Prior cancer and risk of monoclonal gammopathy of undetermined significance: a population-based study in Iceland and Sweden.

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 a 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 constitute a target population for MGUS screening. This two-part study is the first study to evaluate a relationship between 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=1.10; 95% confidence interval: 1.00-1.20). This excess risk was limited to prior cancers in the year preceding MGUS screening. A history of prior cancer was associated with progression of MGUS, except for myeloid malignancies which were associated with a lower risk of progression (hazard ratio=0.37; 95% confidence interval: 0.16-0.89; P=0.028). Our findings indicate that a prior cancer is not a significant etiological factor in plasma cell disorders. The findings do not warrant MGUS screening or different management of MGUS in those with a prior cancer.

Loss in life expectancy in patients with stage II-III cutaneous melanoma in Sweden: A population-based cohort study.

BACKGROUND: Survival in cutaneous melanoma (CM) is heterogeneous. Loss in life expectancy (LLE) measures impact of CM on remaining lifespan compared to general population. OBJECTIVES: Investigating LLE in operated stage II-III CM patients. METHODS: Data from 8061 patients (aged 40-80 years) with stage II-III CM in Sweden, diagnosed between 2005 and 2018, were analyzed (Swedish Melanoma Registry). A flexible parametric survival model estimated life expectancy and LLE. RESULTS: Based on 2018 diagnoses, stage II and III CM patients lost 2209 and 1902 life years, respectively. LLE was higher in stage III: 5.2 versus 10.9 years (stage II vs III 60-year-old females). Younger patients had higher LLE: 10.7 versus 3.9 years (stage II CM in 40 vs 70-year-old males). In stage II, females had lower LLE than males; 50-year-old females and males stage II CM had LLE equal to 7.3 and 8.3 years, respectively. LLE increased with higher substages, stage IIB resembling IIIB and IIC resembling IIIC-D. LIMITATIONS: Extrapolation was used to estimate LLE. Varying stage group sizes require caution. CONCLUSIONS: Our results are both clinically relevant and easy-to-interpret measures of the impact of CM on survival, but the results also summarize the prognosis over the lifetime of a CM patient.

Assessing lead time bias due to mammography screening on estimates of loss in life expectancy.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Standardised survival probabilities: a useful and informative tool for reporting regression models for survival data.

BACKGROUND: When interested in studying the effect of a treatment (or other exposure) on a time-to-event outcome, the most popular approach is to estimate survival probabilities using the Kaplan-Meier estimator. In the presence of confounding, regression models are fitted, and results are often summarised as hazard ratios. However, despite their broad use, hazard ratios are frequently misinterpreted as relative risks instead of relative rates. METHODS: We discuss measures for summarising the analysis from a regression model that overcome some of the limitations associated with hazard ratios. Such measures are the standardised survival probabilities for treated and untreated: survival probabilities if everyone in the population received treatment and if everyone did not. The difference between treatment arms can be calculated to provide a measure for the treatment effect. RESULTS: Using publicly available data on breast cancer, we demonstrated the usefulness of standardised survival probabilities for comparing the experience between treated and untreated after adjusting for confounding. We also showed that additional important research questions can be addressed by standardising among subgroups of the total population. DISCUSSION: Standardised survival probabilities are a useful way to report the treatment effect while adjusting for confounding and have an informative interpretation in terms of risk.

Estimating causal effects in the presence of competing events using regression standardisation with the Stata command standsurv.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Inverse probability weighting and doubly robust standardization in the relative survival framework.

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.

Direct modelling of age standardized marginal relative survival through incorporation of time-dependent weights.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Understanding disparities in cancer prognosis: An extension of mediation analysis to the relative survival framework.

Mediation analysis can be applied to investigate the effect of a third variable on the pathway between an exposure and the outcome. Such applications include investigating the determinants that drive differences in cancer survival across subgroups. However, cancer disparities may be the result of complex mechanisms that involve both cancer-related and other-cause mortality differences making it difficult to identify the causing factors. Relative survival, a commonly used measure in cancer epidemiology, can be used to focus on cancer-related differences. We extended mediation analysis to the relative survival framework for exploring cancer inequalities. The marginal effects were obtained using regression standardization, after fitting a relative survival model. Contrasts of interests included both marginal relative survival and marginal all-cause survival differences between exposure groups. Such contrasts include the indirect effect due to a mediator that is identifiable under certain assumptions. A separate model was fitted for the mediator and uncertainty was estimated using parametric bootstrapping. The avoidable deaths under interventions can also be estimated to quantify the impact of eliminating differences. The methods are illustrated using data for individuals diagnosed with colon cancer. Mediation analysis within relative survival allows focus on factors that account for cancer-related differences instead of all-cause differences and helps improve our understanding on cancer inequalities.

