Brenda J. Crowe

Genotypes and phenotypes in children with short stature: clinical indicators of SHOX haploinsufficiency

Background: Short stature affects approximately 2% of children, representing one of the more frequent disorders for which clinical attention is sought during childhood. Despite assumed genetic heterogeneity, mutations or deletions of the short stature homeobox-containing gene (SHOX) are found quite frequently in subjects with short stature. Haploinsufficiency of the SHOX gene causes short stature with highly variable clinical severity, ranging from isolated short stature without dysmorphic features to Léri-Weill syndrome, and with no functional copy of the SHOX gene, Langer syndrome. Methods: To characterise the clinical and molecular spectrum of SHOX deficiency in childhood we assessed the association between genotype and phenotype in a large cohort of children of short stature from 14 countries. Results: Screening of 1608 unrelated individuals with sporadic or familial short stature revealed SHOX mutations or deletions in 68 individuals (4.2%): complete deletions in 48 (70.6%), partial deletions in 4 (5.9%) and point mutations in 16 individuals (23.5%). Although mean height standard deviation score (SDS) was not different between participants of short stature with or without identified SHOX gene defects (–2.6 vs –2.6), detailed examination revealed that certain bone deformities and dysmorphic signs, such as short forearm and lower leg, cubitus valgus, Madelung deformity, high-arched palate and muscular hypertrophy, differed markedly between participants with or without SHOX gene defects (p<0.001). Phenotypic data were also compared for 33 children with Turner syndrome in whom haploinsufficiency of SHOX is thought to be responsible for the height deficit. Conclusion: A phenotype scoring system was developed that could assist in identifying the most appropriate subjects for SHOX testing. This study offers a detailed genotype-phenotype analysis in a large cohort of children of short stature, and provides quantitative clinical guidelines for testing of the SHOX gene.

Prospective Safety Surveillance of GH-Deficient Adults: Comparison of GH-Treated vs Untreated Patients.

Context: In clinical practice, the safety profile of GH replacement therapy for GH-deficient adults compared with no replacement therapy is unknown. Objective: The objective of this study was to compare adverse events (AEs) in GH-deficient adults who were GH-treated with those in GH-deficient adults who did not receive GH replacement. Design and Setting: This was a prospective observational study in the setting of US clinical practices. Patients and Outcome Measures: AEs were compared between GH-treated (n = 1988) and untreated (n = 442) GH-deficient adults after adjusting for baseline group differences and controlling the false discovery rate. The standardized mortality ratio was calculated using US mortality rates. Results: After a mean follow-up of 2.3 years, there was no significant difference in rates of death, cancer, intracranial tumor growth or recurrence, diabetes, or cardiovascular events in GH-treated compared with untreated patients. The standardized mortality ratio was not increased in either group. Unexpected AEs (GH-treated vs untreated, P ≤ .05) included insomnia (6.4% vs 2.7%), dyspnea (4.2% vs 2.0%), anxiety (3.4% vs 0.9%), sleep apnea (3.3% vs 0.9%), and decreased libido (2.1% vs 0.2%). Some of these AEs were related to baseline risk factors (including obesity and cardiopulmonary disease), higher GH dose, or concomitant GH side effects. Conclusions: In GH-deficient adults, there was no evidence for a GH treatment effect on death, cancer, intracranial tumor recurrence, diabetes, or cardiovascular events, although the follow-up period was of insufficient duration to be conclusive for these long-term events. The identification of unexpected GH-related AEs reinforces the fact that patient selection and GH dose titration are important to ensure safety of adult GH replacement.

Meta-analysis of clinical trial safety data in a drug development program: Answers to frequently asked questions:

Background Meta-analyses of clinical trial safety data have risen in importance beyond regulatory submissions. During drug development, sponsors need to recognize safety signals early and adjust the development program accordingly, so as to facilitate the assessment of causality. Once a product is marketed, sponsors add postapproval clinical trial data to the body of information to help understand existing safety concerns or those that arise from other postapproval data sources, such as spontaneous reports. Purpose This article focuses on common questions encountered when designing and performing a meta-analysis of clinical trial safety data. Although far from an exhaustive set of questions, they touch on some basic and often misunderstood features of conducting such meta-analyses. Methods The authors reviewed the current literature and used their combined experience with regulatory and other uses of meta-analysis to answer common questions that arise when performing meta-analyses of safety data. Results We addressed the following topics: choice of studies to pool, effects of the method of ascertainment, use of patient-level data compared to trial-level data, the need (or not) for multiplicity adjustments, heterogeneity of effects and sources of it, and choice of fixed effects versus random effects. Limitations The list of topics is not exhaustive and the opinions offered represent only our perspective; we recognize that there may be other valid perspectives. Conclusions Meta-analysis can be a valuable tool for evaluating safety questions, but a number of methodological choices need to be made in designing and conducting any meta-analysis. This article provides advice on some of the more commonly encountered choices.

Planning and core analyses for periodic aggregate safety data reviews

Background In 2009, the Safety Planning, Evaluation and Reporting Team gave detailed recommendations for a well-planned and systematic approach for safety data collection and analysis. Important aspects of this approach included regular reviews of aggregate data by a multidisciplinary team focusing on safety. Purpose This article provides information to facilitate the planning and implementation of aggregate data reviews. Methods Our recommendations are based on experience of the authors and review of relevant literature. Results We present information regarding the planning of aggregate data reviews as well as examples of data displays that are useful for many different compounds. A subset of these data displays could form a set of ‘core’ analyses to be generated for aggregate data reviews.

Seeing is believing: good graphic design principles for medical research.

