Dataset Information

Landmark risk prediction of residual life for breast cancer survival.

ABSTRACT: The importance of developing personalized risk prediction estimates has become increasingly evident in recent years. In general, patient populations may be heterogenous and represent a mixture of different unknown subtypes of disease. When the source of this heterogeneity and resulting subtypes of disease are unknown, accurate prediction of survival may be difficult. However, in certain disease settings, the onset time of an observable short-term event may be highly associated with these unknown subtypes of disease and thus may be useful in predicting long-term survival. One approach to incorporate short-term event information along with baseline markers for the prediction of long-term survival is through a landmark Cox model, which assumes a proportional hazards model for the residual life at a given landmark point. In this paper, we use this modeling framework to develop procedures to assess how a patient's long-term survival trajectory may change over time given good short-term outcome indications along with prognosis on the basis of baseline markers. We first propose time-varying accuracy measures to quantify the predictive performance of landmark prediction rules for residual life and provide resampling-based procedures to make inference about such accuracy measures. Simulation studies show that the proposed procedures perform well in finite samples. Throughout, we illustrate our proposed procedures by using a breast cancer dataset with information on time to metastasis and time to death. In addition to baseline clinical markers available for each patient, a chromosome instability genetic score, denoted by CIN25, is also available for each patient and has been shown to be predictive of survival for various types of cancer. We provide procedures to evaluate the incremental value of CIN25 for the prediction of residual life and examine how the residual life profile changes over time. This allows us to identify an informative landmark point, t(0) , such that accurate risk predictions of the residual life could be made for patients who survive past t(0) without metastasis.

SUBMITTER: Parast L

PROVIDER: S-EPMC3744612 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

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Landmark risk prediction of residual life for breast cancer survival.

Parast Layla L Cai Tianxi T

Statistics in medicine 20130314 20

The importance of developing personalized risk prediction estimates has become increasingly evident in recent years. In general, patient populations may be heterogenous and represent a mixture of different unknown subtypes of disease. When the source of this heterogeneity and resulting subtypes of disease are unknown, accurate prediction of survival may be difficult. However, in certain disease settings, the onset time of an observable short-term event may be highly associated with these unknown ...[more]

PMID: 23494768

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Project description:ImportanceExcess adiposity confers higher risk of breast cancer for women. For women who have lost substantial weight, it is unclear whether previous obesity confers residual increased baseline risk of breast cancer compared with peers without obesity.ObjectivesTo determine whether there is a residual risk of breast cancer due to prior obesity among patients who undergo bariatric surgery.Design, setting, and participantsRetrospective matched cohort study of 69 260 women with index date between January 1, 2010, and December 31, 2016. Patients were followed up for 5 years after bariatric surgery or index date. Population-based clinical and administrative data from multiple databases in Ontario, Canada, were used to match a cohort of women who underwent bariatric surgery for obesity (baseline body mass index [BMI] ≥35 with comorbid conditions or BMI ≥40) to women without a history of bariatric surgery according to age and breast cancer screening history. Nonsurgical controls were divided into 4 BMI categories (<25, 25-29, 30-34, and ≥35). Data were analyzed on October 21, 2021.ExposuresWeight loss via bariatric surgery.Main outcomes and measuresResidual hazard of breast cancer after washout periods of 1, 2, and 5 years. Comparisons were made between the surgical and nonsurgical cohorts overall and within each of the BMI subgroups.ResultsIn total, 69 260 women were included in the analysis, with 13 852 women in each of the 5 study cohorts. The mean (SD) age was 45.1 (10.9) years. In the postsurgical cohort vs the overall nonsurgical cohort (n = 55 408), there was an increased hazard for incident breast cancer in the nonsurgical group after washout periods of 1 year (hazard ratio [HR], 1.40 [95% CI, 1.18-1.67]), 2 years (HR, 1.31 [95% CI, 1.12-1.53]), and 5 years (HR, 1.38 [95% CI, 1.21-1.58]). When the postsurgical cohort was compared with the nonsurgical cohort with BMI less than 25, the hazard of incident breast cancer was not significantly different regardless of the washout period, whereas there was a reduced hazard for incident breast cancer among postsurgical patients compared with nonsurgical patients in all high BMI categories (BMI ≥25).Conclusions and relevanceFindings suggest that bariatric surgery was associated with a reduced risk of developing breast cancer for women with prior obesity equivalent to that of a woman with a BMI less than 25 and a lower risk when compared with all groups with BMI greater than or equal to 25.

Project description:BackgroundSeveral gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n?=?123) and test set (n?=?81), respectively. Gene sets from eleven previously published gene signatures are included in the study.Principal findingsTo investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p?=?0.005; breast cancer death: p?=?0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p?=?0.003; breast cancer death: p?=?0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction.ConclusionCombining the predictive strength of multiple gene signatures improves prediction of breast cancer survival. The presented methodology is broadly applicable to breast cancer risk assessment using any new identified gene set.

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Landmark risk prediction of residual life for breast cancer survival.

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Landmark risk prediction of residual life for breast cancer survival.

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