Longitudinal Quality Of Life Data Research Articles

Frontline Management Strategy and Quality of Life in Follicular Lymphoma: A Multi-Institutional Prospective Cohort Study

Background: Understanding the symptoms and quality of life (QOL) over time of adults with follicular lymphoma (FL) is important for treatment decision-making and clinical management. However, there are limited population-level data on the long-term QOL of adults with FL. We aim to describe the real-world, long-term QOL of adults with newly diagnosed FL up to 5/6 years after diagnosis. Methods: We used the Mayo Clinic/Iowa Molecular Epidemiology Resource (MER) and the multi-institutional Lymphoma Epidemiology of Outcomes (LEO) to identify adults with grade 1-3A FL who completed QOL surveys at baseline and follow-up. Participants with lymphoma were prospectively enrolled within 9 months of diagnosis in the MER cohort from 2002-2015 and within 6 months of diagnosis in the multi-institutional LEO cohort study across 8 cancer centers from 2015-2020. The LEO/MER cohort studies systematically collected information on disease status, QOL, health behaviors, and functional assessment. Treatments, disease relapses, and deaths were verified by medical record review. Treating physicians determined clinical management. QOL was measured using the Functional Assessment of Cancer Therapy-General (FACT-G) at years 1, 2, 3, and 5/6 post-diagnosis. A higher FACT-G total score (range 0-108) indicated better QOL across 4 subscales (range): physical (0-28), social/family (0-28), emotional (0-24), and functional (0-28) well-being. We categorized participants based on their frontline management at time of diagnosis: “observation” (surveillance), “treatment” (systemic treatment with immunotherapy +/- chemotherapy), and “local” (radiation) groups. We employed a generalized linear mixed model to evaluate and compare the changes in QOL scores (as a continuous variable) from baseline for the observation, treatment, and local groups, adjusting for sex, race/ethnicity, age, FLIPI risk, and cohort (i.e., LEO or MER). We also evaluated QOL changes by FLIPI risk. Results: Our study included 1,544 participants with FL and QOL data. At the time of enrollment, median age was 61 years (range 19-91), 88% were non-Hispanic White, and 49% were female. Based on initial management, 529 (34%) were in the observation group, 880 (57%) were in the treatment group, and 135 (9%) were in the local group. FLIPI risk was high for 17% in the observation group, 29.3% in the treatment group, and 3% in the local group. The mean (standard deviation) baseline FACT-G total score was lowest in the treatment group with the following baseline scores: 86 (13) for the observation group, 83 (14) for the treatment group, and 88 (12) for the local group (Table 1). The observation and local groups reported a greater worsening of their baseline FACT-G total score vs the treatment group at both the 2-year (-1.2, -0.8 vs +1.8, respectively) and 5/6 year (-5.4, -6 vs. -1.6, respectively) timepoints (Figure 1). This appeared to be driven by worsened physical and functional well-being in the observation (-1.3, -2.2, respectively) and local (-0.9, -2.4, respectively) groups vs the treatment group (0, -0.3, respectively). These differences were statistically significant between the observation vs treatment groups. There were no statistically significant differences in the other subscales, i.e., social/family and emotional well-being, by frontline management. Social/family well-being decreased across all groups by 5/6 years (-2.3 in observation, -2.2 in treatment, and -3 in local, p<0.05). Regardless of FLIPI risk, the total FACT-G scores decreased within each FLIPI risk group at 5/6 years (-4.4 for low, -3.8 for intermediate, and -4.9 for high, p<0.05). This was primarily due to worsened social/family well-being (-2.4 for low, -2.2 for intermediate, and -3 for high, p<0.05) at 5/6 years. There was no statistically significant difference in QOL between FLIPI risk groups. Conclusion: This is one of the first and largest studies with real-world longitudinal QOL data for FL. Our study suggests that frontline systemic treatment initially improved QOL and resulted in a lower degree of QOL decline over time. Social/family well-being decreased over time regardless of frontline management or FLIPI risk. Further research is warranted to explore the impact of timing and specific systemic treatments on QOL, incorporate the patient/caregiver experience to address social/family well-being, and identify the clinical significance of our findings.

