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Cancer survivors who fully participate in the PROFILES registry have better health-related quality of life than those who drop out.

ABSTRACT: PURPOSE:Attrition and subsequent missing data pose a challenge in longitudinal research in oncology. This study examined factors associated with attrition in the PROFILES registry, and its impact on observed health-related quality of life (HRQOL) estimates. METHODS:Sociodemographic, clinical, and HRQOL data were collected annually from a cohort of 2625 colorectal cancer survivors between 2010 and 2015. Participant characteristics according to time of dropout were compared using analysis of variance and chi-square tests. Predictors of attrition were examined in logistic regression analysis. Multilevel linear mixed models were constructed to investigate associations between attrition and HRQOL over time. RESULTS:Participants who dropped out were more likely to be female (OR?=?1.23, CI?=?1.02-1.47), older (OR?=?1.20, CI?=?1.09-1.33), less educated (OR?=?1.64, CI?=?1.30-2.11), and to have depressive symptoms (OR?=?1.84, CI?=?1.39-2.44) than full responders, and less likely to have high socioeconomic status (OR?=?0.74, CI?=?0.61-0.94). Participants who dropped out earlier reported significantly worse HRQOL, functioning, and psychosocial symptoms, which declined at a steeper rate over time, than full responders. CONCLUSIONS:Cancer survivors' HRQOL may be overestimated in longitudinal research due to attrition of the most unwell participants. IMPLICATIONS FOR CANCER SURVIVORS:Cancer survivors with the poorest health are at risk of dropping out of PROFILES and possibly withdrawing from other activities. Optimizing participation in PROFILES-a potential mechanism for providing information and access to support-is an avenue for keeping this group engaged.

SUBMITTER: Ramsey I

PROVIDER: S-EPMC6881419 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Cancer survivors who fully participate in the PROFILES registry have better health-related quality of life than those who drop out.

Ramsey Imogen I de Rooij Belle H BH Mols Floortje F Corsini Nadia N Horevoorts Nicole J E NJE Eckert Marion M van de Poll-Franse Lonneke V LV

Journal of cancer survivorship : research and practice 20190906 6

<h4>Purpose</h4>Attrition and subsequent missing data pose a challenge in longitudinal research in oncology. This study examined factors associated with attrition in the PROFILES registry, and its impact on observed health-related quality of life (HRQOL) estimates.<h4>Methods</h4>Sociodemographic, clinical, and HRQOL data were collected annually from a cohort of 2625 colorectal cancer survivors between 2010 and 2015. Participant characteristics according to time of dropout were compared using an ...[more]

PMID: 31493162

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Project description:BackgroundLong-term colon cancer survivors present heterogeneous health-related quality of life (HRQOL) outcomes. We determined unobserved subgroups (classes) of survivors with similar HRQOL patterns and investigated their stability over time and the association of clinical covariates with these classes.Materials and methodsData from the population-based PROFILES registry were used. Included were survivors with nonmetastatic (TNM stage I-III) colon cancer (n = 1,489). HRQOL was assessed with the Dutch translation of the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire C30 version 3.0. Based on survivors' HRQOL, latent class analysis (LCA) was used to identify unobserved classes of survivors. Moreover, latent transition analysis (LTA) was used to investigate changes in class membership over time. Furthermore, the effect of covariates on class membership was assessed using multinomial logistic regression.ResultsLCA identified five classes at baseline: class 1, excellent HRQOL (n = 555, 37.3%); class 2, good HRQOL with prevalence of insomnia (n = 464, 31.2%); class 3, moderate HRQOL with prevalence of fatigue (n = 213, 14.3%); class 4, good HRQOL with physical limitations (n = 134, 9.0%); and class 5, poor HRQOL (n = 123, 8.3%). All classes were stable with high self-transition probabilities. Longer time since the diagnosis, no comorbid conditions, and male sex were associated with class 1, whereas older age was associated with class 4. Clinical covariates were not associated with class membership.ConclusionThe identified classes are characterized by distinct patterns of HRQOL and can support patient-centered care. LCA and LTA are powerful tools for investigating HRQOL in cancer survivors.Implications for practiceLong-term colon cancer survivors show great heterogeneity in their health-related quality of life. This study identified five distinct clusters of survivors with similar patterns of health-related quality of life and showed that these clusters remain stable over time. It was also shown that these clusters do not significantly differ in tumor characteristics or received treatment. Cluster membership of long-term survivors can be identified by sociodemographic characteristics but is not predetermined by diagnosis and treatment.

