Psychiatric Clinical Trials Research Articles

Medication Adherence:An Important Uncontrolled Variable in Clinical Trials

Over the past decade, there have been thousands of controlled clinical trials assessing the efficacy and safety of drugs used to treat a range of illnesses. The purpose of this review is to document how often medication adherence is controlled in pharmaceutical drug research. Authors of this study focused specifically on psychiatric drugs and clinical drug trials between the years 2002 and 2012. The automated searches included the use of search engines designed to scan documents for key words; limiters were set to narrow the search queries to human subjects, clinical drug trials, and publications within the past 10 years (2002 to 2012). Databases reviewed included: PubMed / Medline, Science Direct, Scirus, and Scopus. The variable, control for adherence, occurred in a low frequency among articles published, and statistical significance was found between drug classes as well as queried search phrases that examined adherence versus compliance. Overall, mention of control for adherence or compliance is missing in a significant and large portion of published articles involving clinical drug trials. The results revealed that the majority of articles written, across all four databases and all seven drug categories, did not control for medication adherence and compliance. At the conservative end, results show that approximately 67% of articles on clinical drug trials neglected to mention or control for compliance. Results call into question the validity of clinical drug trial claims, as well as the safety and efficacy of pharmaceuticals in psychiatric practice.

Structural Models Describing Placebo Treatment Effects in Schizophrenia and Other Neuropsychiatric Disorders

Large variation in placebo response within and among clinical trials can substantially affect conclusions about the efficacy of new medications in psychiatry. Developing a robust placebo model to describe the placebo response is important to facilitate quantification of drug effects, and eventually to guide the design of clinical trials for psychiatric treatment via a model-based simulation approach. In addition, high dropout rates are very common in the placebo arm of psychiatric clinical trials. While developing models to evaluate the effect of placebo response, the data from patients who drop out of the trial should be considered for accurate interpretation of the results. The objective of this paper is to review the various empirical and semi-mechanistic models that have been used to quantify the placebo response in schizophrenia trials. Pros and cons of each placebo model are discussed. Additionally, placebo models used in other neuropsychiatric disorders like depression, Alzheimer's disease and Parkinson's disease are also reviewed with the objective of finding those placebo models that could be useful for clinical studies of both acute and chronic schizophrenic disease conditions. Better understanding of the patterns of dropout and the factors leading to dropouts are crucial in identifying the true placebo response. We therefore also review dropout models that are used in the development of models for treatment effects and in the optimization of clinical trials by simulation approaches. The use of an appropriate modelling strategy that is capable of identifying the potential sources of variable placebo responses and dropout rates is recommended for improving the sensitivity in discriminating between the effects of active treatment and placebo.

Evaluation of performance of some enrichment designs dealing with high placebo response in psychiatric clinical trials

Dealing with high placebo response remains a big challenge to conventional clinical trials for psychiatric disorders. A widely-used design strategy is to implement a placebo lead-in phase prior to randomization. The sequentially parallel design (SPD) proposed by Fava et al., which contains two consecutive double-blind treatment stages, has recently been promoted to reduce both the high placebo response and the required sample size in clinical trials for psychiatric disorders. Our work aims to study these two design strategies and evaluate the relevant statistical approaches for continuous measures under SPD in the presence of missing data. Based on the FDA archived database, we found that a longer placebo lead-in period seemed to help in identifying more placebo responders and thus increase the chance to detect a drug–placebo difference on continuous efficacy endpoint. Using a simple weighted ordinary least square test statistic Z OLS, we analytically showed that, under the SPD with re-randomization of placebo non-responders at the second stage (SPD-ReR), Z OLS can be used as a viable alternative to the weighted test statistic based on seemingly unrelated regression estimate Z SUR proposed by Tamura and Huang to assess treatment efficacy. Results from simulation study comparing three imputation methods (last-observation-carried-forward approach, multiple imputation, and mixed-effects model for repeated measures (MMRM)) demonstrate that, when data are missing-at-random under SPD-ReR and the dropout rate is moderate, the weighted test statistic based on MMRM estimates appears to be the most robust test statistic for SPD-ReR in terms of type I error control, power performance, and estimation accuracy.

