Variación de la proteína mayoritaria de la capa S (MCSG, “Mayor Cell Surface Glycoprotein”).

Osteoporosis A. Introduction

Estimates of intended drug effects derived from observational (non-interventional) studies may be subject to bias1 _{when differences between treatment and comparison groups can be partly attributed to} prescribers’ choice of treatment, particularly when this choice is influenced by the anticipated treatment effect.2 _{In addition, when these studies are conducted in administrative insurance claims databases,} important potential confounders such as lifestyle factors (e.g., diet, smoking), laboratory test results, and other clinical measurements are often missing. Commonly used methods to adjust for confounding such as stratification or covariate adjustment address confounding by measured variables. IV methods have been proposed as a potential approach to adjust for unmeasured confounding.3 _{Pharmacoepidemiologic} studies using IV methods based on administrative databases have used a variety of co-Rxs,

comorbidities, and other variables as instruments.4-9

The identified safety concerns of BPs10, 11 _{combined with the introduction of OP medications}12 _in recent years have led to increasing interest in assessment of the risk-benefit profile of OP medications. Few head-to-head clinical trials comparing the relative effectiveness of these drugs have been

conducted.13-17 _{In addition, only a small number of observational studies have directly compared the} effect of different OP drugs on the risk of Fx or other outcomes.18-21 _{A major limitation of these studies}

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particularly those based on claims or other administrative databases, is that important potential

confounders, such as bone mineral density, BMI, physical activity, family history of OP and frailty, were not available for analyses.

IV methods, which may control for these unmeasured confounders, have yet to be applied to the study of comparative effectiveness of OP medications. The decision to prescribe one OP drug over another may depend on several factors such as convenience, drug tolerability, and adherence to dosing schedules.22, 23 _{The most widely prescribed OP medications are BP, which are potent antiresorptive} drugs that slow or prevent the dissolving of bone, thereby maintaining or increasing bone strength.24 _BP has been associated with safety concerns including osteonecrosis of the jaw10 _{and atypical femoral Fx.}11 Other OP mediations, such as the anabolic drug teriparatide, increase the rate of bone formation and thus rebuilds bone.25 _{We hypothesized that physicians’ Rx preferences for OP medications (BP versus other} OP medications) might serve as a potential IV.

In this study, we constructed an IV based on physician preference for prescribing BP versus other OP drugs and evaluated the assumptions of the IV method in assessing the potential effect of these drugs on risk of osteoporotic Fx among women with postmenopausal osteoporosis (PMO). The validity of IV methods requires three primary assumptions: (1) the IV is associated with the treatment received; (2) covariate values do not differ over levels of the IV (independence assumption); and (3) the IV does not affect the outcome except through its influence on treatment (exclusion restriction). In addition, an IV analysis could be biased for the average effect of treatment if the strength of the IV differs over covariate strata.3 _{We evaluated these assumptions by testing (1) the strength of the IV as reflected by its}

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association with the index treatment; (2) the association of the IV with osteoporotic Fx independent of its association with the index treatment; (3) the balance of covariates across levels of the IV; and (4) the heterogeneity of IV strength by covariate strata.

B. METHODS

Data Source

UH administrative databases consist of medical and pharmacy administrative claims submitted by 11 UH Group-affiliate health plans. Data are available for 15 million patient lives per year, including approximately 1,379,000 members aged ≥ 55 years old, from 1993 to the present. Data consist of claims submitted by healthcare professionals for covered services (claims collected from hospital inpatient, outpatient and emergency room [ER] settings), surgery centers and physician offices), claims submitted by pharmacies, enrollment data to track plan membership for billing purposes, and provider data to track participating physicians who have contracted with health plans to provide services to their enrollees. Data are relatively complete for all billable medical transactions.

Study population

Women aged 55 years and older who were continuously enrolled in their health plan for at least 12 months prior to initiating a BP or other OP medications (index treatment) were identified from January 2008 to June 2011. Patients in the cohort had a wash-out (baseline) period of at least 12 months

preceding the date of receiving the index treatment (index date) during which they did not receive any OP medications. Women with a diagnosis of Paget’s disease of bone (ICD-9 731.0), a diagnosis of cancer

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(excluding non-melanoma skin cancer), or treatment with chemotherapy, hormonal therapy, or radiation therapy for cancer during the 12-month baseline period were excluded from analyses.

