Colorectal cancer prognosis varies substantially with socioeconomic status. We investigated differences in life expectancy between socioeconomic groups and estimated the potential gain in life-years ...if cancer-related survival differences could be eliminated.
This population-based study included 470,000 individuals diagnosed with colon and rectal cancers between 1998 and 2013 in England. Using flexible parametric survival models, we obtained a range of life expectancy measures by deprivation status. The number of life-years that could be gained if differences in cancer-related survival between the least and most deprived groups were removed was also estimated.
We observed up to 10% points differences in 5-year relative survival between the least and most deprived. If these differences had been eliminated for colon and rectal cancers diagnosed in 2013 then almost 8231 and 7295 life-years would have been gained respectively. This results for instance in more than 1-year gain for each colon cancer male patient in the most deprived group on average. Cancer-related differences are more profound earlier on, as conditioning on 1-year survival the main reason for socioeconomic differences were factors other than cancer.
This study highlights the importance of policies to eliminate socioeconomic differences in cancer survival as in this way many life-years could be gained.
A commonly reported measure when interested in the survival of cancer patients is relative survival. Relative survival circumvents issues with inaccurate cause of death information by incorporating ...the expected mortality rates of cancer individuals from population lifetables of the general population. A summary of the cancer population prognosis can be obtained using the marginal relative survival. To explore differences between exposure groups, such as socioeconomic groups, the difference in marginal relative survival between exposed and unexposed can be obtained and under assumptions is interpreted as the average causal effect of exposure to survival. In a modeling context, this is usually estimated by applying regression standardization as the average of the individual‐specific estimates after fitting a relative survival model. Regression standardization yields an estimator that consistently estimates the causal effect under standard causal inference assumptions and if the relative survival model is correctly specified. We extend inverse probability weighting (IPW) and doubly robust standardization methods in the relative survival framework as additional valuable tools for obtaining average causal effects when correct model specification might not hold for the relative survival model. IPW yields an unbiased estimate of the average causal effect if a correctly specified model has been fitted for the exposure (propensity score) whereas doubly robust standardization requires that at least one of the propensity score model or the relative survival model is correctly specified. An example using data on melanoma is provided and a simulation study is conducted to investigate how sensitive are the methods to model misspecification, including different ways for obtaining standard errors.
An increasingly popular measure for summarising cancer prognosis is the loss in life expectancy (LLE), i.e. the reduction in life expectancy following a cancer diagnosis. The proportion of life lost ...(PLL) can also be derived, improving comparability across age groups as LLE is highly age-dependent. LLE and PLL are often used to assess the impact of cancer over the remaining lifespan and across groups (e.g. socioeconomic groups). However, in the presence of screening, it is unclear whether part of the differences across population groups could be attributed to lead time bias. Lead time is the extra time added due to early diagnosis, that is, the time from tumour detection through screening to the time that cancer would have been diagnosed symptomatically. It leads to artificially inflated survival estimates even when there are no real survival improvements.
In this paper, we used a simulation-based approach to assess the impact of lead time due to mammography screening on the estimation of LLE and PLL in breast cancer patients. A natural history model developed in a Swedish setting was used to simulate the growth of breast cancer tumours and age at symptomatic detection. Then, a screening programme similar to current guidelines in Sweden was imposed, with individuals aged 40-74 invited to participate every second year; different scenarios were considered for screening sensitivity and attendance. To isolate the lead time bias of screening, we assumed that screening does not affect the actual time of death. Finally, estimates of LLE and PLL were obtained in the absence and presence of screening, and their difference was used to derive the lead time bias.
The largest absolute bias for LLE was 0.61 years for a high screening sensitivity scenario and assuming perfect screening attendance. The absolute bias was reduced to 0.46 years when the perfect attendance assumption was relaxed to allow for imperfect attendance across screening visits. Bias was also present for the PLL estimates.
The results of the analysis suggested that lead time bias influences LLE and PLL metrics, thus requiring special consideration when interpreting comparisons across calendar time or population groups.
When interested in a time-to-event outcome, competing events that prevent the occurrence of the event of interest may be present. In the presence of competing events, various estimands have been ...suggested for defining the causal effect of treatment on the event of interest. Depending on the estimand, the competing events are either accommodated or eliminated, resulting in causal effects with different interpretations. The former approach captures the total effect of treatment on the event of interest while the latter approach captures the direct effect of treatment on the event of interest that is not mediated by the competing event. Separable effects have also been defined for settings where the treatment can be partitioned into two components that affect the event of interest and the competing event through different causal pathways.
We outline various causal effects that may be of interest in the presence of competing events, including total, direct and separable effects, and describe how to obtain estimates using regression standardisation with the Stata command standsurv. Regression standardisation is applied by obtaining the average of individual estimates across all individuals in a study population after fitting a survival model.
With standsurv several contrasts of interest can be calculated including differences, ratios and other user-defined functions. Confidence intervals can also be obtained using the delta method. Throughout we use an example analysing a publicly available dataset on prostate cancer to allow the reader to replicate the analysis and further explore the different effects of interest.