Criteria for the ethical conduct of psychiatric neurosurgery clinical trials

There is an urgent need for an effective therapy for treatment-refractory mental illness. Trials ongoing globally that explore surgical treatment, such as deep brain stimulation, for refractory psychiatric disease have produced some promising early results. However, diverse inclusion criteria and variable methodological and ethical standards, combined with the sordid past of neuromodulation, confound trial interpretation and threaten the integrity of a new and emerging science. What is required is a standard of ethical practice, globally applied, for neurosurgical trials in psychiatry that protects patients and maintains a high ethical benchmark for clinicians and researchers to meet. With mental illness, as well as treatment resistance, reaching epidemic proportions, ethically and scientifically sound clinical trials will lead to effective and safe surgical treatments that will become vital components of the clinicians' armamentarium. Ethical criteria, such as the ones proposed here, need to be established now and applied in earnest if the field is to move forward and if patients with no other therapeutic options are to receive much-needed treatment.

Changes in Clinical Trials Methodology Over Time: A Systematic Review of Six Decades of Research in Psychopharmacology

BackgroundThere have been many changes in clinical trials methodology since the introduction of lithium and the beginning of the modern era of psychopharmacology in 1949. The nature and importance of these changes have not been fully addressed to date. As methodological flaws in trials can lead to false-negative or false-positive results, the objective of our study was to evaluate the impact of methodological changes in psychopharmacology clinical research over the past 60 years.Methodology/Principal FindingsWe performed a systematic review from 1949 to 2009 on MEDLINE and Web of Science electronic databases, and a hand search of high impact journals on studies of seven major drugs (chlorpromazine, clozapine, risperidone, lithium, fluoxetine and lamotrigine). All controlled studies published 100 months after the first trial were included. Ninety-one studies met our inclusion criteria. We analyzed the major changes in abstract reporting, study design, participants' assessment and enrollment, methodology and statistical analysis. Our results showed that the methodology of psychiatric clinical trials changed substantially, with quality gains in abstract reporting, results reporting, and statistical methodology. Recent trials use more informed consent, periods of washout, intention-to-treat approach and parametric tests. Placebo use remains high and unchanged over time.Conclusions/SignificanceClinical trial quality of psychopharmacological studies has changed significantly in most of the aspects we analyzed. There was significant improvement in quality reporting and internal validity. These changes have increased study efficiency; however, there is room for improvement in some aspects such as rating scales, diagnostic criteria and better trial reporting. Therefore, despite the advancements observed, there are still several areas that can be improved in psychopharmacology clinical trials.

Comprehensibility of Translated Informed Consent Documents Used in Clinical Research in Psychiatry

Background:Informed consent forms are required in all clinical trials which are approved by an independent Ethics Committee before practical use in the trials. However, how much the average subject actually understands of the information contained in these informed consent forms is uncertain.Aim:In a cross sectional study, the translated informed consent forms used in psychiatric clinical trials were assessed with respect to their ease of readability.Materials and Methods:We analyzed 30 informed consent forms translated from English to Hindi used in multinational and multicentre psychiatric clinical trials sponsored by different sponsors. We examined consent forms for readability scores and factors that might relate to readability.Results:The mean readability score for the informed consent forms, determined by the Flesch-Kincaid Grade Level Index (FKGL) was grade levels of 13.66. The ease of readability assessed by the Flesch Reading Ease Score (FRES) was 46.08 suggesting significant complexity of the texts. These values carry even more significance when the average years of schooling for India as a whole are 6.2 years.Conclusion:Our results show that the most informed consent forms were too complex to understand by an average adult subject. We suggest reducing this complexity and increasing the ease of readability so those average subjects receive the intended information as exactly as it could be. This can be achieved by few simple measures like improving the deficiencies in translation processes, encouraging the investigators to participate while preparing these forms, and enhanced understanding of the site specific requirements, namely culture, language (dialect), general literacy rate, etc.

