Browse
Submit Data
Databases
API
Help

Dataset Information

26 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Estimating the optimal dynamic antipsychotic treatment regime: Evidence from the sequential multiple assignment randomized CATIE Schizophrenia Study.

ABSTRACT: Treatment of schizophrenia is notoriously difficult and typically requires personalized adaption of treatment due to lack of efficacy of treatment, poor adherence, or intolerable side effects. The Clinical Antipsychotic Trials in Intervention Effectiveness (CATIE) Schizophrenia Study is a sequential multiple assignment randomized trial comparing the typical antipsychotic medication, perphenazine, to several newer atypical antipsychotics. This paper describes the marginal structural modeling method for estimating optimal dynamic treatment regimes and applies the approach to the CATIE Schizophrenia Study. Missing data and valid estimation of confidence intervals are also addressed.

SUBMITTER: Shortreed SM

PROVIDER: S-EPMC3475611 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Estimating the optimal dynamic antipsychotic treatment regime: Evidence from the sequential multiple assignment randomized CATIE Schizophrenia Study.

Shortreed Susan M SM Moodie Erica E M EE

Journal of the Royal Statistical Society. Series C, Applied statistics 20120531 4

Treatment of schizophrenia is notoriously difficult and typically requires personalized adaption of treatment due to lack of efficacy of treatment, poor adherence, or intolerable side effects. The Clinical Antipsychotic Trials in Intervention Effectiveness (CATIE) Schizophrenia Study is a sequential multiple assignment randomized trial comparing the typical antipsychotic medication, perphenazine, to several newer atypical antipsychotics. This paper describes the marginal structural modeling meth ...[more]

PMID: 23087488

Similar Datasets

Antipsychotic effects on estimated 10-year coronary heart disease risk in the CATIE schizophrenia study.

Project description:Persons with schizophrenia die earlier than the general population, in large part due to cardiovascular disease. The study objective was to examine effects of different antipsychotic treatments on estimates of 10-year coronary heart disease (CHD) risk calculated by the Framingham Heart Study formula.Change in 10-year risk for CHD was compared between treatment groups in 1125 patients followed for 18 months or until treatment discontinuation in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) Schizophrenia Trial.The covariate-adjusted mean change in 10-year CHD risk differed significantly between treatments. Olanzapine was associated with a 0.5% (SE 0.3) increase and quetiapine, a 0.3% (SE 0.3) increase; whereas risk decreased in patients treated with perphenazine, -0.5% (SE 0.3), risperidone, -0.6% (SE 0.3), and ziprasidone -0.6% (SE 0.4). The difference in 10-year CHD risk between olanzapine and risperidone was statistically significant (p=0.004). Differences in estimated 10-year CHD risk between drugs were most marked in the tertile of subjects with a baseline CHD risk of at least 10%. Among individual CHD risk factors used in the Framingham formula, only total and HDL cholesterol levels differed between treatments.These results indicate that the impact on 10-year CHD risk differs significantly between antipsychotic agents, with olanzapine producing the largest elevation in CHD risk of the agents studied in CATIE.

| S-EPMC2614656 | biostudies-literature

Optimal allocation to treatments in a sequential multiple assignment randomized trial.

Project description:One of the main questions in the design of a trial is how many subjects should be assigned to each treatment condition. Previous research has shown that equal randomization is not necessarily the best choice. We study the optimal allocation for a novel trial design, the sequential multiple assignment randomized trial, where subjects receive a sequence of treatments across various stages. A subject's randomization probabilities to treatments in the next stage depend on whether he or she responded to treatment in the current stage. We consider a prototypical sequential multiple assignment randomized trial design with two stages. Within such a design, many pairwise comparisons of treatment sequences can be made, and a multiple-objective optimal design strategy is proposed to consider all such comparisons simultaneously. The optimal design is sought under either a fixed total sample size or a fixed budget. A Shiny App is made available to find the optimal allocations and to evaluate the efficiency of competing designs. As the optimal design depends on the response rates to first-stage treatments, maximin optimal design methodology is used to find robust optimal designs. The proposed methodology is illustrated using a sequential multiple assignment randomized trial example on weight loss management.

| S-EPMC8649474 | biostudies-literature

Prediction of quality of life in schizophrenia using machine learning models on data from Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial.

