Relationship between Bisphosphonate (BP) Treatment and General Infection & Osteonecrosis of the Jaw:

Findings from Marginal Structural Models

Trevor McMullan

Background

• Bisphosphonates (BP) maintain bone strength• BP are most commonly prescribed meds for

osteoporosis• BP treatment has been associated with

Osteonecrosis of the Jaw (ONJ) • Infection is suggested to play a pivotal role in

the pathogenesis of ONJ• Reported BP incidence rates; 8799 (Infection)

and 1 (ONJ) per 100000 subject years

Objective

• To develop causal relationships between osteoporotic meds including BP and their risk factors General Infection and ONJ using observational claims data

• Need to address; confounding by indication and informative censoring bias

• Observational data is unblinded, contains selection bias and time dependent confounding

Time Dependent Confounding

Fragility Fracture

OsteoporosisTreatment 1

OsteoporosisTreatment 2

Infections or ONJ

Data & Study Population• Marketscan commercial claims database• Meds, diagnoses, procedures, in/out patient• Data from 1st Jan 2004 to 30th June 2011• N=469432 subjects; 1050567 subject years of data

• Postmenopausal women with osteoporosis• Age > 55 years• dx osteoporosis, osteo fracture or osteo med• ≥ 12 months of continuous enrollment

Data:

Study Population Inclusion Criteria (PMO Index date):

Baseline Demographic & Subject Characteristics Table

Covariate BP (Ptyrs=464728) Other OP (Ptyrs=67860) No Treat (Ptyrs=517979)

% % %

Age (55-64 yrs) 51.6 52.5 42.2

(>= 65 yrs) 48.4 47.5 57.8

Diabetes Type II 12.8 13.2 19.1

Fragility fracture 3.7 5.5 15.5

Serious infection 4.2 5.0 5.4

CCI (mean,std) 0.5 (0.8) 0.5 (1.0) 0.6 (1.0)

Corticosteroids 26.3 27.0 22.2

Immunosuppresants 1.1 0.9 0.8

No physician visits (mean, std)

7.2 (6.1) 7.7 (6.6) 7.5 (6.4)

CCI=Charlson Comorbidity Index

Exposure & Endpoint(s)

• Treatment accessed via drug/procedure codes• 3 Cohorts; BP, Other Osteo Meds, No treat• Treat duration: Days supplied + 60 days

• Followed from inclusion criteria until disenrollment, dx/trt malignancy/Paget’s disease, end of study period, ONJ or general infection event

Exposure:

Endpoint(s): Time to Event

Covariates

• Time fixed (baseline) and time varying covars• Time fixed: demographics, prior BP use,

healthcare utilization• Time varying: comorbidities, concomitant

meds, risk factors for ONJ or general infection• Chronic diseases (diabetes) once identified,

where carried forward throughout the study period

Visit Window (Data Organization)Unstructured Visit Record Window

X

Time varying covariates: 6 months

Time axis

t t1 t2 t3 t4 t5

Time fixed covariates: 12 months

Data record at time t is activated by a treatment switch, ONJ/infection event, or censoring, time dep var status fragility fracture updated at each new rec,time at risk defined as days supplied + 60 days (on-treatment)

Time at risk

Data Structure: 1 Hypothetical SubjectId Base-

lineDate

Treat-ment

SwitchDate

Cohort Time(t)

Days

Event Base-line

Covars

Time VaryingCovars

BPProb

Other OsteoProb

NoOsteoTreatProb

1 3Jun10 3Jun05 BP 456 0 x y1 0.42 0.30 0.28

1 3Jun10 1Sep06 Other 123 0 x y2 0.30 0.44 0.26

1 3Jun10 1Jan07 No Trt 702 0 x y3 0.40 0.30 0.30

1 3Jun10 2Dec08 Other 151 1 x y4 0.50 0.25 0.25

x, y1,y2,y3,y4 are vectors of covariatesy1,y2,y3,y4 change over timeLOCF used if data value is missing for a time varying covariate1=Event, 0=censored

Statistical Analysis: MSM model

• IPTW regression models with time dep vars• Treatment weights: multinominal regression• Censoring weights: logistic regression• Wghts inverse cond Prob of obersved treat cat• Subj with high predicted prob: lower weight• Subj with low predicted prob: higher weight• Stabilized weights and truncation introduced to control extreme weights

Stage 1:

Statistical Analysis: MSM Weights

𝑠𝑤𝑖 (𝑡 )=∏𝑘=0

𝑖𝑛𝑡 (𝑡 ) 𝑝𝑟 (𝐴 (𝑘 )=𝑎𝑖(𝑘)∨𝐴 (𝑘−1 )=𝑎𝑖 (𝑘−1 ) ,𝑉=𝑣 𝑖)𝑝𝑟 ( 𝐴 (𝑘 )=𝑎𝑖 (𝑘)∨𝐴 (𝑘−1 )=𝑎𝑖 (𝑘−1 ) ,𝐿 (𝑘 )=𝑙𝑖(𝑘))

