescue or additional medication is available, the effect in adherers could mean the effect regardless of use of these medications or the effect in a counterfactual world where the participant did not take the medication. Adherence can mean continuing to be prescribed a treatment or some arbitrary level of compliance with a medication that has been prescribed. The ICH E9 (R1) estimands framework provides an improved alternative for the description of treatment effects in clinical trials. Identification of important intercurrent events and the strategy used to handle these events is key to determining the treatment effect. When designing a trial, estimands should be properly defined according to this framework. It is time the statistical literature abandoned describing treatment effects as the effect in adherers or the per-protocol effect.

Treatment Detection and Movement Disorder Society-Unified Parkinson's Disease Rating Scale, Part III Estimation Using Finger Tapping Tasks.

The validation of objective and easy-to-implement biomarkers that can monitor the effects of fast-acting drugs among Parkinson's disease (PD) patients would benefit antiparkinsonian drug development. We developed composite biomarkers to detect levodopa/carbidopa effects and to estimate PD symptom severity. For this development, we trained machine learning algorithms to select the optimal combination of finger tapping task features to predict treatment effects and disease severity. Data were collected during a placebo-controlled, crossover study with 20 PD patients. The alternate index and middle finger tapping (IMFT), alternative index finger tapping (IFT), and thumb-index finger tapping (TIFT) tasks and the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III were performed during treatment. We trained classification algorithms to select features consisting of the MDS-UPDRS III item scores; the individual IMFT, IFT, and TIFT; and all three tapping tasks collectively to classify treatment effects. Furthermore, we trained regression algorithms to estimate the MDS-UPDRS III total score using the tapping task features individually and collectively. The IFT composite biomarker had the best classification performance (83.50% accuracy, 93.95% precision) and outperformed the MDS-UPDRS III composite biomarker (75.75% accuracy, 73.93% precision). It also achieved the best performance when the MDS-UPDRS III total score was estimated (mean absolute error: 7.87, Pearson's correlation: 0.69). We demonstrated that the IFT composite biomarker outperformed the combined tapping tasks and the MDS-UPDRS III composite biomarkers in detecting treatment effects. This provides evidence for adopting the IFT composite biomarker for detecting antiparkinsonian treatment effect in clinical trials. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Application Value of Serum Neurofilament Light Protein for Disease Staging in Huntington's Disease.

Neurofilament light protein (NfL) has been proven to be a sensitive biomarker for Huntington's disease (HD). However, these studies did not include HD patients at advanced stages or with larger CAG repeats (>50), leading to a knowledge gap of the characteristics of NfL. Serum NfL (sNfL) levels were quantified using an ultrasensitive immunoassay. Participants were assessed by clinical scales and 7.0 T magnetic resonance imaging. Longitudinal samples and clinical data were obtained. Baseline samples were available from 110 controls, 90 premanifest HD (pre-HD) and 137 HD individuals. We found levels of sNfL significantly increased in HD compared to pre-HD and controls (both P < 0.0001). The increase rates of sNfL were differed by CAG repeat lengths. However, there was no difference in sNfL levels in manifest HD from early to late stages. In addition, sNfL levels were associated with cognitive measures in pre-HD and manifest HD group, respectively. The increased levels of sNfL were also closely related to microstructural changes in white matter. In the longitudinal analysis, baseline sNfL did not correlate with subsequent clinical function decline. Random forest analysis revealed that sNfL had good power for predicting disease onset. Although sNfL levels are independent of disease stages in manifest HD, it is still an optimal indicator for predicting disease onset and has potential use as a surrogate biomarker of treatment effect in clinical trials. © 2023 International Parkinson and Movement Disorder Society.