Project description:While research focus remains mainly on psychotic symptoms, it is questionable whether we are placing enough emphasis on improving the quality of life (QoL) of schizophrenia patients. To date, the predictive power of QoL remained limited. Therefore, this study aimed to accurately predict the QoL within schizophrenia using supervised learning methods. The authors report findings from participants of a large randomized, double-blind clinical trial for schizophrenia treatment. Potential predictors of QoL included all available and non-redundant variables from the dataset. By optimizing parameters, three linear LASSO regressions were calculated (N = 697, 692, and 786), including 44, 47, and 41 variables, with adjusted R-squares ranging from 0.31 to 0.36. Best predictors included social and emotion-related symptoms, neurocognition (processing speed), education, female gender, treatment attitudes, and mental, emotional, and physical health. These results demonstrate that machine learning is an excellent predictive tool to process clinical data. It appears that the patient's perception of their treatment has an important impact on patients' QoL and that interventions should consider this aspect.Trial registration: ClinicalTrials.gov Identifier: NCT00014001.

| S-EPMC8938459 | biostudies-literature

The relationship between suicide and violence in schizophrenia: analysis of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) dataset.

Project description:BackgroundSuicide and violence often co-occur in the general population as well as in mentally ill individuals. Few studies, however, have assessed whether these suicidal behaviors are predictive of violence risk in mental illness.AimsThe aim of this study is to investigate whether suicidal behaviors, including suicidal ideation, threats, and attempts, are significantly associated with increased violence risk in individuals with schizophrenia.MethodData for these analyses were obtained from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) trial, a randomized controlled trial of antipsychotic medication in 1460 adults with schizophrenia. Univariate Cox regression analyses were used to calculate hazard ratios (HRs) for suicidal ideation, threats, and attempts. Multivariate analyses were conducted to adjust for common confounding factors, including: age, alcohol or drug misuse, major depression, antisocial personality disorder, depression, hostility, positive symptom, and poor impulse control scores. Tests of discrimination, calibration, and reclassification assessed the incremental predictive validity of suicidal behaviors for the prediction of violence risk.ResultsSuicidal threats and attempts were significantly associated with violence in both males and females with schizophrenia with little change following adjustment for common confounders. Only suicidal threats, however, were associated with a significant increase in incremental validity beyond age, diagnosis with a comorbid substance use disorder, and recent violent behavior.ConclusionsSuicidal threats are independently associated with violence risk in both males and females with schizophrenia, and may improve violence risk prediction.

| S-EPMC3988953 | biostudies-literature

Targeted pharmacogenetic analysis of antipsychotic response in the CATIE study.

Project description:AimThis study evaluated the impact of 6789 SNPs on treatment response to antipsychotics in Caucasian patients from the CATIE study.Materials & methodsAn Illumina (CA, USA) BeadChip was designed that targeted genes potentially impacting disease risk, disease presentation or antipsychotic response. SNPs tagged regions of linkage disequilibrium or functional variants not detectable using previous genotypes for CATIE. Change in Positive and Negative Syndrome scale total score was modeled using a mixed model repeated measures method that assumed a 30-day lag period. Genetic association analysis was performed using linear regression.ResultsAssociation analysis identified 20 SNPs with p-values of ≤5 × 10(-4). Many of these are in genes previously implicated in schizophrenia and other neuropsychiatric diseases.ConclusionThe targeted approach identified SNPs possibly influencing response to antipsychotic drugs in Caucasian patients suffering from schizophrenia. The findings support a biological link between disease risk and presentation and antipsychotic response.

| S-EPMC3518380 | biostudies-literature

Hostility in schizophrenia: An integrated analysis of the combined Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) and the European First Episode Schizophrenia Trial (EUFEST) studies.

Project description:Phase 1 of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study enrolled a sample of 1493 chronic schizophrenia patients. The European First Episode Schizophrenia Trial (EUFEST) enrolled 498 patients. We have combined these two samples to study the effects of hostility on study discontinuation as well as to examine correlates and predictors of hostility. Individual data from 1154 patients with complete data were used for analyses. Survival analysis demonstrated that higher hostility was associated with earlier all-cause treatment discontinuation. Furthermore, regression analysis indicated that increased hostility was associated with more severe positive symptoms, lower adherence to pharmacological treatment, younger age, impaired insight, and more drug or alcohol consumption. The clinical implications of the results point to the importance of establishing therapeutic alliance while managing patient's symptoms of hostility with antipsychotics such as olanzapine combined with psychosocial interventions to improve insight and reduce substance use.

| S-EPMC4838039 | biostudies-literature

Optimal dynamic treatment regime estimation using information extraction from unstructured clinical text.

