A randomized double-blind, placebo-controlled study of omalizumab for idiopathic anaphylaxis.

BACKGROUND: Idiopathic anaphylaxis (IA) is a diagnosis of exclusion, thus taking away the option of therapeutic management focused on eliminating the inciting agent. Epinephrine and antihistamines followed by systemic corticosteroids are the mainstays of therapy for acute events. There is no prophylactic therapy that reliably prevents anaphylaxis. OBJECTIVE: We sought to determine the efficacy of omalizumab in the management of patients with frequent episodes of IA in a double-blind, placebo-controlled trial. METHODS: We prospectively enrolled 19 patients with frequent IA (≥6 episodes/y) who then underwent a medical evaluation that included a serum tryptase determination, mutational analysis for KIT D816V, and bone marrow evaluation to rule out a clonal mast cell disorder. Computer-generated random numbers were provided by the study pharmacist. The primary end point was anaphylactic events in the 6 months after baseline. Sixteen patients completed the primary trial. RESULTS: No statistically significant difference was demonstrated between the placebo and treated groups. There was a trend for efficacy in the treatment group, particularly after 60 days. Overall, the safety profile was favorable without long-term side effects. CONCLUSIONS: Omalizumab was safely administered to a difficult-to-treat patient population with IA. The efficacy results trended modestly in favor of the treatment group, but no statistically significant differences were detected.

A primer on strong vs weak control of familywise error rate.

Multiple comparison adjustments have a long history, yet confusion remains about which procedures control type 1 error rate in a strong sense and how to show this. Part of the confusion stems from a powerful technique called the closed testing principle, whose statement is deceptively simple, but is sometimes misinterpreted. This primer presents a straightforward way to think about multiplicity adjustment.

Hypomorphic caspase activation and recruitment domain 11 (CARD11) mutations associated with diverse immunologic phenotypes with or without atopic disease.

BACKGROUND: Caspase activation and recruitment domain 11 (CARD11) encodes a scaffold protein in lymphocytes that links antigen receptor engagement with downstream signaling to nuclear factor κB, c-Jun N-terminal kinase, and mechanistic target of rapamycin complex 1. Germline CARD11 mutations cause several distinct primary immune disorders in human subjects, including severe combined immune deficiency (biallelic null mutations), B-cell expansion with nuclear factor κB and T-cell anergy (heterozygous, gain-of-function mutations), and severe atopic disease (loss-of-function, heterozygous, dominant interfering mutations), which has focused attention on CARD11 mutations discovered by using whole-exome sequencing. OBJECTIVES: We sought to determine the molecular actions of an extended allelic series of CARD11 and to characterize the expanding range of clinical phenotypes associated with heterozygous CARD11 loss-of-function alleles. METHODS: Cell transfections and primary T-cell assays were used to evaluate signaling and function of CARD11 variants. RESULTS: Here we report on an expanded cohort of patients harboring novel heterozygous CARD11 mutations that extend beyond atopy to include other immunologic phenotypes not previously associated with CARD11 mutations. In addition to (and sometimes excluding) severe atopy, heterozygous missense and indel mutations in CARD11 presented with immunologic phenotypes similar to those observed in signal transducer and activator of transcription 3 loss of function, dedicator of cytokinesis 8 deficiency, common variable immunodeficiency, neutropenia, and immune dysregulation, polyendocrinopathy, enteropathy, X-linked-like syndrome. Pathogenic variants exhibited dominant negative activity and were largely confined to the CARD or coiled-coil domains of the CARD11 protein. CONCLUSION: These results illuminate a broader phenotypic spectrum associated with CARD11 mutations in human subjects and underscore the need for functional studies to demonstrate that rare gene variants encountered in expected and unexpected phenotypes must nonetheless be validated for pathogenic activity.

Causal estimands and confidence intervals associated with Wilcoxon-Mann-Whitney tests in randomized experiments.

Although the P value from a Wilcoxon-Mann-Whitney test is used often with randomized experiments, it is rarely accompanied with a causal effect estimate and its confidence interval. The natural parameter for the Wilcoxon-Mann-Whitney test is the Mann-Whitney parameter, ϕ, which measures the probability that a randomly selected individual in the treatment arm will have a larger response than a randomly selected individual in the control arm (plus an adjustment for ties). We show that the Mann-Whitney parameter may be framed as a causal parameter and show that it is not equal to a closely related and nonidentifiable causal effect, ψ, the probability that a randomly selected individual will have a larger response under treatment than under control (plus an adjustment for ties). We review the paradox, first expressed by Hand, that the ψ parameter may imply that the treatment is worse (or better) than control, while the Mann-Whitney parameter shows the opposite. Unlike the Mann-Whitney parameter, ψ is nonidentifiable from a randomized experiment. We review some nonparametric assumptions that rule out Hand's paradox through bounds on ψ and use bootstrap methods to make inferences on those bounds. We explore the relationship of the proportional odds parameter to Hand's paradox, showing that the paradox may occur for proportional odds parameters between 1/9 and 9. Thus, large effects are needed to ensure that if treatment appears better by the Mann-Whitney parameter, then treatment improves responses in most individuals. We demonstrate these issues using a vaccine trial.

