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Comorbidity-Adjusted Life Tables: A Tool for Assessing Other Causes Mortality in Cancer Patients Angela Mariotto, Zhuoqiao Wang, Carrie Klabunde, Eric.

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Presentation on theme: "Comorbidity-Adjusted Life Tables: A Tool for Assessing Other Causes Mortality in Cancer Patients Angela Mariotto, Zhuoqiao Wang, Carrie Klabunde, Eric."— Presentation transcript:

1 Comorbidity-Adjusted Life Tables: A Tool for Assessing Other Causes Mortality in Cancer Patients Angela Mariotto, Zhuoqiao Wang, Carrie Klabunde, Eric J. Feuer Methods and Applications for Population Based Survival Frascati, September 20-21, 2010

2 Outline Background motivation Data: SEER, SEER-Medicare and 5% non-cancer sample Methods Step I: Estimating comorbidity index Step II: Estimating survival by comorbidity index Step III: Estimating health-adjusted age Results Discussion

3 Motivation More accurate estimate of competing non-cancer survival taking into account health status. Tool to improve informed decisions regarding: Treatment choices. Age to stop screening.

4 SEER Data The Surveillance, Epidemiology, and End Results (SEER)program collects data on clinical, demographic, and cause of death information for persons with cancer. Data from 11 registries ( ) Representing 14 % of the US population

5 Medicare Program Federal health insurance plan that offers health insurance for the 65 years and older US population. Medicare data contains enrollment and claims data associated with health care paid by Medicare plan. Hospitalization, clinic visit, outpatient tests bills Information on date, diagnosis codes, procedure codes, and cost. 94% of the 65 years and older US population has inpatient and outpatient coverage

6 SEER-Medicare and the 5% Sample Data SEER-Medicare : Medicare claims linked to Medicare eligible cancer patients in the SEER database There is a 93% match 5% sample (non-cancer) : At the time of the linking, NCI creates a file that contains claims, demographic characteristics and life status information for a 5 % random sample of Medicare beneficiaries residing in the SEER areas who do not have cancer. The 5% non-cancer sample can be used as controls Medicare claims data is the same for cancer and non-cancer cases

7 Measuring Comorbidity in SEER-Medicare: Cancer Patients SEER-Medicare: ICD-9-CM codes recorded in claims during the 12 months prior to the cancer diagnosis were used to identify 16 comorbid conditions used by Charlson et al. (J. Chronic Disease, 1987). Algorithm similar to Klabunde et al. (Annals of Epidemiology, 2007) X 1 year prior, claims are evaluated to indentify 16 comorbid conditions Cancer diagnosis Non-cancer death Survival time

8 Measuring Comorbidity in the 5% Sample Data: Non- Cancer Comorbidity identified prior to each birthday Multiple records for each person but each record contributes to 1 survival curve Same algorithm as for cancer patients X Claims are evaluated to indentify 16 conditions prior to each birthday 66 Birthday X 67 Birthday X 68 Birthday X 69 Birthday End of follow-up 66 survival 69 survival … 67 survival

9 Data characteristics: Age, Sex, Race and Life Status SEER- Medicare Cancer Patients 5% sample (non-cancer) Multiple records No.% % Age , , , , , , , , , , , ,3715 SexFemales 524, ,966,27163 Males 583, ,133,56237 RaceWhite 978, ,639,75985 Black 79, ,3537 Other 49, ,7218 Life StatusAlive 897, ,249,85473 Dead 210, ,97927 Total 1,108, ,099,833100

10 Comorbidities Frequencies

11 Step I: Estimating the Comorbidity Index SEER-Medicare data on cancer patients only Cancer patients with more than one cancer are excluded Comorbid conditions measured in the year prior to diagnosis Cox proportional hazard method having sex, age, race and 16 conditions Event: death for non-cancer causes Censoring events: cancer death and lost or end of follow-up

12 Results from Cox proportional hazards model Comorbidity Index Calculation (CI) 1. Diabetes + Congestive heart failure CI= = Diabetes + COPD CI= = COPD+ Congestive heart failure + Liver: CI= = 2.17 VariableEstimateStd.Err. Hazard Ratio Age Female Male Race: White Black Other Acute myocardial infarction AIDS Cerebrovascular disease Chronic renal failure Congestive heart failure COPD Dementia Diabetes Diabetes with sequelae Liver disease Liver disease mod./severe Myocardial infarction Paralysis Rheumathologic disease Ulcer disease Vascular Disease

13 Step II: Estimating age- and sex- specific survival by comorbidity index Both data: SEER-Medicare and 5% non-cancer sample For each age and sex we fit a Cox proportional hazard model using comorbidity Index as cubic-spline linear at the tails, cancer status, and race as covariates People in the 5% sample are included once in each survival curve

14 Step II: Estimating age- and sex-survival by comorbidity index (continued) For each age and sex we fit a Cox proportional hazard model where z is a vector of covariates Comorbidity index (CI) is modeled with a restricted cubic spline with 4 knots at the 5%, 35%, 65% and 95% percentile of each individual age : k 1, k 2 k 3, and k 4. where CI 1 and CI 2 are two function of CI and knots.