Artificial intelligence approach for the analysis of placebo-controlled clinical trials in major depressive disorders accounting for individual propensity to respond to placebo

Treatment effect in clinical trials for major depressive disorders (RCT) can be viewed as the resultant of treatment specific and non-specific effects. Baseline individual propensity to respond non-specifically to any treatment or intervention can be considered as a major non-specific confounding effect. The greater is the baseline propensity, the lower will be the chance to detect any treatment-specific effect. The statistical methodologies currently applied for analyzing RCTs doesn’t account for potential unbalance in the allocation of subjects to treatment arms due to heterogenous distributions of propensity. Hence, the groups to be compared may be imbalanced, and thus incomparable. Propensity weighting methodology was used to reduce baseline imbalances between arms. A randomized, double-blind, placebo controlled, three arms, parallel group, 8-week, fixed-dose study to evaluate efficacy of paroxetine CR 12.5 and 25 mg/day is presented as a cases study. An artificial intelligence model was developed to predict placebo response at week 8 in subjects assigned to placebo arm using changes from screening to baseline of individual Hamilton Depression Rating Scale items. This model was used to predict the probability to respond to placebo in each subject. The inverse of the probability was used as weight in the mixed-effects model applied to assess treatment effect. The analysis with and without propensity weight indicated that the weighted analysis provided an estimate of treatment effect and effect-size about twice larger than the non-weighted analysis. Propensity weighting provides an unbiased strategy to account for heterogeneous and uncontrolled placebo effect making patients’ data comparable across treatment arms.

Estimand in benefit-risk assessment

ABSTRACT ICH E9(R1) introduces the estimand framework to strengthen dialogues between sponsors and regulators during drug development. A well-structured benefit-risk assessment (BRA) framework also intends to facilitate communication among stakeholders. However, the estimand in ICH E9(R1) is written mainly from the perspective of a single measure of treatment effect in clinical trials. There is lack of systematic discussion on estimand in the context of BRA. This paper initiates the BRA discussion under the estimand framework. By identifying two types of BRA approaches, we summarize and discuss completed clinical trials, using the estimand language for BRA. Benefits and challenges of using estimand for BRA are also discussed.

The empirical estimate of the survival and variance using a weighted composite endpoint

BackgroundComposite endpoints for estimating treatment efficacy are routinely used in several therapeutic areas and have become complex in the number and types of component outcomes included. It is assumed that its components are of similar asperity and chronology between both treatment arms as well as uniform in magnitude of the treatment effect. However, these assumptions are rarely satisfied. Understanding this heterogeneity is important in developing a meaningful assessment of the treatment effect.MethodsWe developed the Weighted Composite Endpoint (WCE) method which uses weights derived from stakeholder values for each event type in the composite endpoint. The derivation for the product limit estimator and the variance of the estimate are presented. The method was then tested using data simulated from parameters based on a large cardiovascular trial. Variances from the estimated and traditional approach are compared through increasing sample size.ResultsThe WCE method used all of the events through follow-up and generated a multiple recurrent event survival. The treatment effect was measured as the difference in mean survivals between two treatment arms and corresponding 95% confidence interval, providing a less conservative estimate of survival and variance, giving a higher survival with a narrower confidence interval compared to the traditional time-to-first-event analysis.ConclusionsThe WCE method embraces the clinical texture of events types by incorporating stakeholder values as well as all events during follow-up. While the effective number of events is lower in the WCE analysis, the reduction in variance enhances the ability to detect a treatment effect in clinical trials.

Can We Use Blood Biomarkers as Entry Criteria and for Monitoring Drug Treatment Effects in Clinical Trials? A Report from the EU/US CTAD Task Force.

In randomized clinical trials (RCTs) for Alzheimer's Disease (AD), cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers are currently used for the detection and monitoring of AD pathological features. The use of less resource-intensive plasma biomarkers could decrease the burden to study volunteers and limit costs and time for study enrollment. Blood-based markers (BBMs) could thus play an important role in improving the design and the conduct of RCTs on AD. It remains to be determined if the data available on BBMs are strong enough to replace CSF and PET biomarkers as entry criteria and monitoring tools in RCTs.

Plasma biomarkers predict amyloid pathology in cognitively normal monozygotic twins after 10 years.

