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Measuring the Effectiveness of Inhaled Corticosteroids for COPD Is Not Easy!

Johns Hopkins Bloomberg School of Public Health Baltimore, Maryland

The difficulty in improving the natural history of chronic obstructive pulmonary disease (COPD) frustrates patients and their physicians. Over the last decade, as interest in treating this common disease has increased, randomized clinical trials and observational studies have been conducted to assess the benefits and risks of inhaled corticosteroid therapy. In this issue of AJRCCM (pp. 49–53), Suissa (1) questions the findings of one observational study, a cohort study by Sin and Tu (2) using databases of the Province of Ontario, Canada. The study showed a substantial reduction in risk for rehospitalization for COPD or death in persons receiving an inhaled corticosteroid prescription within a 90-day window after hospitalization for COPD. Suissa proposes that a methodologic error made in the study's definition of therapy has inflated the benefit of corticosteroid therapy, a claim disputed by Sin and Tu (3).

The disagreement between Sin and Tu and Suissa reflects, in part, the challenge of causal interpretation of observational studies of therapeutic agents. The randomized clinical trial is considered the highest level of evidence for evaluating the effect of a treatment on the outcome of a disease, giving an indication of efficacy under optimal, controlled circumstances of a trial (4). It holds the highest place in the evidentiary ladder because randomization provides assurance, within the constraint of chance, that treatment and comparison groups are comparable in characteristics determining outcome. Clinical trials, however, often include highly selected participants, and their size and the nature of the outcomes that can be evaluated are limited by feasibility and other constraints. Consequently, observational data are also used to assess treatment effects, affording the opportunity to assess the effectiveness of therapies in "real world" settings and to incorporate outcome measures, such as hospitalization or mortality, that may not be feasibly studied in a trial. Absent randomization, however, differences between treated and untreated patients may bias observational studies. "Confounding by indication" refers to one particularly intractable bias: the differential prescribing of medication to patients based on severity of underlying disease (5). Typically, multivariable models are used to "adjust" for severity indicators to control for this and other sources of confounding.

Suissa addresses another aspect of the Ontario study's methods. The bias identified by Suissa lies in the details of the study by Sin and Tu, who classified study participants as "exposed" to an inhaled corticosteroid, if a prescription was received in a time window up to 90 days after discharge. Because follow-up of cohort members ended when the outcome occurred, those classified as receiving inhaled steroids had up to 90 days during which they were outcome free by the exposure definition. Sin and Tu counted this "immortal" follow-up time, thereby underestimating the rate of reaching an outcome in the group classified as receiving inhaled steroids. Suissa probes this bias, using a database from the Province of Saskatchewan, Canada. There are differences between the studies in the ages of participants and in the methods of adjustment for underlying severity of COPD and comorbidity. However, these differences are not relevant to Suissa's main point, which correctly demonstrates the biasing effect of the exposure classification method used by Sin and Tu. In the Saskatchewan study, about 10% of the follow-up time was "immortal" for those receiving inhaled corticosteroids. Sin and Tu's reply provides findings of an analysis that excludes persons having an event within the first 90 days, but this does not represent a solution to the creation of immortal follow-up time.

The ideal analysis would model the relationship between therapy and risk for outcome in a time-dependent fashion that reflects understanding of the temporal dynamics of the effect of inhaled corticosteroids on the underlying inflammatory process. Although this understanding is still incomplete, the relevant time scale is likely weeks or months (6). The fixed exposure classification approach used by Sin and Tu (2) assigns all follow-up time after the first prescription for inhaled corticosteroids as "exposed," even with no subsequent prescriptions. Of course, it is likely that receipt of one inhaled corticosteroid prescription predicts further use of the drug, but a more complete analysis would incorporate the information on all prescriptions after entry into the cohort, and classify follow-up time in a time-varying fashion, the time-dependent analysis of Suissa. This approach is preferred, if needed data over time are available. Analytic methods are well developed for this purpose and have been elegantly applied, for example, in assessing effectiveness at the population level of highly active antiretroviral therapy on outcomes in persons with HIV/AIDS (7–9).

Both studies indicate the difficulty of dealing with differences between treated and untreated patients. In the studies, substantial differences are evident in the medication profiles given the groups receiving and not receiving inhaled steroids, with the latter generally receiving less medication. This pattern is consistent with confounding by indication, which the authors attempted to control using multivariate models. These models can adjust for differences in characteristics of groups, if the relevant variables have been measured without error and the model used for the adjustment is specified correctly from a biomedical perspective. The propensity score is one widely used approach for handling differences that influence treatment likelihood (10). Model results should be interpreted with caution and substantial changes in apparent benefits with adjustment should raise concern; for example, in the analysis by Sin and Tu (2), risk was reduced by 10% in the crude analysis and by 26% in the adjusted analysis. Residual confounding cannot be excluded with full confidence and its possibility is a principal limitation of observational studies in comparison with clinical trials.

Differences between findings of observational studies and randomized clinical trials have long been of interest; the recent findings of the Women's Health Initiative (11), in comparison with earlier observational studies of hormone replacement therapy, have intensified discussion of their differences (12, 13). As with inhaled corticosteroids and COPD, both lines of evidence are needed to evaluate efficacy and effectiveness. However, care must be taken in the design and analysis of observational studies to guard against avoidable sources of bias and methods available for this purpose need to be used.

Where does this report by Suissa leave the weight of evidence on inhaled corticosteroids and COPD? The controlled trials provide evidence of reduced exacerbations but only a weak indication for reduced mortality (14). Suissa did not find an effect on mortality and hospitalization, although his study had only 979 persons and could not estimate benefit with great precision. The Sin and Tu findings should be set aside for the moment, until the data are further analyzed. For inhaled corticosteroids and COPD, the evidence from trials and observational studies appears coherent for the more severe outcome measures of hospitalization and death, showing little indication at present of benefit.

FOOTNOTES

Related letters from Sin, Man, and Tu, and from Pride, Vestbo, Soriano, and Kiri appear in the Correspondence section in this issue.

REFERENCES

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3. Sin DD, Man SFP, Tu JV. Letter in response to "Effectiveness of inhaled corticosteroids in COPD: bias in observational studies." Am J Respir Crit Care Med (In press)
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