Cost-effectiveness measures, methods, and policy implications from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) for schizophrenia

Sales of atypical antipsychotics have expanded rapidly as their use for treatment of schizophrenia and other illnesses has increased. The CATIE trial was designed to evaluate the cost-effectiveness of these medications. Cost-effectiveness analysis in CATIE examined the benefits and costs of treatments and attempted to determine whether one treatment would result in better outcomes and lower costs than another one. While the data from CATIE on cost-effectiveness have not been analyzed yet, it is still possible to consider the potential implications of the analysis, including how the roles of researchers and stakeholders should be considered in deciding optimal clinical practices and public policies.     

The National Institute of Mental Health Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) project: schizophrenia trial design and protocol development

The National Institute of Mental Health initiated the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) program to evaluate the effectiveness of antipsychotic drugs in typical settings and populations so that the study results will be maximally useful in routine clinical situations. The CATIE schizophrenia trial blends features of efficacy studies and large, simple trials to create a pragmatic trial that will provide extensive information about antipsychotic drug effectiveness over at least 18 months. The protocol allows for subjects who receive a study drug that is not effective to receive subsequent treatments within the context of the study. Medication dosages are adjusted within a defined range according to clinical judgment. The primary outcome is all-cause treatment discontinuation because it represents an important clinical endpoint that reflects both clinician and patient judgments about efficacy and tolerability. Secondary outcomes include symptoms, side effects, neurocognitive functioning, and cost-effectiveness. Approximately 50 clinical sites across the United States are seeking to enroll a total of 1,500 persons with schizophrenia. Phase 1 is a double-blinded randomized clinical trial comparing treatment with the second generation antipsychotics olanzapine, quetiapine, risperidone, and ziprasidone to perphenazine, a midpotency first generation antipsychotic. If the initially assigned medication is not effective, subjects may choose one of the following phase 2 trials: (1) randomization to open-label clozapine or a double-blinded second generation drug that was available but not assigned in phase 1; or (2) double-blinded randomization to ziprasidone or another second generation drug that was available but not assigned in phase 1. If the phase 2 study drug is discontinued, subjects may enter phase 3, in which clinicians help subjects select an open-label treatment based on individuals' experiences in phases 1 and 2.     

Science to services: consumers need "real-world" science

Over the past decade there has been a revolution in pharmacotherapy for schizophrenia and related disorders. The second generation, or atypical, antipsychotic medications have demonstrated efficacy and generally better side-effect profiles. However, from the perspective of policy makers the higher costs associated with these newer medications leads to tough decisions regarding their continued use in light of an escalating fiscal crisis. For consumers, both persons with the illness and their family caregivers, the budgetary cutbacks leave many scrambling for answers to questions that most treatment (efficacy) studies were never designed to answer. "Should we oppose formulary restrictions on principle alone, or is there scientific data that can be relied on to inform our position?" On a more personal note, many are asking whether or not to switch to one of the newer medications and which medication would be best for them. Unlike CATIE, efficacy studies were never designed to answer such questions. In this article, we start by highlighting how CATIE will fill important gaps in translating the results of efficacy studies to effectiveness in the real world. Both the development of the CATIE methodology and the study design itself reflect what we will refer to as "real-world science": i.e., science that sheds light on effectiveness in vivo and can inform decisions consumers, clinicians, and policy makers are faced with day-to-day. We discuss CATIE in the context of the fiscal crisis hitting MEDICAID programs leading many policy makers to take the more expensive, atypical antipsychotics off the list of medications made available to patients. We argue that studies like CATIE will be highly informative and ultimately vital to policy makers wishing to create mental health policies that will succeed. Throughout, we highlight how CATIE, and real-world science more generally, are vital to consumers striving to find the medication(s) that works best for them. Given the organic research design process, which arguably relies on a fuller range of stakeholders than any study of its kind before, we remain hopeful that CATIE can succeed in generating an unprecedented amount of real-world science that consumers can use.     