Finite sample pointwise confidence intervals for a survival distribution with right-censored data.

We review and develop pointwise confidence intervals for a survival distribution with right-censored data for small samples, assuming only independence of censoring and survival. When there is no censoring, at each fixed time point, the problem reduces to making inferences about a binomial parameter. In this case, the recently developed beta product confidence procedure (BPCP) gives the standard exact central binomial confidence intervals of Clopper and Pearson. Additionally, the BPCP has been shown to be exact (gives guaranteed coverage at the nominal level) for progressive type II censoring and has been shown by simulation to be exact for general independent right censoring. In this paper, we modify the BPCP to create a 'mid-p' version, which reduces to the mid-p confidence interval for a binomial parameter when there is no censoring. We perform extensive simulations on both the standard and mid-p BPCP using a method of moments implementation that enforces monotonicity over time. All simulated scenarios suggest that the standard BPCP is exact. The mid-p BPCP, like other mid-p confidence intervals, has simulated coverage closer to the nominal level but may not be exact for all survival times, especially in very low censoring scenarios. In contrast, the two asymptotically-based approximations have lower than nominal coverage in many scenarios. This poor coverage is due to the extreme inflation of the lower error rates, although the upper limits are very conservative. Both the standard and the mid-p BPCP methods are available in our bpcp R package. Published 2016. This article is US Government work and is in the public domain in the USA.

Assessment of clinical findings, tryptase levels, and bone marrow histopathology in the management of pediatric mastocytosis.

BACKGROUND: The management of children with pediatric mastocytosis poses a challenge. This is because there is limited information as to the application of clinical and laboratory findings and bone marrow histopathology as they relate to medical intervention and communication. OBJECTIVE: We sought to examine clinical aspects of pediatric mastocytosis in relationship to serum tryptase levels and bone marrow pathology to provide practical guidance for management. METHODS: Between 1986 and 2012, 105 children were evaluated at the National Institutes of Health. Organomegaly was confirmed by means of ultrasound. Baseline tryptase levels and at least 1 subsequent tryptase measurement was available in 84 and 37 of these children, respectively. Fifty-three children underwent a bone marrow examination. These data were used to examine relationships between clinical findings, tryptase levels, and marrow histopathology. RESULTS: In patients with high tryptase levels and severe mediator symptoms, all with organomegaly had systemic disease, and none without organomegaly had systemic disease. Serum tryptase levels differed significantly between patients with urticaria pigmentosa and those with diffuse cutaneous (P < .0001) and systemic mastocytosis (P < .0001) and in all 3 categories versus control subjects (P < .0001). Tryptase levels and symptoms decreased over time in most patients, and tryptase levels correlated with bone marrow mast cell burden in patients with systemic mastocytosis (P < .0001). There was a significant relationship between clinical resolution and the percentage decrease in tryptase levels (P = .0014). CONCLUSIONS: The majority of children experienced major or complete disease resolution (57%), whereas the remainder exhibited partial improvement. Organomegaly was a strong indicator of systemic disease. Serum tryptase levels furthered classification and reflected clinicopathologic findings, while sequential tryptase measurements were useful in supplementing clinical judgment as to disease course.

Pointwise confidence intervals for a survival distribution with small samples or heavy censoring.

We propose a beta product confidence procedure (BPCP) that is a non-parametric confidence procedure for the survival curve at a fixed time for right-censored data assuming independent censoring. In such situations, the Kaplan-Meier estimator is typically used with an asymptotic confidence interval (CI) that can have coverage problems when the number of observed failures is not large, and/or when testing the latter parts of the curve where there are few remaining subjects at risk. The BPCP guarantees central coverage (i.e. ensures that both one-sided error rates are no more than half of the total nominal rate) when there is no censoring (in which case it reduces to the Clopper-Pearson interval) or when there is progressive type II censoring (i.e. when censoring only occurs immediately after failures on fixed proportions of the remaining individuals). For general independent censoring, simulations show that the BPCP maintains central coverage in many situations where competing methods can have very substantial error rate inflation for the lower limit. The BPCP gives asymptotically correct coverage and is asymptotically equivalent to the CI on the Kaplan-Meier estimator using Greenwood's variance. The BPCP may be inverted to create confidence procedures for a quantile of the underlying survival distribution. Because the BPCP is easy to implement, offers protection in settings when other methods fail, and essentially matches other methods when they succeed, it should be the method of choice.

A valid formulation of the analysis of noninferiority trials under random effects meta-analysis.

