Bayesian Meta-analysis of Multiple Continuous Treatments with Individual Participant-Level Data: An Application to Antipsychotic Drugs.

Modeling dose-response relationships of drugs is essential to understanding their safety effects on patients under realistic circumstances. While intention-to-treat analyses of clinical trials provide the effect of assignment to a particular drug and dose, they do not capture observed exposure after factoring in nonadherence and dropout. We develop a Bayesian method to flexibly model the dose-response relationships of binary outcomes with continuous treatment, permitting multiple evidence sources, treatment effect heterogeneity, and nonlinear dose-response curves. In an application, we examine the risk of excessive weight gain for patients with schizophrenia treated with the second-generation antipsychotics paliperidone, risperidone, or olanzapine in 14 clinical trials. We define exposure as total cumulative dose (daily dose × duration) and convert to units equivalent to 100 mg of olanzapine (OLZ doses). Averaging over the sample population of 5891 subjects, the median dose ranged from 0 (placebo randomized participants) to 6.4 OLZ doses (paliperidone randomized participants). We found paliperidone to be least likely to cause excessive weight gain across a range of doses. Compared with 0 OLZ doses, at 5.0 OLZ doses, olanzapine subjects had a 15.6% (95% credible interval: 6.7, 27.1) excess risk of weight gain; corresponding estimates for paliperidone and risperidone were 3.2% (1.5, 5.2) and 14.9% (0.0, 38.7), respectively. Moreover, compared with nonblack participants, black participants had a 6.8% (1.0, 12.4) greater risk of excessive weight gain at 10.0 OLZ doses of paliperidone. Nevertheless, our findings suggest that paliperidone is safer in terms of weight gain risk than risperidone or olanzapine for all participants at low to moderate cumulative OLZ doses

Risk of weight gain for specific antipsychotic drugs: a meta-analysis.

People with schizophrenia are at considerably higher risk of cardiometabolic morbidity than the general population. Second-generation antipsychotic drugs contribute to that risk partly through their weight gain effects, exacerbating an already high burden of disease. While standard as-randomized analyses of clinical trials provide valuable information, they ignore adherence patterns across treatment arms, confounding estimates of realized treatment exposure on outcome. We assess the effect of specific second-generation antipsychotics on weight gain, defined as at least a 7% increase in weight from randomization, using a Bayesian hierarchical model network meta-analysis with individual patient level data. Our data consisted of 14 randomized clinical trials contributing 5923 subjects (mean age = 39 [SD = 12]) assessing various combinations of olanzapine (n = 533), paliperidone (n = 3482), risperidone (n = 540), and placebo (n = 1368). The median time from randomization to dropout or trial completion was 6 weeks (range: 0-60 weeks). The unadjusted probability of weight gain in the placebo group was 4.8% across trials. For each 10 g chlorpromazine equivalent dose increase in olanzapine, the odds of weight gain increased by 5 (95% credible interval: 1.4, 5.3); the effect of risperidone (odds ratio = 1.6 [0.25, 9.1]) was estimated with considerable uncertainty but no different from paliperidone (odds ratio = 1.3 [1.2, 1.5])

Changes in Physician Antipsychotic Prescribing Preferences, 2002–2007

Objective Physician antipsychotic prescribing behavior may be influenced by comparative effectiveness evidence, regulatory warnings, and formulary and other restrictions on these drugs. This study measured changes in the degree to which physicians are able to customize treatment choices and changes in physician preferences for specific agents after these events. Methods The study used 2002–2007 prescribing data from the IMS Health Xponent database and data on physician characteristics from the American Medical Association for a longitudinal cohort of 7,399 physicians. Descriptive and multivariable regression analyses were conducted of the concentration of prescribing (physician-level Herfindahl index) and preferences for and likelihood of prescribing two first-generation antipsychotics and six second-generation antipsychotics. Analyses adjusted for prescribing volume, specialty, demographic characteristics, practice setting, and education. Results Antipsychotic prescribing was highly concentrated at the physician level, with a mean unadjusted Herfindahl index of .33 in 2002 and .29 in 2007. Psychiatrists reduced the concentration of their prescribing more over time than did other physicians. High-volume psychiatrists had a Herfindahl index that was half that of low-volume physicians in other specialties (.18 versus .36), a difference that remained significant (p<.001) after adjustment for physician characteristics. The share of physicians preferring olanzapine dropped from 29.9% in 2002 to 10.3% in 2007 (p<.001) while the share favoring quetiapine increased from 9.4% to 44.5% (p<.001). Few physicians (<5%) preferred a first-generation antipsychotic in 2002 or 2007. Conclusions Preferences for specific antipsychotics changed dramatically during this period. Although physician prescribing remained heavily concentrated, the concentration decreased over time, particularly among psychiatrists.National Institute of Mental Health (U.S.) (Grant R01MH093359)National Institute of Mental Health (U.S.) (Grant P30 MH090333)National Institute of Mental Health (U.S.) (Grant R01MH087488)Agency for Healthcare Research and Quality (Grant R01HS017695)Robert Wood Johnson Foundation (Investigator Award in Health Policy Research

How Quickly Do Physicians Adopt New Drugs? The Case of Second-Generation Antipsychotics

