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The MEDSTAT Group, 125 Cambridge Park Drive, Cambridge, MA 02140, USA. Tel: + 1 617 492 9309; fax: + 1 617 492 9365
Correspondence: e-mail: Bill_Crown{at}MEDSTAT.com
Declaration of interest W.H.C. received an honorarium and travel expenses from Eli Lilly & Co.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONECONOMIC EVALUATIONS OF...NATURALISTIC ECONOMIC OUTCOME...STATISTICAL METHODS FOR REDUCING...CONCLUSIONREFERENCES |
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Aims To highlight the use of statistical methods in non-randomised studies and the application of those methods to economic analyses.
Method The literature on the observational studies of economic outcomes with alternative antidepressants is reviewed and several statistical methodologies to control for biases that can occur in non-randomised study designs are described.
Results In comparisons of antidepressant drugs, differences in acquisition costs are consistently found to be at least offset by other components of care when broad measures of health care resource utilisation are considered.
Conclusions Economic evaluations of antidepressants should be based on broad measures of health care expenditure and can rely on data generated in real-world settings if appropriate statistical methods are used to control for the potential biases of non-randomisation.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONECONOMIC EVALUATIONS OF...NATURALISTIC ECONOMIC OUTCOME...STATISTICAL METHODS FOR REDUCING...CONCLUSIONREFERENCES |
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Numerous observational studies have been conducted of the economic outcomes associated with alternative antidepressants (Hylan et al, 1998a), and include both prospective and retrospective studies. Prospective, naturalistic economic clinical trials have been proposed as a study design that marries features of a clinical trial (i.e. the randomisation) with features of clinical practice (i.e. the observation of usual care) (Simon et al, 1995). Yet, because of inclusion criteria and other considerations, these studies may still not be generalisable to broader populations. Retrospective studies using large administrative databases offer quick access to large samples of patients in naturalistic settings. Although the ability to observe patients in naturalistic settings improves the generalisability of study findings, a variety of confounding factors may introduce sources of bias in the estimated treatment effect. To this end, a number of economic analyses of antidepressants have used methods designed to mitigate such bias (Hylan et al, 1998a).
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ECONOMIC EVALUATIONS OF ANTIDEPRESSANTS |
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TOPABSTRACTINTRODUCTIONECONOMIC EVALUATIONS OF...NATURALISTIC ECONOMIC OUTCOME...STATISTICAL METHODS FOR REDUCING...CONCLUSIONREFERENCES |
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NATURALISTIC ECONOMIC OUTCOME STUDIES OF ANTIDEPRESSANTS |
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TOPABSTRACTINTRODUCTIONECONOMIC EVALUATIONS OF...NATURALISTIC ECONOMIC OUTCOME...STATISTICAL METHODS FOR REDUCING...CONCLUSIONREFERENCES |
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Hybrid naturalistic trials like this one have garnered attention as a potential solution to the dilemma of maximising both external and internal validity in pharmaco-economic analysis. However, they have limitations and therefore should be viewed as complementary to, rather than replacements for, the data that comes from randomised clinical trials, meta-analyses, decision-analytic models and retrospective studies.
Retrospective studies
In contrast to the scarcity of prospective studies on the treatment of
depression, a large number of retrospective studies have been conducted. These
studies often use large administrative databases such as insurance claims or
other electronic records of patients' resource utilisation. When total health
care expenditures are considered, the various retrospective database studies
are remarkably consistent. In particular, virtually all studies have found
that total direct health care treatment expenditures for patients starting
therapy on selective serotonin reuptake inhibitors (SSRIs) are equal to or
lower than those for patients who start therapy on TCAs (Sclar et al,
1994,
1995; Skaer et al,
1995,
1996;
Forder et al, 1996;
Croghan et al, 1997;
Melton et al, 1997;
Obenchain et al,
1997; Revicki et al,
1997; Crown et al,
1998a,b;
Hylan et al,
1998b; Simon &
Fishman, 1998; McCombs et
al, 1999; Treglia et
al, 1999). These findings demonstrate that the higher drug
acquisition costs for SSRIs relative to TCAs are at least offset, and in some
cases more than offset, by lower expenditures for health care services other
than antidepressant pharmacotherapy.