Have you noticed when you browse a book, journal, study report, or product label how your eye is drawn to figures more than to words and tables? Statistical graphs are powerful ways to transparently and succinctly communicate the key points of medical research. Furthermore, the graphic design itself adds to the clarity of the messages in the data. The goal of this paper is to provide a mechanism for selecting the appropriate graph to thoughtfully construct quality deliverables using good graphic design principles. Examples are motivated by the efforts of a Safety Graphics Working Group that consisted of scientists from the pharmaceutical industry, Food and Drug Administration, and academic institutions.

Comparison of several imputation methods for missing baseline data in propensity scores analysis of binary outcome.

We performed a simulation study comparing the statistical properties of the estimated log odds ratio from propensity scores analyses of a binary response variable, in which missing baseline data had been imputed using a simple imputation scheme (Treatment Mean Imputation), compared with three ways of performing multiple imputation (MI) and with a Complete Case analysis. MI that included treatment (treated/untreated) and outcome (for our analyses, outcome was adverse event [yes/no]) in the imputers model had the best statistical properties of the imputation schemes we studied. MI is feasible to use in situations where one has just a few outcomes to analyze. We also found that Treatment Mean Imputation performed quite well and is a reasonable alternative to MI in situations where it is not feasible to use MI. Treatment Mean Imputation performed better than MI methods that did not include both the treatment and outcome in the imputers model.

Current Practices, Challenges, and Statistical Issues With Product Safety Labeling

Prescribing health care professionals are dependent on product labels to give them the information they need to appropriately prescribe drugs and monitor patients. A well-constructed label with accurate, meaningful, and easy-to-understand information is important for understanding the benefit–risk profile of the compound. Statisticians are uniquely qualified to help. Historically, statisticians have played a major role in defining the efficacy information provided in the label. Similar leadership is needed to develop safety information for products. Statisticians should play a key role in making sure that the safety information from clinical trials is accurate and provides adequate information to health care professionals regarding the likely and potential effects of a drug. In this regard, giving careful thought to (a) the way safety information is defined and collected, (b) the way in which different adverse events (AEs) are categorized and combined over multiple studies, (c) the statistical methods used to analyze the safety data, and (d) the tables and graphs in which safety information is presented is needed. We have touched on aspects of each of these in this article. Though product labeling has improved greatly in the past 20 years, there are still many issues that need statisticians’ leadership and thought processes. In this article, we consider some of the important issues and make recommendations to address them. These include passive versus active elicitation of AEs, grouping of terms for labeling purposes, definitions of treatment-emergent AEs, meta-analytical principles, and analytical methods for estimating incidence and rates. Consideration of these issues can benefit health care professionals and ultimately patients who take medicines.

The FDA's Final Rule on Expedited Safety Reporting: Statistical Considerations

In March 2011, a Final Rule for expedited reporting of serious adverse events took effect in the United States for studies conducted under an Investigational New Drug (IND) application. In December 2012, the U.S. Food and Drug Administration (FDA) promulgated a final Guidance describing the operationalization of this Final Rule. The Rule and Guidance clarified that a clinical trial sponsor should have evidence suggesting causality before defining an unexpected serious adverse event as a suspected adverse reaction that would require expedited reporting to the FDA. The Rules emphasis on the need for evidence suggestive of a causal relation should lead to fewer events being reported but, among those reported, a higher percentage actually being caused by the product being tested. This article reviews the practices that were common before the Final Rule was issued and the approach the New Rule specifies. It then discusses methods for operationalizing the Final Rule with particular focus on relevant statistical considerations. It concludes with a set of recommendations addressed to Sponsors and to the FDA in implementing the Final Rule.

Reporting Adverse Drug Reactions in Product Labels

Product labels are intended to provide health care professionals with clear and concise prescribing information that will enhance the safe and effective use of drug products. In this manuscript, we offer suggestions to improve product labels. First, we recommend that product labels that include comparator data be changed to include adjusted incidence proportions (or adjusted incidence rates when needed and appropriate) for adverse drug reactions that are somewhat common. Second, we believe that including comparator incidence in product labels is a good practice, as it gives health care providers and patients appropriate information to put the absolute risks in perspective. Finally, we recommend changing the practice of reporting extremely rare events based on the “Rule of 3” in the Summary of Product Characteristics in Europe. We recommend that these adverse drug reactions be put in a separate table from other adverse drug reactions with a note that it is difficult to reliably estimate their incidences. In exceptional circumstances, it may be possible to present an estimate of their incidence based on postmarketing data. We believe the proposed changes could help product labels to better reflect the risk of a drug relative to a comparator.

Reference Limits for Outlier Analyses in Randomized Clinical Trials

Background: Reference limits used in clinical medicine to screen and manage patients are typically developed nonparametrically using reference values from a limited number of healthy subjects using a 95th percentile reference interval. We have evaluated alternative methods of computation and the resulting limits for use in the analyses of treatment-emergent outliers in clinical trials. Methods: We developed a set of alternative reference limits for 38 laboratory analytes based on alternative statistical methods and assessed their relative performance in clinical trial analysis. Performance assessment was based on the clinical credibility of the limits, inferential statistical performance, consideration of incidences for the test drug and control (placebo) in cases where the drug was reasonably believed to be associated with a change in an analyte (positive cases), and in cases where prior analyses failed to demonstrate a change associated with the drug (negative cases). Results: Based on consideration of these cases, no single method resulted in optimal limits for all cases considered. However, with the limits developed using clinical trial subjects’ values at baseline as reference values, excluding outliers, the robust method and the 98th percentile interval appeared to produce optimal limits across the greatest number of cases considered. Conclusion: Although no single method of limit computation will result in optimal limits for all outlier analyses for all analytes across all clinical trials, the 98th percentile reference interval robust limits based on clinical trial reference values appeared superior to multiple alternatives considered for such analyses.