Comparative Effectiveness of Adaptive Treatment Strategies for Schizophrenia
Conditions: Schizophrenia, Schizoaffective Disorder
Sponsor: New York State Psychiatric Institute

 
1
 
 
 
Comparative Effectiveness of Adaptive Pharmacotherapy Strategies for Schizophrenia 
 
 
 
 
Protocol prepared by: T. Scott Stroup, MD, MPH, Tobias Gerhard, PhD, Mark Olfson, MD, MPH, Chacku Mathai, 
Melanie Wall, PhD, Zhiqiang Tan, PhD 
 
 
 
 
Project sponsored by PCORI. 
 
 
23 June 2016 
 
 

 
2
 
Background 
Schizophrenia is a serious mental disorder that has a lifetime population risk approaching 1%.1 In the U.S., schizophrenia 
affects approximately 2.4 million individuals.2 It typically begins in early adulthood and is a major cause of 
disability.3  Age-specific all-cause mortality rates in schizophrenia are more than 3 times greater than in the general 
population.4  Outcomes of schizophrenia are variable, ranging from slight to severe disability. For affected individuals and 
their families, schizophrenia can be devastating.  Many people with schizophrenia have substantial social and functional 
impairments that contribute to high rates of homelessness, unemployment, disability, and incarceration.5,6 Globally, 
schizophrenia is the 5th leading cause of years lost to disability for men and 6th leading cause for women.3 
 
Antipsychotic medications are a cornerstone of schizophrenia treatment.7  However, because most people with 
schizophrenia respond only partially or not at all to an antipsychotic medication,8  these medications are changed 
frequently and combined or different classes of psychotropic medication are added.9,10 Preliminary analyses of national 
Medicaid data from 2001-2005 revealed that in the year before starting a new antipsychotic, most patients with 
schizophrenia fill a prescription for an antidepressant (67.3%), a benzodiazepine (53.2%), or a mood stabilizer (55.8%). 
The FDA has not approved any of these classes of drugs to treat schizophrenia, although strong evidence supports their 
use for the conditions for which they have been approved.  Evidence is scare for effectiveness of these medications in 
people with schizophrenia.11 
 
This investigation will address key patient-centered questions about medication strategies for individuals with 
schizophrenia who face common clinical situations, for example: 
• 
“I have schizophrenia and take an antipsychotic drug but still have symptoms that interfere with my daily life.” 
• 
“I have schizophrenia and take an antipsychotic drug but often feel depressed.” 
 
In these scenarios, a doctor may recommend a medication change. Patients want to know: “What are my options? 
Which will best keep me out of the hospital so that I can keep my housing and job?  Which will help me to lead a long 
life?  Which choice will help me avoid the emergency room? I’m worried about side effects—which option will help me 
avoid serious heart problems? Or diabetes?” 
 
A physician working with an individual with schizophrenia might have the following questions: 
• 
What is the best medication to recommend for a person with schizophrenia who is not feeling better with just an 
antipsychotic? 
• 
What is the best medication to recommend for this person who has now developed depression? Or anxiety? Or 
manic symptoms? 
 
This will be the largest study of treatments for schizophrenia ever conducted. By conducting rigorous 
analyses using 10 years of national Medicaid data, the study will provide information to help make personalized treatment 
choices that fit an individual’s clinical situation and preferences with the goal of achieving outcomes that matter to the 
patient. 
Goal 
More than half of individuals diagnosed with schizophrenia have an incomplete response to a prescribed antipsychotic 
medication.10  In spite of the high proportion of patients who experience an incomplete response, little is known about the 
comparative effectiveness of treatment options that are commonly used in this situation.  The proposed research question 
“For patients with schizophrenia who have persistent, emergent or recurrent psychiatric symptoms in spite of 
antipsychotic treatment, what are the medication treatment options and what are their comparative benefits and risks?” 
thus has considerable public health importance is relevant to a large proportion of the approximately 3 million individuals 
with schizophrenia in the U.S. specifically, the project seeks to achieve the following aims: 
 
1. Within five clinically important schizophrenia subgroups, compare the effectiveness and safety of 
alternative medication strategies beyond antipsychotic monotherapy. 
 
2. Within the five subgroups, explore whether the comparative effectiveness and safety of alternative 
treatment strategies varies according to patient age and presence of a substance use disorder. 
 