Blood-based biomarkers could prove useful to predict Alzheimer's disease core pathologies in advance of clinical symptoms. Implementation of such biomarkers requires a solid understanding of their long-term dynamics and the contribution of confounding to their association with Alzheimer's disease pathology. Here we assess the value of plasma amyloid-β1-42/1-40, phosphorylated-tau181 and glial fibrillary acidic protein to detect early Alzheimer's disease pathology, accounting for confounding by genetic and early environmental factors. Participants were 200 monozygotic twins, aged ≥60 years with normal cognition from the european medical information framework for Alzheimer's disease study. All twins had amyloid-β status and plasma samples available at study enrolment. For 80 twins, additional plasma samples were available that had been collected approximately 10 years prior to amyloid-β status assessment. Single-molecule array assays were applied to measure amyloid-β1-42/1-40, phosphorylated-tau181 and glial fibrillary acidic protein. Predictive value of and longitudinal change in these biomarkers were assessed using receiver operating characteristic curve analysis and linear mixed models. Amyloid pathology could be predicted using blood-based biomarkers obtained at the time of amyloid status assessment (amyloid-β1-42/1-40: area under the curve = 0.65, P = 0.01; phosphorylated-tau181: area under the curve = 0.84, P < 0.001; glial fibrillary acidic protein: area under the curve = 0.74, P < 0.001), as well as using those obtained 10 years prior to amyloid status assessment (amyloid-β1-42/1-40: area under the curve = 0.69, P = 0.03; phosphorylated-tau181: area under the curve = 0.92, P < 0.001; glial fibrillary acidic protein: area under the curve = 0.84, P < 0.001). Longitudinally, amyloid-β1-42/1-40 levels decreased [β (SE) = -0.12 (0.01), P < 0.001] and phosphorylated-tau181 levels increased [β (SE) = 0.02 (0.01), P = 0.004]. Amyloid-β-positive individuals showed a steeper increase in phosphorylated-tau181 compared with amyloid-β-negative individuals [β (SE) = 0.06 (0.02), P = 0.004]. Also amyloid-β-positive individuals tended to show a steeper increase in glial fibrillary acidic protein [β (SE) = 0.04 (0.02), P = 0.07]. Within monozygotic twin pairs, those with higher plasma phosphorylated-tau181 and lower amyloid-β1-42/1-40 levels were more likely to be amyloid-β positive [β (SE) = 0.95 (0.26), P < 0.001; β (SE) = -0.28 (0.14), P < 0.05] indicating minimal contribution of confounding by genetic and early environmental factors. Our data support the use of amyloid-β1-42/1-40, phosphorylated-tau181 and glial fibrillary acidic protein as screening tools for Alzheimer's disease pathology in the normal aging population, which is of importance for enrolment of high-risk subjects in secondary, or even primary, prevention trials. Furthermore, these markers show potential as low-invasive monitoring tool of disease progression and possibly treatment effects in clinical trials.

Investigating neurodegenerative heterogeneity in cognitively unimpaired A4 pre‐randomization cohort

AbstractBackgroundThe heterogeneous nature of Alzheimer’s disease and uncertainty of determining disease severity and subtype can confound treatment effects in clinical trials. Computational methods for subtyping can assist in trial enrichment. This study investigates the latent heterogeneity and severity (including absence) of neurodegeneration in the cognitively unimpaired A4 cohort enriched for elevated amyloid.MethodBaseline 3T T1‐weighted MRI data from ADNI (N=687) and A4 pre‐randomization (N=1240, all Aβ+ [Sperling et al. 2020, DOI:10.1001/jamaneurol.2020.0387]) studies were included. FreeSurfer was used to estimate regional brain volumes, of which 13 features were input to SuStaIn (Young et al., 2018, DOI:10.1038/s41467‐018‐05892‐0) to train two cohort‐specific models of neurodegeneration subtypes. Volumes were first adjusted for age, sex, education, and intracranial volume, and z‐scored relative to controls (Aβ– in A4; cognitively normal in ADNI). For each feature, z‐score events of z=1 (subtle abnormality) and z=2 (atrophy) were used. The number of subtypes was determined using cross‐validation.ResultFigure 1 shows our SuStaIn model for ADNI, replicating the three‐subtype finding of Young et al. (2018). Figure 2 shows our SuStaIn model for A4, which estimated a similar three‐subtype model: typical, cortical, subcortical (named for the earliest atrophy events). Notable differences between the models are in the subtle abnormality (z=1) events in A4 that might, or might not, develop into more prominent atrophy (z=2), such as with the caudate which stalls at z=1 (Figure 2, top‐left). Our results indicate the presence of AD‐like heterogeneity in the A4 cohort. Figure 3 shows boxplots of amyloid PET SUVr — there are no statistically significant differences between subtypes (Mann‐Whitney U test), suggesting amyloid PET screening is insufficient to detect neurodegeneration heterogeneity. There were no statistically significant subtype differences in cognitive measures nor demographics (Table 1).ConclusionOur results show that neurodegeneration heterogeneity exists in the A4 cohort, despite amyloid PET screening. This heterogeneity could have ramifications on the potential success of the A4 trial, particularly if it correlates with different rates of cognitive decline and confounds anti‐amyloid therapeutic effects on trial endpoints. Our results support the use of more holistic, multimodal, data‐driven screening for clinical trials in Alzheimer’s disease to reduce cohort heterogeneity.