Project description:BACKGROUND:The aims of this study were to prospectively assess symptoms of anxiety and depression among survivors of colorectal cancer (CRC), to compare these survivors with a normative population, and to identify subgroups at risk for experiencing symptoms of anxiety and/or depression across a 4-year time period. Also, the impact on health-related quality of life (HRQOL) was studied. METHODS:The population-based Eindhoven Cancer Registry was used to select patients diagnosed with CRC between 2000 and 2009. The Hospital Anxiety and Depression Scale and the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (HRQOL) were completed by patients in 2010 (n?=?2625 [73% response rate]), 2011, 2012, and 2013 and by an age- and sex-matched normative sample (n?=?315) in 2011. RESULTS:Patients reported a significantly higher prevalence of depression (19.0% vs 12.8%) and anxiety (20.9% vs 11.8%) in comparison with the norm. Anxiety was stable, whereas depression scores changed over time, although this was not clinically relevant. A longer time since diagnosis was associated with fewer depressive symptoms over time, whereas older age and being male were associated with less anxiety and more depression. Being married was associated with less anxiety and depression, and a low education level and comorbid conditions were associated with more anxiety and depression. Higher levels of symptoms of depression and anxiety were associated with a lower global quality of life and lower physical, role, cognitive, emotional, and social functioning over time. CONCLUSIONS:Because of the increased prevalence of depression and anxiety among patients with CRC and their negative effect on HRQOL, screening and referral are of the utmost importance, especially among those who are single, have a low educational level, and have comorbid conditions, even years after diagnosis and treatment. Cancer 2018;124:2621-8. © 2018 The Authors. Cancer published by Wiley Periodicals, Inc. on behalf of American Cancer Society. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Project description:BackgroundResearch into the clustering of symptoms may improve the understanding of the underlying mechanisms that affect survivors' symptom burden. This study applied network analyses in a balanced sample of cancer survivors to 1) explore the clustering of symptoms and 2) assess differences in symptom clustering between cancer types, treatment regimens, and short-term and long-term survivors.MethodsThis study used cross-sectional survey data, collected between 2008 and 2018, from the population-based Patient Reported Outcomes Following Initial Treatment and Long Term Evaluation of Survivorship registry, which included survivors of 7 cancer types (colorectal cancer, breast cancer, ovarian cancer, thyroid cancer, chronic lymphocytic leukemia, Hodgkin lymphoma, and non-Hodgkin lymphoma). Regularized partial correlation network analysis was used to explore and visualize the associations between self-reported symptoms (European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire) and the centrality of these symptoms in the network (ie, how strongly a symptom was connected to other symptoms) for the total sample and for subgroups separately.ResultsIn the total sample (n = 1330), fatigue was the most central symptom in the network with moderate direct relationships with emotional symptoms, cognitive symptoms, appetite loss, dyspnea, and pain. These relationships persisted after adjustments for sociodemographic and clinical characteristics. Connections between fatigue and emotional symptoms, appetite loss, dyspnea, and pain were consistently found across all cancer types (190 for each), treatment regimens, and short-term and long-term survivors.ConclusionsIn a heterogenous sample of cancer survivors, fatigue was consistently the most central symptom in all networks. Although longitudinal data are needed to build a case for the causal nature of these symptoms, cancer survivorship rehabilitation programs could focus on fatigue to reduce the overall symptom burden.