Exposures

OP medications were identified using NDC, AHFS drug codes, or HCPCS procedure codes. The BP included in this study were restricted to those indicated for the treatment of PMO: intravenous BP including zoledronic acid infusion (Reclast) and ibandronate injection, and oral BP including alendronate sodium, alendronate sodium/vitamin D tablet (Fosamax plus D), ibandronate sodium, risedronate, and risedronate sodium/calcium carbonate. Other OP medications included calcitonin injection and spray, teriparatide injection and raloxifene tablets.

Outcomes

Osteoporotic Fx was defined as closed Fx at the hip, spine, pelvis, femur, humerus, radius or ulna, open Fx at the distal radius or ulna, or pathologic Fx at the spine based upon an algorithm comprised of ICD-9 diagnosis codes and procedure codes.26 _{Fxs associated with an ICD-9 E code for} major trauma associated with vehicle accidents, falls, and other accidents (E800-E848, E881-E884, E908-E909, E916-E928) were excluded. To ensure identification of incident cases, we required that there was no claim for the same Fx site within the 6-month period preceding the Fx.

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Covariates

Demographic factors, comorbidities, concomitant medications including specific risk factors for osteoporotic Fx and the Charlson comorbidity score, assessed over the 12-month baseline period, were evaluated as baseline covariates.

Follow-up

Follow-up began on the index date and continued until the first incident osteoporotic Fx. Censoring occurred at the earliest of disenrollment from the health plan, death, diagnosis of Paget’s disease, diagnosis of malignancy (excluding non-melanoma skin cancer), treatment with chemotherapy, hormonal therapy or radiation therapy for cancer or the end of the study period.

Instrumental Variables based on Physician Medication Preference

Two IVs (Figure 5.1) describing physician preference for BP versus other OP medications were created: (1) ‘Day IV’ based on the last claim for any OP medication (BP vs other OP medication) by the physician prior to the patient’s index date; and (2) ‘Year IV’ which was based on a physician’s Rxs within the year prior to the index date. For year IV, physicians were classified as having a preference for BP if the proportion of BP claims by the same physician in the year prior to the patient’s index date was greater than the proportion of BP claims in the entire pharmacy claims database. Otherwise they were classified as having a preference for other OP medications. Analyses were restricted to PMO patients whose index OP treatment was recorded in pharmacy claims data (which included 92% of the PMO OP treatment claims in the database during the study period) because the DEA, an encrypted unique ID for each

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physician which we used to obtain the associated physician’s prescribing history, is located in pharmacy claims but not in medical services claims of the database.

Statistical Analysis

To evaluate the first assumption that the IV is associated with the index treatment, we cross- tabulated physician preference (IV) by the index treatment and assessed the strength of the IV based on risk differences. We evaluated the second (independence) assumption that covariates are balanced across levels of the IV by comparing the distribution of baseline covariates across patients with different IV values versus across patients with different index treatments. We assessed the third assumption that the IV does not predict the outcome except through its influence on the treatment by estimating the incidence rate ratio (IRR) of osteoporotic Fx in a series of Cox proportional hazard regression models which included: (1) the index treatment only (BP vs other OP medications), (2) the IV only (BP versus other OP medications), and (3) the IV and index treatment. The heterogeneity of treatment effect was evaluated by computing the IV strength in strata defined by the presence of each covariate; the relative likelihood that a medication complier (i.e., a patient who was prescribed BP versus another OP

medication by a physician who preferred that OP drug group) possessed a characteristic (i.e., covariate) was calculated as the IV strength in each covariate stratum divided by the IV strength in the full

population.27 _{As the first Rx for a patient is more likely to reflect the preference of the treating physician,} we excluded refills of OP medications when defining physicians’ preference for the primary analysis. All analyses were conducted using SAS 9.3 (SAS Institute Inc., Cary, North Carolina).

58 C. RESULTS

A total of 66,125 women were identified as initiators of BP or other OP medications. The first assumption regarding IV strength was assessed by evaluating the risk difference (i.e., the probability of a patient receiving a BP medication from a physician who preferred BPs compared to the probability of a patient receiving a BP medication from a physician who preferred other OP medications). The risk difference (95% CI) was 0.17 (0.16, 0.18) for Day IV; the IV strength was lower for Year IV at 0.12 (0.12, 0.13). After excluding Rxs for OP medication refills from our primary analysis, the IV strength was reduced slightly for Day IV (0.15 (0.14, 0.16), N=62,982), but was similar for Year IV (0.13 (0.12, 0.13, N=65,079).