Examining the correlation between treatment effects in clinical trials and economic modeling

ABSTRACT Introduction Many diseases have a sequential treatment pathway. Compared with patients without previous treatment, patients who fail initial treatment may have lower success rates with a second treatment. This phenomenon can be explained by a correlation between treatment effects. Methods We developed a statistical model of covariance for the underlying unobserved correlation between treatments and established a mathematical expression for the magnitude of the latent correlation term. We conducted a simulation study of clinical trials to investigate the correlation between two treatments and explored clinical examples based on published literature to illustrate the identification and evaluation of these correlations. Results Our simulation study confirmed that a treatment correlation reduces the probability of success for the second treatment, compared with no correlation. We found that treatment correlations may be observable in clinical trials, such as for depression and lung cancer, and the magnitude of correlation may be estimated. We illustrated that treatment correlations can be incorporated into an economic model, with possible impacts on cost-effectiveness results. Additional applications of correlation concepts are also discussed. Conclusions We evaluated the correlation between treatment effects and our approach can be applied to clinical trial design and economic modeling of sequential clinical treatment pathways.

Information extraction from free text for aiding transdiagnostic psychiatry: constructing NLP pipelines tailored to clinicians’ needs

BackgroundDeveloping predictive models for precision psychiatry is challenging because of unavailability of the necessary data: extracting useful information from existing electronic health record (EHR) data is not straightforward, and available clinical trial datasets are often not representative for heterogeneous patient groups. The aim of this study was constructing a natural language processing (NLP) pipeline that extracts variables for building predictive models from EHRs. We specifically tailor the pipeline for extracting information on outcomes of psychiatry treatment trajectories, applicable throughout the entire spectrum of mental health disorders (“transdiagnostic”).MethodsA qualitative study into beliefs of clinical staff on measuring treatment outcomes was conducted to construct a candidate list of variables to extract from the EHR. To investigate if the proposed variables are suitable for measuring treatment effects, resulting themes were compared to transdiagnostic outcome measures currently used in psychiatry research and compared to the HDRS (as a gold standard) through systematic review, resulting in an ideal set of variables. To extract these from EHR data, a semi-rule based NLP pipeline was constructed and tailored to the candidate variables using Prodigy. Classification accuracy and F1-scores were calculated and pipeline output was compared to HDRS scores using clinical notes from patients admitted in 2019 and 2020.ResultsAnalysis of 34 questionnaires answered by clinical staff resulted in four themes defining treatment outcomes: symptom reduction, general well-being, social functioning and personalization. Systematic review revealed 242 different transdiagnostic outcome measures, with the 36-item Short-Form Survey for quality of life (SF36) being used most consistently, showing substantial overlap with the themes from the qualitative study. Comparing SF36 to HDRS scores in 26 studies revealed moderate to good correlations (0.62—0.79) and good positive predictive values (0.75—0.88). The NLP pipeline developed with notes from 22,170 patients reached an accuracy of 95 to 99 percent (F1 scores: 0.38 – 0.86) on detecting these themes, evaluated on data from 361 patients.ConclusionsThe NLP pipeline developed in this study extracts outcome measures from the EHR that cater specifically to the needs of clinical staff and align with outcome measures used to detect treatment effects in clinical trials.

Estimation of treatment effects in short-term depression studies. An evaluation based on the ICH E9(R1) estimands framework.