Project description:ObjectiveOur aim was to describe and compare self-reported causal attributions (interpretations of what caused an illness) among cancer survivors and to assess which sociodemographic and clinical characteristics are associated with them.MethodsData from five population-based PROFILES registry samples (i.e. lymphoma (n = 993), multiple myeloma (n = 156), colorectal (n = 3989), thyroid (n = 306), endometrial (n = 741), prostate cancer (n = 696)) were used. Causal attributions were assessed with a single question.ResultsThe five most often reported causal attributions combined were unknown (21%), lifestyle (19%), biological (16%), other (14%), and stress (12%). Lymphoma (49%), multiple myeloma (64%), thyroid (55%), and prostate (64%) cancer patients mentioned fixed causes far more often than modifiable or modifiable/fixed. Colorectal (33%, 34%, and 33%) and endometrial (38%, 32%, and 30%) cancer survivors mentioned causes that were fixed, modifiable, or both almost equally often. Colorectal, endometrial, and prostate cancer survivors reported internal causes most often, whereas multiple myeloma survivors more often reported external causes, while lymphoma and thyroid cancer survivors had almost similar rates of internal and external causes. Females, those older, those treated with hormonal therapy, and those diagnosed with prostate cancer were less likely to identify modifiable causes while those diagnosed with stage 2, singles, with ≥2 comorbid conditions, and those with endometrial cancer were more likely to identify modifiable causes.ConclusionIn conclusion, this study showed that patients report both internal and external causes of their illness and both fixed and modifiable causes. This differsbetween the various cancer types.Implications for cancer survivorsAlthough the exact cause of cancer in individual patients is often unknown, having a well-informed perception of the modifiable causes of one's cancer is valuable since it can possibly help survivors with making behavioural adjustments in cases where this is necessary or possible.

Project description:Study questionDoes inverse probability weighting (IPW) provide a more valid estimate of the cumulative incidence of live birth after multiple cycles of IVF?Summary answerIPW can provide a more accurate estimate of treatment success for counseling and decision-making regarding IVF.What is known alreadyDifferent approaches have been used to define and calculate IVF success; however, many of these approaches have limitations and potentially violate statistical assumptions. IPW can address potential selection bias that arises when people do not continue IVF treatment after a failed cycle.Study design, size, durationData were derived from a cohort study of women undergoing their first fresh embryo transfer IVF cycle at our institution between 1 January 1995 and 31 December 2014. All autologous cycles (fresh and frozen) were included, up to six total cycles.Participants/materials, setting, methodsWe identified 20 015 women who underwent 47 079 IVF cycles and had 10 031 live births during the study period. The cumulative incidence of live birth was calculated using three approaches. First, we used a standard Kaplan-Meier approach, 'the optimistic approach', censoring women when they dropped out of treatment. Second, we used a 'conservative' Kaplan-Meier approach that assumed women who dropped out of treatment did not achieve a live birth. Finally, we used IPW to calculate the probability of remaining in treatment, while accounting for differences in treatment drop out. IPW up-weights the data of those remaining under observation who resembled the women who dropped out of treatment, thereby decreasing the potential selection bias resulting from loss to follow-up. The IPW was incorporated into a Kaplan-Meier approach.Main results and the role of chanceThe cumulative incidence of live birth was 72.1% (95% CI: 71.0-73.1%) for the optimistic approach, 50.1% (49.4-50.8%) for the conservative approach and 66.8% (65.5-68.1%) for the IPW approach. Among women < 38 years of age, the cumulative incidence of live birth calculated by the IPW was slightly higher than that calculated by the optimistic approach. For women 41-42 years of age, the IPW cumulative incidence of live birth was slightly lower. The IPW was similar to the optimistic approach for the other age groups. The conservative estimate was lowest for all age groups.Limitations, reasons for cautionOnly clinical data recorded by the providers during an IVF cycle were used to generate weights for IPW. Covariates included: age, gravidity and year at the start of the cycle; primary infertility diagnosis; procedure type (i.e. whether a fresh or frozen embryo was transferred); number of mature oocytes retrieved; number of embryos transferred; cycle cancellation; pregnancy loss in the cycle; and insurance status. We were unable to determine exact reasons for treatment drop out (e.g. cessation of IVF treatment, transfer to another institution or spontaneous pregnancy). Our IPW model was moderately predictive based on the c-statistic from the calculation of the denominator of the weight; however, residual selection bias may remain due to the limited range of covariate data.Wider implications of the findingsIPW can be used in a variety of settings to address selection bias introduced by differential loss to follow up or treatment drop out.Study funding/competing interest(s)AMM was supported by National Institutes of Health (NIH) T32 HD052458-Boston University Reproductive, Perinatal and Pediatric Epidemiology Training Program. The authors report no conflicts of interest.Trial registration numberN/A.

Dataset Information

Cancer survivors who fully participate in the PROFILES registry have better health-related quality of life than those who drop out.

Publications

Cancer survivors who fully participate in the PROFILES registry have better health-related quality of life than those who drop out.

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