In evaluating the second assumption, slight imbalances in the distribution of most covariates for patients receiving different index treatments were improved in analyses comparing patients with different IV values (i.e., Mahalanobis distances of 0.7505 and 0.6978 for index treatment and IV groups,

respectively, for Day IV excluding refills) (Table 5.1). This pattern was observed for Day IV including refills as well as Year IV with or without refills (Table 5.4). The prevalence balance in antihypertensive treatment, OP, rheumatoid arthritis, cerebrovascular disease, moderate or severe renal disease, diabetes, and Charlson Comorbidity Index score were most improved by Day IV. The prevalence difference ratios (i.e., PDR; difference of prevalences in the covariates between IV groups compared to the prevalence difference between treatment groups) were smallest for fragility Fx, obesity, OP, and ulcer.

To evaluate the third assumption, the IRRs for index treatment and IV groups were estimated (Table 5.2). For the analysis among patients with Day IV defined (without refills), the IRR for index

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treatment was 0.57 (0.52, 0.64) comparing BP to other OP medication. The IRR for Day IV (BP vs. other OP medication) was 0.96 (0.86, 1.07), which rose slightly to 1.05 (0.94, 1.18) after adjusting for treatment. Additional adjustment for baseline covariates and both treatment and covariates did not result in

substantial changes in the IRR for Day IV. Similar results were found for Year IV (Table 5.5) and the inclusion of refill Rxs did not substantially change the result for either IV.

Possible bias due to treatment effect heterogeneity was reflected by moderate differences in IV strength across groups defined by the presence of specific covariates (Table 5.3). Relative to the IV strength (95% CI) of 0.15 (0.14, 0.16) on the absolute scale for Day IV, IV strength ranged from a minimum of 0.12 (0.08, 0.16) for patients with connective tissue disease to a maximum of 0.50 (-0.19, 1.00) for patients with any tumor, leukemia, or lymphoma. Compared to the IV strength (95% CI) of 0.13 (0.12, 0.13) for Year IV using new Rxs (Table 5.6), IV strength ranged from a minimum of 0.09 (0.06, 0.12) for patients with rheumatoid arthritis or connective tissue disease to a maximum of 0.32 (-0.02, 0.66) for patients with moderate or severe liver disease. Furthermore, the relative likelihood that compliers had a particular baseline characteristic ranged from 0.66 to 3.39 for Day IV (Table 5.3) and 0.72 to 2.56 for Year IV (Table 5.6).

D. DISCUSSION

We evaluated two physician-preference IVs derived from insurance claims to assess the comparative effectiveness of BP versus other OP medications with respect to incidence of osteoporotic fragility Fx in women with PMO, one based upon the last prior OP medication Rx and the other based on the proportion of OP medication Rxs in the prior year. Comprehensive evaluation of different definitions

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of an IV is critical to identifying the IV with maximum strength, that satisfies assumptions needed to assure validity of the IV, and that provides an effect estimate whose interpretation best suits the question being addressed (e.g., risk ratio or risk difference). Our study is the first to explore the use of refill information to determine whether a Rx was the first that a patient received for a medication. Overall, the IV strength on the absolute scale was higher for Day IV than for Year IV with or without refills, suggesting that new Rxs for drugs rather than refills may not necessarily be more indicative of physician prescribing decisions for all drugs and settings. We found few differences in results of analyses based on the two IVs, although in a previous study, the IV based on last prior OP Rx appeared to provide a more variable, though less biased, instrument relative to the IV based on the proportion of Rxs in the prior year.4 _IV strength observed in the current study (0.12 to 0.17 on the absolute scale) is within the range of that found (0.03 to 0.23) in prior pharmacoepidemiologic studies.3-9, 28

We found that both IVs balanced the prevalence of some strong confounders such as prior fragility Fx, but not others such as corticosteroid use, suggesting that there may have been differences in patient case mix or physician specialties which may be indicative of a potential violation of the IV assumptions. In this case, corticosteroid use would still need to be adjusted for in the analysis by other methods. Furthermore, relative to received treatment, the IV did not improve balance in the prevalence of several other diagnoses and medications, particularly those not anticipated to affect the choice of OP medication, such as cerebrovascular disease and dementia. In general, there was not a substantial difference in the distribution of most of the measured covariates across the treatment groups, and the IV generally reduced the prevalence imbalance among most covariates but did not eliminate it. These results are