The CATIE schizophrenia trial: results, impact, controversy

The CATIE (Clinical Antipsychotic Trials for Intervention Effectiveness) Schizophrenia Trial was designed to examine fundamental issues about second-generation antipsychotic (SGA) medications (olanzapine, risperidone, quetiapine, and ziprasidone) - their relative effectiveness and their effectiveness compared to a first-generation antipsychotic (FGA), perphenazine. This article reviews these and other findings from this important trial and offers a perspective regarding their meaning for practice and their significance for the advancement of research in psychiatry. The primary outcome measure, time to discontinuation, served as an index of effectiveness and was remarkably short; only 26% of subjects completed the 18-month trial on the medicine to which they were initially randomized. Subjects receiving olanzapine experienced a slightly longer time to discontinuation. Based on this single criterion, olanzapine showed greater effectiveness than the other agents despite its association with significant metabolic disturbance, especially weight gain. Perphenazine unexpectedly showed comparable levels of effectiveness and produced no more extrapyramidal side effects than the other agents. Despite modest prolactin elevation, risperidone was the best-tolerated medication. Ziprasidone was associated with weight loss and with positive impact on lipids and blood glucose. In Phase 2, clozapine demonstrated better effectiveness compared to other SGAs for subjects who discontinued their Phase 1 medication because of efficacy. Olanzapine and risperidone showed greater effectiveness in the tolerability pathway. CATIE secondary outcomes are currently being examined. Improvements in cognition were modest among all the agents in Phase 1, and perphenazine was no less effective in improving cognitive performance than the SGAs. Cost-effectiveness analysis revealed a significant advantage for perphenazine, due to the impact of the high-priced, brand-name SGAs on overall health care costs.     

Effectiveness of olanzapine, quetiapine, and risperidone in patients with chronic schizophrenia after discontinuing perphenazine: a CATIE study

OBJECTIVE: The relative effectiveness of newly started antipsychotic drugs for individuals with schizophrenia may depend on multiple factors, including each patient's previous treatment response and the reason for a new medication trial. This randomized, double-blind study compared olanzapine, quetiapine, and risperidone in patients who had just discontinued the older antipsychotic perphenazine. METHOD: Subjects with schizophrenia (N=114) who had been randomly assigned to and then discontinued perphenazine in phase 1 of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia study were reassigned randomly to double-blinded treatment with olanzapine, 7.5-30.0 mg/day (N=38); quetiapine, 200-800 mg/day (N=38); or risperidone, 1.5-6.0 mg/day (N=38). The primary aim was to determine whether there were differences among these three treatments in effectiveness, as measured by time to treatment discontinuation for any reason. Secondary outcomes included reasons for treatment discontinuation and measures of drug tolerability. RESULTS: The time to treatment discontinuation was longer for patients treated with quetiapine (median, 9.9 months) and olanzapine (7.1 months) than with risperidone (3.6 months). There were no significant differences between treatments on discontinuation due to inefficacy, intolerability, or patient decision. CONCLUSIONS: Among this group of patients with chronic schizophrenia who had just discontinued the older antipsychotic perphenazine, quetiapine and olanzapine were more effective than risperidone, as reflected by longer time to discontinuation for any reason. In the context of other results from the CATIE study, the effectiveness and acceptability of antipsychotic drugs appears to vary considerably according to clinical circumstances.     

Move over LOCF: principled methods for handling missing data in sleep disorder trials

Missing data, e.g. patient attrition, are endemic in sleep disorder clinical trials. Common approaches for dealing with this situation include complete-case analysis (CCA) and last observation carried forward (LOCF). Although these methods are simple to implement, they are deeply flawed in that they may introduce bias and underestimate uncertainty, leading to erroneous conclusions. There are alternative principled approaches, however, that are available in statistical software namely mixed-effects models and multiple imputation. In this paper we introduce terminology used to describe different assumptions about missing data. We emphasize that understanding reasons for missingness is a critical step in the analysis process. We describe and implement both linear mixed-effects models and an inclusive multiple imputation strategy for handling missing data in a randomized trial examining sleep outcomes. These principled strategies are compared with "complete-case analysis" and LOCF. These analyses illustrate that methodologies for accommodating missing data can produce different results in both direction and strength of treatment effects. Our goal is for this paper to serve as a guide to sleep disorder clinical trial researchers on how to utilize principled methods for incomplete data in their trial analyses.     