Understanding the impact of socioeconomic differences in colorectal cancer survival: potential gain in life-years.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: This study highlights the importance of policies to eliminate socioeconomic differences in cancer survival as in this way many life-years could be gained.

Estimating the impact of a cancer diagnosis on life expectancy by socio-economic group for a range of cancer types in England.

BACKGROUND: Differences in cancer survival exist across socio-economic groups for many cancer types. Standard metrics fail to show the overall impact for patients and the population. METHODS: The available data consist of a population of ∼2.5 million patients and include all patients recorded as being diagnosed with melanoma, prostate, bladder, breast, colon, rectum, lung, ovarian and stomach cancers in England between 1998 and 2013. We estimated the average loss in expectation of life per patient in years and the proportion of life lost for a range of cancer types, separately by deprivation group. In addition, estimates for the total number of years lost due to each cancer were also obtained. RESULTS: Lung and stomach cancers result in the highest overall loss for males and females in all deprivation groups in terms of both absolute life years lost and loss as a proportion of expected life remaining. Female lung cancer patients in the least- and most-deprived group lose 14.4 and 13.8 years on average, respectively, that is translated as 86.1% and 87.3% of their average expected life years remaining. Melanoma, prostate and breast cancers have the lowest overall loss. On the basis of the number of patients diagnosed in 2013, lung cancer results in the most life years lost in total followed by breast cancer. Melanoma and bladder cancer account for the lowest total life years lost. CONCLUSIONS: There are wide differences in the impact of cancer on life expectancy across deprivation groups, and for most cancers the most affluent lose less years.

Causal inference in survival analysis using pseudo-observations.

Causal inference for non-censored response variables, such as binary or quantitative outcomes, is often based on either (1) direct standardization ('G-formula') or (2) inverse probability of treatment assignment weights ('propensity score'). To do causal inference in survival analysis, one needs to address right-censoring, and often, special techniques are required for that purpose. We will show how censoring can be dealt with 'once and for all' by means of so-called pseudo-observations when doing causal inference in survival analysis. The pseudo-observations can be used as a replacement of the outcomes without censoring when applying 'standard' causal inference methods, such as (1) or (2) earlier. We study this idea for estimating the average causal effect of a binary treatment on the survival probability, the restricted mean lifetime, and the cumulative incidence in a competing risks situation. The methods will be illustrated in a small simulation study and via a study of patients with acute myeloid leukemia who received either myeloablative or non-myeloablative conditioning before allogeneic hematopoetic cell transplantation. We will estimate the average causal effect of the conditioning regime on outcomes such as the 3-year overall survival probability and the 3-year risk of chronic graft-versus-host disease. Copyright © 2017 John Wiley & Sons, Ltd.

Income disparities in loss in life expectancy after colon and rectal cancers: a Swedish register-based study.

BACKGROUND: Differences in the prognosis after colorectal cancer (CRC) by socioeconomic position (SEP) have been reported previously; however, most studies focused on survival differences at a particular time since diagnosis. We quantified the lifetime impact of CRC and its variation by SEP, using individualised income to conceptualise SEP. METHODS: Data included all adults with a first-time diagnosis of colon or rectal cancers in Sweden between 2008 and 2021. The analysis was done separately for colon and rectal cancers using flexible parametric models. For each cancer and income group, we estimated the life expectancy in the absence of cancer, the life expectancy in the presence of cancer and the loss in life expectancy (LLE). RESULTS: We found large income disparities in life expectancy after a cancer diagnosis, with larger differences among the youngest patients. Higher income resulted in more years lost following a cancer diagnosis. For example, 40-year-old females with colon cancer lost 17.64 years if in the highest-income group and 13.68 years if in the lowest-income group. Rectal cancer resulted in higher LLE compared with colon cancer. Males lost a larger proportion of their lives. All patients, including the oldest, lost more than 30% of their remaining life expectancy. Based on the number of colon and rectal cancer diagnoses in 2021, colon cancer results in almost double the number of years lost compared with rectal cancer (24 669 and 12 105 years, respectively). CONCLUSION: While our results should be interpreted in line with what individualised income represents, they highlight the need to address inequalities.