Short-course Palliative Hypofractionated Radiotherapy in Patients with Poor-prognosis High-grade Glioma: Survival and Quality of Life Outcomes from a Prospective Phase II Study

AimsTo report longitudinal quality of life (QoL) outcomes and survival in patients with poor-prognosis high-grade glioma (HGG) treated with palliative hypofractionated radiotherapy. Materials and methodsPatients with poor-prognosis HGG were accrued on a prospective study of short-course palliative hypofractionated radiotherapy (35 Gy/10 fractions/2 weeks). The European Organization for Research and Treatment of Cancer QoL core questionnaire (QLQ-C30) and brain cancer module (BN20) were used in English or validated Indian vernacular languages (Hindi and Marathi) for QoL assessment at baseline (before radiotherapy), the conclusion of radiotherapy, 1 month post-radiotherapy and subsequently at 3-monthly intervals until disease progression/death. Baseline QoL scores were compared with corresponding scores from a historical HGG cohort. Summary QoL scores were compared longitudinally over time by related samples Friedman's two-way test. Progression-free survival and overall survival were calculated using the Kaplan–Meier method and reported as 1-year estimates with 95% confidence intervals. ResultsForty-nine (89%) of 55 patients completed the planned course of hypofractionated radiotherapy. Longitudinal QoL data were available in 42 (86%) of 49 patients completing radiotherapy, comprising the present cohort. The median age of included patients, comprised mainly of glioblastoma patients (81%), was 57 years, with an interquartile range (IQR) of 50–66 years and a median baseline Karnofsky score of 60 (IQR = 50–60). Baseline QoL scores were significantly worse for several domains compared with a historical institutional cohort of HGG patients treated previously with conventionally fractionated radiotherapy, indicating negative selection bias. QoL scores remained stable for most domains after palliative hypofractionated radiotherapy, with statistically significant improvements in fatigue (P = 0.032), dyspnoea (P = 0.042) and motor dysfunction (P = 0.036) over time. At a median follow-up of 8 months, Kaplan–Meier estimates of 1-year progression-free survival and overall survival were 33.3% (95% confidence interval 21.7–51.1%) and 38.1% (95% confidence interval 25.9–56%), respectively. ConclusionShort-course palliative hypofractionated radiotherapy in patients with poor-prognosis HGG is associated with stable and/or improved QoL scores in several domains, making it a viable resource-sparing regimen.

Longitudinal Changes of Quality of Life and Hearing Following Radiosurgery for Vestibular Schwannoma.

Simple SummarySince vestibular schwannomas are slow-growing tumors that can be controlled with different treatment modalities such as neurosurgery or radiotherapy, preserving quality of life is an important consideration. In this study, we analyzed how quality of life (QoL) changes for patients who receive stereotactic radiation in the months and years after treatment and if there is a correlation between changes in QoL and changes in hearing function. The results suggest that proper hearing of the unaffected ear might compensate for a hearing loss of the other ear due to the tumor or the treatment and in turn preserve QoL. However, this association should be confirmed in additional studies.Background: Most existing publications on quality of life (QoL) following stereotactic radiosurgery (SRS) for vestibular schwannomas (VS) gather information retrospectively by conducting surveys several years after treatment. The purpose of this study is therefore to provide longitudinal QoL data and assess how changes in hearing impact QoL. Methods: Patients completed the 12-item short-form (SF-12) health survey prior to treatment and at every follow-up visit. One hundred and seventy-five patients who had complete forms prior to treatment as well as at an early and at a late follow-up were included in the analysis. For 51 of these patients, longitudinal audiometry data were available. Results: Median follow-up was 7.2 years. Patients experienced a significant reduction in the physical composite score (PCS, p = 0.011) compared to before treatment. The mental composite score (MCS) increased significantly (p = 0.032). A decrease in PCS was not significantly correlated with an increased hearing threshold on the affected but rather on the unaffected ear (r(49) = −0.32, p = 0.023). Conclusions: It is unclear whether the decline in the PCS is due to treatment-related toxicity or the normal decline of PCS with age. Ensuring proper hearing on the untreated ear might be crucial to ensure good QoL for patients treated with SRS for VS, though this association should be confirmed in additional studies.