What have we learned from CATIE about the pharmacologic treatment of schizophrenia?

The Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study sponsored by the National Institute of Mental Health compared the effectiveness of four second-generation antipsychotics (olanzapine, risperidone, quetiapine, and ziprasidone), and one first-generation antipsychotic (perphenazine) in 1460 individuals with chronic schizophrenia. A hybrid of effectiveness and efficacy study design, the primary outcome was antipsychotic “effectiveness,” as measured by discontinuation of the assigned antipsychotic because of inadequate efficacy, inadequate tolerability, patient decision, or other reason. Most (64% to 82%) patients discontinued the phase-I antipsychotic before 18 months of treatment (approximately two-thirds subjects continued treatment with a different antipsychotic in phase 2). The five antipsychotics varied significantly in effectiveness, efficacy, and side-effect profiles. CATIE baseline results also find the general health of individuals with chronic schizophrenia to be poor, highlighting concerns about added risk from metabolic side effects of some antipsychotics. Analysis of comparative cost effectiveness, impact on cognition, rates of recovery, phase 2 results, and many other analyses are forthcoming, and the cumulative findings from the CATIE study will provide an evidence base for clinicians, health care administrators, patients and their families to make pharmacologic treatment choices.     

Neurocognitive assessment in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) project schizophrenia trial: development,methodology, and rationale

Patients with schizophrenia are severely impaired in crucial aspects of neurocognitive function. This impairment is the strongest clinical correlate of poor long-term outcome and adaptive dysfunction. Reports of neurocognitive enhancement with second generation antipsychotic medications have thus offered promise for improvement in the long-term outcome of patients with schizophrenia. However, the majority of these studies have had serious weaknesses in methodology, such as open-label design, small samples, or inappropriate dosing of medications. More recent studies have addressed these methodological issues but have been of short duration and have largely been sponsored by pharmaceutical companies. The Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) project is a unique opportunity to address the comparative neurocognitive effectiveness of available antipsychotic medications. This article describes the neurocognitive methods used in the schizophrenia trial of the CATIE project, including the selection and training of neurocognitive raters, patient inclusion criteria for assessment, rationale for the choice of neurocognitive instruments, and methodology for each neurocognitive test.     

National Institute of Mental Health Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE): Alzheimer disease trial methodology.

The authors describe the development of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) protocol for Alzheimer disease (AD), a trial developed in collaboration with the National Institute of Mental Health (NIMH), assessing the effectiveness of atypical antipsychotics for psychosis and agitation occurring in AD outpatients. They provide an overview of the methodology utilized in the trial as well as the clinical-outcomes and effectiveness measures that were implemented.     

What CATIE found: results from the schizophrenia trial

The authors provide an overview of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) sponsored by the National Institute of Mental Health. CATIE was designed to compare a proxy first-generation antipsychotic, perphenazine, to several newer drugs. In phase 1 of the trial, consenting patients were randomly assigned to receive olanzapine, perphenazine, quetiapine, risperidone, or ziprasidone for up to 18 months on a double-blind basis. Patients with tardive dyskinesia were excluded from being randomly assigned to perphenazine and were assigned to one of the four second-generation antipsychotics in phase 1A. Clozapine was included in phase 2 of the study. Overall, olanzapine had the longest time to discontinuation in phase 1, but it was associated with significant weight and metabolic concerns. Perphenazine was not significantly different in overall effectiveness, compared with quetiapine, risperidone, and ziprasidone. Also, perphenazine was found to be the most cost-effective drug. Clozapine was confirmed as the most effective drug for individuals with a poor symptom response to previous antipsychotic drug trials, although clozapine was also associated with troublesome adverse effects. There were no differences in neurocognitive or psychosocial functioning in response to medications. Subsequent randomizations suggest that a poor response to an initial medication may mean that a different medication will be more effective or better tolerated. Although the CATIE results are controversial, they are broadly consistent with most previous antipsychotic drug trials and meta-analyses; however, the results may not generalize well to patients at high risk of tardive dyskinesia. Patient characteristics and clinical circumstances affected drug effectiveness; these patient factors are important in making treatment choices.     