Drs. Damaraju, Olson, and Canuso Reply

Back to table of contents Previous article Next article Letters to the EditorFull AccessDrs. Damaraju, Olson, and Canuso ReplyC.V. DAMARAJU Ph.D.WILLIAM OLSON Ph.D.CARLA M. CANUSO M.D.,C.V. DAMARAJU Ph.D.Search for more papers by this authorWILLIAM OLSON Ph.D.Search for more papers by this authorCARLA M. CANUSO M.D.Search for more papers by this author,Published Online:1 Nov 2009https://doi.org/10.1176/appi.ajp.2009.09070959rrAboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail To the editor: We appreciate the insightful comments made by Drs. Potkin and Siu in their reference to our article. As the editorial accompanying our article observed, the proportion of missing data in our study was small (<20%). The likelihood-based mixed-model repeated-measures analysis is generally robust to departures from the missing at random assumption when the sample size is large and the number of dropouts is small (1) . In our study, results were consistent between the last observation carried forward and mixed-model repeated-measures analyses of the primary efficacy endpoint (day 14) based on the intent-to-treat data set. The mixed-model repeated-measures analyses utilized PANSS score data obtained at all visits, including the dropout visit. Missing data arise under different conditions. Missing completely at random means that the observed data are observed at random and the missing data are missing at random, whereas missing at random means only that the missing data are missing at random (2) . It is not possible to test the missing at random assumption, since by definition it involves data we do not have (i.e., the missing data). However, it is possible to examine whether the data are consistent with the missing completely at random assumption because the definition of missing completely at random involves observed data, and, in particular, we can examine whether the observed data are observed at random. Although dropout plots are helpful, these could be misleading in instances in which few dropouts occur. We have tested the missing completely at random assumption based on the dropouts during the monotherapy period using a nonparametric method suggested by Diggle et al. (3 , 4) . We do not find evidence to reject the null hypothesis of completely random dropouts during the monotherapy treatment period in this study (p=0.52). In general, the mixed-model repeated-measures models are better suited than last observation carried forward models to handle longitudinal data in the presence of dropouts commonly arising in psychiatric clinical trials in which the missing at random assumption appears reasonably plausible. However, we do acknowledge that missing data in psychiatric clinical trials pose serious threats to valid statistical inference when such data are not missing at random. Because the validity of the missing at random assumption cannot be assessed from the data, a sensible approach to inference is to perform sensitivity analyses that account for varying degrees of selection bias (5) . Finally, we thank Drs. Potkin, Sui, and Hamer for their thoughtful comments and the Journal for providing the forum to share this discussion. We hope that such exchanges will help researchers as they explore methodologies to best interpret the complexities of clinical trial data.Titusville, N.J.The authors’ disclosures accompany the original article.This letter (doi: 10.1176/appi.ajp.2009.09070959rr) was accepted for publication in August 2009.