NQPC-03 PROGNOSTIC SIGNIFICANCE OF QUALITY-OF-LIFE EVALUATION OVER TIME IN CLINICAL PRACTICE FOR PATIENTS WITH GLIOBLASTOMA

BACKGROUNDSEvaluation of quality of life (QOL) has been considered as an indispensable modality for assessment of treatment impact for patients with malignant brain tumor, especially glioblastoma. However, changes in patients’ QOL under clinical practice with current standard of care (SOC) have not been clearly and routinely explored, so that solid baseline QOL data under SOC are not available for reliable comparison with those with novel treatments. Here we retrospectively examined changes in QOL during SOC in glioblastoma patients.PATIENTS AND METHODSPatients with histologically confirmed glioblastoma treated in our institute from April 2016 to April 2019, who underwent QOL evaluations using EORTC QLQ-C-30/BN-20 were eligible. Outcomes were assessed with clinical factors including therapeutic regimens.RESULTSForty-two patients, median age 64 yo (25–87), male/female 26/16, were identified having longitudinal QOL data along with medical records. Median initial KPS and mini-mental state examination (MMSE) score were 70 (20–90) and 27, respectively, suggesting this cohort containing those in good performance status. In four patients whose QOL queries were answered by a family, median MMSE was 16, indicating the impaired NCF affect self-report ability. Long term survivors without progression remained at an adequate functional scale level, while those who recurred declined in functional scale after progression, often accompanied with an increase in symptom scales associated with tumor location. The domains of declined functional scales varied among patients, and there was no clear tendency associated with patients’ backgrounds such as age and gender. The functional scale level improved in most cases when the recurrent disease was successfully treated, but it gradually declined in a stepwise fashion by repeated recurrences.CONCLUSIONSChanges in QOL in patients with glioblastoma were found to associate with disease status. The small number of patients who could be evaluated for QOL over time prevented extracting significant factors affecting QOL outcomes.

The OncoLifeS data-biobank for oncology: a comprehensive repository of clinical data, biological samples, and the patient\u2019s perspective

BackgroundUnderstanding cancer heterogeneity, its temporal evolution over time, and the outcomes of guided treatment depend on accurate data collection in a context of routine clinical care. We have developed a hospital-based data-biobank for oncology, entitled OncoLifeS (Oncological Life Study: Living well as a cancer survivor), that links routine clinical data with preserved biological specimens and quality of life assessments. The aim of this study is to describe the organization and development of a data-biobank for cancer research.ResultsWe have enrolled 3704 patients aged ≥ 18 years diagnosed with cancer, of which 45 with hereditary breast-ovarian cancer (70% participation rate) as of October 24th, 2019. The average age is 63.6 ± 14.2 years and 1892 (51.1%) are female. The following data are collected: clinical and treatment details, comorbidities, lifestyle, radiological and pathological findings, and long-term outcomes. We also collect and store various biomaterials of patients as well as information from quality of life assessments.ConclusionEmbedding a data-biobank in clinical care can ensure the collection of high-quality data. Moreover, the inclusion of longitudinal quality of life data allows us to incorporate patients’ perspectives and inclusion of imaging data provides an opportunity for analyzing raw imaging data using artificial intelligence (AI) methods, thus adding new dimensions to the collected data.

Quality of life for patients diagnosed with bipolar disorder: Lifestyle and treatment

