Matthew Rotelli

Association between early and rapid weight gain and change in weight over one year of olanzapine therapy in patients with schizophrenia and related disorders.

Abstract: Weight gain is an important issue in the use of atypical antipsychotics, including olanzapine. A retrospective analysis of patterns of weight gain and possible covariates was performed for 1191 patients diagnosed with schizophrenia or schizoaffective disorder who were treated with olanzapine for up to 52 weeks. Patients were dichotomized into 2 main groups according to the percentage of body weight gained during the first 6 weeks of treatment with olanzapine: (1) patients who gained ≥7% of their body weight (Rapid Weight Gain Group [RWG]), and (2) patients who lost weight, gained no weight, or gained <7% of their body weight (Nonrapid Weight Gain Group [NRWG]). Results demonstrated that approximately 15% of the patient population showed rapid increases in weight (RWG group), whereas 85% of patients gained weight more slowly or not at all (NRWG group). Patients in the RWG group gained an average of 4% of their body weight (approximately 4-7 lb) within the first 2 weeks of treatment with olanzapine. Furthermore, patients in the RWG group were younger, had a lower baseline body mass index, were more likely to report an increase in appetite, and showed a more robust clinical response compared with patients in the NRWG group. Over the course of 52 weeks, patients in the RWG group gained significantly more weight and reached a higher plateau for mean weight increase at 38 weeks compared with the mean increase observed for patients in the NRWG group. By measuring the weight of patients during the first few weeks of olanzapine treatment and by assessing changes in appetite, clinicians may be able to identify those patients at risk for substantial weight gain.

Defining “good” and “poor” outcomes in patients with schizophrenia or schizoaffective disorder: A multidimensional data-driven approach

The studys goal was to characterize the typology of patient outcomes based on social and occupational functioning and psychiatric symptoms following antipsychotic drug treatment, and to explore predictors of group membership representing the best/worst outcomes. A hierarchical cluster analysis was used to define groups of patients (n=1449) based on endpoint values for psychiatric symptoms, social functioning, and useful work measured up to 30 weeks of treatment. Stepwise logistic regression was used to construct predictive models of cluster membership for baseline predictors, and with 2/4/8 weeks of treatment. Five distinct clusters of patients were identified at endpoint (Clusters A-E). Patients in Cluster A (25.6%, best outcome) had minimal psychiatric symptoms and mild functional impairment, while patients in Cluster D (14.3%) and E (14.8%) (worst outcome) had moderate-to-severe symptoms and severe functional impairment. Occupational functioning, disorganized thinking, and positive symptoms were sufficient to describe the clusters. Membership in the best/worst clusters was predicted by baseline scores for functioning and symptom severity, and by early changes in symptoms with treatment. Psychiatric symptoms and functioning provided complementary information to describe treatment outcomes. Early symptom response significantly improved the prediction of outcome, suggesting that early monitoring of treatment response may be useful in clinical practice.

Flexible-dose clinical trials: predictors and outcomes of antipsychotic dose adjustments.

Abstract: In a new approach to the interpretation of data from flexible-dose studies, we examined the safety and efficacy measurements that preceded and followed dose changes, to identify clinical factors that predict dose change as well as subsequent outcome of clinical status with dose change. This was a post hoc analysis of 3 flexible-dosed olanzapine studies: acutely ill bipolar I patients with an index manic episode (N = 452) who received olanzapine (5-20 mg/d) or haloperidol (3-15 mg/d); acutely ill patients with schizophrenia (N = 339) who received olanzapine (10-20 mg/d) or risperidone (4-12 mg/d) for 28 weeks; and remitted bipolar I patients (N = 361) who received olanzapine (5-20 mg/d) or placebo for 48 weeks. The major findings of this analysis were: an increase in dose was predicted by baseline illness severity in the acute studies, and a decrease in dose was predicted by illness symptom improvement or worsening of adverse events. Dose decrease was followed by significantly decreased efficacy for patients with acute mania treated with olanzapine or haloperidol, and olanzapine dose increases were followed by improved efficacy. Treatment-emergent extrapyramidal symptom adverse events and akathisia typically predicted dose decreases. Techniques used in this analysis may prove to be useful in assessing the relationship between dose change and safety and efficacy measures.

A New Model for Communicating Risk Information in Direct-to-Consumer Print Advertisements

Direct-to-consumer (DTC) pharmaceutical print ads are required by law to carry a “fair balance” of risks and benefits. There are little quantitative data on the effectiveness of risk communication to the consumer. A questionnaire-based method was used to compare consumer reactions to DTC print advertisements that varied in the amount and format of health risk information presented. The highest-scoring ads contained risk information in a prominent risk window. As the number of side effects listed (4, 8, or 12) increased, more consumers recalled no side effects correctly (37%, 45%, and 53%, respectively). On the basis of these results, communication of risk information to consumers could be improved by highlighting risks using a window format, and limiting the number of common side effects listed.

