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

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

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.

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 (1992-2005) Representing 14 % of the US population

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

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

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

Cancerdiagnosis

●

Non-cancer death

Survival time

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

Data characteristics: Age, Sex, Race and Life Status

SEER- Medicare Cancer Patients

5% sample (non-cancer)

Multiple records

No. % No. %Age 66-69 211,849 19 673,786 22

70-74 293,324 26 821,570 27 75-79 269,384 24 689,356 22 80-84 188,485 17 486,045 16 85-89 99,993 9 273,705 9 90+ 45,050 4 155,371 5

Sex Females 524,625 47 1,966,271 63 Males 583,460 53 1,133,562 37

Race White 978,633 88 2,639,759 85 Black 79,921 7 223,353 7 Other 49,531 4 236,721 8

Life Status Alive 897,368 81 2,249,854 73 Dead 210,717 19 849,979 27 Total 1,108,085 100 3,099,833 100

Comorbidities Frequencies

No. % No. %Acute myocardial infarction 14,466 1.3 30,653 1.0

AIDS 279 0.0 430 0.0Cerebrovascular disease 65,711 5.9 159,874 5.2

Chronic renal failure 22,952 2.1 43,948 1.4Congestive heart failure 106,067 9.6 207,307 6.7

COPD 169,780 15.3 277,638 9.0Dementia 16,305 1.5 60,806 2.0Diabetes 171,688 15.5 408,201 13.2

Diabetes with sequelae 33,743 3.0 80,461 2.6Liver disease mild 6,430 0.6 7,475 0.2

Liver disease mod/severe 2,171 0.2 2,407 0.1Myocardial infarction 22,270 2.0 44,584 1.4

Paralysis 8,000 0.7 19,456 0.6Rheumathologic disease 22,606 2.0 59,259 1.9

Ulcer disease 20,218 1.8 37,923 1.2Vascular Disease 47,195 4.3 101,248 3.3

No Comorbidity 665,135 60.0 2,125,944 68.6

Total 1,108,085 100.0 3,099,833 100.0

SEER-Medicare Cancer Patients

5% sample (non-cancer)Multiple records

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

Results from Cox proportional hazards model

Comorbidity Index Calculation (CI)

1. Diabetes + Congestive heart failure CI=0.34+0.74=1.08

2. Diabetes + COPD CI=034+0.56=0.88

3. COPD+ Congestive heart failure + Liver: CI= 0.56+0.74+0.87= 2.17

Variable Estimate Std.Err. Hazard Ratio

Age 0.09 0.000   1.1Female        

Male 0.23 0.004   1.3Race: White        

Black 0.15 0.008   1.2Other -0.09 0.011   0.9

Acute myocardial infarction 0.15 0.015   1.2AIDS 0.54 0.154   1.7

Cerebrovascular disease 0.35 0.008   1.4Chronic renal failure 0.68 0.012   2.0

Congestive heart failure 0.74 0.006   2.1COPD 0.56 0.006   1.8

Dementia 0.72 0.013   2.1Diabetes 0.34 0.006   1.4

Diabetes with sequelae 0.24 0.012   1.3Liver disease 0.87 0.026   2.4

Liver disease mod./severe 0.66 0.042   1.9Myocardial infarction 0.06 0.014   1.1

Paralysis 0.38 0.020   1.5Rheumathologic disease 0.26 0.014   1.3

Ulcer disease 0.09 0.014   1.1Vascular Disease 0.31 0.009   1.4

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

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

exp( )

, ,( | ) ( ) age sex age sexS t S tβz

z

00 01 02 03 cancer black Other race CI spline βz

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 : k1, k2 k3, and k4.

10 11 1 12 2 CI spline CI CI CI

where CI1 and CI2 are two function of CI and knots.3 3

3 4 4 4

4 3 4 3

( ) ( ) ( ) ( )( ) , 1, 2.

( ) ( )

j jj j

CI k k k CI k k kCI CI k j

k k k k

0 0

0

if uu

u if u

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.

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

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

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

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.

Estimating health-adjusted age: physiologic age

Example: White women age 66 with no comorbidity

66, 0 ( )CIS tis 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 66, 0 ( ) ( | )CIS t P t x and

( )P t is the cumulative probability of surviving age t obtained from the 2000 life tables for white women in the US

( )( | )

( )P t

P t xP x

is the cumulative probability of surviving age t, conditional on being alive at age x.

10

66 01

50 75

,CIt

{ x }min | S ( t ) P( t | x )| .

White women diagnosed with cancer at 70 years of age and selected comorbidity indexes (solid) to the best fitted US life table (dashed lines).

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 10

Pro

po

rtio

n s

urv

ivin

g

Years

US LT Age=65

Age=70, Comorb.=0

US LT, Age=70

Age=70, Comorb.=0.2

US LT Age=76

Age=70, Comorb.=0.5

US LT, Age=83

Age=70, Comorb.=1.0

US LT Age=88

Age=70, Comorb.=1.5US LT Age=92

Age=70, Comorb.=2.0

Acute myocardial infarction

COPD

Diabetes + COPD, Diabetes+ CHF

Diabetes + COPD+ CHFDementia + COPD +CHF

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

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.

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

One Dataset

Net probability of dying of Cancer

Net probability of dying of Cancer

Net probability of dying of Other Causes

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 Cancer

Net probability of dying of Other Causes

Net probability of dying of Other Causes

Dataset 2 Non-cancer

Cox Model 2

Cox Model 1

Crude probabilities dying of Cancer and Other CausesCrude probabilities dying of Cancer and Other Causes

Crude probabilities dying of Cancer and Other CausesCrude 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 SE’s 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, 1998.

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 – CI’s of cumulative incidence computed using delta method

Equations are the same

Thank you!!!

Villa Mondragone