3. Conduct instrumental variable and sensitivity analyses to examine the robustness of the treatment effectiveness 
results that are based on propensity score methods. 
 
The project will yield unbiased, generalizable, and patient-centered information on the comparative effectiveness 
and safety of alternative medication strategies for patients with schizophrenia who have an incomplete response to 

 
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standard antipsychotic monotherapy.  New information will be generated regarding the comparative effectiveness and 
safety of commonly used but poorly understood second-line medication treatment strategies. We will compare the 
effectiveness of these medication treatment strategies for schizophrenia in five subgroups based on presenting symptoms 
and syndromes that lead to initiation of a new psychotropic strategy: 1) schizoaffective, 2) depressive, 3) anxiety, 4) 
manic, and 5) uncomplicated schizophrenia. 
 
The project is designed to address critical knowledge gaps in the comparative effectiveness of pharmacological 
management of patients with schizophrenia, broadly conceived to include the diagnoses of schizophrenia, early course 
schizophrenia (schizophreniform disorder), and schizoaffective disorder. Schizoaffective disorder, a subtype of 
schizophrenia characterized by psychotic symptoms and persistent mood symptoms, is often diagnosed in the Medicaid 
population.12   The pharmacologic treatment of schizoaffective disorder has not been extensively studied in controlled 
trials,13 though treatment for schizoaffective disorder shows patterns that are distinct from other schizophrenia 
subgroups.12   Symptoms of anxiety,14 depression,15  and mania16 also commonly occur in adults with schizophrenia.  
Nevertheless, clinical trials of standard pharmacological treatments for anxiety, depression, and mania routinely exclude 
patients with schizophrenia and related psychotic disorders.  As a result, little is known about the comparative 
effectiveness of medication options for treating schizoaffective disorder, anxiety, depression, and mania in 
schizophrenia.17 
 
There is little evidence on pharmacologic treatments in schizophrenia other than antipsychotics. The first step in 
medication treatment for schizophrenia is generally a single antipsychotic.11,18,19  However, most patients with 
schizophrenia have an incomplete response to antipsychotic monotherapy. Switching antipsychotics is a common 
strategy for breakthrough symptoms, but the evidence suggests switching is more likely to affect side effects than to 
reduce core symptoms.20,21  The one medicine consistently shown to often work when others do not is clozapine, which 
has an indication for both treatment-resistant schizophrenia and for reducing persistent suicidal behaviors11  but is rarely 
prescribed.22   Support for adding a second antipsychotic agent, though common in clinical practice,23 is largely 
confined to case reports and open-label trials24,25 rather than double- blind clinical trials.26   The evidence for adding 
agents from different psychotropic classes is summarized in Table 1. 
 
Current practice patterns vary widely. In our preliminary analyses, we found wide variation in prescribing of 
different classes of psychotropic medications to patients with schizophrenia in the year prior to initiation of a new 
antipsychotic medication, providing additional evidence regarding uncertainty of these treatment strategies.  Across states, 
rates varied by 2 to 3 fold.  Specifically, the initiation of benzodiazepines varied from 9.2% (Kentucky) to 29.7% 
(Vermont); antidepressants from 19.8% (Vermont) to 48.3% (Oklahoma); and mood stabilizers from 6.6% (North Dakota) 
to 18.9% (Texas).  Such variation underscores deficiencies in widely accepted clinical evidence concerning these 
important treatment decisions. 
 
There is no professional consensus on treatments for important subgroups with schizophrenia.  
Schizophrenia treatment guidelines and algorithms11,18,19 
have concluded that there is little evidence to guide selection of 
pharmacologic strategies for schizophrenia beyond the initial recommendation for antipsychotic monotherapy and then 
for clozapine for people with treatment-resistant illness. 
 