BackgroundAlthough bipolar disorder (BP) is associated with impaired quality of life (QOL), little is known about the clinical features associated with QOL. Better knowledge about this relationship may improve treatment. MethodsThis cross-sectional and retrospective study of 160 bipolar outpatients is part of an extensive study battery that includes patient-rated QOL with the World Health Organization QOL-100. The subscale “overall QOL” was used for analysis. QOL was divided into subgroups denoted “low”, “mid”, and “high”. Clinical data such as disease-specific factors, treatment efforts, and lifestyle were gathered from personal interviews and medical records. ResultsCompared to mid QOL, single analysis adjusted for age and sex revealed that low QOL was associated with BP II diagnosis, no previous hospitalization, low grade of current lithium medication, high grade of current antiepileptic medication, short disease duration with lithium, long disease duration without lithium, inactive lifestyle, high BMI, young age, and pre-menopausal women. Compared to mid QOL, high QOL was associated with a hypomanic/manic first affective episode, low BMI, non-smoker, and not currently using anxiolytic or sedatives. LimitationsNo longitudinal QOL data were collected. ConclusionsQOL for bipolar patients is determined by serval factors that potentially could be altered. To improve QOL, lithium prophylaxis and lifestyle factors seem the most promising.

Weight loss results in significant improvements in quality of life for patients with nonalcoholic fatty liver disease: A prospective cohort study.

Nonalcoholic fatty liver disease (NAFLD) is highly prevalent and associated with decreased quality of life (QOL). The currently available treatment is weight loss through lifestyle modification. However, longitudinal QOL data are lacking on whether weight loss improves QOL. We prospectively enrolled 151 patients with NAFLD from 2009 to 2014. All patients received a liver biopsy, lifestyle assessment, blood tests, and QOL tools, including the Chronic Liver Disease Questionnaire (CLDQ), a validated health-related quality of life measurement. All patients were followed with a repeat CLDQ at 6 months. The cohort included 91 (60%) men, ages 51.5 ± 12.6 years, 46 (30%) of whom were diabetic. Thirty (21%) had advanced fibrosis or cirrhosis and 67 (47%) had a NAFLD Activity Score >4. Overall, 47 (31%) patients achieved at least a 5% reduction in weight. The cohort's median baseline total CLDQ value was 5.6 (interquartile range: 4.8-6.2). Those who achieved at least a 5% reduction in weight had a 0.45 (95% confidence interval [CI]: 0.24-0.66; P < 0.0001) point improvement in the total CLDQ, compared to 0.003 (95% CI: -0.12-0.12; P = 0.95) in those who did not. Nondiabetic patients with nonalcoholic steatohepatitis and without advanced fibrosis are most likely to achieve QOL benefits from weight loss. For every decrement in body mass index (BMI), there was a corresponding increase of 0.09 (95% CI: 0.03-0.16) points in the CLDQ scale (P = 0.005), adjusting for histology, diabetes, sex, age, and change in alanine aminotransferase level and change in FIB-4 index. A decrease by 5 points in BMI leads to a 10% adjusted improvement in QOL. Patients with NAFLD can experience significant improvements in QOL that appear specific to weight loss and not biochemical improvements.

The 22q11.2 deletion syndrome as a model to investigate the role of T-cells in psychosis?

Although bipolar disorder (BP) is associated with impaired quality of life (QOL), little is known about the clinical features associated with QOL. Better knowledge about this relationship may improve treatment.This cross-sectional and retrospective study of 160 bipolar outpatients is part of an extensive study battery that includes patient-rated QOL with the World Health Organization QOL-100. The subscale “overall QOL” was used for analysis. QOL was divided into subgroups denoted “low”, “mid”, and “high”. Clinical data such as disease-specific factors, treatment efforts, and lifestyle were gathered from personal interviews and medical records.Compared to mid QOL, single analysis adjusted for age and sex revealed that low QOL was associated with BP II diagnosis, no previous hospitalization, low grade of current lithium medication, high grade of current antiepileptic medication, short disease duration with lithium, long disease duration without lithium, inactive lifestyle, high BMI, young age, and pre-menopausal women. Compared to mid QOL, high QOL was associated with a hypomanic/manic first affective episode, low BMI, non-smoker, and not currently using anxiolytic or sedatives.No longitudinal QOL data were collected.QOL for bipolar patients is determined by serval factors that potentially could be altered. To improve QOL, lithium prophylaxis and lifestyle factors seem the most promising.

Jointly modeling longitudinal proportional data and survival times with an application to the quality of life data in a breast cancer trial.