Objective The authors examined physician adoption of second-generation antipsychotic medications and identified physician-level factors associated with early adoption. Methods The authors estimated Cox proportional-hazards models of time to adoption of nine second-generation antipsychotics by 30,369 physicians who prescribed antipsychotics between 1996 and 2008, when the drugs were first introduced, and analyzed the total number of agents prescribed during that time. The models were adjusted for physicians’ specialty, demographic characteristics, education and training, practice setting, and prescribing volume. Data were from IMS Xponent, which captures over 70% of all prescriptions filled in the United States, and the American Medical Association Physician Masterfile. Results On average, physicians waited two or more years before prescribing new second-generation antipsychotics, but there was substantial heterogeneity across products in time to adoption. General practitioners were much slower than psychiatrists to adopt second-generation antipsychotics (hazard ratios (HRs) range .10−.35), and solo practitioners were slower than group practitioners to adopt most products (HR range .77−.89). Physicians with the highest antipsychotic-prescribing volume adopted second-generation antipsychotics much faster than physicians with the lowest volume (HR range .15−.39). Psychiatrists tended to prescribe a broader set of antipsychotics (median=6) than general practitioners and neurologists (median=2) and pediatricians (median=1). Conclusions As policy makers search for ways to control rapid health spending growth, understanding the factors that influence physician adoption of new medications will be crucial in the efforts to maximize the value of care received by individuals with mental disorders as well as to improve medication safety.National Institute of Mental Health (U.S.) (R01 MH093359)Robert Wood Johnson Foundation (Investigator Award in Health Policy Research)Agency for Healthcare Research and Quality (R01HS017695)National Institute of Mental Health (U.S.) ((NIMH) R34 MH082682)National Institute of Mental Health (U.S.) ((NIMH) P30 MH090333)National Institute of Mental Health (U.S.) ((NIMH) R01 MH087488)National Science Foundation (U.S.) (0915674

Regional Variation in Physician Adoption of Antipsychotics: Impact on US Medicare expenditures

Background—Regional variation in US Medicare prescription drug spending is driven by higher prescribing of costly brand-name drugs in some regions. This variation likely arises from differences in the speed of diffusion of newly-approved medications. Second-generation antipsychotics were widely adopted for treatment of severe mental illness and for several off-label uses. Rapid diffusion of new psychiatric drugs likely increases drug spending but its relationship to non-drug spending is unclear. The impact of antipsychotic diffusion on drug and medical spending is of great interest to public payers like Medicare, which finance a majority of mental health spending in the U.S.National Institute of Mental Health (U.S.) (R01 MH093359

Empirically identified networks of healthcare providers for adults with mental illness

Background Policies target networks of providers who treat people with mental illnesses, but little is known about the empirical structures of these networks and related variation in patient care. The goal of this paper is to describe networks of providers who treat adults with mental illness in a multi-payer database based medical claims data in a U.S. state. Methods Provider networks were identified and characterized using paid inpatient, outpatient and pharmacy claims related to care for people with a mental health diagnosis from an all-payer claims dataset that covers both public and private payers. Results Three nested levels of network structures were identified: an overall network, which included 21% of providers (N = 8256) and 97% of patients (N = 476,802), five communities and 24 sub-communities. Sub-communities were characterized by size, provider composition, continuity-of-care (CoC), and network structure measures including mean number of connections per provider (degree) and average number of connections who were connected to each other (transitivity). Sub-community size was positively associated with number of connections (r = .37) and the proportion of psychiatrists (r = .41) and uncorrelated with network transitivity (r = −.02) and continuity of care (r = .00). Network transitivity was not associated with CoC after adjustment for provider type, number of patients, and average connection CoC (p = .85). Conclusions These exploratory analyses suggest that network analysis can provide information about the networks of providers that treat people with mental illness that is not captured in traditional measures and may be useful in designing, implementing, and studying interventions to improve systems of care. Though initial results are promising, additional empirical work is needed to develop network-based measures and tools for policymakers

Targeted learning in observational studies with multi-valued treatments: An evaluation of antipsychotic drug treatment safety

We investigate estimation of causal effects of multiple competing (multi-valued) treatments in the absence of randomization. Our work is motivated by an intention-to-treat study of the relative cardiometabolic risk of assignment to one of six commonly prescribed antipsychotic drugs in a cohort of nearly 39,000 adults with serious mental illnesses. Doubly-robust estimators, such as targeted minimum loss-based estimation (TMLE), require correct specification of either the treatment model or outcome model to ensure consistent estimation; however, common TMLE implementations estimate treatment probabilities using multiple binomial regressions rather than multinomial regression. We implement a TMLE estimator that uses multinomial treatment assignment and ensemble machine learning to estimate average treatment effects. Our multinomial implementation improves coverage, but does not necessarily reduce bias, relative to the binomial implementation in simulation experiments with varying treatment propensity overlap and event rates. Evaluating the causal effects of the antipsychotics on three-year diabetes risk or death, we find a safety benefit of moving from a second-generation drug considered among the safest of the second-generation drugs to an infrequently prescribed first-generation drug known for having low cardiometabolic risk

Improving Quality And Diffusing Best Practices: The Case Of Schizophrenia

The slow diffusion of empirically supported treatments and the rapid diffusion of treatments lacking empirical support play a significant role in the quality gap in the care of people with severe mental illnesses. Further, the rapid diffusion of treatments of low cost-effectiveness limits the system's ability to provide the full gamut of high-value treatments available to treat this vulnerable population. Using the case of schizophrenia as an illustrative case study, we review the context in which these paradoxical patterns of diffusion have occurred and propose policy solutions