NAMI perspective on CATIE: policy and research implications

The Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) was designed to assess effectiveness of antipsychotic medication for people with schizophrenia. The authors, who are administrators of the National Alliance on Mental Illness (NAMI), discuss CATIE and related policy and research studies and their implications. CATIE has answered some important questions for consumers and their families and raises many more. The prevalence of medical risk factors in the population with schizophrenia is an important part of advancing prevention. Poor adherence to medications randomly prescribed by CATIE physicians in a blinded procedure is also a key finding and points to the need for individually tailoring medication regimens. Policy makers may be tempted to oversimplify the results of CATIE by restricting access to the costlier second-generation medications. However, doing so will hurt clinical care, and any savings to state and community mental health programs may be illusory. Policy can be constructed to focus on clinical outcomes and not merely restrict access to medications on the basis of cost. Research is urgently needed on a new generation of medications with benign side effects and greater efficacy than their predecessors for people with schizophrenia.     

A systematic method for estimating individual responses to treatment with antipsychotics in CATIE

ObjectiveIn addition to comparing drug treatment groups, the wealth of genetic and clinical data collected in the Clinical Antipsychotic Trials of Intervention Effectiveness study offers tremendous opportunities to study individual differences in response to treatment with antipsychotics. A major challenge, however, is how to estimate the individual responses to treatments. For this purpose, we propose a systematic method that condenses all information collected during the trials in an optimal, empirical fashion.MethodsOur method comprises three steps. First, we test how to best model treatment effects over time. Next, we screen many covariates to select those that will further improve the precision of the individual treatment effect estimates which, for example, improves power to detect predictors of individual treatment response. Third, Best Linear Unbiased Predictors (BLUPs) of the random effects are used to estimate for each individual a treatment effect based on the model empirically indicated to best fit the data. We illustrate our method for the Positive and Negative Syndrome Scale (PANSS).ResultsA model assuming it takes on average about 30 days for a treatment to exert an effect that will then remain about the same for the rest of the trial showed the best fit to the data. Of all screened covariates, only two improved the precision of the individual treatment effect estimates. Finally, correlations between drug effects and PANSS scales suggested that in CATIE it cannot be recommended to simply combine treatment effects across drugs (e.g. to study common drug mechanisms), but it is sensible to study how a given drug affects multiple symptom dimensions.ConclusionsWe demonstrate that treatment effects can be estimated in a way that condenses all information collected in an optimal, empirical fashion. We expect the proposed method to be valuable for other clinical outcomes in CATIE and potentially other clinical trials.     

Statistical analysis of latency outcomes in behavioral experiments

In experimental designs of animal models, memory is often assessed by the time for a performance measure to occur (latency). Depending on the cognitive test, this may be the time it takes an animal to escape to a hidden platform (water maze), an escape tunnel (Barnes maze) or to enter a dark component (passive avoidance test). Latency outcomes are usually statistically analyzed using ANOVAs. Besides strong distributional assumptions, ANOVA cannot properly deal with animals not showing the performance measure within the trial time, potentially causing biased and misleading results. We propose an alternative approach for statistical analyses of latency outcomes. These analyses have less distributional assumptions and adequately handle results of trials in which the performance measure did not occur within the trial time. The proposed method is well known from survival analyses, provides comprehensible statistical results and allows the generation of meaningful graphs. Experiments of behavioral neuroscience and anesthesiology are used to illustrate this method.     

Comparing the PANSS in Chinese and American inpatients: cross-cultural psychiatric analyses of instrument translation and implementation

This article compares data from the Positive and Negative Syndrome Scale (PANSS) collected from Chinese and American inpatients diagnosed with schizophrenia to show how patterned differences in item ratings may reflect cultural attitudes of the raters. The Chinese sample (N=553) was based on consecutive admissions to four academic hospitals in Changsha, China. Only patients ill for 3 or more years were included in the analysis to match the chronically ill sample represented in the US CATIE sample. A total of 261 PANSS assessments were completed during a month when CATIE subjects had been hospitalized for 15 days or more to optimize equivalence of the US and Chinese samples. Controlling for age and gender, the total PANSS and the three sub-scores were all significantly lower in the Chinese than in the US CATIE sample by 5-8% (all p<.05). However, on 9 items, the Chinese sample scored 10-30% higher than the US sample (all p<.05) and on 5 items they scored over 20% higher (all p<.0001). These items rated increased hostility, poorer attention, lack of judgment and insight, disturbance of volition, and poorer impulse control. We ascribe these differences to cultural variations in the ways individuals relate to others in their social environment within Chinese and American societies.     