In contrast to the uniformity of conclusions just described, the concern is often raised that database studies arrive at contradictory conclusions and that these disparities appear to be related to the sources of funding for the studies. These differences are more apparent than real and arise from the fact that the studies often evaluate different measures of health care expenditure (e.g. mental health expenditure or antide-pressant expenditure). When viewed from the perspective of total direct health care expenditure, the vast majority of studies support a common conclusion — that differences in antidepressant acquisition costs are at least offset, and in some cases more than offset, by savings in other areas. This finding is consistent across comparisons of TCAs and SSRIs and between the SSRIs (Russell et al, 1999).
Total direct health care expenditure can provide a more comprehensive assessment of economic outcome than narrower measures. Studies that focus on drug costs alone are less useful because the results of these studies are driven by the acquisition costs, which may not capture the full consequences of the initial drug selection. In the absence of a link between treatment costs and clinical outcome, inexpensive but ineffective drugs, with serious side-effect profiles that cause early discontinuation of therapy, may appear to be less costly than more expensive medications that are used more effectively. Studies that look just at antidepressant prescription volume and expenditure (Smith & Sherrill, 1996; Singletary et al, 1997; Viale, 1998) find differences between the antidepressants that are highly correlated with their unit cost. However, such conclusions may be incorrect if the patterns of antidepressant use lead to differences in broader health care resource utilisation such as concomitant pharmaceutical prescribing, physician visits or hospitalisations.
Studies that look only at depression-related or mental health care expenditure suffer from the same general criticism, although to a lesser degree. For this reason, guideline panels have recommended that pharmaco-economic studies should use the broadest measures of expenditure available (Task Force on Principles for Economic Analysis of Health Care Technology, 1995). Although some payers may face only pharmaceutical costs or mental health care treatment costs, it is still important to consider the impact of initial treatment selection on total direct health care expenditure. Treatments that appear to result in lower antidepressant costs initially may raise expenditure in other parts of the health care system. This could raise health care expenditure overall and have unintended consequences. For example, attempts by a health plan to minimise expenditure on antidepressant therapy by prescribing TCAs as first-line therapy may actually increase expenditure if patients experience higher rates of depression relapse as a result. Considering the total health care expenditure associated with initial treatment selection is also consistent with providing care to the greatest number of recipients for a given budget, an objective for many health care systems.
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STATISTICAL METHODS FOR REDUCING THE EFFECTS OF SELECTION BIAS IN RETROSPECTIVE STUDIES |
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TOPABSTRACTINTRODUCTIONECONOMIC EVALUATIONS OF...NATURALISTIC ECONOMIC OUTCOME...STATISTICAL METHODS FOR REDUCING...CONCLUSIONREFERENCES |
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Statistical methods can be applied to retrospective data to help control for both observed factors as well as unobserved factors correlated with initial treatment selection and subsequent outcomes. These methods use the data to construct variables that can be used to control for the effects of unobserved factors such as underlying disease severity or physician prescribing patterns. Using observable data to construct variables to act as proxies for unobserved factors is not new in the area of outcomes research (Von Korff et al, 1992; Johnson et al, 1994). Three broad statistical modelling approaches are considered here: instrumental variables, parametric selection bias methods and propensity score models.
Instrumental variables
The instrumental variables technique is widely used by econometricians to
correct for a variety of statistical problems in regression analysis —
most notably, simultaneous equations bias and errors in measurement (e.g.
Kennedy, 1992;
Greene, 1993). All such
problems have the characteristic that the explanatory variables are correlated
with the error terms of the estimated equations. An instrumental variable is
one that has the characteristic of being highly correlated with the variable
for which it is intended to serve as an instrument without its being
correlated with the error terms. Non-random selection into treatment groups
essentially results in a problem of missing variables measurement error in the
statistical model. The effect of unobserved variables that are important for
explaining drug selection will be captured by the error term of the drug
selection equation. If, as a result, the error term of the drug selection
equation is correlated with treatment outcomes, estimates of the treatment
effect will be biased. A major difficulty with the implementation of
instrumental variables techniques is the challenge of finding variables that
are highly correlated with the variable of interest (e.g. drug selection) but
uncorrelated with the outcome variable (e.g. treatment cost).