Dropouts and Missing Data in Psychiatric Clinical Trials

Back to table of contents Previous article Next article Letters to the EditorFull AccessDropouts and Missing Data in Psychiatric Clinical TrialsSTEVEN G. POTKIN M.D.,CYNTHIA O. SIU Ph.D.,STEVEN G. POTKIN M.D.Search for more papers by this author,CYNTHIA O. SIU Ph.D.Search for more papers by this author,Published Online:1 Nov 2009https://doi.org/10.1176/appi.ajp.2009.09070959AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail To The Editor: In the June 2009 issue of the Journal , the editorial by Robert M. Hamer, Ph.D., and Pippa M. Simpson, Ph.D. (1) , discussed last observation carried forward and mixed models as described in the article by Carla M. Canuso, M.D., et al. (2) . Drs. Hamer and Simpson concluded that “mixed models are preferable to last observation carried forward when the number of subjects is sufficiently large and the proportion of missing data is small enough” (dropout rate <20%) ( 1 . p. 641). However, since most psychiatric clinical trials report dropout rates of >20%, the problem of missing data remains a major challenge for analysis. The key issue is whether those who drop out differ from those who remain in the study and how this relates to individuals’ treatment outcomes. A simple graphical method we"ve previously described (3) addresses this matter directly. In a 40-week randomized study of ziprasidone and haloperidol, we divided the entire study sample into distinct subgroups corresponding to individual dropout times and included the completer subgroup. Missing mechanism assumptions could then be checked based on the longitudinal responses among completers and in each of the distinct dropout cohorts. Plotting the trajectory of Positive and Negative Syndrome Scale (PANSS) scores for the dropout cohorts showed that the missing completely at random assumption was violated and pointed to a key source of bias: the subjects receiving ziprasidone, 80–160 mg/day (standardized as a z score), who discontinued early in the first 6 weeks had higher PANSS scores than the haloperidol subjects, but this difference was not seen at other time points. In the Canuso et al. study, the primary efficacy analysis was based on the last observation carried forward, which assumed that the responses following dropout remained constant at the last observed value prior to dropout, a not very likely event. A mixed-model repeated-measures analysis was also performed for observed cases occurring within a visit window at each time point. It is not clear whether PANSS scores obtained at all visits (and most importantly the dropout visit) were included in this mixed-model repeated-measures analysis (e.g., 24 PANSS scores missing at day 5, while only 20 dropouts during the 2-week monotherapy phase for the paliperidone group). The data supplement accompanying the online version of this letter summarizes the missing data pattern extracted from Figure 2 of the Canuso et al. article. Because no data are available to verify whether missing data at day-5 visits are the same as the set of observed values (missing completely at random assumption and ignorable) or whether they are dropout-related (nonignorable), it would be difficult to interpret the early responses to paliperidone therapy at day 5 relative to quetiapine (p<0.05) and placebo (p<0.001) (2) . The use of a dropout cohort plot would allow accounting for all dropouts at each study time point and the identifying of potential bias. Irvine, Calif.Ringoes, N.J.Dr. Potkin has served as a consultant to and received grant support or funding from Pfizer (contributing to the findings discussed in this letter); he has also served as a consultant to or received grant support or funding from AstraZeneca AB, Bioline, Bristol-Myers Squibb, Ceregene, Cortex, Dainippon Sumitomo, Elan Corp., Eli Lilly, Forest Laboratories, Janssen, Minster Pharmaceuticals, Novartis, Organon/Schering Plough, Otsuka, Solvay, Roche Laboratories, Wyeth Research, and Vanda; and he has served as a consultant to or received grant support or funding from APA, the Alzheimer’s Association, Brigham and Women’s Hospital, Harvard Massachusetts General Hospital, International Society for CNS Clinical Trials and Methodology, NIH (National Institute on Alcohol Abuse and Alcoholism, National Institute of Biomedical Imaging and BioEngineering, National Center for Research Resources), and the University of Southern California. Dr. Siu has served as a consultant to Pfizer, Dainippon Sumitomo Pharma America, Memory Pharmaceutical, Wyeth, and Prescient.This letter (doi: 10.1176/appi.ajp.2009.09070959) was accepted for publication in August 2009.

Last Observation Carried Forward Versus Mixed Models in the Analysis of Psychiatric Clinical Trials

Last Observation Carried Forward Versus Mixed Models in the Analysis of Psychiatric Clinical Trials

Sequential Allocation to Balance Prognostic Factors in a Psychiatric Clinical Trial

OBJECTIVE:This paper aims to describe and discuss a minimization procedure specifically designed for a clinical trial that evaluates treatment efficacy for OCD patients.METHOD:Aitchison’s compositional distance was used to calculate vectors for each possibility of allocation in a covariate adaptive method. Two different procedures were designed to allocate patients in small blocks or sequentially one-by-one.RESULTS:We present partial results of this allocation procedure as well as simulated data. In the clinical trial for which this procedure was developed, successful balancing between treatment arms was achieved. Separately, in an exploratory analysis, we found that if the arrival order of patients was altered, most patients were allocated to a different treatment arm than their original assignment.CONCLUSION:Our results show that the random arrival order of patients determine different assignments and therefore maintains the unpredictability of the allocation method. We conclude that our proposed procedure allows for the use of a large number of prognostic factors in a given allocation decision. Our method seems adequate for the design of the psychiatric trials used as models. Trial registrations are available at clinicaltrials.gov NCT00466609 and NCT00680602.