Assessment of the persistence of anacetrapib and evacetrapib concentrations using two pharmacokinetic modeling approaches.

Anacetrapib, a cholesterol ester transfer protein (CETP) inhibitor, has been reported to have longer elimination half‐life after longer treatment. Two pharmacokinetic model‐based approaches were used to assess whether evacetrapib, another CETP inhibitor, could behave similarly. Using population pharmacokinetic (PopPK) modeling, evacetrapib and anacetrapib pharmacokinetics were characterized using available concentration‐time data, and steady‐state conditions were simulated. Published 2‐compartment models for each compound were adapted to include a hypothetical third compartment representing a depot into which drug could partition. Physiologically based pharmacokinetic (PBPK) modeling was used to predict steady‐state conditions and terminal half‐life based on known physicochemical and dispositional properties. The PopPK model described the anacetrapib data well, showing a likely third compartment with estimated apparent volume of 40,700 L. Anacetrapibs estimated half‐life for this compartment was 550 days. Simulations for evacetrapib using a hypothetical 3‐compartment model, the third compartment being consistent with that of the anacetrapib model, produced predictions inconsistent with reported results, indicating that evacetrapib did not substantially accumulate into a large compartment. The PBPK simulations were consistent with PopPK results, predicting accumulation for anacetrapib (but not evacetrapib) followed by very slow elimination. Based on available data and known physicochemical properties, evacetrapib is not expected to accumulate substantially during long‐term treatment.

Ethical Considerations for Increased Transparency and Reproducibility in the Retrospective Analysis of Health Care Data

In the field of health care, researchers and decision makers are increasingly turning toward retrospective observational studies of administrative claims and electronic health record databases to improve outcomes for patients. For many important questions, randomized studies have not been conducted, and even when they have been, such studies often inadequately reflect the realities of patients’ lives or care. However, use of retrospective studies not only increases methodological complexity but also requires more subjectivity for those attempting to perform statistical analysis. The hurdles for establishing the reproducibility of such research to ensure accuracy and generalizability are therefore also higher, as are the requirements for transparency to limit the impact of bias. The ethical statistical practitioner will therefore need to take additional steps to enable results to be interpreted and acted upon with confidence. These include increased transparency regarding the impact of database selection, database quality, database content, and design decisions on the robustness of statistical conclusions. A number of approaches to increase the reproducibility of retrospective health care research are also presented, along with some discussion regarding responsibilities of data owners, statistical practitioners, publishers, and users of results.

Response to “Statistical Thinking and Methods in Quality Improvement: A Look to the Future” by Roger Hoerl and Ronald Snee

Eli Lilly and Company, Indianapolis, IN I commend Drs. Hoerl and Snee for an insightful paper highlighting the challenges faced by the statistics profession and our failure to respond to these in the past. Particularly enlightening is the comparison of the work environment from the time of Dr. Hoerl’s father in the 1950s to today. The prevalence of statistical software and training in statistical methods has enabled anyone with data and a computer to ‘‘do statistics.’’ This has led to a lack of clarity in the role of a statistician in many industries. Further, the lack of interest from the statistics community in emerging data-driven applications (i.e., data mining, Six Sigma, etc.) have required this void to be filled with other disciplines. The solutions are often abundant in the application of statistical methodology combined with clever, heuristic approaches that, despite little development of rigorous theoretical foundations, are sometimes very effective in practice. The question then arises, ‘‘What is the role of an industry statistician?’’ The authors propose that the statistics community shift their focus from methods research to ‘‘statistical engineering,’’ defined as the use of statistics to benefit mankind. They provide steps showing how this discipline should be inserted into university curricula, applied within organizations by embedding statistical thinking and methods into organizational processes, and used to solve problems organizations face not just at the functional or business unit level but in the broadest sense; for example, how to respond to the current financial crisis. One point that cannot be brought out clearly enough is why it is essential that statisticians embrace this change in philosophy and, equally as important, why organizations should demand it. Ultimately, statistics is the study of uncertainty: how to measure it, how to model it, and how to reduce it. Whether in data mining or process improvement, organizations face uncertainty in inputs, uncertainty in the relationship between inputs and outputs, and uncertainty in the outputs themselves. Where many of the ‘‘nonstatistical’’ approaches have achieved success is by identifying a local solution that appears robust to this uncertainty in a collection of data. Statisticians need to recognize the value of this approach and ensure organizations understand Address correspondence to Matthew Rotelli, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA. E-mail: Rotel@Lilly.com Quality Engineering, 22:133–134, 2010 Copyright # Taylor & Francis Group, LLC ISSN: 0898-2112 print=1532-4222 online DOI: 10.1080/08982111003800513