Although there is limited information on the clinical comparisons we propose to study, recent evidence from a 
Finnish observational study of psychotropic polypharmacy in schizophrenia, which used methods that are similar to those 
that we propose, found substantial effect sizes.27   In that study, for example, there was a significant increase in mortality 
associated with adjunctive benzodiazepine use compared to antipsychotic monotherapy and adjunctive antidepressant use 
was associated with markedly reduced risk of suicide 
 
Despite the widespread use of various combinations of classes of psychotropic medicines in schizophrenia, their 
comparative safety and effectiveness have not been established.  By determining which options are most effective in 
achieving desired outcomes in specific clinical situations, this project will promote personalized, evidence-based medicine 
and has the potential to reduce wasteful or harmful medication management approaches. By helping doctors and patients 
find effective treatments earlier than by simple trial and error, this project has the potential to reduce the potential harms 
and inconvenience of failed medication trials, adverse effects, and unnecessary hospitalizations. 
Protocol Details 
 
Overview of study design 
The study is a retrospective cohort study of adults with schizophrenia that will compare outcomes of new users of 
alternative psychotropic medication strategies using 10 years of Medicaid data. The primary comparative effectiveness 
analyses will focus on subgroups of patients with schizophrenia facing common clinical situations. 

 
4
 
Data source and cohort identification 
The data source will be national (45-state) Medicaid Analytic Extracts data (2001-2010). The cohort will consist of adults 
who are 18 to 64 years old and diagnosed with schizophrenia who initiate a new psychotropic medication after a period of 
stable antipsychotic treatment.  Individuals who are 65 or older because will be excluded because they also have Medicare 
and receive pharmacy benefits through Medicare Part D. 
 
Schizophrenia will be defined as ≥2 outpatient claims or ≥1 inpatient claim for schizophrenia [ICD-9-CM: 295] during 
365 days of consecutive Medicaid enrollment immediately prior to the index date.28 Stable antipsychotic monotherapy 
will be defined by filled prescriptions for only one second-generation antipsychotic (excluding clozapine), and no other 
psychotropics, for ≥ 90 days immediately preceding the start of the index medication (t0). After the ≥90 days of stable 
treatment with a single second-generation antipsychotic, study patients will have had a change in therapy defined as (1) 
addition of a second antipsychotic or (2) addition of a different psychotropic drug class (antidepressant, mood stabilizer, 
or benzodiazepine).  To ensure the patients are in active treatment there must be an active supply of antipsychotic 
medication on t0. 
 
The eligibility criteria select a cohort of individuals that are likely to have schizophrenia,28 have received antipsychotic 
monotherapy prior to the index date, and are still experiencing problems for which a new psychotropic medication 
strategy was initiated. A 1-year period of eligibility prior to follow-up initiation ensures sufficient time to collect service 
use related covariates to characterize cohort members.  
 
Patients with stable clozapine monotherapy will be excluded because clozapine is largely reserved for the most severely ill 
patients. Patients who implement more than one change in therapy at the index date (e.g., addition of a mood stabilizer 
and an antidepressant) will also be excluded. 
 
 
Subgroups 
From the base study population described above, 5 clinical subgroups will be defined based on the presence of 
psychiatric diagnoses during the 30 days prior to and inclusive of the treatment change under study (index date) and 
who have this same diagnostic code from more than one provider to increase the validity of the diagnostic groups.  
The subgroups will be defined by codes to capture 1) uncomplicated schizophrenia (with schizophrenia codes from at 
least 2 different providers and without any diagnoses that would qualify them for the other subgroups during the 365 
days prior to the index date), 2) schizoaffective disorder (ICD-9: 295.7) as the most recent schizophrenia-spectrum 
diagnosis, 3) depressive (ICD-9: 293.83, 296.2, 296.3. 296.9, 298.0, 300.4, 311), 4) manic (296.0, 296.4-296.8), and 
5) anxiety (293.84, 300.00-300.02, 300.09, 300.20-300.23, 300.29, 300.3, 300.7, 308.0-308.9, 309.21, 309.81, 
312.39, 313.0, 313.21, 313.23).  These subgroups are defined to reflect the reason for the change in treatment. 
 
 
Comparators 
Pharmacological treatment options for patients with schizophrenia who are nonresponsive to antipsychotic monotherapy 
will include (1) initiation of a second antipsychotic, (2) initiation of an antidepressant, (3) initiation of a mood stabilizer 
and (4) initiation of a benzodiazepine.  
 