To date, the instrumental variables approach has not been used in any published study of depression. However, in landmark studies McClellan and colleagues (McClellan et al, 1994; McClellan, 1995) demonstrated the application of instrumental variables to mortality outcomes for elderly patients with acute myocardial infarction. These studies used differences in distance from treatment centres as an instrumental variable to control for the confounding effects of unobserved case-mix variation. The successful application of parametric selection bias models in depression studies suggests that instrumental variables techniques will soon find their way into the depression literature.
Parametric selection bias methods
Parametric sample selection models are closely related to the instrumental
variables approach. Originally developed in the econometrics literature to
assess labour market outcomes and the effects of job and education training
programmes (Heckman, 1976,
1979;
Heckman & Smith, 1995),
these models have been increasingly applied to economic evaluations of
health-care utilisations (e.g. Dowd et
al, 1996; Hylan et al,
1997,
1998b; Crown et
al,
1998a,b).
Sample selection models use a two-stage econometric approach to construct a
variable that controls for the bias due to unobserved factors associated with
treatment selections.
The estimation of sample selection models proceeds in two stages. In the
first stage, a model of treatment selection is estimated. From this model, the
errors in correctly predicting treatment selection are used to construct an
adjustment factor, 
, calculated for each patient. In the second stage,
the adjustment factor is included as one of the explanatory variables in the
outcome model.
Including in the outcome (e.g. expenditure) equation helps control
for underlying differences across the patient group in their probability of
receiving the selected antidepressant. A feature of sample selection models is
that the adjustment factor permits a direct test of whether selection bias is
present and if so, what the direction of its impact is. Specifically, if the
coefficient on the adjustment factor, , in the outcome equation is
statistically significant, this indicates that selection bias is present and
that the results of the treatment effect would have been biased had the
adjustment not been made. The sign on the adjustment factor also indicates the
direction in which the results would have been biased.
As with the instrumental variables approach, however, the estimation of parametric selection bias models requires the identification of variables that are correlated with treatment selection but uncorrelated with outcomes. Recently, analysts have proposed several such variables that seem to work reasonably well for sample selection models. For example, the time between the launch date for a particular pharmaceutical product and the date of the prescription (as a proxy for the diffusion of information about the product to physicians) seems to be a good predictor of treatment choice, but is uncorrelated with treatment costs.
Propensity score models
Propensity score analysis has received growing attention as a methodology
for reducing the bias due to unobserved differences in treatment groups
(Rosenbaum & Rubin, 1984;
Robins et al, 1992;
Drake & Fisher, 1995). As
with sample selection models, the method of propensity scores involves first
estimating the conditional probability of a treatment outcome. Patients are
then sorted into groups with similar probabilities, or propensity scores.
Finally, treatment effects are evaluated within each of the patient
sub-populations with similar propensity scores. The propensity score approach
attempts to deal with the effects of unobserved variables by matching
recipients and non-recipients who have similar predicted probabilities of
receiving treatment based on observed variables. Typically, this matching is
done for several groups (for example, those with low predicted probabilities,
mid-range predicted probabilities and high predicted probabilities). The
effect of treatment on outcomes is assessed for each of these subsamples and
the results are then combined to determine the overall effect of treatment on
the outcome of interest.
The propensity score approach avoids the necessity of specifying variables that are correlated with treatment selection but uncorrelated with outcomes, which is the fundamental challenge of the instrumental variables and sample selection models. However, it does not provide a direct test of the presence of selection bias, nor does it provide an estimate of the magnitude of selection bias if it is present. None the less, if a test for selection bias is not required, Angrist (1997) has argued that the propensity score process of matching patients with similar probabilities of receiving particular treatment accomplishes much the same thing as sample selection models. Obenchain & Melfi (1998) compared the propensity score method to the parametric sample selection approach using as an application differences in total health care expenditure for patients treated with a TCA or fluoxetine. Although the two methods resulted in similar conclusions about the economic differences between the two drugs, Obenchain & Melfi concluded that the propensity score method was the preferred approach because it is easier to understand and explain, and less sensitive to underlying model assumptions.