Motivated by the joint analysis of longitudinal quality of life data and recurrence free survival times from a cancer clinical trial, we present in this paper two approaches to jointly model the longitudinal proportional measurements, which are confined in a finite interval, and survival data. Both approaches assume a proportional hazards model for the survival times. For the longitudinal component, the first approach applies the classical linear mixed model to logit transformed responses, while the second approach directly models the responses using a simplex distribution. A semiparametric method based on a penalized joint likelihood generated by the Laplace approximation is derived to fit the joint model defined by the second approach. The proposed procedures are evaluated in a simulation study and applied to the analysis of breast cancer data motivated this research.

Construction of quality of life change patterns: example in oncology in a phase III therapeutic trial (FFCD 0307).

ObjectiveQuality of life data in cancerology are often difficult to summarize due to missing data and difficulty to analyze the pattern of evolution in different groups of patients. The aim of this work was to apply a new methodology to construct Quality of Life (QoL) change patterns within patients included in a clinical trial comparing to regimen of treatment in locally advanced eosogastric cancer.Materials and methodsIn this trial, QoL was assessed every 2 months by self-reported EORTC QLQ-C30 questionnaire. Physical dimension scores were analyzed. After multiple imputation of missing data, 27 statistical measures aiming to describe the variation of QoL measures among follow-up were computed for each patient. Based on these measures, patient were grouped into homogenous groups in terms of QoL variation pattern using a K-Means classification method. The mean QoL score at each time was graphically represented in each obtained pattern. Finally, clinical characteristic of patients in each pattern of QoL were described and compared.ResultsThe trial included 416 patients and 1023 questionnaire were collected. 74 % of patients were male with a mean ± SD age of 62 ± 11 years. 43 % of scores were missing. Patients were grouped into four classes of homogeneous QoL variation patterns. 1) a Pattern of 24 (6 %) patients showing improvement in QoL with a mean variation of +10.7 points on the 0–100 scale, 2) a Pattern of 171 (41 %) patients showing a stability 3) two Patterns of 78 (19 %) and 143 (34 %) patients respectively showing a deterioration of QoL with a mean variation of −67.2 and −67.6, respectively. There were no difference between patterns in terms of gender or age. Patients within “degradation” pattern had significantly lower performance status (p = 0.015), higher severe after-effects rate (p < 10-3) and death rate (p < 10-3).ConclusionThis work opens up perspectives for longitudinal data analysis with a high probability of missing values while providing a relevant graphical summary. Patterns of QoL evolution with clinical relevance may help to interpret longitudinal QoL data in Cancer studies.Electronic supplementary materialThe online version of this article (doi:10.1186/s12955-015-0342-1) contains supplementary material, which is available to authorized users.

Causal inference for bivariate longitudinal quality of life data in presence of death by using global odds ratios

In longitudinal clinical trials, if a subject drops out due to death, certain responses, such as those measuring quality of life (QoL), will not be defined after the time of death. Thus, standard missing data analyses, e.g., under ignorable dropout, are problematic because these approaches implicitly 'impute' values of the response after death. In this paper we define a new survivor average causal effect for a bivariate response in a longitudinal quality of life study that had a high dropout rate with the dropout often due to death (or tumor progression). We show how principal stratification, with a few sensitivity parameters, can be used to draw causal inferences about the joint distribution of these two ordinal quality of life measures.

331P - Impact of Response Shift On Time to Quality of Life Scores Deterioration in Breast Cancer Patients: Is it Time to Move For QOL Recist Criterion?