Within each clinical subgroup, treatment options may be refined by exclusions at the drug class or individual agent 
level. For example, antidepressants are generally not considered treatment alternatives in patients presenting with 
mania, but could reasonably be considered for patients in the remaining four schizophrenia subgroups: depression, 
anxiety, schizoaffective disorder, and uncomplicated schizophrenia. Unless it is not considered a reasonable treatment 
option for a specific subgroup, initiation of a new antipsychotic will serve as the reference category because it is a 
frequently recommended approach to improving treatment response29 and is a common treatment strategy across all of 
the clinical subgroups under study. A survey on the prescribing behaviors of clinicians who prescribe psychotropic 
medications for individuals with schizophrenia will inform these choices.   
 

 
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Outcomes of interest 
 
Primary 
The primary effectiveness outcome will be time to psychiatric hospitalization.   
 
Secondary 
We will also investigate time to index treatment discontinuation, time to introduction of another psychotropic medication, 
psychiatric emergency department visits, all-cause hospitalization, and death. 
 
We will investigate adverse effects of medications including incidence of major cardiovascular events and diabetes 
mellitus. 
 
 
Approach to confounding 
 
Physicians make treatment choices in light of available clinical information and as a result there is a risk of confounding 
by clinical indication for the treatments under study. Our design minimizes confounding by indication by focusing on 
diagnostic subgroups.  To further reduce confounding, we will control for a broad range of potentially relevant factors 
from claims histories.  These factors will be identified in the 365-day period before drug initiation and used to calculate 
propensity scores.  In addition we will conduct sensitivity analyses to estimate the extent of unmeasured confounding 
necessary to fully explain our observed findings and we will conduct instrumental variable analyses if a suitable 
instrument is identified. 
 
 
Follow-up and censoring 
Follow-up will begin at the date of new treatment initiation (t0 or index date) and will end after 365 days regardless of 
changes in pharmacotherapy (intent-to-treat approach). We will also conduct secondary analyses using a 180-day follow-
up period and an as-treated specification..  In the as-treated approach, follow-up will end at discontinuation of the new 
treatment (>10 days without drug) or addition of another psychotropic agent (antipsychotic, mood stabilizer, 
antidepressant, or benzodiazepine).  All analyses will be censored at loss of Medicaid (because that is the data source) 
with one exception—in intent-to-treat mortality analyses, follow-up will not be censored at loss of Medicaid because 
date of death data comes from the Social Security Master File, not Medicaid.  
Outcomes 
 
 
 
Death 
Date of death will be defined by the Social Security Death Master File. 
Psychiatric Hospitalization 
Hospital claims will define inpatient admissions with a first listed mental disorder diagnosis (ICD-
9:290-310). High levels of agreement between diagnoses in inpatient medical records and inpatient 
claims have been reported for several mental disorder groups, including schizophrenia (100.0%), major 
mood disorders (98.7%), alcohol dependence (97.8%), and drug dependence (96.4%).30 
Psychiatric ED Visits 
Emergency department claims are a reliable source of diagnostic information in relation to medical 
records
31,32
and a valid source of illness severity information.33 Emergency department visits in which the first 
listed diagnosis is a mental disorder (ICD-9-CM: 290-319) will be defined as emergency department mental 
health visits. 
Self-Injurious Behavior 
Deliberate self-harm will be defined by an E-code of E950-E958, excluding late effects of deliberate self-
harm (E959), on any inpatient, emergency department, or outpatient claim. High positive predictive values 
have been reported.34 
Diabetes Mellitus 
One or more claims with a physician diagnosis of diabetes will define diabetes mellitus.  Using self-
reported diabetes status as the criterion standard, ≥1 physician claim with any diagnosis of diabetes or a 
diabetes complication (ICD-9 codes: 295.00-2950.93, 357.2, 362.0-362.02, 366.41) has acceptable 
sensitivity (68.5%) and specificity (96.9%), and (kappa=0.76).35Sensitivity of 90% and specificity of 
93% with self-reported diabetes.104 
Cardiovascular Events: 
Acute myocardial infarction (AMI) (ICD-9-CM: 410) or stroke (430-438, except 435) will be defined by 
daignostic codes on inpatient claims of ≥3 days in duration.36-39  With this AMI definition 75% met criteria 
for definite or probable AMI on independent review of cardiac biomarkers, electrocardiograms, presenting 
symptoms, and medical history, and an additional 12% had a suspected AMI.36-39 

 
6
 
Analysis plan 
 
AIM 1 Comparative Effectiveness: Main Effects:  Within five clinically important schizophrenia subgroups, 
compare the effectiveness and safety of alternative medication strategies beyond antipsychotic monotherapy. 
 