𝑠𝑤𝑖∗ (𝑡 )=∏

𝑘=0

𝑖𝑛𝑡 (𝑡 ) 𝑝𝑟 (𝐶 (𝑘 )=0∨𝐶 (𝑘−1 )=0 , 𝐴 (𝑘−1 )=𝑎𝑖 (𝑘−1 ) ,𝑉=𝑣 𝑖)𝑝𝑟 ¿¿

¿

Treatment stabilized weights: Multinominal logistic regression model

Censoring stabilized weights: Logistic regression model

=Treatment =Treatment history =Time fixed covariates =censoring =Time Varying covariate history (includes Time Fixed covars) =Censoring history

Statistical Analysis: MSM Cox model

(t|V) =

Where:

(t|V) is the hazard of ONJ or General infection at time t among subjectswith baseline covariates V in the source population had, contrary to fact, all subjects followed each treatment cohort history through time t

the scalar and row vector are unknown parameters

is an unspecified baseline hazard

Need to account for within subject correlation: Robust Sandwich Covariance Estimator

Weight and MSM models use different time axes

Stage 2:

General Infection Results TableTreatment Number

of PtyrsNumber of Cases

Multivariate Cox Reg Model I

Multivariate Cox Reg Model II MSM:Model III

No Osteo Treatment

330429 78634 1 1 1

BP 335976 82963 1.11 (1.10, 1.13) 1.08 (1.06, 1.09) 0.84 (0.83, 0.85)

Other OP Meds

47433 12882 1.17 (1.14, 1.19) 1.13 (1.11, 1.15) 0.92 (0.90, 0.93)

Model I: Unweighed Cox model with time fixed covariatesModel II: Unweighted Cox model with time fixed and time varying covariatesModel III: IPTW weighed Cox model with time fixed and time varying covariates

Time fixed covars: demographics, healthcare utilizationTime varying covars: risk factors for general infection (hiv,lupus,diabetes etc.), comorbidity status, select concomitant medications, malnutrition, obesity, fragility fracture, etc.

ONJ Results TableTreatment Number

of PtyrsNumber of Cases

Multivariate Cox Reg Model I

Multivariate Cox Reg Model II MSM

No Osteo Treatment

515903 108 1 1 1

BP 465060 99 1.04 (0.73, 1.48) 1.03 (0.69, 1.53) 0.94 (0.64, 1.37)

Other OP Meds

67683 8 0.56 (0.26 1.17) 0.55 (0.26, 1.18) 0.58 (0.27, 1.22)

Model I: Unweighed Cox model with time fixed covariatesModel II: Unweighted Cox model with time fixed and time varying covariatesModel III: IPTW weighed Cox model with time fixed and time varying covariates

Time fixed covars: demographics, healthcare utilizationTime varying covars: risk factors for ONJ (age, gingival bleeding, dental fistula, diabetes etc.), comorbidity status, select concomitant medications, fragility fracture, etc.

MSM Assumptions

• No unmeasured confounders

• Positivity

• Model mis-specification

• Weight Truncation

Claims data does not collect all variables that may impact treatment and outcome, For instance, bone mineral density (BMD)

All modeled covariates should have a +ve probability for outcome category

The correct model is selected for determining the IPTWs such as using amultinominal logistic regression model and not an ordinal logistic regressionmodel when treatments > 2 and are not ordinal

Trade-off between control of confounding and precision of MSM weight estimates

Conclusion & Other Approaches

• Unweighted Cox models indicated an increased risk of general infection for subjects on BP and other OP meds

• Adjusting for time varying confounding covariates such as fragility fracture using inverse probability of treatment weights indicated a reduced risk of general infection for BP and other OP med subjects

• ONJ results were inconclusive due to their low occurrence rate

• IPTW Kaplan Meier curves are another possible way to conduct this statistical analysis

ReferencesHernan MA, Brumback B & Robins JM Marginal Structural Models to Estimate the Causal Effect of Zidovudine on the Survival of HIV-Positive Men. Epidemiology 2000;11(5): 561-570Westreich D et al. Time Scale and Adjusted Survival Curves for Marginal Structural Cox Models. Practice of Epidemiology 2010;171(6): 691-700Wang O, Kilpatrick RD et al. Relationship between Epoetin Alfa Dose and Mortality: Findings from a Marginal Structural Model. Clin J Am Soc Nephrol. 2010; 5: 182-188Xue F, Tchetgen Tchetgen E, McMullan T et al. Marginal Structural Model to Estimate the Effect of Cumulative Osteoporosis Medication on Infection and Potential Osteonecrosis of the Jaw (ONJ) Using Claims Data (manuscript under progress)Spreeuwenberg MD et al. The Multiple Propensity Score as Control for Bias in the Comparison of More Than Two Treatment Arms. Medical Care 2010; 48(2): 166-174

Additional Slides

Statistical Analysis: Weighed KM

𝑆𝑥 (𝑡 )=∏𝑡

1−𝑑𝑡𝑥

𝑟 𝑡𝑥

𝑑𝑡𝑥=∑𝑖=1

𝑁

𝑊 𝑖𝑡𝑌 𝑖𝑡 (𝑋 𝑖𝑡=𝑥¿)¿

Survivor Function:

where:

= IPTW weighed number of events for treatment x at week t=IPTW weight at time t for subject i= Event indicator with 1=Event 0=No event = Subject risk set at time t for treatment t

Measured & Unmeasured Confounding

Fragility Fracture

OsteoporosisTreatment 1

OsteoporosisTreatment 2

Infections or ONJ

Unmeasured Confounders