ABSTRACT Background Time to quality of life (QoL) score deterioration (TD) is a method of longitudinal QoL data analysis that has been proposed for breast cancer (BC) patients (Hamidou et al Oncologist 2011). As for RECIST criteria, the optimal definitions dealing with reference should be explored. This study aims to study the impact of changes in internal standards (CIS) of response-shift (RS) and the influence of baseline QoL expectancies on TD. Methods A prospective multicenter study including all women hospitalized for a primary BC was conducted. The EORTC-QLQ-C30 and BR-23 questionnaires were used to assess the QoL at baseline, at the end of 1st hospitalization, and 3 and 6 months after. CIS was investigated by the then-test method. QoL expectancy was assessed at baseline using Likert scale. Deterioration was defined as a decrease in QoL scores reaching at least the mean difference identified as minimal clinically important difference (MCID) using Jaeschke's transition question. Sensitivity analyses were done using the then-test score as reference score, and considering 5 and 10 points as MCID. TD was estimated using Kaplan-Meier method. Cox regression analyses were used to identify factors influencing TD. Results From February 2006 to February 2008, 381 women were included. For role functioning dimension, the median TD increased from 3.2 months [95% CI: 3.1-3.36] to 4.76 months [3.3-6.2] when adjusting on CIS. For body image when adjusting on CIS, sentinel lymph node biopsy became significantly associated with longer TD (HR: 0.64[0.43-0.94]) as compared to axillary lymph node dissection, radiotherapy to a shorter TD (HR: 0.63[0.42-0.95] and the type of surgery had no effect on TD. For global health, cognitive and social functioning dimensions, patients expecting deterioration in their QoL had a significantly shorter TD. For fatigue and breast symptom scales, patients expecting no change had a significantly shorter TD, as compared to patients expecting an improvement. Sensitivity analyses using a MDCS of 5 or 10 confirmed these results. Conclusions Our results suggest that it would be more accurate to take into account CIS component of RS as well as QoL expectancies to estimate TD of QoL scores in patient with BC. Disclosure All authors have declared no conflicts of interest.

Impact of response shift on time to quality-of-life scores deterioration in patients with breast cancer.

1584 Background: Time to quality of life (QoL) score deterioration (TD) is a method of longitudinal QoL data analysis that has been proposed for breast cancer (BC) patients (Hamidou et al Oncologist 2011). As for RECIST criteria, the optimal definitions dealing with reference should be explored. This study aims to study the impact of changes in internal standards (CIS) of response-shift (RS) and the influence of baseline QoL expectancies on TD. Methods: A prospective multicenter study including all women hospitalized for a primary BC was conducted. The EORTC-QLQ-C30 and BR-23 questionnaires were used to assess the QoL at baseline, at the end of 1st hospitalization, and 3 and 6 months after. CIS was investigated by the then-test method. QoL expectancy was assessed at baseline using Likert scale. Deterioration was defined as a decrease in QoL scores reaching at least the mean difference identified as minimal clinically important difference (MCID) using Jaeschke’s transition question. Sensitivity analyses were done using the then-test score as reference score, and considering 5 and 10 points as MCID. TD was estimated using Kaplan-Meier method. Cox regression analyses were used to identify factors influencing TD. Results: From February 2006 to February 2008, 381 women were included. For role functioning dimension, the median TD increased from 3.2 months [95% CI: 3.1-3.36] to 4.76 months [3.3-6.2] when adjusting on CIS. For body image when adjusting on CIS, sentinel lymph node biopsy became significantly associated with longer TD (HR: 0.64[0.43-0.94]) as compared to axillary lymph node dissection, radiotherapy to a shorter TD (HR: 0.63[0.42-0.95] and the type of surgery had no effect on TD. For global health, cognitive and social functioning dimensions, patients expecting deterioration in their QoL had a significantly shorter TD. For fatigue and breast symptom scales, patients expecting no change had a significantly shorter TD, as compared to patients expecting an improvement. Sensitivity analyses using a MDCS of 5 or 10 confirmed these results. Conclusions: Our results suggest that it would be more accurate to take into account CIS component of RS as well as QoL expectancies to estimate TD of QoL scores in patient with BC.

Concordance of quality of life assessments following pediatric hematopoietic stem cell transplantation

