Investigation of relative survival from colorectal cancer between NHS organisations

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1 School Cancer of Epidemiology something Group FACULTY OF OTHER MEDICINE AND HEALTH Investigation of relative survival from colorectal cancer between NHS organisations Katie Harris

2 Background Survival from colorectal cancer in UK is poor Increasing demand for NHS to publish data on clinical outcomes to inform patient choice Cancer survival rates are an effective representation of quality of health care performance Health status of an individual maybe determined by factors operating at a higher level

3 Aim To assess variation between Hospital Trusts in survival for colorectal cancer patients

4 Data National Cancer Data Repository All patients diagnosed in England with colorectal cancer between 1998 and Allocated to a Trust NHS trusts ( patients) Stage missing for 25% of patients Complete data for patients Variables: Survival, Demographics, Treatment

5 What to do about missingness? Why are data missing? Options: Only use complete cases Missing data indicator Replace missing value with mean value Distributing all cases with unknown stage proportionally to the known stages Imputation Is data missing at random?

6 Multiple Imputation Multiple imputation model includes: Stage Imputed Deprivation score Imputed Age at diagnosis Sex Vital status (Alive/ Dead) Cancer site Year of diagnosis Survival time Admission type Procedure type Charlson comorbidity index Trust work load Trust Network Missing at random observed values are predictive of missing values Survival data outcome and survival time must be imputed Clustering Trust modelled as fixed effect Five imputations and five iterations Software: mice in R

7 Imputation Results Stage Before Imputation (All) 9% 25% 26% 15% 25% Before Imputation (Staged only) 12% 34% 34% 20% After Imputation 12% 31% 31% 26%

8 Exploration of data Survival 0.6 Survival Curves by NHS Trust Days

9 Statistical Modelling Aim: to examine differences in cancer survival between Trusts Data from population-based cancer registries Cause of death? Multiple patients nested within multiple hospitals Adjust for casemix Separate models for colon and rectal cancer Colon patients ( Trust range) Rectal patients ( Trust range)

10 Relative Survival Regression framework Generalised Linear Mixed Effects Model with Poisson errors Individual level data Five year survival Complete approach Software: lme4 in R

11 Model specification Outcome: Alive or dead (censoring inidcator) Offset: log Survival time Link function: log Error distribution: Poisson Hierarchy: Trust Explanatory variables: Stage, Sex, Age, Deprivation, Admission, Procedure, Charlson Model specification in R: glmer (Outcome ~ 1 + (1 Trust) + offset(log(survtime)), family = Poisson)

12 Fixed effect coefficients Rectal Cancer C20 Colon Cancer C18 C19 Fixed effects Original Pooled Original Pooled Stage Stage Stage Sex (F) Dep Dep Dep Dep Age Admission (Emergency) Proc LE Proc NS Proc palliative Charlson Charlson Charlson

13 Results Significant cluster variation No notable difference in fixed and random coefficients from complete case analysis and imputed models Imputation reduces standard errors of coefficients All fixed effects significant with narrow 95% CI Rectal cancer has slightly better survival Excess deaths = 45% for Rectal Cancer Excess deaths = 52% for Colon Cancer

14 Identifying Outlying Trusts Convert survival data into a summary value Excess hazard ratio for each Trust Trusts that are beyond limits of normal variation Options: Confidence interval Caterpillar plot Funnel plot (Intercept)

15 Funnel plots Established technique To identify Trusts that have excess hazard of death higher (above) or lower (below) than the risk of death from cancer in all trusts combined Plot excess hazard ratio against work load of Trust 95% and 99% control limits Software: funnelcompar in Stata

16 Rectal Cancer 1.4 Trust Trust patient number C20

17 Colon Cancer 1.4 Trust Trust patient number C18 C19

18 Conclusions High quality source of data Relative survival models and funnel plots are effective methods for assessing Trust disparities in cancer survival Significant variation in survival of colorectal cancer patients exists between hospital Trusts in England Possible inequalities in the level of care between Trusts Outlying Trusts to be notified of results

19 Any Questions?

20 Acknowledgments Eva Morris Paul Finan Philip Quirke James Thomas Louise Whitehouse John Wilkinson

21 Rectal Model with 95%CI Fixed effects Original 95% CI Pooled 95% CI Stage (1.16,1.21) 1.19 (1.16, 1.21) Stage (1.50,1.57) 1.57 (1.54, 1.59) Stage (3.46,3.63) 3.48 (3.42, 3.55) Sex (F) 0.94 (0.92,0.95) 0.95 (0.94, 0.96) Dep (1.00,1.05) 1.03 (1.01, 1.05) Dep (1.04,1.08) 1.07 (1.05, 1.09) Dep (1.06,1.11) 1.09 (1.07, 1.11) Dep (1.11,1.16) 1.15 (1.13, 1.17) Age 1.02 (1.02,1.02) 1.02 (1.02, 1.02) Admission (Emergency) 1.57 (1.52,1.62) 1.66 (1.61, 1.71) Proc LE 1.26 (1.21,1.30) 1.25 (1.22, 1.28) Proc NS 1.76 (1.73,1.80) 1.98 (1.95, 2.01) Proc palliative 2.23 (2.16,2.30) 2.24 (2.19, 2.29) Charlson (1.20,1.26) 1.26 (1.23, 1.28) Charlson (1.32,1.44) 1.41 (1.37, 1.45) Charlson (1.55,1.74) 1.54 (1.49, 1.61)

22 Colon Model with 95% CI Fixed effects Original 95% CI Pooled 95% CI Stage (1.08, 1.12) 1.11 (1.09,1.13) Stage (1.60, 1.65) 1.65 (1.62,1.67) Stage (4.33, 4.49) 4.41 (4.34,4.48) Sex (F) 0.98 (0.97, 0.99) 0.99 (0.99,1.00) Dep (1.02, 1.05) 1.05 (1.04,1.06) Dep (1.06, 1.08) 1.09 (1.08,1.10) Dep (1.09, 1.12) 1.13 (1.12,1.15) Dep (1.13, 1.16) 1.17 (1.16,1.18) Age 1.02 (1.02, 1.02) 1.02 (1.02,1.02) Admission (Emergency) 1.52 (1.51, 1.54) 1.55 (1.53,1.56) Proc LE 1.18 (1.14, 1.23) 1.08 (1.05,1.11) Proc NS 2.07 (2.04, 2.09) 2.41 (2.38,2.43) Proc palliative 2.42 (2.36, 2.48) 2.48 (2.44,2.53) Charlson (1.20, 1.22) 1.23 (1.21,1.24) Charlson (1.40, 1.45) 1.43 (1.41,1.45) Charlson (1.75,1.84) 1.70 (1.66,1.73)

On-going and planned colorectal cancer clinical outcome analyses

On-going and planned colorectal cancer clinical outcome analyses Eva Morris Cancer Research UK Bobby Moore Career Development Fellow National Cancer Data Repository Numerous routine health data sources