Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR

Shed urinary ALCAM is an independent prognostic biomarker of three-year overall survival after cystectomy in patients with bladder cancer.

Proteins involved in tumor cell migration can potentially serve as markers of invasive disease. Activated Leukocyte Cell Adhesion Molecule (ALCAM) promotes adhesion, while shedding of its extracellular domain is associated with migration. We hypothesized that shed ALCAM in biofluids could be predictive of progressive disease. ALCAM expression in tumor (n = 198) and shedding in biofluids (n = 120) were measured in two separate VUMC bladder cancer cystectomy cohorts by immunofluorescence and enzyme-linked immunosorbent assay, respectively. The primary outcome measure was accuracy of predicting 3-year overall survival (OS) with shed ALCAM compared to standard clinical indicators alone, assessed by multivariable Cox regression and concordance-indices. Validation was performed by internal bootstrap, a cohort from a second institution (n = 64), and treatment of missing data with multiple-imputation. While ALCAM mRNA expression was unchanged, histological detection of ALCAM decreased with increasing stage (P = 0.004). Importantly, urine ALCAM was elevated 17.0-fold (P < 0.0001) above non-cancer controls, correlated positively with tumor stage (P = 0.018), was an independent predictor of OS after adjusting for age, tumor stage, lymph-node status, and hematuria (HR, 1.46; 95% CI, 1.03-2.06; P = 0.002), and improved prediction of OS by 3.3% (concordance-index, 78.5% vs. 75.2%). Urine ALCAM remained an independent predictor of OS after accounting for treatment with Bacillus Calmette-Guerin, carcinoma in situ, lymph-node dissection, lymphovascular invasion, urine creatinine, and adjuvant chemotherapy (HR, 1.10; 95% CI, 1.02-1.19; P = 0.011). In conclusion, shed ALCAM may be a novel prognostic biomarker in bladder cancer, although prospective validation studies are warranted. These findings demonstrate that markers reporting on cell motility can act as prognostic indicators

Omission of surgery in older women with early breast cancer has an adverse impact on breast cancer specific survival

Background: Primary endocrine therapy (PET) is used as an alternative to surgery in up to 40% of UK women with early breast cancer over age 70. This study has investigated the impact of surgery versus PET on breast cancer specific survival (BCSS) in older women. Methods: Cancer registration data were obtained from two English regions from 2002 to 2010 (n=23,961). A retrospective analysis was performed for women with ER positive disease, using statistical modelling to show the effect of treatment (surgery or PET) and age/health status on BCSS. Missing data was handled using multiple imputation. Results: After data pre-processing, 18,730 (78.5%) were identified as having ER positive disease; of these, 10,087 (54%) had surgery and 8,643 (46%) had PET. BCSS was worse in the PET group compared with the surgical group (5 year BCSS: 69% v 90% respectively). This was true for all strata considered, though the differential was lessened in the cohort with the greatest degree of comorbidity. For older, frailer patients the hazard of breast cancer death has less relative impact on overall survival. Selection for surgery on the basis of predicted life expectancy may permit selection of women for whom surgery confers little benefit. This model is being used to develop an on-line algorithm to aid management of older women with early breast cancer (Age Gap Risk Prediction Tool). Conclusion: BCSS in older women with ER positive disease is worse if surgery is omitted. This treatment choice may, therefore, contribute to inferior cancer outcomes

SPHR Diabetes Prevention Model: Detailed Description of Model Background, Methods, Assumptions and Parameters

Type-2 diabetes is a complex disease with multiple risk factors and health consequences whose prevention is a major public health priority. We have developed a microsimulation model written in the R programming language that can evaluate the effectiveness and cost-effectiveness of a comprehensive range of different diabetes prevention interventions, either in the general population or in subgroups at high risk of diabetes. Within the model individual patients with different risk factors for diabetes follow metabolic trajectories (for body mass index, cholesterol, systolic blood pressure and glycaemia), develop diabetes, complications of diabetes and related disorders including cardiovascular disease and cancer, and eventually die. Lifetime costs and quality-adjusted life-years are collected for each patient. The model allows assessment of the wider social impact on employment and the equity impact of different interventions. Interventions may be population-based, community-based or individually targeted, and administered singly or layered together. The model is fully enabled for probabilistic sensitivity analysis (PSA) to provide an estimate of decision uncertainty. This discussion paper provides a detailed description of the model background, methods and assumptions, together with details of all parameters used in the model, their sources and distributions for PSA

