Estimating heterogeneity of treatment effect in psychiatric clinical trials

Currently, placebo-controlled clinical trials report mean change and effect sizes, which masks information about heterogeneity of treatment effects (HTE). Here, we present a method to estimate HTE and evaluate the null hypothesis (H 0 ) that a drug has equal benefit for all participants (HTE=0). We developed measure termed 'estimated heterogeneity of treatment effect' or eHTE , which estimates variability in drug response by comparing distributions between study arms. This approach was tested across numerous large placebo-controlled clinical trials. In contrast with variance-based methods which have not identified heterogeneity in psychiatric trials, reproducible instances of treatment heterogeneity were found. For example, heterogeneous response was found in a trial of venlafaxine for depression (p eHTE =0.034), and two trials of dasotraline for binge eating disorder (Phase 2, p eHTE =0.002; Phase 3, 4mg p eHTE =0.011; Phase 3, 6mg p eHTE =0.003). Significant response heterogeneity was detected in other datasets as well, often despite no difference in variance between placebo and drug arms. The implications of eHTE as a clinical trial outcomes independent from central tendency of the group is considered and the important of the eHTE method and results for drug developers, providers, and patients is discussed.

A Novel Method for Deriving Adverse Event Prevalence in Randomized Controlled Trials: Potential for Improved Understanding of Benefit-Risk Ratio and Application to Drug Labels

INTRODUCTION

Adverse event (AE) data in randomized controlled trials (RCTs) allow quantification of a drug's safety risk relative to placebo and comparison across medications. The standard US label for Food and Drug Administration-approved drugs typically lists AEs by MedDRA Preferred Term that occur at ≥ 2% in drug and with greater incidence than in placebo. We suggest that the drug label can be more informative for both patients and physicians if it includes, in addition to AE incidence (percent of subjects who reported the AE out of the total subjects in treatment), the absolute prevalence (percent of subject-days spent with an AE out of the total subject-days spent in treatment) and expected duration (days required for AE incidence to be reduced by half). We also propose a new method to analyze AEs in RCTs using drug-placebo difference in AE prevalence to improve safety signal detection.

METHODS

AE data from six RCTs in schizophrenia were analyzed (five RCTs of the dopamine D2 receptor-based antipsychotic lurasidone and one RCT of the novel trace amine-associated receptor 1 [TAAR1] agonist ulotaront). We determined incidence, absolute prevalence, and expected duration of AEs for lurasidone and ulotaront vs respective placebo. We also calculated areas under the curve of drug-placebo difference in AE prevalence and mean percent contribution of each AE to this difference.

RESULTS

A number of AEs with the same incidence had different absolute prevalence and expected duration. When accounting for these two parameters, AEs that did not appear in the 2% incidence tables of the drug label turned out to contribute substantially to drug tolerability. The percent contribution of a drug-related AE to the overall side effect burden increased the drug-placebo difference in AE prevalence, whereas the percent contribution of a placebo-related AE decreased such difference, revealing a continuum of risk between drug and placebo. AE prevalence curves for drug were generally greater than those for placebo. Ulotaront exhibited a small drug-placebo difference in AE prevalence curves due to a relatively low incidence and short duration of AEs in the ulotaront treatment arm as well as the emergence of disease-related AEs in the placebo arm.

CONCLUSION

Reporting AE absolute prevalence and expected duration for each RCT and incorporating them in the drug label is possible, is clinically relevant, and allows standardized comparison of medications. Our new metric, the drug-placebo difference in AE prevalence, facilitates signal detection in RCTs. We piloted this metric in RCTs of several neuropsychiatric indications and drugs, offering a new way to compare AE burden and tolerability among treatments using existing clinical trial information.

Characterization of specific and distinct patient types in clinical trials of acute schizophrenia using an uncorrelated PANSS score matrix transform (UPSM)

Understanding the specificity of symptom change in schizophrenia can facilitate the evaluation antipsychotic efficacy for different symptom domains. Previous work identified a transform of PANSS using an uncorrelated PANSS score matrix (UPSM) to reduce pseudospecificity among symptom domains during clinical trials of schizophrenia. Here we used UPSM-transformed factor scores to identify 5 distinct patient types, each having elevated and specific severity among each of 5 symptom domains. Subjects from placebo-controlled clinical trials of acute schizophrenia were clustered (baseline) and classified (post-baseline) by a machine-learning algorithm. At baseline, all 5 patient types were similar in PANSS total score. Post-baseline, subjects' memberships among the 5 UPSM patient types were relatively stable over treatment duration and were relatively insensitive to overall improvements in symptoms, in contrast to other methods based on untransformed PANSS items. Using UPSM-transformed PANSS, drug treatment effect sizes versus placebo were doubly-dissociated for specificity across symptom domains and within specific patient types. This approach illustrates how broader clinical trial populations can nevertheless be utilized to characterize the specificity of new mechanisms across the dimensions of schizophrenia psychopathology.