Despite multi-disciplinary team discussions the socioeconomic disparities persist in the oncological treatment of non-metastasized colorectal cancer.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Marginal measures and causal effects using the relative survival framework.

BACKGROUND: In population-based cancer survival studies, the event of interest is usually death due to cancer. However, other competing events may be present. Relative survival is a commonly used measure in cancer studies that circumvents problems caused by the inaccuracy of the cause of death information. A summary of the prognosis of the cancer population and potential differences between subgroups can be obtained using marginal estimates of relative survival. METHODS: We utilize regression standardization to obtain marginal estimates of interest in a relative survival framework. Such measures include the standardized relative survival, standardized all-cause survival and standardized crude probabilities of death. Contrasts of these can be formed to explore differences between exposure groups and under certain assumptions are interpreted as causal effects. The difference in standardized all-cause survival can also provide an estimate for the impact of eliminating cancer-related differences between exposure groups. The potential avoidable deaths after such hypothetical scenarios can also be estimated. To illustrate the methods we use the example of survival differences across socio-economic groups for colon cancer. RESULTS: Using relative survival, a range of marginal measures and contrasts were estimated. For these measures we either focused on cancer-related differences only or chose to incorporate both cancer and other cause differences. The impact of eliminating differences between groups was also estimated. Another useful way for quantifying that impact is the avoidable deaths under hypothetical scenarios. CONCLUSIONS: Marginal estimates within the relative survival framework provide useful summary measures and can be applied to better understand differences across exposure groups.

Robustness of individual and marginal model-based estimates: A sensitivity analysis of flexible parametric models.

BACKGROUND: Flexible parametric survival models (FPMs) are commonly used in epidemiology. These are preferred as a wide range of hazard shapes can be captured using splines to model the log-cumulative hazard function and can include time-dependent effects for more flexibility. An important issue is the number of knots used for splines. The reliability of estimates are assessed using English data for 10 cancer types and the use of online interactive graphs to enable a more comprehensive sensitivity analysis at the control of the user is demonstrated. METHODS: Sixty FPMs were fitted to each cancer type with varying degrees of freedom to model the baseline excess hazard and the main and time-dependent effect of age. For each model, we obtained age-specific, age-group and internally age-standardised relative survival estimates. The Akaike Information Criterion and Bayesian Information Criterion were also calculated and comparative estimates were obtained using the Ederer II and Pohar Perme methods. Web-based interactive graphs were developed to present results. RESULTS: Age-standardised estimates were very insensitive to the exact number of knots for the splines. Age-group survival is also stable with negligible differences between models. Age-specific estimates are less stable especially for the youngest and oldest patients, of whom there are very few, but for most scenarios perform well. CONCLUSION: Although estimates do not depend heavily on the number of knots, too few knots should be avoided, as they can result in a poor fit. Interactive graphs engage researchers in assessing model sensitivity to a wide range of scenarios and their use is highly encouraged.

Potential gain in life years for Swedish women with breast cancer if stage and survival differences between education groups could be eliminated - Three what-if scenarios.

Many studies have found evidence of socioeconomic differences in breast cancer survival. This study aimed to quantify the impact of removing differences in stage distribution and stage-specific relative survival between education groups in Swedish women with breast cancer. Using information from a breast cancer research database, the study population contained 62 121 women diagnosed with breast cancer in three healthcare regions of Sweden from 1992 to 2012. The loss in expectation of life and life years lost due to breast cancer were estimated using flexible parametric relative survival models by education group and age at diagnosis. The potential gain in life years and postponable deaths were calculated by applying the 1) stage distribution, 2) stage-specific relative survival, and 3) both stage distribution and stage-specific relative survival of the high education group to the low and medium education groups. For a cohort of around 3500 women diagnosed with breast cancer residing in three Swedish healthcare regions in a typical calendar year, we estimated that removing stage differences would postpone an additional 25 deaths at five years after diagnosis, and result in a gain of approximately 573 life years. Alternatively, if stage-specific breast cancer survival could be equated, approximately 692 life years could be saved and an additional 26 deaths could be postponed five years after diagnosis. Results such as these can help guide decisions on interventions intended to minimise socioeconomic differences in breast cancer outcomes.