Prediction of cancer survival for cohorts of patients most recently diagnosed using multi-model inference

Turning a Curve: How People Use Everyday Resources to Negotiate Recovery From Cancer Treatment With Curative Intent

Many more cancers are treated with intent to cure now than in previous decades, but for most, this involves significant effects from which people need to recover psychologically and socially, as well as physically. This longitudinal photo-elicitation interview study uses grounded theory to explain how people discharged from specialist care made use of everyday social and material resources to manage this process at home. Recovery is presented as a curve in life's pathway requiring gradual reorientation, drawing on social worlds and domestic resources to calibrate this process. Findings are described in three stages: (1) responding to diagnosis and treatment, (2) using social resources for meaning-making, and (3) developing assets for recovery. During treatment, participants drew on past identities to reinforce their sense of self, and personalized health care communication supported this process. In the weeks after treatment, new frameworks of understanding were constructed from perspectives on cancer encountered in the family, workplace, and outpatient clinics. Recovery processes included the negotiation of personal change, the renegotiation of close relationships, and the use of everyday resources to regain three sensations: control, comfort, and continuity. Supportive care would benefit from an individualized exploration of the assets that can help people to negotiate this challenging phase as treatment comes to a close. Possibilities for self-care (the maintenance of health and well-being in the context of everyday life) can be explored and assessed through personalized discussion around the identities, social worlds, and everyday resources available to each individual.

Using temporal recalibration to improve the calibration of risk prediction models in competing risk settings when there are trends in survival over time

We have previously proposed temporal recalibration to account for trends in survival over time to improve the calibration of predictions from prognostic models for new patients. This involves first estimating the predictor effects using data from all individuals (full dataset) and then re-estimating the baseline using a subset of the most recent data whilst constraining the predictor effects to remain the same. In this article, we demonstrate how temporal recalibration can be applied in competing risk settings by recalibrating each cause-specific (or subdistribution) hazard model separately. We illustrate this using an example of colon cancer survival with data from the Surveillance Epidemiology and End Results (SEER) program. Data from patients diagnosed in 1995-2004 were used to fit two models for deaths due to colon cancer and other causes respectively. We discuss considerations that need to be made in order to apply temporal recalibration such as the choice of data used in the recalibration step. We also demonstrate how to assess the calibration of these models in new data for patients diagnosed subsequently in 2005. Comparison was made to a standard analysis (when improvements over time are not taken into account) and a period analysis which is similar to temporal recalibration but differs in the data used to estimate the predictor effects. The 10-year calibration plots demonstrated that using the standard approach over-estimated the risk of death due to colon cancer and the total risk of death and that calibration was improved using temporal recalibration or period analysis.

An investigation of cancer survival inequalities associated with individual-level socio-economic status, area-level deprivation, and contextual effects, in a cancer patient cohort in England and Wales

BACKGROUND

People living in more deprived areas of high-income countries have lower cancer survival than those in less deprived areas. However, associations between individual-level socio-economic circumstances and cancer survival are relatively poorly understood. Moreover, few studies have addressed contextual effects, where associations between individual-level socio-economic status and cancer survival vary depending on area-based deprivation.

METHODS

Using 9276 individual-level observations from a longitudinal study in England and Wales, we examined the association with cancer survival of area-level deprivation and individual-level occupation, education, and income, for colorectal, prostate and breast cancer patients aged 20-99 at diagnosis. With flexible parametric excess hazard models, we estimated excess mortality across individual-level and area-level socio-economic variables and investigated contextual effects.

RESULTS

For colorectal cancers, we found evidence of an association between education and cancer survival in men with Excess Hazard Ratio (EHR) = 0.80, 95% Confidence Interval (CI) = 0.60;1.08 comparing "degree-level qualification and higher" to "no qualification" and EHR = 0.74 [0.56;0.97] comparing "apprenticeships and vocational qualification" to "no qualification", adjusted on occupation and income; and between occupation and cancer survival for women with EHR = 0.77 [0.54;1.10] comparing "managerial/professional occupations" to "manual/technical," and EHR = 0.81 [0.63;1.06] comparing "intermediate" to "manual/technical", adjusted on education and income. For breast cancer in women, we found evidence of an association with income (EHR = 0.52 [0.29;0.95] for the highest income quintile compared to the lowest, adjusted on education and occupation), while for prostate cancer, all three individual-level socio-economic variables were associated to some extent with cancer survival. We found contextual effects of area-level deprivation on survival inequalities between occupation types for breast and prostate cancers, suggesting wider individual-level inequalities in more deprived areas compared to least deprived areas. Individual-level income inequalities for breast cancer were more evident than an area-level differential, suggesting that area-level deprivation might not be the most effective measure of inequality for this cancer. For colorectal cancer in both sexes, we found evidence suggesting area- and individual-level inequalities, but no evidence of contextual effects.

CONCLUSIONS

Findings highlight that both individual and contextual effects contribute to inequalities in cancer outcomes. These insights provide potential avenues for more effective policy and practice.