A noninferiority (NI) trial is sometimes employed to show efficacy of a new treatment when it is unethical to randomize current patients to placebo because of the established efficacy of a standard treatment. Under this framework, if the NI trial determines that the treatment advantage of the standard to the new drug (i.e. S-N) is less than the historic advantage of the standard to placebo (S-P), then the efficacy of the new treatment (N-P) is established indirectly. We explicitly combine information from the NI trial with estimates from a random effects model, allowing study-to-study variability in k historic trials. Existing methods under random effects, such as the synthesis method, fail to account for the variability of the true standard versus placebo effect in the NI trial. Our method effectively uses a prediction interval for the missing standard versus placebo effect rather than a confidence interval of the mean. The consequences are to increase the variance of the synthesis method by incorporating a prediction variance term and to approximate the null distribution of the new statistic with a t with k-1 degrees of freedom instead of the standard normal. Thus, it is harder to conclude NI of the new to (predicted) placebo, compared with traditional methods, especially when k is small or when between study variability is large. When the between study variances are nonzero, we demonstrate substantial Type I error rate inflation with conventional approaches; simulations suggest that the new procedure has only modest inflation, and it is very conservative when between study variances are zero. An example is used to illustrate practical issues.

Genetically defined individual reference ranges for tryptase limit unnecessary procedures and unmask myeloid neoplasms.

Serum tryptase is a biomarker used to aid in the identification of certain myeloid neoplasms, most notably systemic mastocytosis, where basal serum tryptase (BST) levels >20 ng/mL are a minor criterion for diagnosis. Although clonal myeloid neoplasms are rare, the common cause for elevated BST levels is the genetic trait hereditary α-tryptasemia (HαT) caused by increased germline TPSAB1 copy number. To date, the precise structural variation and mechanism(s) underlying elevated BST in HαT and the general clinical utility of tryptase genotyping, remain undefined. Through cloning, long-read sequencing, and assembling of the human tryptase locus from an individual with HαT, and validating our findings in vitro and in silico, we demonstrate that BST elevations arise from overexpression of replicated TPSAB1 loci encoding canonical α-tryptase protein owing to coinheritance of a linked overactive promoter element. Modeling BST levels based on TPSAB1 replication number, we generate new individualized clinical reference values for the upper limit of normal. Using this personalized laboratory medicine approach, we demonstrate the clinical utility of tryptase genotyping, finding that in the absence of HαT, BST levels >11.4 ng/mL frequently identify indolent clonal mast cell disease. Moreover, substantial BST elevations (eg, >100 ng/mL), which would ordinarily prompt bone marrow biopsy, can result from TPSAB1 replications alone and thus be within normal limits for certain individuals with HαT.

Does treatment effect depend on control event rate? Revisiting a meta-analysis of suicidality and antidepressant use in children.

BACKGROUND: Weisberg et al. (Clin Trials 2009, 6: 109-18) illustrate that entry criteria in randomized trials can lead to results that are biased for the target population. Presenting data from a previously published meta-analysis of the effect of antidepressants in children on suicidality, they suggest a relationship between the control event rate and relative risk. PURPOSE: Our purpose is threefold: (1) to show that natural methods of demonstrating a relationship between the control event rate and relative treatment benefit are fraught with danger, (2) to develop better methods, and (3) to assess whether there is evidence of such a relationship in the suicidality meta-analysis. METHODS: We propose an improved graphical method and an exact and approximate test of whether the treatment effect increases (or decreases) with the control event rate. RESULTS: The apparent relationship in the naive plot of relative risk against control rate is actually no stronger than what would be expected by chance. Results of our test do not support the conclusion that the odds ratio for suicidality varies with the control rate (one-sided p = 0.39). LIMITATIONS: We are not able to apply the exact test to this data set. Simulation results indicate that the relationship would have to be very strong to detect it with these sample sizes and control event rates. CONCLUSIONS: The difficulty in showing that the treatment effect in clinical trials differs by control rate is caused by 3 factors: (1) artificial correlation between a relative risk and control rate, (2) regression dilution bias because the independent variable - the control proportion - is subject to random variability, and (3) low power because we are trying to detect an interaction. Our graph and test are useful tools. Using them, we found no support for a relationship between the control event rate and treatment effect on suicidality.

On Causal Inferences for Personalized Medicine: How Hidden Causal Assumptions Led to Erroneous Causal Claims About the D-Value.

Personalized medicine asks if a new treatment will help a particular patient, rather than if it improves the average response in a population. Without a causal model to distinguish these questions, interpretational mistakes arise. These mistakes are seen in an article by Demidenko [2016] that recommends the "D-value," which is the probability that a randomly chosen person from the new-treatment group has a higher value for the outcome than a randomly chosen person from the control-treatment group. The abstract states "The D-value has a clear interpretation as the proportion of patients who get worse after the treatment" with similar assertions appearing later. We show these statements are incorrect because they require assumptions about the potential outcomes which are neither testable in randomized experiments nor plausible in general. The D-value will not equal the proportion of patients who get worse after treatment if (as expected) those outcomes are correlated. Independence of potential outcomes is unrealistic and eliminates any personalized treatment effects; with dependence, the D-value can even imply treatment is better than control even though most patients are harmed by the treatment. Thus, D-values are misleading for personalized medicine. To prevent misunderstandings, we advise incorporating causal models into basic statistics education.