Project description:The wide-scale adoption of electronic health records (EHRs) provides extensive information to support precision medicine and personalized health care. In addition to structured EHRs, we leverage free-text clinical information extraction (IE) techniques to estimate optimal dynamic treatment regimes (DTRs), a sequence of decision rules that dictate how to individualize treatments to patients based on treatment and covariate history. The proposed IE of patient characteristics closely resembles "The clinical Text Analysis and Knowledge Extraction System" and employs named entity recognition, boundary detection, and negation annotation. It also utilizes regular expressions to extract numerical information. Combining the proposed IE with optimal DTR estimation, we extract derived patient characteristics and use tree-based reinforcement learning (T-RL) to estimate multistage optimal DTRs. IE significantly improved the estimation in counterfactual outcome models compared to using structured EHR data alone, which often include incomplete data, data entry errors, and other potentially unobserved risk factors. Moreover, including IE in optimal DTR estimation provides larger study cohorts and a broader pool of candidate tailoring variables. We demonstrate the performance of our proposed method via simulations and an application using clinical records to guide blood pressure control treatments among critically ill patients with severe acute hypertension. This joint estimation approach improves the accuracy of identifying the optimal treatment sequence by 14-24% compared to traditional inference without using IE, based on our simulations over various scenarios. In the blood pressure control application, we successfully extracted significant blood pressure predictors that are unobserved or partially missing from structured EHR.

| S-EPMC9185731 | biostudies-literature

Pharmacogenetics of antipsychotic response in the CATIE trial: a candidate gene analysis.

Project description:The Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) Phase 1 Schizophrenia trial compared the effectiveness of one typical and four atypical antipsychotic medications. Although trials such as CATIE present important opportunities for pharmacogenetics research, the very richness of the clinical data presents challenges for statistical interpretation, and in particular the risk that data mining will lead to false-positive discoveries. For this reason, it is both misleading and unhelpful to perpetuate the current practice of reporting association results for these trials one gene at a time, ignoring the fact that multiple gene-by-phenotype tests are being carried out on the same data set. On the other hand, suggestive associations in such trials may lead to new hypotheses that can be tested through both replication efforts and biological experimentation. The appropriate handling of these forms of data therefore requires dissemination of association statistics without undue emphasis on select findings. Here we attempt to illustrate this approach by presenting association statistics for 2769 polymorphisms in 118 candidate genes evaluated for 21 pharmacogenetic phenotypes. On current evidence it is impossible to know which of these associations may be real, although in total they form a valuable resource that is immediately available to the scientific community.

| S-EPMC2986499 | biostudies-literature

Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens.

Project description:Dynamic treatment regimens (DTRs) are sequential treatment decisions tailored by patient's evolving features and intermediate outcomes at each treatment stage. Patient heterogeneity and the complexity and chronicity of many diseases call for learning optimal DTRs that can best tailor treatment according to each individual's time-varying characteristics (eg, intermediate response over time). In this paper, we propose a robust and efficient approach referred to as Augmented Outcome-weighted Learning (AOL) to identify optimal DTRs from sequential multiple assignment randomized trials. We improve previously proposed outcome-weighted learning to allow for negative weights. Furthermore, to reduce the variability of weights for numeric stability and improve estimation accuracy, in AOL, we propose a robust augmentation to the weights by making use of predicted pseudooutcomes from regression models for Q-functions. We show that AOL still yields Fisher-consistent DTRs even if the regression models are misspecified and that an appropriate choice of the augmentation guarantees smaller stochastic errors in value function estimation for AOL than the previous outcome-weighted learning. Finally, we establish the convergence rates for AOL. The comparative advantage of AOL over existing methods is demonstrated through extensive simulation studies and an application to a sequential multiple assignment randomized trial for major depressive disorder.

| S-EPMC6191367 | biostudies-literature

New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes.

Project description:Dynamic treatment regimes (DTRs) are sequential decision rules for individual patients that can adapt over time to an evolving illness. The goal is to accommodate heterogeneity among patients and find the DTR which will produce the best long term outcome if implemented. We introduce two new statistical learning methods for estimating the optimal DTR, termed backward outcome weighted learning (BOWL), and simultaneous outcome weighted learning (SOWL). These approaches convert individualized treatment selection into an either sequential or simultaneous classification problem, and can thus be applied by modifying existing machine learning techniques. The proposed methods are based on directly maximizing over all DTRs a nonparametric estimator of the expected long-term outcome; this is fundamentally different than regression-based methods, for example Q-learning, which indirectly attempt such maximization and rely heavily on the correctness of postulated regression models. We prove that the resulting rules are consistent, and provide finite sample bounds for the errors using the estimated rules. Simulation results suggest the proposed methods produce superior DTRs compared with Q-learning especially in small samples. We illustrate the methods using data from a clinical trial for smoking cessation.

| S-EPMC4517946 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data