P.8.a.013 An independent clinician group of excellence to improve psychiatric clinical trials in France

Chronological age estimation from the dental parameters is becoming increasingly important. The London atlas of tooth development is the most recent developed method and represents a modification of the previous older methods.The aim of this study was to evaluate the accuracy of the London atlas for the dental age estimation in the Portuguese population.The study sample included 736 radiographic images (498 females and 238 males) of Portuguese origin, patients of Dental Clinic of Superior Institute of Health Sciences Egas Moniz and Dental Medicine Faculty, University of Lisbon. The age range of the individuals was between 3 and 24 years. Estimated age was compared with the chronological age using the paired t-test.The results showed that there was no statistically significant difference between left and right side of the jaw (p > 0.05). Both sides showed an average overestimation of age by one month approximately. Moreover, the significant difference between chronological and estimated age was not observed in the females. However, the significant difference was observed in a sample coming from males (right: p = 0.008; left: p = 0.003).Our results showed that the London atlas can be potentially used as a tool for age estimation. However, the difference between sexes clearly suggests that separate charts should be made for each sex. Further studies, which will have as a final goal the development of a new method for age estimation using dental parameters, are needed.

Sequential Allocation and Balancing Prognostic Factors in a Psychiatric Clinical Trial

AbstractIn controlled clinical trials, each of several prognostic factors should be balanced across the trial arms. Traditional restricted randomization may be proved inadequate especially with small sample sizes. In psychiatric disorders such as obsessive compulsive disorder (OCD), small trials prevail. Therefore, procedures to minimize the chance of imbalance between treatment arms are advisable. This paper describes a minimization procedure specifically designed for a clinical trial that evaluates treatment efficacy for OCD patients. Aitchison's compositional distance was used to calculate vectors for each possibility of allocation in a covariate adaptive method. Two different procedures were designed to allocate patients in small blocks or sequentially one-by-one. Partial results of this allocation procedure as well as simulated ones are shown. In the clinical trial for which this procedure was developed, the balancing between treatment arms was achieved successfully. Simulations of results considering different arrival order of patients showed that most of the patients are allocated in a different treatment arm if arrival order is modified. Results show that a random factor is maintained with the random arrival order of patients. This specific procedure allows the use of a large number of prognostic factors for the allocation decision and was proved adequate for a psychiatric trial design.

Law Ensures Public Access to Clinical Trial Disclosures

Law Ensures Public Access to Clinical Trial Disclosures

Primary care patients in psychiatric clinical trials: a pilot study using videoconferencing.

BackgroundWhile primary care physicians play a pivotal role in the treatment of depression, collaboration between primary care and psychiatry in clinical research has been limited. Primary care settings provide unique opportunities to improve the methodology of psychiatric clinical trials, by providing more generalizable and less treatment-resistant patients. We examined the feasibility of identifying, recruiting, screening and assessing primary care patients for psychiatric clinical trials using high-quality videoconferencing in a mock clinical trial.Methods1329 patients at two primary care clinics completed a self-report questionnaire. Those screening positive for major depression, panic, or generalized anxiety were given a diagnostic interview via videoconference. Those eligible were provided treatment as usual by their primary care physician, and had 6 weekly assessments by the off-site clinician via videoconferencing.Results45 patients were enrolled over 22 weeks, with 36 (80%) completing the six-week study with no more than two missed appointments. All diagnostic groups improved significantly; 94% reported they would participate again, 87% would recommend participation to others, 96% felt comfortable communicating via videoconference, and 94% were able to satisfactorily communicate their feelings via video.ConclusionResults showed that primary care patients will enroll, participate in and complete psychiatric research protocols using remote interviews conducted via videoconference.

An examination of the efficiency of the sequential parallel design in psychiatric clinical trials

Psychiatric clinical trials have a high failure rate, even among agents which are known to be effective. Because of this high failure rate, a novel clinical trial design has been proposed which incorporates a second phase in which non-responders to placebo are randomly reassigned to drug or placebo. The purpose of this research is to examine the efficiency of this new design compared to the conventional two arm clinical trial. We consider both binary and continuous endpoints. The limiting distribution of a class of weighted average test statistics is obtained for the binary case which allows analytic calculation of the power for a given set of parameters. For the continuous case, we examine the efficiency of seemingly unrelated regression and a weighted average statistic via simulation. The novel design reduces the sample size 20-25% compared to the standard design under a wide range of parameters. There are no actual trials with the novel design therefore assumptions of the effect size across two periods for actual psychiatric agents is unknown. The new design reduces sample size which in turn should reduce the cost of clinical trials. Further refinements of the design are possible including alternative test statistics and incorporation of additional data from placebo responders.

