Post-market Authorized Generic Evaluation (PAGE) U01FD005272-02 November 18 th , 2016 Peggy L. Peissig Richard Berg Michael Caldwell James Linneman Richard Hansen Jingjing Qian Motiur Rahman Ning Cheng Yasser Alatawi David Page Enrique Seoane- Vazquez
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Post-market Authorized Generic Evaluation (PAGE)
U01FD005272-02 November 18th, 2016
Peggy L. Peissig
Richard Berg
Michael Caldwell
James Linneman
Richard Hansen
Jingjing Qian
Motiur Rahman
Ning Cheng
Yasser Alatawi
David Page
Enrique Seoane-Vazquez
Project Overview
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Specific Aim 1
• To determine and compare switchback rates, medical service utilization, and clinical outcomes between authorized generics (AGs) and generics using healthcare claim data with electronic medical records.
3
Aim 1 Methods
• Study design: a retrospective cohort study
• Study population: Marshfield Clinic (MC) Security Health Plan (SHP) claims data with linked electronic health records (EHR) data in 1999-2014
• Inclusion criteria:– Continuous enrollment (CE) in 6-mon prior to generic introduction
through at least the first Rx fill after generic availability
– At least 1 brand Rx in 6-mon prior and 1 Rx fill of a medication in the therapeutic area within 12-mon after generic availability
– At least 1 MC healthcare encounter/year during eligible periods
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Aim 1 Methods (cont’d)
Branded treatment initiation
Generic entry
Generic switch
Non-switchers: stay on brand
SwitchbackIndex date for generic
switch
Index date for switchback
Switchers: stay on generic
Switchers: switchback to brand
Pre-index
Brand
Brand
Generic
Generic switch: switch from a brand drug to an authorized or independent generic drug within 30 months following generic entry
Switchback: among those who had a brand to generic switch, generic to brand switchback rates were calculated in 30 months following the index switch date
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Aim 1 Methods (cont’d)
• Revised drugs of interest:
Drugs Generic Switch Switchback
alendronate x x
amlodipine x x
citalopram x x
gabapentin x x
glimepiride x
losartan x
metformin x
paroxetine x x
sertraline x x
simvastatin x x
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Rates of Switching
Drugs Switch Type
Brand to AG Brand to OG
All drugs (n=5234) 1138 (23%) 3762 (77%)
Alendronate (n=930) 41 (5%) 832 (95%)
Amlodipine (n=1487) 289 (20%) 1156 (80%)
Citalopram (n=813) 74 (10%) 670 (90%)
Gabapentin (n=279) 25 (11%) 199 (89%)
Paroxetine (n=669) 302 (48%) 328 (52%)
Sertraline (n=730) 278 (40%) 417 (60%)
Simvastatin (n=636) 176 (30%) 408 (70%)
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*Factors associated with a higher likelihood of generic switch in the overall drug cohort included pre-index defined daily dose and all-cause hospitalization
Time from generic entry to generic switch by drug
Rates of Switching Back to Brand
Factors Brand to Generic Switchback
Hazard
Ratio
95% Confidence
Interval
IG (REF=AG) 0.86 0.65-1.15
Age1.02* 1.01-1.02
Male 0.59* 0.44-0.80
Proportion of pre-index brand
medication use 2.43* 1.45-4.07
Defined daily dose prior to switching 0.85 0.72-1.01
Charlson comorbidity index 1.02 0.90-1.16
Pre-index hospitalization0.87 0.52-1.48
Pre-index ED visit 1.02 0.67-1.56
Count of pre-index outpatient visits 1.02 1.00-1.02
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*P<0.05, multivariable Cox proportional hazards model
Brand switchback rate from AG vs. OG
Health Services Use & Outcomes
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*Medical service utilization and outcomes were measured during a 12-month period after generic switch. Generalized logistic regression was used to model binary outcomes and negative binomial regression were used to mode count outcomes. Age, defined daily dose, and Charlson score were controlled as covariates in these models.
Adjusted medical service utilization and outcomes for AG vs. OG users
Outcome Estimate
Lower
CI
Upper
CI P-Value
1.05 1 1.1 0.071
1.08 0.9 1.29 0.395
All-cause emergency department visits
Any visit 1.33 1.11 1.61 0.003
Number per year 1.23 1.02 1.47 0.026
All-cause hospitalizations
Any visit 1.14 0.91 1.43 0.257
Number per year 1.09 0.81 1.46 0.582
Medication discontinuation 0.95 0.8 1.12 0.508
Number of all-cause
outpatient visists per year
Number of all-cause urgent
care visits per year
1Estimate
0.5 2Favors OG Favors AG
Specific Aim 2
• To analyze brand versus generic adverse event reporting rates in the FDA Adverse Event Reporting System (FAERS)
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Methods
• Data– FAERS data 2004-2014
• Approach– Method 1 – verbatim assignment by name
– Method 2 – assign reports to manufacturer• Exclude direct reports
• Sensitivity analyses around which reports to include, such as primary suspect, serious, US only, validated
• Analyses– Disproportionality analyses
– Segmented regression11
Drugs
• Alendronate
• Amlodipine
• Azithromycin
• Carbamazepine XR
• Escitalopram
• Lamotrigine
• Leflunomide
• Losartan
• Metoprolol XR
• Modafinil
• Oxcarbazepine
• Sertraline
• Venlafaxine ER
• Zolpidem
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Events
• Coded by Preferred Terms (PT) or Standardized MedDRA Query (SMQ)
• To develop a pilot surveillance system to compare patient experience for authorized generics and independent generics with brand name drugs for differential adverse event signal detection.
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Marshfield’s Population
Security Health Plan MC Primary Service Area
Analysis Decision Points• Analysis cohort (e.g. first-time users of the drug vs. switch cohort)
• Maximum length of observation time (e.g. 365 days vs. 30 days)
• Inclusion / Exclusion of those with events prior to drug exposure
• Inclusion / Exclusion of those with events documented on a single date
• Statistical model (e.g. PH model of time to first event, negative binomial)
• Covariates
• Gender
• Age
• Charlson comorbidity score
• Diabetes indicator
• Smoking indicator
• MESA residency indicator
• Estimated SHP rate for the event of interest
• Review the raw data
• Initial model based analysis
• Are numbers small?
• Review unadjusted/adjusted significance
• Are hazard ratios reasonable?
Initial screening
• Evaluate results after varying decision points
• Evaluate subsetting cohort to limit date of first exposure
• Gather extensive data pre-exposure
• Evaluate associations with B/G and outcome
• Develop propensity score to improve B/G comparability