Discontinuing and switching antipsychotic medications: understanding the CATIE schizophrenia trial

A new standard in effectiveness research on schizophrenia medications has been established by the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. The study used an innovative approach to determining relative effectiveness of medications by using time until medication discontinuation or switch as the primary outcome criterion. The study is perhaps best known for the overall high proportion of subjects (74%) meeting all-cause discontinuation (ACD) criteria within the 18-month time frame of being assigned to their phase 1 antipsychotic. However, some of the drawbacks of the ACD approach are not well understood, in part because of unfamiliarity with the way ACD was assessed and problems with the use of hierarchical criteria to establish the primary reason for medication discontinuation. Using the time until ACD as an endpoint cannot by itself capture the complexity of the trajectory of a patient's response to a new medication. In particular, it is quite plausible that switching medications upon entering CATIE phase 1 would reduce some symptoms, which then would lead to a greater desire to make another medication switch. Using ACD criteria, a comparison with CATIE subjects who coincidentally remained on treatment with their preswitch medication would make it seem that switching was detrimental when in fact it could have been helpful. Another major limitation of the ACD was omitting the recording of the reason for stopping study medication whenever the ACD was considered to be a "patient-decision" discontinuation. This means that patient-initiated discontinuations could never be classified as a tolerability discontinuation. Since the ACD was done by the patients' clinicians, this approach may have underestimated the proportion of side effect discontinuations whenever the patient disagreed with his or her clinician. Moreover, retaining the "patient-decision" discontinuation subgroup in the attributable risk estimates of tolerability discontinuations further minimizes the attributable risk estimate of the role of side effects relative to other causes of discontinuation. For these assumptions to be valid would require the very optimistic assumption that CATIE clinicians never underestimated tolerability concerns in their patients. Otherwise, this mutually exclusive approach will lead to significant underestimation of the proportion of CATIE discontinuations caused by tolerability problems. It can be argued that excluding the "patient decision" subgroup from the attributable risk estimate of role of tolerability in medication discontinuation is a better approach to mitigate against these biases. A reanalysis using an adjusted N of 1061 evaluable subjects changes the attributable portion of tolerability discontinuations from 14.9% to 38.5%. Regarding specific side effect-medication pairs of interest, the attributable risk of extrapyramidal symptoms as a reason for discontinuing perphenazine increases from 8% to 21%, and weight-related discontinuations from olanzapine from 9% to 28%. Therefore, the clinical implications of the CATIE phase 1 findings may depend, in part, on the underlying assumptions of the ACD outcome measure.     

Using global statistical tests in long‐term Parkinson's disease clinical trials

Parkinson's disease (PD) impairments are multidimensional, making it difficult to choose a single primary outcome when evaluating treatments to stop or lessen the long‐term decline in PD. We review commonly used multivariate statistical methods for assessing a treatment's global impact, and we highlight the novel Global Statistical Test (GST) methodology. We compare the GST to other multivariate approaches using data from two PD trials. In one trial where the treatment showed consistent improvement on all primary and secondary outcomes, the GST was more powerful than other methods in demonstrating significant improvement. In the trial where treatment induced both improvement and deterioration in key outcomes, the GST failed to demonstrate statistical evidence even though other techniques showed significant improvement. Based on the statistical properties of the GST and its relevance to overall treatment benefit, the GST appears particularly well suited for a disease like PD where disability and impairment reflect dysfunction of diverse brain systems and where both disease and treatment side effects impact quality of life. In future long term trials, use of GST for primary statistical analysis would allow the assessment of clinically relevant outcomes rather than the artificial selection of a single primary outcome. © 2009 Movement Disorder Society     