Use of OSWALD for analyzing longitudinal data with informative dropout

OSWALD (Object-oriented Software for the Analysis of Longitudinal Data) is flexible and powerful software written for S-PLUS for the analysis of longitudinal data with dropout for which there is little other software available in the public domain. The implementation of OSWALD is described through analysis of a psychiatric clinical trial that compares antidepressant effects in an elderly depressed sample and a simulation study. In the simulation study, three different dropout mechanisms: completely random dropout (CRD), random dropout (RD) and informative dropout (ID), are considered and the results from using OSWALD are compared across mechanisms. The parameter estimates for ID-simulated data show less bias with OSWALD under the ID missing data assumption than under the CRD or RD assumptions. Under an ID mechanism, OSWALD does not provide standard error estimates. We supplement OSWALD with a bootstrap procedure to derive the standard errors. This report illustrates the usage of OSWALD for analyzing longitudinal data with dropouts and how to draw appropriate conclusions based on the analytic results under different assumptions regarding the dropout mechanism.

Industry Funding and Author-Industry Affiliation in Clinical Trials in Psychiatry

Back to table of contents Previous article Next article Letters to the EditorFull AccessIndustry Funding and Author-Industry Affiliation in Clinical Trials in PsychiatryJOHN H. MONTGOMERY D.O.,JOHN H. MONTGOMERY D.O.Search for more papers by this author,Published Online:1 Jun 2006AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail To the Editor: The recent study by Roy H. Perlis, M.D., and colleagues (1) addressed an important relationship between author-industry affiliation and study outcomes in clinical trials. The authors examined funding source and author financial conflict of interest in 397 clinical trials published in four psychiatric journals over a 3-year period. The authors defined conflict of interest as “any report of consulting or speaking fees…stock ownership, or employment by the study sponsor” (p. 1957). Their results suggested that author conflict of interest was prevalent and significantly associated with positive trial outcomes in all of the 162 randomized, double-blind, placebo-controlled studies. Further, the authors stated that in these studies, “industry support itself was not significantly associated with a positive outcome” (p. 1958). Dr. Perlis and colleagues also noted that, to their knowledge, no previous studies have investigated the “extent and implications” of funding source and author-industry affiliation in psychiatric clinical trials (p. 1957). I would like to bring to the authors’ attention a published study in which my colleagues and I investigated relationships between funding source, study quality, author-industry affiliation, and study outcome in all published randomized, double-blind trials (N=86) of second-generation antipsychotics for the treatment of schizophrenia (2) . We defined a study as having author-industry affiliation if the primary author was listed as an employee or agent of the sponsoring pharmaceutical company. Although our results showed a correlation between author-industry affiliation and study outcome in industry-funded studies, this was not significant. We did find a significant relationship between funding source and study outcome, with industry-funded studies favoring the innovative (sponsored) treatments over standard therapies to a greater degree than nonindustry-funded studies. The relationship between funding source and study outcome was investigated in several other published studies (3 – 8) . Similar to our findings, these studies all demonstrated that industry support was associated with positive study outcome. Findings reported by Dr. Perlis and colleagues are interesting in that industry support itself was not associated with positive study outcomes, yet author conflict of interest was. I invite the authors to comment further on this possible discrepancy. I applaud the call of Dr. Perlis and colleagues for strategies to ensure transparency in the reporting of all clinical trials, regardless of outcome. Hopefully, the psychiatric community will continue to meaningfully address the subject of bias and conflict of interest in industry-supported research.Whitfield, Miss.