Several causal effects can be defined in the presence of competing events and, under assumptions, estimates of those can be obtained using regression standardisation with the Stata command standsurv. The choice of which causal effect to define should be given careful consideration based on the research question and the audience to which the findings will be communicated.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
When quantifying the probability of survival in cancer patients using cancer registration data, it is common to estimate marginal relative survival, which under assumptions can be interpreted as ...marginal net survival. Net survival is a hypothetical construct giving the probability of being alive if it was only possible to die of the cancer under study, enabling comparisons between populations with differential mortality rates due to causes other the cancer under study. Marginal relative survival can be estimated non-parametrically (Pohar Perme estimator) or in a modeling framework. In a modeling framework, even when just interested in marginal relative survival it is necessary to model covariates that affect the expected mortality rates (e.g. age, sex and calendar year). The marginal relative survival function is then obtained through regression standardization. Given that these covariates will generally have non-proportional effects, the model can become complex before other exposure variables are even considered.
We propose a flexible parametric model incorporating restricted cubic splines that directly estimates marginal relative survival and thus removes the need to model covariates that affect the expected mortality rates. In order to do this the likelihood needs to incorporate the marginal expected mortality rates at each event time taking account of informative censoring. In addition time-dependent weights are incorporated into the likelihood. An approximation is proposed through splitting the time scale into intervals, which enables the marginal relative survival model to be fitted using standard software. Additional weights can be incorporated when standardizing to an external reference population.
The methods are illustrated using national cancer registry data. In addition, a simulation study is performed to compare different estimators; a non-parametric approach, regression-standardization and the new marginal relative model. The simulations study shows the new approach is unbiased and has good relative precision compared to the non-parametric estimator.
The approach enables estimation of standardized marginal relative survival without the need to model covariates that affect expected mortality rates and thus reduces the chance of model misspecification.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
There is some evidence that a prior cancer is a risk factor for the development of multiple myeloma (MM). If this is true, prior cancer should be associated with higher prevalence or increased ...progression rate of monoclonal gammopathy of undetermined significance (MGUS), the precursor of MM and related disorders. Those with a history of cancer might therefore present a target population for MGUS screening. This two-part study is the first study to evaluate the relationship of MGUS and prior cancers. First, we evaluated whether prior cancers were associated with having MGUS at the time of screening in the Iceland Screens Treats or Prevents Multiple Myeloma (iStopMM) study that includes 75,422 individuals screened for MGUS. Next, we evaluated the association of prior cancer and the progression of MGUS to MM and related disorders in a population-based cohort of 13,790 Swedish individuals with MGUS. A history of prior cancer was associated with a modest increase in the risk of MGUS (odds ratio (OR)= 1.10; 95% confidence interval (CI): 1.00-1.20). This excess risk was limited to prior cancers in the year preceding MGUS screening. A history of prior cancer associated with the progression of MGUS, except for myeloid malignancies which were associated with lower risk of progression (hazard ratio (HR)=0.37; 95%CI: 0.16-0.89; p=0.028). Our findings indicate that a prior cancer are not a significant aetiological factor in plasma cell disorders. The findings do not warrant MGUS screening or different management of MGUS in those with a prior cancer.
The introduction of national guidelines should eliminate previously observed associations between socioeconomic status (SES) and colorectal cancer treatment. The aim of the study was to investigate ...whether inequalities remain.
CRCBaSe, a register-linkage originating from the Swedish Colorectal Cancer Registry, was used to identify information on patient and tumour characteristics, for 83,460 patients with stage I-III disease diagnosed 2008–2021. SES was measured as disposable income (quartiles) and the highest level of education. Outcomes of interest were emergency surgery, multidisciplinary team (MDT) conference discussion, and oncological treatment. Differences in treatment between SES groups were explored using multivariable logistic regression adjusted for year of diagnosis, age at diagnosis, sex, civil status, comorbidities, tumour location and stage.
Patients in the highest income quartile had a lower risk of emergency surgery (OR 0.73 95%CI 0.68–0.80), a higher chance of being discussed at the preoperative (OR 1.39 95%CI 1.28–1.51) and postoperative MDT (OR 1.41 95%CI 1.30–1.53), receiving neoadjuvant (OR 1.15 95%CI 1.06–1.25) and adjuvant treatment (OR 2.04 95%CI 1.88–2.20). Higher education level increased the odds of MDT discussion but was not associated with oncological treatment. The proportion of patients discussed at the MDT increased, with almost all patients discussed since 2016. Despite this, treatment differences remained when patients diagnosed since 2016 were analysed separately.
There were significant differences in how patients with different SES were treated for colorectal cancer. Further action is required to investigate the drivers of these differences as well as their impact on mortality and, ultimately, eliminate the inequalities.
•Emergency surgery for colorectal cancer is more common in patients most deprived.•Most deprived patients get less oncological treatment for resected colorectal cancer.•Almost all patients were discussed at Multidisciplinary team conferences by 2016.•Despite MDT discussion, differences in treatment remained.
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