We will construct Cox models to estimate effects of the study treatments.  We will first examine unadjusted treatment 
effects and then will conduct analyses using basic adjustment for demographic factors. Then we will construct Cox 
models using weighted propensity scores.  Follow-up time will be censored at discontinuation or change of second drugs, 
death, or loss of Medicaid.  
 
 
Propensity score estimation and application 
Propensity score methods involve two steps: estimation of the propensity scores and application of the propensity score.40   
Estimation of the propensity scores involves decisions about which covariates to include in the prediction model of the 
propensity scores and what form the model should take.  We will use expert opinion to select the variables to include in 
the propensity score.  To estimate the propensity scores we will first use logistic regression including all baseline 
predictors occurring prior to treatment assignment.41 In addition, we will augment our analysis with another non-
parametric method for building the propensity score model such as a generalized boosting algorithm implemented with 
regression trees.42  We will assess the sensitivity of our results to the choice of the propensity score model.    
 
For the application of the propensity scores, we will use weighting by the odds to estimate the average treatment effect on 
the treated sample and weighting by the inverse probability of treatment weights to estimate the average treatment effect 
in the population.40  
 
We will examine the covariate balance using standardized differences in covariate means across the treatment groups after 
weighting, and address any significant imbalances. 
 
 
AIM 2 Heterogeneity of Treatment Effects:  Within the five subgroups, explore whether the comparative 
effectiveness and safety of alternative treatment strategies varies according to patient age and presence of a 
substance use disorder. 
 
We will use similar Cox model techniques for the HTE analyses. First we will assess interaction of age and substance use 
variables with treatment. If such interactions are found, we will then perform analyses examining alternative treatment 
strategies stratified by age group or substance use. 
 
The rationale for investigating patient age as a moderator of antipsychotic treatment effects follows from evidence that 
individuals early in the course of psychosis may respond differently to medications than people with more chronic illness.   
The rationale for investigating comorbid diagnosis of a substance use disorder as a moderator of treatment effects follows 
from evidence that people diagnosed with schizophrenia and comorbid substance use disorders respond more poorly to 
antipsychotic medications than those without comorbid substance use disorders. 
 
 
AIM 3 Instrumental Variable and Other Sensitivity Analyses 
 
Instrumental variable analyses   
 
We will also seek to use instrumental variable analyses to control for unobservable confounders.  First, we will try to 
identify a feasible instrument and, if one is identified, we will apply it to our analyses.  Instruments to be considered 
includes zip code, county, or state of patient residence; and, if there are significant time trends in the use of medications, 
year of prescription. We will also consider the prescribing patterns of prescribers but are concerned that this data may not 
be reliably available in the data.  
 

 
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If the candidate instrument meets the following basic criteria, we will integrate it into our statistical analyses: 1) the 
instrument is associated with use of the treatment; 2) the instrument does not cause the outcomes except through its 
relationship with the treatment (known as exclusion restriction); and 3) the instrument is not confounded with the 
outcomes (known as independence assumption). 
 
We will evaluate the plausibility of exclusion restriction by examining whether a candidate instrumental variable is related 
to potentially hazardous co-prescriptions, and study whether the independence assumption needs to be made conditionally 
on observed patient characteristics.. We will employ methods rigorously developed for causal inference using 
instrumental variables, such as instrumental variable propensity score methods or near-far matching. These methods are 
applicable to various types of outcomes and to situations where the instrument is related to observed patient 
characteristics. These methods involve construction of instrument groups that, by weighting or matching, are similar in 
terms of observed covariates but differ in the instrument values. With these methods, treatment effects can be estimated 
by difference-in-difference, i.e., the ratio of the average outcome difference over the average treatment difference between 
the instrument groups. A complication of instrumental variable analysis is that the causal estimands inferred are in general 
distinct from the average treatment effects, overall or on the treated samples, as in the intention-to-treat analysis. We will 
perform bounding and sensitivity analyses to investigate the relationship between the instrumental variable estimates and 
intention-to-treat estimates and examine its implications for the interpretation of the final results.  
 