Estimands aim to incorporate intercurrent events in design, data collection and estimation of treatment effects in clinical trials. Our aim was to understand what estimands may correspond to efficacy analyses commonly employed in clinical trials conducted before publication of ICH E9(R1). We re‐analysed six clinical trials evaluating a new anti‐depression treatment. We selected the following analysis methods—ANCOVA on complete cases, following last observation carried forward (LOCF) imputation and following multiple imputation; mixed‐models for repeated measurements without imputation (MMRM), MMRM following LOCF imputation and following jump‐to‐reference imputation; and pattern‐mixture mixed models. We included a principal stratum analysis based on the predicted subset of the study population who would not discontinue due to adverse events or lack of efficacy. We translated each analysis into the implicitly targeted estimand, and formulated corresponding clinical questions. We could map six estimands to analysis methods. The same analysis method could be mapped to more than one estimand. The major difference between estimands was the strategy for intercurrent events, with other attributes mostly the same across mapped estimands. The quantitative differences in MADRS10 population‐level summaries between the estimands were 4–8 points. Not all six estimands had a clinically meaningful interpretation. Only a few analyses would target the same estimand, hence only few could be used as sensitivity analyses. The fact that an analysis could estimate different estimands emphasises the importance of prospectively defining the estimands targeting the primary objective of a trial. The fact that an estimand can be targeted by different analyses emphasises the importance of prespecifying precisely the estimator for the targeted estimand.

USING PAIN TOLERABILITY THRESHOLDS TO CHARACTERIZE CLINICALLY MEANINGFUL TREATMENT OUTCOMES IN KNEE OSTEOARTHRITIS: POST HOC ANALYSIS OF A PHASE 2B TRIAL OF INTRA-ARTICULAR LORECIVIVINT

Purpose: To evaluate the performance of responder definitions using cut-offs in the pain NRS that correlate with patient-reported pain tolerability for randomized clinical trials. The construct of pain tolerability reveals the complex burden of chronic knee osteoarthritis (OA) pain that informs decisions regarding clinically meaningful treatment effects in clinical trials. A recent survey of 537 patients with chronic pain demonstrated that respondents who reported pain numeric rating scale (NRS) scores of 4 and below almost exclusively report their pain as tolerable.

Acute Medication Use in Patients With Migraine Treated With Monoclonal Antibodies Acting on the CGRP Pathway: Results From a Multicenter Study and Proposal of a New Index.

IntroductionAssessing the impact of migraine preventive treatments on acute medication consumption is important in clinical evaluation. The number of acute medication intakes per each monthly migraine day (MMD) could provide insights on migraine burden and represent a new proxy of treatment effectiveness in clinical trials and real-life studies. We evaluated the effect of monoclonal antibodies acting on calcitonin gene-related peptide (CGRP) pathway on the consumption of migraine acute medication in real-life.MethodsIn two headache centers in Prague (CZ), we included and followed up to 6 months consecutive patients treated with MoAbs acting on CGRP (erenumab or fremanezumab). For each month of treatment, we reported monthly drug intake (MDI) in doses of any medication, migraine-specific (MS), and non-migraine-specific (non-MS) medications, and computed a ratio between MMDs and MDI, i.e., Migraine Medication Index (MMI) for MS and non-MS medications.ResultsWe included 90 patients (91.1% women) with a median age of 47 [interquartile range (IQR) 42–51] years; 81 (90.0%) treated with erenumab and 9 (10.0%) with fremanezumab. Median MMDs decreased from 11 (IQR 8–14) at baseline to 4 (IQR 2–5) at Month 3 (p < 0.001 vs. baseline) and 3 (IQR 2–6) at Month 6 (p < 0.001 vs. baseline). Median MDI decreased from 15 drug intakes (IQR 11–20) at baseline to four drug intakes (IQR 2–7) at Month 3 (p < 0.001) and four drug intakes (IQR 2–7) at Month 6 (p < 0.001).The corresponding MDIs for MS medications were 10 (IQR 6–14) at baseline, 3 (IQR 1–5, p < 0.001) at Month 3, and 2 (IQR 0–4, p < 0.001) at Month 6. Monthly drug intakes for non-MS medications were 4 (IQR 0–9) at baseline, 1 (IQR 0–3, p < 0.001) at Month 3 and at Month 6.Median MMI decreased from 1.32 (IQR 1.11–1.68) at baseline to 1.00 (IQR 1.00–1.50, p < 0.001) at Month 3 and 1.00 (IQR 1.00–1.34, p < 0.001) at Month 6.ConclusionsWe confirmed that MoAbs acting on CGRP pathway decrease acute migraine medication consumption. We proposed a new index that can be easily applied in clinical practice to quantify migraine burden and its response to acute medication. Our index could help optimizing migraine acute treatment in clinical practice.