The Effects of Anthropometry and Angiogenesis on Breast Cancer Etiology

Factors such as mammographic breast density and angiogenesis may be related to breast cancer development, though numerous questions about the etiologic mechanisms remain. Percent density is positively associated with breast cancer risk, yet is negatively associated with another breast cancer risk factor, body mass index (BMI). Vascular endothelial growth factor (VEGF) is a primary regulator of angiogenesis, yet its relationship to breast cancer risk is unclear. We evaluated the longitudinal association between BMI and breast density in the Study of Women's Health Across the Nation (SWAN) Mammographic Density Substudy (N=834). Using adjusted random intercept models, changes in BMI were not associated with changes in dense breast area (Beta=-0.0105, p=0.34), but were strongly negatively associated with changes in percent density (Beta=-1.18, p<0.001). Thus, effects of changes in anthropometry on percent breast density may reflect effects on non-dense tissue, rather than on the dense tissue where cancers arise. Breast density was measured from routine screening mammograms which were not timed with SWAN visits. We developed a method to align the off-schedule mammogram data to the study visit times using linear interpolation with multiple imputation. Our method was shown to be valid, with an average bias for dense breast area of 0.11 cm2. In the random intercept models, use of a simple matching algorithm to estimate breast density produced different (Beta=-0.0155, p=0.04), and likely incorrect, results. Our linear interpolation with multiple imputations method may be applicable to other longitudinal datasets with important data collected off-schedule. In a separate case-control study, the Mammograms and Masses Study (MAMS), we evaluated the association between serum VEGF levels and breast cancer (N=407). Geometric mean VEGF levels were higher among cases (331.4 pg/mL) than controls (291.4 pg/mL; p=0.21). In a multivariable logistic regression model, VEGF greater than or equal to 314.2 pg/mL was positively associated with breast cancer (odds ratio 1.37, 95% confidence interval 0.88-2.12), albeit non-significantly. Higher levels of VEGF may increase breast cancer risk. We have identified roles for anthropometry and angiogenesis in breast carcinogenesis. Enhancing knowledge of breast cancer etiology is a significant contribution to public health and may lead to improved opportunities for prevention or early detection

Development of Prognosis in Palliative care Study (PiPS) predictor models to improve prognostication in advanced cancer: prospective cohort study

OBJECTIVE: To develop a novel prognostic indicator for use in patients with advanced cancer that is significantly better than clinicians' estimates of survival. DESIGN: Prospective multicentre observational cohort study. SETTING: 18 palliative care services in the UK (including hospices, hospital support teams, and community teams). PARTICIPANTS: 1018 patients with locally advanced or metastatic cancer, no longer being treated for cancer, and recently referred to palliative care services. MAIN OUTCOME MEASURES: Performance of a composite model to predict whether patients were likely to survive for "days" (0-13 days), "weeks" (14-55 days), or "months+" (>55 days), compared with actual survival and clinicians' predictions. RESULTS: On multivariate analysis, 11 core variables (pulse rate, general health status, mental test score, performance status, presence of anorexia, presence of any site of metastatic disease, presence of liver metastases, C reactive protein, white blood count, platelet count, and urea) independently predicted both two week and two month survival. Four variables had prognostic significance only for two week survival (dyspnoea, dysphagia, bone metastases, and alanine transaminase), and eight variables had prognostic significance only for two month survival (primary breast cancer, male genital cancer, tiredness, loss of weight, lymphocyte count, neutrophil count, alkaline phosphatase, and albumin). Separate prognostic models were created for patients without (PiPS-A) or with (PiPS-B) blood results. The area under the curve for all models varied between 0.79 and 0.86. Absolute agreement between actual survival and PiPS predictions was 57.3% (after correction for over-optimism). The median survival across the PiPS-A categories was 5, 33, and 92 days and survival across PiPS-B categories was 7, 32, and 100.5 days. All models performed as well as, or better than, clinicians' estimates of survival. CONCLUSIONS: In patients with advanced cancer no longer being treated, a combination of clinical and laboratory variables can reliably predict two week and two month survival

Prognostic modelling of breast cancer patients: a benchmark of predictive models with external validation

