Impact and implications of Cancer Death Status Reporting Delay on Population- Based Relative Survival Analysis with Presumed-Alive Assumption

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1 Impact and implications of Cancer Death Status Reporting Delay on Population- Based Relative Survival Analysis with Presumed-Alive Assumption X Dong, Y Ren, R Wilson, and K Zhang NAACCR

2 Introduction Population-based survival analysis is a core application of cancer surveillance data to better understand the impact of cancer epidemic Relative survival methodology is becoming the default method for population-based cancer survival analysis Because of the difficulties to ascertain up-to-date dates of last contact for some cancer patients, presumed-alive assumption is adopted to facilitate the analysis of some cancer surveillance data RELIABLE TRUSTED SCIENTIFIC DCPC

3 Introduction Date of diagnosis, date of last contact and vital status determine cancer survival estimation Reporting issues may contribute biases into cancer survival estimates Incidence case Date of diagnosis Date of last contact Vital status RELIABLE TRUSTED SCIENTIFIC DCPC

4 Introduction Incidence reporting delay The cases are reported outside designated reporting window and are not known to the current study Our study reported in 2016 NAACCR conference demonstrated the possible incidence reporting delay induced underestimation biases in population-based cancer survival with presumed alive assumption Date of diagnosis Missing and erroneous dates of diagnosis Very slight in NPCR data and the biasing potential low RELIABLE TRUSTED SCIENTIFIC DCPC

5 RELIABLE TRUSTED SCIENTIFIC DCPC Introduction Date of last contact NPCR states are not funded for active follow-up, which makes dated date of last contact a concern for survival estimation By employing presumed alive assumption, reliable survival estimates can be computed with NPCR data

6 RELIABLE TRUSTED SCIENTIFIC DCPC Introduction Vital status Vital status provides essential event (death) information to survival analysis Death status reporting delay (DSRD): Death status of a cancer patient is not reported to a cancer surveillance system at the same year death occurred. The damages lay in two areas: Incomplete death counts will cause underestimation of hazard rate (death rate) which will in turn cause overestimation of survival Presumed alive assumption erroneously will regard these DSRD cases as alive and attribute much longer survival time than they should have, resulting in overestimation of survival again

7 Objectives To study DSRD patterns in NPCR data To quantify biases induced by DSRD to the population-based cancer relative survival analysis with presumed-alive assumption RELIABLE TRUSTED SCIENTIFIC DCPC

8 Death Status Reporting Delay in NPCR data

9 Data and Method Data: NPCR data submissions: November 2002-November 2014 ( ) From 16 states that data quality met survival analysis criteria and had consistent patient IDs between Submission Method: The cases experienced death between were tracked among submissions The dates of the 1 st appearance of the status Dead in NPCR data were reported RELIABLE TRUSTED SCIENTIFIC DCPC

10 RELIABLE TRUSTED SCIENTIFIC DCPC Results: DSRD in NPCR Data (16 Sates) Year of Death Yrs of Delay Total Total Table 1: DSDR of all cancer patients diagnosed between and died between

11 RELIABLE TRUSTED SCIENTIFIC DCPC Results: DSRD in NPCR Data (16 States) Year of Death (Diagnosis year = 2001) Yrs of Delay Total Total Table 2: DSDR of cancer patients diagnosed in 2001 and died between

12 RELIABLE TRUSTED SCIENTIFIC DCPC Results: DSRD in NPCR Data (DXYR=2001) Year of Death (Diagnosis year = 2001) Yrs of Delay Total Total Table 3: Effects of DSDR on survival time assigned Compare effects of DSDR with a conceptual cohort (diagnosed in 2001 and follow-up to 2007) between Submission 2009 and 2014 Cases diagnosed in 2001, in green, reported as Dead in Submission 2009 Cases diagnosed in 2001, in colors, reported as Dead in Submission 2014 Years of survival erroneously assigned: cutoff year (2007) less death year Death 2001: 6-7 years of 781 cases Death 2002: 5-6 years of 955 cases Death 2003: 4-5 years of 662 cases Death 2004: 3-4 years of 511 cases Death 2005: 2-3 years of 435 cases Death 2006: 1-2 years of 362 cases Death 2007: 0-1 years of 645cases 4351 DSDR cases of the cohort are wrongly assigned, by presumed alive assumption, 0-7 more years of survival in Submission 2009 than those if the study were done with Submission 2014 data