 
Sensitivity Analyses 
We will conduct sensitivity analyses to confirm findings from the primary analyses and to examine their 
generalizability.  In addition to the primary 360-day intention-to-treat analyses we will conduct 180-day intention-to-
treat analyses and as-treated analyses. Another sensitivity analysis will examine subgroups of people with 
depression, anxiety, or mania diagnoses that are listed by more than one provider. 
 
Quantitative Sensitivity Analyses for Unmeasured Confounders. Because the observed relative risk is a closed- form 
function of the balance of confounders among exposure groups, the strength of the confounder, and the prevalence of the 
confounder, it is possible to estimate the extent of unmeasured and thus unadjusted confounding necessary to fully explain 
the observed findings, i.e., would move the observed relative risk to statistical insignificance (the null).  
 
To quantify how strong unmeasured confounding would have to be to explain the observed effect sizes among our 
comparison groups,44 ,we will use the array approach to understand how the strength of unmeasured confounders with 
known associations with our outcomes, such as BMI and risk of diabetes,3 and  the confounder imbalance among study 
groups might affect the observed relative risk.  
 
 
Sample size 
Table 5 presents sample size estimates for national 2001-2010 Medicaid data based on our preliminary analyses. For 
example, we expect that the following treatments with corresponding numbers will be options for the depression symptom 
subgroup: second antipsychotic (n = 2,192), mood stabilizer (n = 1,296), antidepressant (n = 4,646), and benzodiazepine 
(n =1,199). We will then, for example, compare 2,192 patient episodes with a second antipsychotic to 2,192  propensity-
score matched patient episodes that were treated with an antidepressant.  The 1:1 matching method uses a nearest 
neighbor algorithm.45,46  As recommended for observational studies, we will use a caliper width of 0.2 of the standard 
deviation of the logit of the propensity score to ensure appropriate matching.36   
 
 
Our power analysis is based on two-sided (alpha = .05) tests for differences in proportions between treatment groups, 
which depend upon the sample size of each group and the probability of the outcome. To demonstrate power, we 
conservatively use the expected number of distinct patients rather than episodes (distinct patients equal approximately 
70% of the episodes in Table 5).  Final statistical analyses of individual episodes will have greater power (i.e., be able to 
detect even smaller percent differences) and will incorporate random effects to account for patients who contribute more 
than one episode.45,46 
 
Table 5: Estimated sample sizes of selected schizophrenia treatment episodes (2001-2010) 

 
8
  
Uncomplicated 
Schizophrenia 
Schizoaffective 
Disorder 
Depression 
Mania 
Anxiety 
Second Antipsychotic 
27,005 
6,393 
2,192 
1,580 
611 
Mood Stabilizer 
9,954 
4,734 
1,296 
2,107 
NA 
Antidepressant 
25,061 
9,692 
4,646 
,NA 
1,110 
Benzodiazepine 
12,248 
   3,681 
1,199 
840 
1,099 
 
We demonstrate the smallest detectable differences in proportions with >80% power for three examples.  For example, 
in the depression group, 1534 distinct patients per group (70% of 2,192) distinct patients per group (antidepressant vs. 
second antipsychotic) will provide at least 80% power to detect a difference of 5.0% in the rate of hospital admission 
in 1 year of follow-up.  Also within the depression group, the smallest treatment group (839 distinct patients of 1,199 
initiating episodes of benzodiazepines), will provide at least 80% power to detect differences of 6.8% between the 
reference and the comparator group.  In the smallest group overall (611 episodes in anxiety patients starting mood 
stabilizers), there is still >80% power to detect differences as small as 10.0% in their outcomes compared to those with 
anxiety in the comparator group. Because of the larger sample sizes, all comparisons in the uncomplicated 
schizophrenia and schizoaffective disorder groups will have power much greater than 80% to detect a 4% difference in 
proportions. 
 
 
These large sample sizes permit identification of statistically significant differences of small magnitude, not all of 
which may have substantial clinical and policy importance. We will work with our patient, clinical, and policy 
stakeholders to interpret the meaning of the magnitude of observed treatment effects.  The ability to detect small 
differences (Aim 1) will enable detection of clinically meaningful treatment heterogeneity (Aim 2). 
 