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores – Sistemas Digitais e Percepcionais pela Universidade Nova de Lisboa, Faculdade de Ciências e TecnologiaThere are several clinical prognostic models in the medical field. Prior to clinical use, the outcome models of longitudinal cohort data need to undergo a multi-centre evaluation of their predictive accuracy. This thesis evaluates the possible gain in predictive accuracy in multicentre evaluation of a flexible model with Bayesian regularisation, the (PLANN-ARD), using a reference data set for breast cancer, which comprises 4016 records from patients diagnosed during 1989-93 and reported by the BCCA, Canada, with follow-up of 10 years. The method is compared with the widely used Cox regression model. Both methods were fitted to routinely acquired data from 743 patients diagnosed during 1990-94 at the Christie Hospital, UK, with follow-up of 5 years following surgery. Methodological advances developed to support the external validation of this neural network with clinical data include: imputation of missing data in both the training and validation data sets; and a prognostic index for stratification of patients into risk groups that can be extended to non-linear models. Predictive accuracy was measured empirically with a standard discrimination index, Ctd, and with a calibration measure, using the Hosmer-Lemeshow test statistic. Both Cox regression and the PLANN-ARD model are found to have similar discrimination but the neural network showed marginally better predictive accuracy over the 5-year followup period. In addition, the regularised neural network has the substantial advantage of being suited for making predictions of hazard rates and survival for individual patients. Four different approaches to stratify patients into risk groups are also proposed, each with a different foundation. While it was found that the four methodologies broadly agree, there are important differences between them. Rules sets were extracted and compared for the two stratification methods, the log-rank bootstrap and by direct application of regression trees, and with two rule extraction methodologies, OSRE and CART, respectively. In addition, widely used clinical breast cancer prognostic indexes such as the NPI, TNM and St. Gallen consensus rules, were compared with the proposed prognostic models expressed as regression trees, concluding that the suggested approaches may enhance current practice. Finally, a Web clinical decision support system is proposed for clinical oncologists and for breast cancer patients making prognostic assessments, which is tailored to the particular characteristics of the individual patient. This system comprises three different prognostic modelling methodologies: the NPI, Cox regression modelling and PLANN-ARD. For a given patient, all three models yield a generally consistent but not identical set of prognostic indices that can be analysed together in order to obtain a consensus and so achieve a more robust prognostic assessment of the expected patient outcome

Comparison of methods for handling missing data on immunohistochemical markers in survival analysis of breast cancer

Background:Tissue micro-arrays (TMAs) are increasingly used to generate data of the molecular phenotype of tumours in clinical epidemiology studies, such as studies of disease prognosis. However, TMA data are particularly prone to missingness. A variety of methods to deal with missing data are available. However, the validity of the various approaches is dependent on the structure of the missing data and there are few empirical studies dealing with missing data from molecular pathology. The purpose of this study was to investigate the results of four commonly used approaches to handling missing data from a large, multi-centre study of the molecular pathological determinants of prognosis in breast cancer.Patients and Methods:We pooled data from over 11 000 cases of invasive breast cancer from five studies that collected information on seven prognostic indicators together with survival time data. We compared the results of a multi-variate Cox regression using four approaches to handling missing data-complete case analysis (CCA), mean substitution (MS) and multiple imputation without inclusion of the outcome (MI) and multiple imputation with inclusion of the outcome (MI). We also performed an analysis in which missing data were simulated under different assumptions and the results of the four methods were compared.Results:Over half the cases had missing data on at least one of the seven variables and 11 percent had missing data on 4 or more. The multi-variate hazard ratio estimates based on multiple imputation models were very similar to those derived after using MS, with similar standard errors. Hazard ratio estimates based on the CCA were only slightly different, but the estimates were less precise as the standard errors were large. However, in data simulated to be missing completely at random (MCAR) or missing at random (MAR), estimates for MI were least biased and most accurate, whereas estimates for CCA were most biased and least accurate.Conclusion:In this study, empirical results from analyses using CCA, MS, MI and MI were similar, although results from CCA were less precise. The results from simulations suggest that in general MI is likely to be the best. Given the ease of implementing MI in standard statistical software, the results of MI and CCA should be compared in any multi-variate analysis where missing data are a problem. © 2011 Cancer Research UK. All rights reserved

Investigating the inequalities in route to diagnosis amongst patients with diffuse large B-cell or follicular lymphoma in England