Application of these methods
Of the three methods discussed above, the parametric sample selection
approach has been most widely applied in depression studies. A number of
studies have applied the two-state sample selection method to assess
differences in total health care expenditure between TCAs and SSRIs
(Croghan et al, 1997;
Crown et al,
1998a,b).
These studies consistently find that differences in antidepressant acquisition
costs are offset (and in some cases more than offset) by broader measures of
health care resource utilisation.
Other studies have extended the two-stage sample selection model to look at additional outcomes, including differences in the number of benzodiazepine co-prescriptions between different SSRIs (Hylan et al, 1997; Treglia et al, 1998). These studies find that after controlling for unobserved factors that may be correlated with initial SSRI selection, patients who start therapy on paroxetine have a higher rate of benzodiazepine prescriptions than patients who start therapy on fluoxetine. Of course, the higher co-prescribing of benzodiazepines with paroxetine could be the result of treatment for comorbid anxiety disorder. Paroxetine patients might be more likely to have comorbid anxiety and depression because paroxetine is indicated for both conditions. In reality, statistical approaches may never be able to control fully for such biases. However, controlling for the non-random selection into initial treatment is particularly important in depression studies, because it may be that selection of a particular drug may be influenced by marketing initiatives, previous treatment non-response and comorbid conditions (such as anxiety).
Corroborating prospective studies are useful to confirm the findings of retrospective studies. It is interesting to note the similarity in economic outcomes between fluoxetine and TCAs found in the prospective study of Simon et al (1996) and the retrospective study of Croghan et al (1997), which used statistical methods to control for potential biases due to non-randomisation. Both of these studies found that the total direct health care expenditure for fluoxetine equalled that for the TCAs; patients who began therapy on fluoxetine, however, were more likely to have a dose and duration of therapy consistent with recommended treatment guidelines. Similar prospective studies comparing the SSRIs and other new antidepressants are necessary to provide further corroboration of the findings from retrospective databases.
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CONCLUSION |
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TOPABSTRACTINTRODUCTIONECONOMIC EVALUATIONS OF...NATURALISTIC ECONOMIC OUTCOME...STATISTICAL METHODS FOR REDUCING...CONCLUSIONREFERENCES |
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Retrospective database studies have been commonly used to assess the economic outcomes of alternative antidepressants in clinical practice. Because of the potential selection bias inherent in non-randomised samples, it is important to control for the potential presence of unobserved factors that may be correlated with the initial treatment selection and economic outcomes. Because the presence or absence of selection bias is ultimately an empirical issue, it is important to test for it in retrospective studies. We have yet to realise the full application of existing statistical methods to retrospective studies in health care technology evaluation, particularly as applied to antidepressants. The methods discussed in this review may also be applicable to evaluating different antipsychotic agents in the treatment of schizophrenia.
Retrospective studies comparing the economic outcomes of alternative antidepressants have consistently found that differences in drug acquisition costs appear to be offset — and in some cases more than offset — by differences in broader measures of health care resource utilisation. This suggests that antidepressant acquisition costs are not a good predictor of total direct health care expenditure. As a consequence, decisions based on antidepressant acquisition costs alone may result in unintended clinical and economic outcomes. It is necessary for health care decision-makers to take a broader perspective when making decisions about paying for depression treatment. This broader budgetary perspective is consistent with an objective of providing the greatest benefit for a given population and budget, an approach that maximises the value of health care spending.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONECONOMIC EVALUATIONS OF...NATURALISTIC ECONOMIC OUTCOME...STATISTICAL METHODS FOR REDUCING...CONCLUSIONREFERENCES |
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| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
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