To examine the concordance between pediatric patient's self-report and parent-report regarding a patient's quality of life (QoL) prior to and following hematopoietic stem cell transplantation (HSCT) and to identify potential medical and demographic covariates of concordance. Utilizing the PedsQL 4.0, the longitudinal QoL data were obtained from 68 pediatric HSCT patient-parent dyads prior to and up to two years post-transplantation. Reliability based upon Intraclass Correlation Coefficients (ICC) indicates a parabolic pattern of concordance being significantly poorer in the acute phase of treatment 3 months post-HSCT, followed by a return to pre-transplant levels at subsequent assessments and a substantial rise at one- and two-year follow-up assessments (Baseline ICC = 0.42; 3 months = 0.11; 6 months = 0.54). Paired t-tests further indicate that concordance was highest for observable domains of functioning (Physical and School) with greater inter-rater discrepancies on the subjective domains (Emotional and Social) of QoL at baseline and 6 months post-transplantation. Children typically rated their QoL as higher than parents at all time points and in virtually all domains; however, both perspectives are vital in providing a more accurate depiction of a patient's treatment experience. Dyads speaking the same language exhibited higher levels of QoL agreement than those that did not, while concordance among female-patient-dyads was more disparate than male-patient-dyads. Such findings highlight the importance of implementing psychosocial assessments and possible interventions for patients and parents proactively during the HSCT to effectively address the impact of the treatment and recovery experiences.

Longitudinal quality of life data can provide insights on the impact of adjuvant treatment for pancreatic cancer—Subset analysis of the ESPAC‐1 data

The European Study Group for Pancreatic Cancer (ESPAC-1) study is the largest study of adjuvant treatment for pancreatic ductal adenocarcinoma to date and confirmed a survival advantage for adjuvant chemotherapy but not for chemoradiation. The importance of parallel evaluation of survival and quality of life (QoL) has been recognized as fundamental and the aim was to assess QoL and quality adjusted survival. A longitudinal QoL study on a subset of ESPAC-1 patients who prospectively completed the EORTC QLQ C-30 questionnaire during treatment and follow-up. An integrated quality-survival product method was used to adjust any treatment effect on survival by a function of measured QoL, calculated over a restricted 24-month-period (QALM-24). Three hundred and sixteen patients completed 1,201 questionnaires. There were no differences between treatment groups in dimension scores at baseline (randomization). For the chemotherapy group, the mean Quality Adjusted Life Months over 24 months (QALM-24) was 9.6 (95% CI: 8.7, 11.2) months compared with the mean QALM-24 of 8.6 (95% CI: 7.6, 10.5) months for the no chemotherapy group. For the chemoradiation group, the mean QALM-24 was 7.1 (95% CI: 6.0, 9.0) months compared with the mean QALM-24 of 8.1 (95% CI: 7.0, 10.0) months for the no chemoradiation group. The previously reported survival advantage supporting the use of adjuvant chemotherapy is maintained when adjusted using quality adjusted survival methodology. Chemotherapy provided on average an additional 1.0 quality-adjusted life months within a restricted 2-year time period from the time of resection.

Latent variable modelling of recovery trajectories: sexual function following radical prostatectomy.

As treatments improve and survival time lengthens, the course of recovery and long-term quality of life (QoL) is of greater interest. The application of latent class growth models to longitudinal QoL data provides unique insights into recovery that are not evident with marginal analyses. We examine this approach in the context of a large population-based observational study. This is the first attempt to characterize individuals' recovery over time following prostate cancer surgery, and to determine factors associated with varying recovery experiences of prostate cancer patients. Four major patterns emerged that illustrate typical patterns of recovery following radical prostatectomy. Given a man's baseline data, this method produces estimates of the probability of belonging to each recovery class. The method is presented as a useful tool for identifying hypotheses associated with recovery and potential antecedents of importance.

Pattern mixture models for longitudinal quality of life studies in advanced stage disease.

Analyses of longitudinal quality of life (QOL) for patients with advanced stage disease are frequently plagued by problems of non-random drop-out attributable to deteriorating health and/or death. As an example, Moinpour et al. cite specific challenges which limited their report and assessment of QOL for patients treated for advanced stage colorectal cancer in a clinical trial of several chemotherapeutic regimes performed by the Southwest Oncology Group. A particular source of confusion that arises in studies of advanced stage disease is whether or not to differentiate loss of follow-up due to death from drop-out where the patient is still alive but has dropped from the study. In this paper we examine exploratory data techniques for longitudinal QOL data with non-random missingness due to drop-out and censorship by death. We propose a pattern mixture model for longitudinal QOL, time of drop-out and survival, which allows for straightforward implementation of sensitivity analyses and explicit comparisons to the raw data. Our method is illustrated in the context of analysing the data and addressing the challenges posed by Moinpour et al.