Grey matter network markers identify individuals with prodromal Alzheimer's disease who will show rapid clinical decline.

Individuals with prodromal Alzheimer’s disease show considerable variability in rates of cognitive decline, which hampers the ability to detect potential treatment effects in clinical trials. Prognostic markers to select those individuals who will decline rapidly within a trial time frame are needed. Brain network measures based on grey matter covariance patterns have been associated with future cognitive decline in Alzheimer’s disease. In this longitudinal cohort study, we investigated whether cut-offs for grey matter networks could be derived to detect fast disease progression at an individual level. We further tested whether detection was improved by adding other biomarkers known to be associated with future cognitive decline [i.e. CSF tau phosphorylated at threonine 181 (p-tau181) levels and hippocampal volume]. We selected individuals with mild cognitive impairment and abnormal CSF amyloid β1–42 levels from the Amsterdam Dementia Cohort and the Alzheimer’s Disease Neuroimaging Initiative, when they had available baseline structural MRI and clinical follow-up. The outcome was progression to dementia within 2 years. We determined prognostic cut-offs for grey matter network properties (gamma, lambda and small-world coefficient) using time-dependent receiver operating characteristic analysis in the Amsterdam Dementia Cohort. We tested the generalization of cut-offs in the Alzheimer’s Disease Neuroimaging Initiative, using logistic regression analysis and classification statistics. We further tested whether combining these with CSF p-tau181 and hippocampal volume improved the detection of fast decliners. We observed that within 2 years, 24.6% (Amsterdam Dementia Cohort, n = 244) and 34.0% (Alzheimer’s Disease Neuroimaging Initiative, n = 247) of prodromal Alzheimer’s disease patients progressed to dementia. Using the grey matter network cut-offs for progression, we could detect fast progressors with 65% accuracy in the Alzheimer’s Disease Neuroimaging Initiative. Combining grey matter network measures with CSF p-tau and hippocampal volume resulted in the best model fit for classification of rapid decliners, increasing detecting accuracy to 72%. These data suggest that single-subject grey matter connectivity networks indicative of a more random network organization can contribute to identifying prodromal Alzheimer’s disease individuals who will show rapid disease progression. Moreover, we found that combined with p-tau and hippocampal volume this resulted in the highest accuracy. This could facilitate clinical trials by increasing chances to detect effects on clinical outcome measures.

Directly and Simultaneously Expressing Absolute and Relative Treatment Effects in Medical Data Models and Applications.

Logistic regression is widely used in the analysis of medical data with binary outcomes to study treatment effects through (absolute) treatment effect parameters in the models. However, the indicative parameters of relative treatment effects are not introduced in logistic regression models, which can be a severe problem in efficiently modeling treatment effects and lead to the wrong conclusions with regard to treatment effects. This paper introduces a new enhanced logistic regression model that offers a new way of studying treatment effects by measuring the relative changes in the treatment effects and also incorporates the way in which logistic regression models the treatment effects. The new model, called the Absolute and Relative Treatment Effects (AbRelaTEs) model, is viewed as a generalization of logistic regression and an enhanced model with increased flexibility, interpretability, and applicability in real data applications than the logistic regression. The AbRelaTEs model is capable of modeling significant treatment effects via an absolute or relative or both ways. The new model can be easily implemented using statistical software, with the logistic regression model being treated as a special case. As a result, the classical logistic regression models can be replaced by the AbRelaTEs model to gain greater applicability and have a new benchmark model for more efficiently studying treatment effects in clinical trials, economic developments, and many applied areas. Moreover, the estimators of the coefficients are consistent and asymptotically normal under regularity conditions. In both simulation and real data applications, the model provides both significant and more meaningful results.