Statistical analysis for Aim 2

DEA variable in the United Healthcare Database

The DEA or Drug Enforcement Administration number variable in the UH database is an encrypted identifier (ID) for a physician or group of physicians which is unique over time. A preliminary analysis of the UH database showed that there were nearly 3 million OP medication Rx claims between January 2008 and June 2011. 13.1% of these claims had unpopulated DEA fields and there were about 127,000 unique DEAs. Furthermore, on average there were 23.2 OP medication Rxs per DEA or approximately 6.6 Rxs per year. Although it is possible that the DEA represents a group of physicians rather than a single physician, these preliminary findings indicate that the DEA variable is likely specific to each physician as 6.6 Rxs is a plausible number of Rxs per year for each physician. Therefore, this variable is suitable for use in the current project as a basis of the physician preference instrument.

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The DEAs associated with subjects in the cohort were identified and the Rx history for each DEA was obtained. This doctor-level information was then be linked back to the patient-level information for the PMO initiator cohort. From this, each patient’s physician was categorized as having a preference for either BP or other OP medication.

Constructing the physician/provider preference instrument

Using the lag function in SAS, previous fill date information within each physician’s or provider’s prescribing history was identified. Physician/provider-level information was then linked to the patient-level dataset for the PMO initiator cohorts. Analytic datasets were constructed for the two IVs (Figure 5.4): provider preference based on (1) the most recent Rx fill prior to the index date and (2) the proportion of Rx fills within the year prior to the index date.

Evaluating the assumptions of the IV method

For the IVs, the three assumptions were evaluated. The first assumption was evaluated by conducting a cross-tabulation (using proc freq in SAS) of physician/provider preference (IV) by the drug filled on the index date to evaluate the association between the IV and treatment. The second assumption was assessed by comparing baseline covariates (using proc means in SAS) across patients with different IV and index drugs. The third assumption was evaluated by tabulating the number of cases and the person time for each index drug and IV group and by conducting time-to-event analysis (using proc phreg in SAS) with fragility Fx as the dependent variable and, in a series of models, using as the independent variables (1) the index drug (BP vs. Other OP medication), (2) the IV, (3) the IV and covariates, (4) the IV and index drug, and (5) the IV, index drug, and covariates. We used covariate-adjusted Cox models to assess the third assumption by comparing results from a model with IV as the independent variable and IV+treatment as independent variables. This comparison may inform the evaluation of whether any effect of the IV on the outcome is related to the effect of the IV to treatment exposure.

For an IV analysis to be valid, the IV should be correlated with the treatment, the distribution of risk factors should not differ by level of IV, and the IV should not be associated with the outcome except through its correlation with the treatment.

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Potential Improvement of defining incidence medication use with refill information

Furthermore, the “FST_FILL” (i.e., first fill) variable in the UH database, an indicator for new Rxs when the variable has a value of “Y”, was used to restrict to pharmacy claims for new Rxs only. Although “FST_FILL” may equal “Y” due to change in pharmacies or for other reasons, "FST_FILL” not equal to “Y” may be evidence that a Rx is not new. Therefore, restricting the analysis to “FST_FILL=Y” may more accurately capture physician prescribing patterns as compared to the analysis which includes refills since new Rxs rather than refills would be more representative of physician prescribing decisions. An

additional consideration is that patients may change their Rxs at some point during their treatment course. This may be due to changes in provider preference or for other reasons, such as a situation where the drug prescribed initially was not well tolerated by the patient. This change should be captured also by using the "FST_FILL” variable, since each patient in the PMO initiator cohorts may have more than one instance for which the "FST_FILL” variable is equal to “Y” if they had been prescribed more than one study drug during the study period.

I. Statistical analysis for Aim 3

Applying the IV methods to time-to-event analyses

Kaplan Meier models were used to estimate survival and hazard for the treatment and IV groups in the UH and database. An important aspect of the Kaplan-Meier estimate is that it can take into account censored data (i.e., right-censored) which occurs when a patient becomes lost before the outcome is observed or the study is ended.

The Kaplan-Meier product-limit estimator is:

where S(ti) is the survival probability for any particular one of the t time periods, ti; di is the number of subjects who die during time period ti and ni is the number of subjects at risk at the start of time period ti. The cumulative hazard is equal to –ln(S(ti)).

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To apply the IV methods to time-to-event of osteoporotic Fx in our cohorts, using the two-stage approach, the IV estimate would be the differences in the Kaplan Meier estimate of the cumulative hazard between IV groups at a particular time (numerator) between IV groups scaled by the difference in the expected value of exposure in the IV groups (denominator) at that time. We estimated the risk of Fx within groups defined by levels of the IV at 1, 2, and 3 years after the index date for each characterization of the IV. Differences in the risk between IV groups were divided by IV strength to derive an IV estimator of the effect of treatment choice on Fx risk:

Risk difference = 1 − 𝑆(𝑡 ) 𝑍 = 1_{𝑋 = 1 𝑍 = 1} 1 − 𝑆(𝑡 ) 𝑍 = 0_{𝑋 = 1 𝑍 = 0} ,

where 𝑆(𝑡 ) denotes the estimated survival probability for particular time period estimated by Kaplan-Meier, X is an indicator for treatment receipt, and Z is the IV, and Pr(X=1|Z) was the relative frequency of treatment within levels of the IV.

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REFERENCES

1. Warriner AJ, Patkar NM, Curtis JR, Delzell E, Gary L, Kilgore M, Saag K. Which fractures are most attributable to osteoporosis? J of Clin Epid. 2011; 64;46-53.

2. Holmberg AH, Johnell O, Nilsson PM, Nilsson J, Berglund G, Akesson K. Risk factors for fragility fracture in middle age. A prospective population-based study of 33,000 men and women. Osteoporos Int. 2006; 17:1065-1077.

3. Dontas IA, Yiannakopoulos CK. Risk factors and prevention of osteoporosis-related fractures. J Musculoskelet Nuronal Interact. 2007; 7(3):268-272.

4. Schneeweiss S, Wang PS, Avorn J, Glynn R. Improved Comorbidity Adjustment for Predicting Mortality in Medicare Populations. Health Serv Res. 2003; 38(4):1103-1120.

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CHAPTER IV. RESULTS:Identifying the prescribing physician in U.S. healthcare claims data