Longitudinal effects of high-dose chemotherapy and autologous stem cell transplantation on quality of life in the treatment of metastatic breast cancer.

This study determined the effects of high-dose chemotherapy (HDCT) with autologous blood stem cell transplantation (ASCT) on quality of life (QL) in women with metastatic breast cancer prior to, and during treatment, and up to 1-year post-ASCT. Thirty-three women diagnosed with metastatic breast cancer participated in a phase 1 clinical trial of a new combination of cyclophosphamide (CTX) and mitoxantrone (MXT), with dose escalation of paclitaxel. Longitudinal QL data were collected using the functional living index-cancer (FLIC) and symptom scales at seven time periods: pre-induction chemotherapy (CT), post-induction CT, post-high dose CT (HDCT), and at 3, 6, 9 and 12 months post-ASCT. FLIC scores indicated that the worst problems for patients were feelings of hardship on themselves and their families, followed by psychological functioning and physical functioning problems. The time around diagnosis of the metastatic disease and following HDCT were the worst times for all levels of quality of life, but anxiety and depression symptoms continued to increase in severity across the entire follow-up period. The symptoms that were most problematic were worry about the future, loss of sexual interest, anxiety about the treatment, general worrying, and joint pain. These data highlight the problems that women with metastatic breast cancer encounter at different stages of the disease and treatment process, and can be used to tailor psychosocial interventions appropriate for treating the relevant issues at different points in time.

A joint analysis of quality of life and survival using a random effect selection model

In studies of patients with advanced disease, longitudinal quality of life data may be truncated as a result of early death. Since survival and quality of life are likely to be related, modelling of the quality of life response needs to account for these different survival patterns. Here we discuss the application of a random effect selection model, in the form of a trivariate Normal model for the joint analysis of quality of life response (intercept and slope) and log survival time. Under certain assumptions this can give an unbiased description of the quality of life responses and valid inferences comparing treatment strategies in a clinical trial. It also indicates how quality of life and survival are related, by estimating the expected quality of life responses conditional on different survival times. Model parameters can be estimated using a restricted iterative generalized least-squares (RIGLS) procedure within standard software, extended to handle censoring of survival outcome using an EM algorithm. The model is applied to a physical quality of life score and survival data from a trial of treatment for patients with colorectal hepatic metastases. Survival differed between the treatment groups, and quality of life repsonse tended to be worse, both in initial level and change over time, for those patients who died earlier. The parameter estimates obtained agreed well with those from analysing the extended trial data set with complete survival information. Residual diagnostics used to check the necessary underlying assumptions of the model are exemplified. We conclude that such models can give an informative description of longitudinal responses when these are truncated by differential survival patterns. Copyright © 2000 John Wiley & Sons, Ltd.

A joint analysis of quality of life and survival using a random effect selection model.

In studies of patients with advanced disease, longitudinal quality of life data may be truncated as a result of early death. Since survival and quality of life are likely to be related, modelling of the quality of life response needs to account for these different survival patterns. Here we discuss the application of a random effect selection model, in the form of a trivariate Normal model for the joint analysis of quality of life response (intercept and slope) and log survival time. Under certain assumptions this can give an unbiased description of the quality of life responses and valid inferences comparing treatment strategies in a clinical trial. It also indicates how quality of life and survival are related, by estimating the expected quality of life responses conditional on different survival times. Model parameters can be estimated using a restricted iterative generalized least-squares (RIGLS) procedure within standard software, extended to handle censoring of survival outcome using an EM algorithm. The model is applied to a physical quality of life score and survival data from a trial of treatment for patients with colorectal hepatic metastases. Survival differed between the treatment groups, and quality of life repsonse tended to be worse, both in initial level and change over time, for those patients who died earlier. The parameter estimates obtained agreed well with those from analysing the extended trial data set with complete survival information. Residual diagnostics used to check the necessary underlying assumptions of the model are exemplified. We conclude that such models can give an informative description of longitudinal responses when these are truncated by differential survival patterns.