15 Parameters Estimates Set of 7 parameters for each age (66-95) and sex (males and females) Summarize parameters graphically Hazard ratios of dying of other causes by age due to: Diabetes vs. healthy Cancer vs. non cancer Race For selected ages we show the effect of comorbidity index on the risk of dying of other causes for white women.

16 Hazard ratio estimates of dying of other causes than cancer due to diabetes (CI=0.34) Reference is whites, no-comorbidities and no-cancer: Hazard Ratio=1

17 Hazard ratio estimates of dying of other causes than cancer due to diabetes (CI=0.34), race and cancer status. Reference is whites, no-comorbidities and no-cancer: Hazard Ratio=1

18 Hazard ratio estimates of dying of other causes than cancer due to diabetes (CI=0.34), race and cancer status. Reference is whites, no-comorbidities and no-cancer: Hazard Ratio=1

19 Estimating Health-Adjusted Age or Physiologic Age Motivation: usually doctors subjectively assign a physiological age to patients depending on their health status and health behaviors People in good health and with healthy: lower physiological age People in poor health: higher physiological age By comparing each age and comorbidity specific survival curve with US life tables we will more objectively try to calculate physiological age Life tables represent all causes mortality in the US population.

20 Estimating health-adjusted age: physiologic age Example: White women age 66 with no comorbidity is the estimated cumulative probability of surviving age t for a white women diagnosed with cancer at age 66 and no comorbidities Health- Adjusted age is the age x that minimizes distance between is the cumulative probability of surviving age t obtained from the 2000 life tables for white women in the US is the cumulative probability of surviving age t, conditional on being alive at age x.

21 White women diagnosed with cancer at 70 years of age and selected comorbidity indexes (solid) to the best fitted US life table (dashed lines). Acute myocardial infarction COPD Diabetes + COPD, Diabetes+ CHF Diabetes + COPD + CHF Dementia + COPD +CHF

22 Limitations Comorbidities measured from claims data Estimates for ages 66+ only 2 step analysis: Cancer patients to estimate comorbidity index Cancer patients + cancer free people to estimate survival by comorbidities In one analysis we would have to take into account of the correlation of comorbidities before consecutive birthdays on the cancer free population

23 Discussion and Conclusions Comorbidity, cancer status, sex and race are important predictors of other cause mortality, however their effect is attenuated as age increases. Not clear why cancer status is a predictor of worse other causes survival Misclassification of cause of death? Future analysis: restrict analysis to women with early breast cancer and do matching with 5% cancer random sample to investigate if their other causes survival is still worse.

24 Discussion and Conclusions This tool will paired with cancer prognosis (net cancer survival) to provide more individualized probabilities of dying from cancer and of dying of other causes Inclusion of other cause mortality in decision of cancer treatment and screening are particularly important for patients diagnosed at older ages and with more indolent tumors (e.g. prostate cancer) Health-adjusted age might be a useful tool for clinicians in general

25 One Dataset Net probability of dying of Cancer Net probability of dying of Other Causes Cox Model 2 Cox Model 1 Dataset 1 Cancer Patients Net probability of dying of Cancer Net probability of dying of Other Causes Dataset 2 Non-cancer Cox Model 2 Cox Model 1 Crude probabilities dying of Cancer and Other Causes No need for independence assumption Minjung used a continuous time model where estimates are computed using counting process* Estimates and SEs of cumulative incidence are identical if independence is assumed or not (Nonidentifiability: Tsiatis,1975) * Cheng SC, Fine JP, Wei LJ, Prediction of the Cumulative Incidence Function under the Proportional Hazards Model, Biometrics, 54, Needs independence assumption of competing risk and that populations are similar* Can take advantage of the richness of alternative different data sources. Use discrete time model – CIs of cumulative incidence computed using delta method Equations are the same

26 Thank you!!!

27 Villa Mondragone


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