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generally consistent with the expectation that the physician preference-based IV is not associated with patient-level characteristics that may confound the treatment effect. The IV may not always be superior in balancing baseline characteristics, especially for those that are not associated with the choice of

treatment and thus are not potential confounders. Though the analysis in the current study is based on measured potential confounders, it may shed light on the improvement in the adjustment for unmeasured confounders potentially achieved by physician preference-based IV.

For the IV to have less asymptotic bias compared to ordinary least squares (OLS) regression, the absolute prevalence difference ratio (PDR) of unmeasured confounders, defined as the prevalence difference between IV groups divided by the prevalence differences between treatment groups, should be less than the strength of the instrument.3 _{Although one cannot estimate this quantity for an unmeasured} variable, one can take the observed variables, particularly those that are likely strong confounders, as a proxy for the unmeasured ones. For the current study, this threshold is 12 to 17% according to the strength of physician preferences IVs. For OP and fragility Fx, both strong confounders, the PDR was less than the IV strength threshold but this was not the case for most of the other covariates. For this study, most prevalence differences were close to 0 and fluctuations in the PDR could be due to these small prevalence differences. It would be informative to apply this IV to situations where greater

imbalances across treatment groups were observed to more comprehensively evaluate the performance of the current IV. The low prevalence and small imbalances in the measured covariates in this study likely reflect the small amount of measurable confounding that can be addressed based on data available in administrative databases rather than the absence of confounding. Many of the risk factors for

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osteoporotic Fx, such as low bone mineral density or low BMI, are not assessable in administrative claims data. The usage of IVs in adjusting for unmeasured confounding variables is of particular value in this context. Besides measuring PDR, we conducted a statistical analysis to test whether the IV was superior to OLS methods in this study (Choi B, personal communication). We failed to reject the null hypothesis of the IV being equivalent or worse than OLS methods. Therefore, the IV may not have been an

improvement in this instance, but it is likely not worse than standard OLS methods.

To evaluate the third assumption, we compared treatment groups and IV groups with regard to the incidence of osteoporotic Fx. Assuming the IV is valid and that there is no unmeasured confounding, after adjusting for treatment, the effect of the IV should be attenuated because any effect of the IV on osteoporotic Fx is only realized through the mediating effect of the index treatment. However, in the current study the IRR for the index treatment and the IV were not associated with the outcome in a similar manner. This may be due to unmeasured confounding; the IRR for index treatment is likely confounded by unmeasured factors while the IRR for the IV is not confounded unless there are sufficient differences in patient case mix between the IV and treatment groups due to imperfect correlation between the IV and treatment. After adjusting for treatment when estimating the IRR comparing the IV groups, the estimate increased away from the null which is expected given that the IRRs of the IV and the index treatment were associated with the outcome in opposite directions. Previous studies have cited similar results.4

Lastly, we found differential IV strength by measured potential confounders suggesting potential bias due to heterogeneity by unmeasured confounders. When we assessed the relative likelihood of the

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presence of each characteristic for compliers, we found that in general, the compliers were similar to the overall population in characteristics measured by the covariates.

A physician’s drug preference is not easily quantified. In general, the preference-based IV assumes that physicians or groups of providers differ in their preferences for medical treatments or procedures for similar patients.29 _{Differences in hospital capacity, drug benefit plans or formularies may} lead to regional differences in medical decision making among groups of physicians.30 _{Other factors such} as marketing by pharmaceutical companies, a physician’s clinical experience, emerging evidence about safety and efficacy of a drug, patient case mix, drug availability and reimbursement may also influence a physician’s prescribing decision. However, as long as these factors contributing to physician prescribing decisions are not related to patients’ characteristics, the assumptions of the IV are not violated. For OP medications, a physician’s prescribing preferences may be influenced by patient preference which may depend on affordability,22 _{potential side effects,}31, 32 _{convenience of dosing schedule,}23 _{and route of} administration.33 _{To the extent that these physician preferences are associated with potential} confounding variables, the validity of the IV may be compromised.