13 Impact of DSRD on Population-Based Cancer Survival with Presumed Alive Assumption

14 RELIABLE TRUSTED SCIENTIFIC DCPC Data and Method Data (N=2,240,010) NPCR data submissions Study cohort: Cases diagnosed between with follow-up to 2007 From 16 states that met the data quality standards of survival analysis and had consistent patient IDs between submission Method Assume all death of the cohort were reported in 2014 submission Removed all cases with incidence reporting delay since our early study indicated underestimation biases All factors were controlled except for leaving the vital status as the sole varying factor among submissions Simulate estimation of 5-year relative survival in each submission sequentially from 2009 to 2014 for the same cohort Used customized NPCR Relative Survival SAS tool to estimate the relative survival

15 Results: Impact of DSDR Cancer site Submission 5-year relative survival Standard error DSDR Induced Overestimation Biases All sites Combined % 0.0% *0.9% All sites Combined % 0.0% *0.5% All sites Combined % 0.0% *0.3% All sites Combined % 0.0% *0.3% All sites Combined % 0.0% 0.1% All sites Combined % 0.0% 0.0% Thyroid % 0.2% 0.2% Thyroid % 0.2% 0.1% Thyroid % 0.2% 0.1% Thyroid % 0.2% 0.0% Thyroid % 0.2% 0.0% Thyroid % 0.2% 0.0% Liver & IBD % 0.3% *1.6% Liver & IBD % 0.3% *1.0% Liver & IBD % 0.3% *0.8% Liver & IBD % 0.3% 0.6% Liver & IBD % 0.3% 0.1% Liver & IBD % 0.3% 0.0% All sites combined Maximum bias 0.9% Significant bias exists till the 4 th data submission Thyroid (high survival) Maximum bias 0.2% No significant bias Liver & IBD (low survival) Maximum bias 1.6% Significant bias exists till the 3 rd data submission Table 4: Examples of impact of DSDR on relative survival RELIABLE TRUSTED SCIENTIFIC DCPC

16 RELIABLE TRUSTED SCIENTIFIC DCPC Results: Impact of DSDR Cancer Site Submission 5-Year Survival Standard Error Biases Submissions with Significant Bias Cervix % 0.4% 0.8% 0 Hodgkin lymphoma % 0.4% 0.4% 0 Testis % 0.2% 0.1% 0 Thyroid % 0.2% 0.2% 0 Larynx % 0.5% 1.1% 1 Brain & other nervous system % 0.3% 1.2% 1 Myeloma % 0.4% 1.4% 1 Ovary % 0.3% 1.0% 1 Leukemia % 0.3% 1.0% 1 Oral cavity & pharyn % 0.3% 1.0% 1 Non-Hodgkin lymphoma % 0.2% 0.9% 1 Kidney & renal pelvis % 0.3% 0.7% 1 Corpus & uterus % 0.2% 0.6% 1 Melanoma % 0.2% 0.5% 1 Pancreas % 0.2% 1.3% 4 Liver & IBD % 0.3% 1.6% 3 Esophagus % 0.3% 1.4% 2 Lung & bronchus % 0.1% 1.3% 4 Stomach % 0.3% 1.8% 3 Colon & rectum % 0.1% 1.1% 3 All sites Combined % 0.0% 0.9% 4 Urinary bladder % 0.2% 1.1% 2 Breast % 0.1% 0.6% 2 Prostate % 0.1% 0.6% 3 Table 5: Summary of DSRD on relative survival in 24 cancer sites

17 RELIABLE TRUSTED SCIENTIFIC DCPC Conclusion: Impact of DSRD DSRD is substantially presented in NPCR data The level of DSRD s impact on survival time is diagnosis year specific and depends on study cohort cutoff date and actual death date The impact of DSRD on survival is significant, but also cancer specific, especially in Cancers with high case load Cancers with low survival Statistical comparisons on temporal scales of these cancer sites should be reconsidered, if without any precautionary adjustments to study populations

18 Possible Next Steps: Study the combined effects of reporting delays, incidence reporting delay and death status reporting delay, on survival A long-term study to investigate the factors that might contribute to DSDR (need to involve NPCR states) Annually monitoring reporting delay related biases in NPCR survival outcomes It might be necessary to publish an annual NPCR relative survival study population selection guidelines RELIABLE TRUSTED SCIENTIFIC DCPC

19 Thank You and Questions! Contact Information Xing Dong Reda Wilson