Statistical methods for longitudinal research on bipolar disorders

Objectives: Outcomes research in bipolar disorders, because of complex clinical variation over-time, offers demanding research design and statistical challenges. Longitudinal studies involving relatively large samples, with outcome measures obtained repeatedly over-time, are required. In this report, statistical methods appropriate for such research are reviewed.
Methods: Analytic methods appropriate for repeated measures data include: (i) endpoint analysis; (ii) endpoint analysis with last observation carried forward; (iii) summary statistic methods yielding one summary measure per subject; (iv) random effects and generalized estimating equation (GEE) regression modeling methods; and (v) time-to-event survival analyses.
Results: Use and limitations of these several methods are illustrated within a randomly selected (33%) subset of data obtained in two recently completed randomized, double blind studies on acute mania. Outcome measures obtained repeatedly over 3 or 4 weeks of blinded treatment in active drug and placebo sub-groups included change-from-baseline Young Mania Rating Scale (YMRS) scores (continuous measure) and achievement of a clinical response criterion (50% YMRS reduction). Four of the methods reviewed are especially suitable for use with these repeated measures data: (i) the summary statistic method; (ii) random/mixed effects modeling; (iii) GEE regression modeling; and (iv) survival analysis.
Conclusions: Outcome studies in bipolar illness ideally should be longitudinal in orientation, obtain outcomes data frequently over extended times, and employ large study samples. Missing data problems can be expected, and data analytic methods must accommodate missingness.     

Estimating treatment effects from longitudinal clinical trial data with missing values: comparative analyses using different methods

The selection of a method for estimating treatment effects in an intent-to-treat analysis from clinical trial data with missing values often depends on the field of practice. The last observation carried forward (LOCF) analysis assumes that the responses do not change after dropout. Such an assumption is often unrealistic. Analysis with completers only requires that missing values occur completely at random (MCAR). Ignorable maximum likelihood (IML) and multiple imputation (MI) methods require that data are missing at random (MAR). We applied these four methods to a randomized clinical trial comparing anti-depressant effects in an elderly depressed group of patients using a mixed model to describe the course of the treatment effects. Results from an explanatory approach showed a significant difference between the treatments using LOCF and IML methods. Statistical tests indicate violation of the MCAR assumption favoring the flexible IML and MI methods. IML and MI methods were repeated under the pragmatic approach, using data collected after termination of protocol treatment and compared with previously reported results using piecewise splines and rescue (treatment adjustment) pragmatic analysis. No significant treatment differences were found. We conclude that attention to the missing-data mechanism should be an integral part in analysis of clinical trial data.     

Prevalence of the metabolic syndrome in patients with schizophrenia: baseline results from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial and comparison with national estimates from NHANES III

One important risk factor for cardiovascular disease is the metabolic syndrome (MS), yet limited data exist on its prevalence in US patients with schizophrenia. METHODS: Using baseline data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) Schizophrenia Trial, assessment of MS prevalence was performed based on National Cholesterol Education Program (NCEP) criteria, and also using a fasting glucose threshold of 100 mg/dl (AHA). Subjects with sufficient anthropometric data, data on use of antihypertensives, hypoglycemic medications or insulin, and fasting glucose and lipid values >8 h from last meal were included in the analysis. Comparative analyses were performed using a randomly selected sample from NHANES III matched 1:1 on the basis of age, gender and race/ethnicity. RESULTS: Of 1460 CATIE baseline subjects, 689 met analysis criteria. MS prevalence was 40.9% and 42.7%, respectively using the NCEP and AHA derived criteria. In females it was 51.6% and 54.2% using the NCEP and AHA criteria, compared to 36.0% (p = .0002) and 36.6% (p = .0003), respectively for males. 73.4% of all females (including nonfasting subjects) met the waist circumference criterion compared to 36.6% of males. In a logistic regression model with age, race and ethnicity as covariates, CATIE males were 138% more likely to have MS than the NHANES matched sample, and CATIE females 251% more likely than their NHANES counterparts. Even when controlling for differences in body mass index, CATIE males were still 85% more likely to have MS than the NHANES male sample, and CATIE females 137% more likely to have MS than females in NHANES. CONCLUSIONS: The metabolic syndrome is highly prevalent in US schizophrenia patients and represents an enormous source of cardiovascular risk, especially for women. Clinical attention must be given to monitoring for this syndrome, and minimizing metabolic risks associated with antipsychotic treatment.