Industry Funding and Author-Industry Affiliation in Clinical Trials in Psychiatry

Back to table of contents Previous article Next article Letters to the EditorFull AccessIndustry Funding and Author-Industry Affiliation in Clinical Trials in PsychiatryJOHN H. MONTGOMERY D.O.,JOHN H. MONTGOMERY D.O.Search for more papers by this author,Published Online:1 Jun 2006AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail To the Editor: The recent study by Roy H. Perlis, M.D., and colleagues (1) addressed an important relationship between author-industry affiliation and study outcomes in clinical trials. The authors examined funding source and author financial conflict of interest in 397 clinical trials published in four psychiatric journals over a 3-year period. The authors defined conflict of interest as “any report of consulting or speaking fees…stock ownership, or employment by the study sponsor” (p. 1957). Their results suggested that author conflict of interest was prevalent and significantly associated with positive trial outcomes in all of the 162 randomized, double-blind, placebo-controlled studies. Further, the authors stated that in these studies, “industry support itself was not significantly associated with a positive outcome” (p. 1958). Dr. Perlis and colleagues also noted that, to their knowledge, no previous studies have investigated the “extent and implications” of funding source and author-industry affiliation in psychiatric clinical trials (p. 1957). I would like to bring to the authors’ attention a published study in which my colleagues and I investigated relationships between funding source, study quality, author-industry affiliation, and study outcome in all published randomized, double-blind trials (N=86) of second-generation antipsychotics for the treatment of schizophrenia (2) . We defined a study as having author-industry affiliation if the primary author was listed as an employee or agent of the sponsoring pharmaceutical company. Although our results showed a correlation between author-industry affiliation and study outcome in industry-funded studies, this was not significant. We did find a significant relationship between funding source and study outcome, with industry-funded studies favoring the innovative (sponsored) treatments over standard therapies to a greater degree than nonindustry-funded studies. The relationship between funding source and study outcome was investigated in several other published studies (3 – 8) . Similar to our findings, these studies all demonstrated that industry support was associated with positive study outcome. Findings reported by Dr. Perlis and colleagues are interesting in that industry support itself was not associated with positive study outcomes, yet author conflict of interest was. I invite the authors to comment further on this possible discrepancy. I applaud the call of Dr. Perlis and colleagues for strategies to ensure transparency in the reporting of all clinical trials, regardless of outcome. Hopefully, the psychiatric community will continue to meaningfully address the subject of bias and conflict of interest in industry-supported research.Whitfield, Miss.

Using trajectories from a bivariate growth curve as predictors in a Cox regression model

An important research objective in most psychiatric clinical trials of maintenance treatment is to find predictors of recurrence of illness. In those trials, patients are first admitted into an open treatment period also called acute treatment. If they respond to the treatment and are considered to have stable remission from the illness, they enter the second phase of the trial where they are randomized into different arms of the 'maintenance treatments'. Often, more than one response variable is measured longitudinally in the acute treatment phase to monitor treatment responses. Trajectories of these response measures are believed to have predictive ability for recurrences in the maintenance phase of the trial. By using a bivariate growth curve from two such longitudinal measures, we developed a method to use the estimated trajectories of each subject in a Cox regression model to predict recurrence in the maintenance phase. To adjust for the parameter estimation errors, we applied a full likelihood approach based on the conditional expectations of the predictors. Simulation studies indicate that the estimation error corrected estimators for the Cox model parameters are less biased when compared to the naive regression estimators without accounting for these errors. The uniqueness of this method lies in estimating trajectories from bivariate unequally spaced longitudinal response measures. An illustrative example is provided with data from a maintenance treatment trial for major depression in an elderly population. Visual Fortran 90 programs were developed to implement the algorithm.

Gender Differences in the Prescribing of Antipsychotic Drugs

Objective: The aim of this article is to offer recommendations and rationale for gender-specific antipsychotic treatment. Method: The author summarizes reviews of recent literature in psychiatric clinical trials, pharmacology, drug safety, toxicology, obstetrics and gynecology, and pediatrics. Results: The pharmacokinetics and pharmacodynamics of antipsychotic drugs differ in women and men and are influenced by gender-specific factors such as body build, diet, smoking, concurrent medication, exercise, substance use, and hormonal transitions. In general, and for some drugs in particular, women require lower doses in order to stay well. Because preliminary drug testing is not done in pregnant women, the issue of effective dosing during pregnancy is unstudied, and safety for fetuses and nursing infants may not become evident until a drug is widely used. Specific adverse effects on issues crucial to women (e.g., parenting) have not been well studied, but some side effects, such as weight gain, passivity, hypotension, and hyperprolactinemia, are reported to be particularly problematic for women. Some serious side effects are more often seen among women than among men. Conclusions: Optimal maintenance regimens of antipsychotics for women and men are not the same.