Limitations 
National Medicaid data offer exceedingly large, reliable and widely used sources of information for comparative 
outcomes research. Although the data do not include some self- reported, recovery-oriented outcomes that might be 
available in prospective studies, outcomes available in our data such as avoidance of serious medical illnesses, 
premature death, and the need for psychiatric emergency services and hospitalization are valued by patients as 
important near-term outcomes that help make long-term recovery possible. 
 
 
The analytic plans for Aims 1 and 2 involve only Medicaid financed patients, which is the dominant source of 
payment for individuals diagnosed with schizophrenia in the U.S.47   We will not be able to evaluate the comparative 
effectiveness of common pharmacological strategies in the relatively small patient population with private insurance. 
Similarly, we will not be able to study the comparative effectiveness of medication treatment strategies in uninsured 
patients. Yet the results of the proposed analysis will represent the experiences of most patients treated for 
schizophrenia in the United States including substantial representation of important groups, such as ethnic/racial 
minorities who are usually underrepresented in schizophrenia outcomes research, and patients with significant medical 
and mental health comorbidities who are often systematically excluded from such research. 
 
 
Potential confounding is a concern in all observational comparative effectiveness research. We address potential 
confounding by treatment indication by restricting our analyses to second-line treatment strategies and comparable 
schizophrenia subgroups to create situations where the magnitude of potential confounding is expected to be limited 
due to a lack of strong evidence supporting one treatment option over others. The clinical diagnoses in claims data 
have the strong advantage of being the basis of real-world decision-making. In addition, propensity score methods will 
be used to minimize residual confounding from a large number of measured demographic and clinical patient 
characteristics. 
 

 
9
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Appendices: 
Appendiz 1: Exclusion Diagnoses 
Appendix 2: Variables included in propensity scores

 
Appendix 1: Exclusion Diagnoses (Contraindications for clozapine, other antipsychotics, or life threatening) 
 
Diagnosis 
ICD-9-CM Codes* 
Narrow angle glaucoma 
365.02 
Myocarditis  
422.x, 391.2x, 398.0x 
Neutrophil disorders  
288.00-288.04, 288.09, 288.1x 
Malignant neoplasms  
140-205 
Epilepsy, seizure disorders 
345.xx 
Alzheimer’s and other cerebral degenerations  
331.xx 
* x indicates inclusion of any 5th digit if available for a diagnosis code. 
  xx indicates inclusion of any 4th and 5th digits if available for a diagnosis code. 

 
13 
Appendix 2: Variables included in the Propensity Score 
 
Demographics  
Gender 
      Male  
      Female 
Age group 
      18-21 
      22-25 
      26-29 
      30-33 
      34-37 
      38-41 
      42-45 
      46-49 
      50-53 
      54-57 
      58-61 
      62-64 
Race/Ethnicity 
      White, non-Hispanic 
      Black, non-Hispanic 
      Hispanic 
      Other 
Medicaid Eligibility 
       Disability 
       Low income 
       Other 
AP treatment Initiation year 
       2002 
       2003 
       2004 
       2005 
       2006 
       2007 

 
14 
       2008 
       2009 
Diagnostic History, past year 
ICD-9-CM Codes* 
      Depression 
293.83, 296.2x, 296.3x, 296.9x, 298.0x, 300.4x, 311.xx 
      Anxiety 
293.84, 300.0x, 300.2x, 300.3x, 308.3x, 309.21, 309.81, 313.0x, 313.2x, 313.89 
      Bipolar 
296.xx (not 296.2x or 296.3x or 296.9x) 
      Substance use disorder 
291.xx, 292.xx, 303.xx, 304.xx 
      Moderate/severe mental retardation 
318.xx 
      HIV 
042, 043, 044, 079.53, V08 
      Neoplasms 
140.xx-239.xx (Exclude people with malignant neoplasms 140.xx-205.xx) 
      Diabetes 
250.xx 
      Obesity 
270.xx 
      Hyperlipidemia 
272.0x-272.4x, 272.7x, 272.9x 
      White blood cell diseases 
288.xx,289.xx (exclude neutrophil d/o 288.00-288.04, 288.09, 288.1x) 
      Anemia 
280.xx–285.xx 
      Drug-related dyskinesia 
333.85 
      Hypertension 
401.xx-405.xx 
      Ischemic heart disease 
410.xx-414.xx 
      Pulmonary circulation diseases 
415.xx-417.xx 
      Cardiac dysrhythmias 
427.xx 
      Heart failure 
428.xx 
      Cerebrovascular disease 
430.xx-438.xx 
       Acute bronchitis and bronchiolitis   
466.xx 
       Chronic bronchitis   
491.xx 
       Pneumonia 
480.xx-486.xx 
       Emphysema 
492.xx 
       Asthma 
493.xx 
       Appendicitis 
540.xx–543.xx  
       Noninfectious enteritis and colitis   
555.xx–558.xx  
       Diverticula of intestine 
562.xx 
       Intestinal obstruction 
560.xx 
       Chronic liver disease and cirrhosis 
571.xx 
       Acute & chronic pancreatitis   
577.0x, 577.1x 
       Cholelithiasis 
574.xx 
       Acute kidney failure   
584.xx 
       Chronic kidney failure 
585.xx 