Introduction: Diagnostic delay is associated with lower chances of cancer survival. Underlying comorbidities are known to affect the timely diagnosis of cancer. Diffuse large B-cell (DLBCL) and follicular lymphomas (FL) are primarily diagnosed amongst older patients, who are more likely to have comorbidities. Characteristics of clinical commissioning groups (CCG) are also known to impact diagnostic delay. We assess the association between comorbidities and diagnostic delay amongst patients with DLBCL or FL in England during 2005–2013. Methods: Multivariable generalised linear mixed-effect models were used to assess the main association. Empirical Bayes estimates of the random effects were used to explore between-cluster variation. The latent normal joint modelling multiple imputation approach was used to account for partially observed variables. Results: We included 30,078 and 15,551 patients diagnosed with DLBCL or FL, respectively. Amongst patients from the same CCG, having multimorbidity was strongly associated with the emergency route to diagnosis (DLBCL: odds ratio 1.56, CI 1.40–1.73; FL: odds ratio 1.80, CI 1.45–2.23). Amongst DLBCL patients, the diagnostic delay was possibly correlated with CCGs that had higher population densities. Conclusions: Underlying comorbidity is associated with diagnostic delay amongst patients with DLBCL or FL. Results suggest a possible correlation between CCGs with higher population densities and diagnostic delay of aggressive lymphomas

Vulvar cancer : patterns of recurrence, quality of life and extended indications for the sentinel node technique

Background: Vulvar cancer is a rare malignancy and few studies have addressed the course of disease and the impact of physical and psychological symptoms on healthrelated quality of life (HRQOL) over time. In addition, extending the indication for sentinel node biopsy in vulvar cancer requires further evaluation. The overall aim of this thesis was to investigate patterns of recurrence and the trajectory of symptoms and HRQOL in a nationwide population of women with vulvar cancer, and to examine the feasibility of sentinel node biopsy in larger and multifocal tumours. Methods: Study I included all women diagnosed with primary vulvar squamous cell carcinoma (VSCC) from 2012-2015 whose health data were recorded in the Swedish Quality Registry for Gynaecologic Cancer (n=489). The cumulative incidences and survival rates for local, groin, and distant recurrences were calculated. In addition, the potential impact of not performing surgical groin staging on survival was assessed. In Studies II and III the relationship between physical and psychological symptoms and HRQOL in a nationwide longitudinal cohort of women with primary vulvar cancer diagnosed from 2019-2021 (n=153) were examined utilizing validated questionnaires (European Organisation for Research and Treatment of Cancer (EORTC)-QLQ C30, the EORTC-QLQ VU34, the Supportive Care Needs Survey, and the Hospital Anxiety and Depression Scale). Anxiety, depression, local vulvar and lymphoedema symptoms and their impact on HRQOL were investigated at the time of diagnosis, as well as 3 and 12 months after treatment. Study IV was a nationwide prospective, single-arm interventional pilot study. Women with VSCC and tumours ≥ 4 cm in diameter (Group 1), multifocal tumours (Group 2) or a first local recurrence (Groups 3 and 4) diagnosed between 2019-2022 (total n=64) underwent sentinel node biopsy in addition to standard inguinofemoral lymphadenectomy. Detection rates and negative predictive values were calculated. Results: In Study I after a median follow-up of 52 months, the recurrence rate was 22.3% (vulva 61%, groin 30%, and distant 9%). Groin and distant recurrences occurred primarily within the first two years after treatment, while the incidence of local recurrences increased continuously during follow-up. The median two-year post-recurrence overall survival was 57.8% for vulvar, 17.2% for groin, and 0% for distant recurrences. Omission of surgical groin staging in 23.7% of the patients with presumed stage IB-II disease was associated with poorer survival. In Studies II and III 140 (92%) of the women completed at least one questionnaire and 105 (69%) completed all three. At the time of diagnosis, 41.8% of the women reported elevated anxiety, a proportion that declined to 29.5% 12 months after treatment. Insomnia, a high need for information and persistent vulvar symptoms were associated with enhanced anxiety. Vulvar symptoms were associated with impaired HRQOL and improved after treatment, whereas symptoms of leg lymphoedema became more common after treatment. Emotional, role, cognitive, and social functioning, as well as global and mental health became better following treatment. In Study IV, the detection rates in Groups 1 and 2 were 94.1–100% per patient and 84.1–85.3% per groin, respectively. There were no false-negative sentinel nodes, i.e., the negative predictive value was 100% (95% CI 91.2%-100% for Group 1 and 83.9%-100% for Group 2). Conclusions: Local recurrences are common in patients with vulvar cancer, with a stable incidence throughout the period of surveillance. Lack of surgical groin staging is associated with poorer survival. Women with primary vulvar cancer report a high prevalence of vulvar symptoms, anxiety, and impaired HRQOL at the time of diagnosis. Alleviating vulvar symptoms, insomnia, and unmet needs for information might reduce anxiety. Extending the application of sentinel node biopsy to women with tumours ≥ 4 cm in diameter, as well as to those with multifocal tumours seems feasible