Transformed PANSS Factors Intended to Reduce Pseudospecificity Among Symptom Domains and Enhance Understanding of Symptom Change in Antipsychotic-Treated Patients With Schizophrenia

Positive and Negative Syndrome Scale (PANSS) total score is the standard primary efficacy measure in acute treatment studies of schizophrenia. However, PANSS factors that have been derived from factor analytic approaches over the past several decades have uncertain clinical and regulatory status as they are, to varying degrees, intercorrelated. As a consequence of cross-factor correlations, the apparent improvement in key clinical domains (eg, negative symptoms, disorganized thinking/behavior) may largely be attributable to improvement in a related clinical domain, such as positive symptoms, a problem often referred to as pseudospecificity. Here, we analyzed correlations among PANSS items, at baseline and change post-baseline, in a pooled sample of 5 placebo-controlled clinical trials (N = 1710 patients), using clustering and factor analysis to identify an uncorrelated PANSS score matrix (UPSM) that minimized the degree of correlation between each resulting transformed PANSS factor. The transformed PANSS factors corresponded well with discrete symptom domains described by prior factor analyses, but between-factor change-scores correlations were markedly lower. We then used the UPSM to transform PANSS in data from 4657 unique schizophrenia patients included in 12 additional lurasidone clinical trials. The results confirmed that transformed PANSS factors retained a high degree of specificity, thus validating that low between-factor correlations are a reliable property of the USPM when transforming PANSS data from a variety of clinical trial data sets. These results provide a more robust understanding of the structure of symptom change in schizophrenia and suggest a means to evaluate the specificity of antipsychotic treatment effects.

Understanding Antipsychotic Drug Treatment Effects: A Novel Method to Reduce Pseudospecificity of the Positive and Negative Syndrome Scale (PANSS) Factors

The Positive and Negative Syndrome Scale (PANSS) is the most widely used efficacy measure in acute treatment studies of schizophrenia. However, interpretation of the efficacy of antipsychotics in improving specific symptom domains is confounded by moderate-to-high correlations among standard (Marder) PANSS factors. The authors review the results of an uncorrelated PANSS score matrix (UPSM) transform designed to reduce pseudospecificity in assessment of symptom change in patients with schizophrenia. Based on a factor analysis of five pooled, placebo-controlled lurasidone clinical trials (N=1,710 patients), a UPSM transform was identified that generated PANSS factors with high face validity (good correlation with standard Marder PANSS factors), and high specificity/orthogonality (low levels of between-factor correlation measuring change during treatment). Between-factor correlations were low at baseline for both standard (Marder) PANSS factors and transformed PANSS factors. However, when measured change in symptom severity was measured during treatment (in a pooled 5-study analysis), there was a notable difference for standard PANSS factors, where changes across factors were found to be highly correlated (factors exhibited pseudospecificity), compared to transformed PANSS factors, where factor change scores exhibited the same low levels of between-factor correlation observed at baseline. At Week 6-endpoint, correlations among PANSS factor severity scores were moderate-to-high for standard factors (0.34-0.68), but continued to be low for the transformed factors (-0.22-0.20). As an additional validity check, we analyzed data from one of the original five pooled clinical trials that included other well-validated assessment scales (MADRS, Negative Symptom Assessment scale [NSA]). In this baseline analysis, UPSM-transformed PANSS factor severity scores (negative and depression factors) were found to correlate well with the MADRS and NSA. The availability of transformed PANSS factors with a high degree of orthogonality/specificity, but which retain a high degree of concurrent and face validity, can reduce pseudospecificity as a measurement confound, and should facilitate the drug development process, permitting a more accurate characterization of the efficacy of putative new agents in targeting specific symptom domains in patients with psychotic illness.

A touch probe method of operating an implantable RFID tag for orthopedic implant identification

The major problem in operating an implantable radio-frequency identification (RFID) tag embedded on an orthopedic implant is low efficiency because of metallic interference. To improve the efficiency, this paper proposes a method of operating an implantable passive RFID tag using a touch probe at 13.56 MHz. This technology relies on the electric field interaction between two pairs of electrodes, one being a part of the touch probe placed on the surface of tissue and the other being a part of the tag installed under the tissue. Compared with using a conventional RFID antenna such as a loop antenna, this method has a better performance in the near field operation range to reduce interference with the orthopedic implant. Properly matching the touch probe and the tag to the tissue and the implant reduces signal attenuation and increases the overall system efficiency. The experiments have shown that this method has a great performance in the near field transcutaneous operation and can be used for orthopedic implant identification.

Design and implementation of a volume conduction based RFID system for smart implants

As the population ages, knee and hip replacement surgeries are more and more popular, and embedding an RFID (radio frequency identification) tag on these implants for identification becomes an important issue. Traditional operation of an RFID tag by wireless means will not work on the implantable knees or hips which are made of metal because of the interference caused by metallic objects degrading the field strength near the RFID tag. This paper proposes a method of operating an RFID tag using volume conduction while avoiding the RF interference in a metallic environment. To increase the efficiency of power transmission, electrodes in this paper are designed and optimized for a real knee implant. Experiments using saline have been conducted and the results have shown that volume conduction has a better performance than wireless methods in that signal attenuation is far less in metallic environments. Finally, the experiment on reading an implanted RFID tag through pig skin shows that volume conduction is an effective method to operate an RFID tag embedded on a metallic implant.