 
15 
       Kidney infections 
590.xx 
       Cellulitis and abscess   
681.xx–682.xx  
       Osteoarthrosis and allied disorders 
715.xx 
       Intervertebral disc disorder 
722.xx 
       Fractures, all sites 
800.xx–829.xx  
       Fracture of neck of femur 
820.xx 
       Poisoning by psychotropic medication 
969.xx 
       Poisonings 
960.xx–989.xx 
       Suicide and self-inflicted injury 
E950-959  
       Intracranial injury 
815.xx-854.xx 
       Severe cardiovascular diseases 
401.0x-404.9x, 410.xx-416.xx, 425.xx-437.xx, 440.xx-447.xx 
      Schizophreniform 
295.4x 
      Schizoaffective 
295.7x 
* x indicates inclusion of any 5th digit if available for a diagnosis code. 
  xx indicates inclusion of any 4th and 5th digits if available for a diagnosis code. 

 
16 
 
Medication history, past year 
Drug class 
      Psychotropic medication 
             Antidepressants 
SSRI/SNRI/mirtazapine, TCA and heterocyclic compounds, MAOI, trazodone-related 
antidepressants, bupropion 
             Mood Stabilizer 
lithium, primary anticonvulsant, secondary anticonvulsant 
             ADHD medication 
psychostimulants, alpha-agonists 
             Anxiolytic/hypnotics 
benzodiazepines, GABA agonists, other newer or older anxiolytic/hypnotic 
             Other psychiatric medication 
modafinil, oxybate, phendimetrazine, benzphetamine 
      OB/GYN medication 
oral contraceptives, other contraception, medroxyprogesterone 
      Metabolic and related medication 
lipid-lowering drugs, hypothyroid treatment, antithyroid agents, anorexiants 
      Cardiovascular medication 
thiazide diuretic, ACE inhibitor/ARBs, other anti-hypertensives, other cardiovascular drugs 
      Respiratory/allergy medication 
non-sedating antihistamines, other antihistamines, Corticosteroids, other Asthma medications, 
Smoking cessation 
      Gastrointestinal medication 
histamine 2 receptor antagonists, proton-pump inhibitors, other prescription dyspepsia, 
Antacids, anti H pylori, phenothiazine antiemetics, Ulcerative colitis treatment 
      Neurologic/musculoskeletal medication 
migraine treatment/prevention, NSAID, includes coxibs, narcotic analgesic, Non-narcotic 
analgesic (acetaminophen), Cyclobenzaprine, other skeletal muscle relaxants, other 
rheumatologic 
      Antibiotics 
      Diabetic medication 
      Hyperlipidemia medication 
      Severe cardiovascular medication 
  
Health care services, past year 
Mental Health Emergency Service 
Outpatient visits for Schizophrenia (0-9, 10-29, 30-49, ≥50) 
Hospital admissions for psychiatric illness (1, 2, 3,≥4) 
Psychotherapy 
Psychosocial Service contacts 
CPT Codes for Psychotherapy or Psychosocial Service Contacts 
Services 
CPT Codes 
Psychotherapy 
90804-90829, 90841-90847, 90849, 90853, 90855, 90857, 90875, 90876 
Psychosocial Service Contacts 
H0036, H2000, H2010, H2011, H2012, H2014, H2017 
 
 

 
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