Impact of Race, Ethnicity, and Socioeconomic Status over Time on the Long-term Survival of Adolescent and Young Adult Hodgkin Lymphoma Survivors

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Abstract

Background:

Although there are growing numbers of adolescent and young adult (AYA) Hodgkin lymphoma (HL) survivors, long-term overall survival (OS) patterns and disparities in this population are underreported. The aim of the current study was to assess the impact of race/ethnicity, socioeconomic status (SES), rurality, diagnosis age, sex, and HL stage over time on long-term survival in AYA HL survivors.

Methods:

The authors used the Surveillance, Epidemiology, and End Results (SEER) registry to identify survivors of HL diagnosed as AYAs (ages 15–39 years) between the years 1980 and 2009 and who were alive 5 years after diagnosis. An accelerated failure time model was used to estimate survival over time and compare survival between groups.

Results:

There were 15,899 5-year survivors of AYA HL identified, with a median follow-up of 14.4 years and range up to 33.9 years from diagnosis. Non-Hispanic black survivors had inferior survival compared with non-Hispanic white survivors [survival time ratio (STR): 0.71, P = 0.002]. Male survivors, older age at diagnosis, those diagnosed at higher stages, and those living in areas of higher SES deprivation had unfavorable long-term survival. There was no evidence of racial or sex-based survival disparities changing over time.

Conclusions:

Racial, SES, and sex-based disparities persist well into survivorship among AYA HL survivors.

Impact:

Disparities in long-term survival among AYA HL survivors show no evidence of improving over time. Studies investigating specific factors associated with survival disparities are needed to identify opportunities for intervention.

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We need to make substantial changes to how we conduct research. First, in response to heightened concern that our published research literature is incomplete and untrustworthy, we need new requirements to ensure research integrity. These include prespecification of studies whenever possible, avoidance of selection and other inappropriate data-analytic practices, complete reporting, and encouragement of replication. Second, in response to renewed recognition of the severe flaws of null-hypothesis significance testing (NHST), we need to shift from reliance on NHST to estimation and other preferred techniques. The new statistics refers to recommended practices, including estimation based on effect sizes, confidence intervals, and meta-analysis. The techniques are not new, but adopting them widely would be new for many researchers, as well as highly beneficial. This article explains why the new statistics are important and offers guidance for their use. It describes an eight-step new-statistics strategy for research with integrity, which starts with formulation of research questions in estimation terms, has no place for NHST, and is aimed at building a cumulative quantitative discipline.

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Modeling Survival Data: Extending the Cox Model

Terry M. Therneau, Patricia M. Grambsch (2013)

This is a book for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Its goal is to extend the toolkit beyond the basic triad provided by most statistical packages: the Kaplan-Meier estimator, log-rank test, and Cox regression model. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyse multiple/correlated event data using marginal and random effects (frailty) models. It covers the use of residuals and diagnostic plots to identify influential or outlying observations, assess proportional hazards and examine other aspects of goodness of fit. Other topics include time-dependent covariates and strata, discontinuous intervals of risk, multiple time scales, smoothing and regression splines, and the computation of expected survival curves. A knowledge of counting processes and martingales is not assumed as the early chapters provide an introduction to this area. The focus of the book is on actual data examples, the analysis and interpretation of the results, and computation. The methods are now readily available in SAS and S-Plus and this book gives a hands-on introduction, showing how to implement them in both packages, with worked examples for many data sets. The authors call on their extensive experience and give practical advice, including pitfalls to be avoided. Terry Therneau is Head of the Section of Biostatistics, Mayo Clinic, Rochester, Minnesota. He is actively involved in medical consulting, with emphasis in the areas of chronic liver disease, physical medicine, hematology, and laboratory medicine, and is an author on numerous papers in medical and statistical journals. He wrote two of the original SAS procedures for survival analysis (coxregr and survtest), as well as the majority of the S-Plus survival functions. Patricia Grambsch is Associate Professor in the Division of Biostatistics, School of Public Health, University of Minnesota. She has collaborated extensively with physicians and public health researchers in chronic liver disease, cancer prevention, hypertension clinical trials and psychiatric research. She is a fellow the American Statistical Association and the author of many papers in medical and statistical journals.