Estimation in discrete time coarsened multivariate longitudinal models

We consider the analysis of longitudinal data of multiple types of events where some of the events are observed on a coarser level (e.g. grouped) at some time points during the follow-up, for example, when certain events, such as disease progression, are only observable during parts of follow-up for some subjects, causing gaps in the data, or when the time of death is observed but the cause of death is unknown. In this case, there is missing data in key characteristics of the event history such as onset, time in state, and number of events. We derive the likelihood function, score and observed information under independent and non-informative coarsening, and conduct a simulation study where we compare bias, empirical standard errors, and confidence interval coverage of estimators based on direct maximum likelihood, Monte Carlo Expectation Maximisation, ignoring the coarsening thus acting as if no event occurred, and artificial right censoring at the first time of coarsening. Longitudinal data on drug prescriptions and survival in men receiving palliative treatment for prostate cancer is used to estimate the parameters of one of the data-generating models. We demonstrate that the performance depends on several factors, including sample size and type of coarsening

Experiences of Banner advertisement on a specialized homepage

Title: Experiences of Banner advertisement on a specialized homepage Author: Marcus Westerberg Supervisor: Marie Hemming Department: Department of Business Administration, IEM Course: Bachelor thesis in Business Administration, FEC 007 Purpose: Create a understanding for how the users on Internet pages experience the occurrence and the shape of the banners. Method: Methods used are quantitative and qualitative, and the facts and figures are based on a pop up inquiry and some telephoneinterviews. I elected pop up inquiry and telephoneinterview as my data collection. My inquiry had a high grade of standardization and the interviews had a low grade of standardization. Result: On the basis of the question of issue I have drawn the conclusion that the users feels that it is important with information in all advertising. It is also important for the users to create a value around the product. The users do not have the time to figure out the message behind advertising, the want simple and massive information about the product/service. It´s important for the users that the banneradvertising are about things that they are interested in. Only then the users will be drawn to the banneradvertising on different homepages.Titel: Upplevelser av bannerreklam på en nischad hemsida Författare: Marcus Westerberg Handledare: Marie Hemming Institution: Institutionen för ekonomi och management, IEM Kurs: Kandidatarbete i företagsekonomi, FEC 007 Syfte: Skapa en förståelse för hur användare på Internet upplever förekomsten och utformningen av bannerreklamen. Metod: Uppsatsen är baserad på en kvantitativ såväl som kvalitativ ansats där fakta har inhämtats med en pop up enkät och telefonintervjuer. För insamlingen av pop up enkäten använde jag mig av strukturerade frågor, där frågor och svarsalternativ redan hade bestämts på förhand. För insamlingen av telefonintervjuer använde jag mig av frågor med låg grad av standardisering. Slutsatser: Utifrån frågeställningen har jag kommit fram till att användarna upplever att det är viktigt med fakta och information i bannerreklamen. Det är viktigt för användarna att skapa ett värde kring produkten. Användarna har inte tid att tänka ut budskapet bakom reklamen, utan de vill ha enkel och mycket information om varan/tjänsten i reklamen. Det är viktigt för användarna att reklamen ska handla om det de är intresserade av. Det är då de dras till bannerreklamen på olika hemsidor

Modelling short and long term consequences of changes in diagnostic activity and treatment

Since the late 90’s the diagnostic activity for prostate cancer has increased in Sweden, primarily due to increased use of PSA testing, and this has led to a large increase in diagnoses. Simultaneously, there have been changes in treatment strategies, and more effective treatments have been introduced. This thesis aims to increase the understanding of short and long term consequences of these changes by use of high quality data on virtually all men diagnosed with prostate cancer in Sweden. In paper I, the survival of men with metastatic prostate cancer at diagnosis was investigatedby use of survival models, including Kaplan-Meier analyses and Cox proportional hazards regression.The median survival from diagnosis increased with 6 months when comparing mendiagnosed 1998-2001 with men diagnosed 2010-2015, and the risk of death decreased with 13%, while median levels of prostate specific antigen at diagnosis dropped with up to 50%. In paper II, the interplay between diagnostic activity, incidence and risk of death by prostate cancer was modelled using a discrete time model. Data on diagnostic activity, e.g. in termsof testing frequencies, was not available and therefore a proxy for the diagnostic activity wasused. The hazards were estimated within the framework of generalized additive models. Two simulations were performed, assuming low and high diagnostic activity respectively, to compare incidence and mortality from 2017-2060. Higher diagnostic activity, compared to lower, led to more men being diagnosed, primarily with lower risk prostate cancer, but in the long run it led to fewer men diagnosed with metastatic disease and fewer prostate cancer deaths

Modelling short and long term consequences of changes in diagnostic activity and treatment

Since the late 90’s the diagnostic activity for prostate cancer has increased in Sweden, primarily due to increased use of PSA testing, and this has led to a large increase in diagnoses. Simultaneously, there have been changes in treatment strategies, and more effective treatments have been introduced. This thesis aims to increase the understanding of short and long term consequences of these changes by use of high quality data on virtually all men diagnosed with prostate cancer in Sweden. In paper I, the survival of men with metastatic prostate cancer at diagnosis was investigatedby use of survival models, including Kaplan-Meier analyses and Cox proportional hazards regression.The median survival from diagnosis increased with 6 months when comparing mendiagnosed 1998-2001 with men diagnosed 2010-2015, and the risk of death decreased with 13%, while median levels of prostate specific antigen at diagnosis dropped with up to 50%. In paper II, the interplay between diagnostic activity, incidence and risk of death by prostate cancer was modelled using a discrete time model. Data on diagnostic activity, e.g. in termsof testing frequencies, was not available and therefore a proxy for the diagnostic activity wasused. The hazards were estimated within the framework of generalized additive models. Two simulations were performed, assuming low and high diagnostic activity respectively, to compare incidence and mortality from 2017-2060. Higher diagnostic activity, compared to lower, led to more men being diagnosed, primarily with lower risk prostate cancer, but in the long run it led to fewer men diagnosed with metastatic disease and fewer prostate cancer deaths

Reasons for Missing Data of Risk Categorisation in NPCR

The risk of prostate cancer (PCa) can be described using ve risk categories based on clinicalassessment involving the risk of cancer and metastasis level. In some cases the information neededto calculate the risk is not registered. The aim is to assess potential dierences in properties likeage, treatment, comorbidity and survival, between men with a dened risk stage categorisation ofprostate cancer compared to men lacking information to calculate the risk stage category. Themain measures involved in the risk stage assessment are Gleason score, prostate specic antigen(PSA) value and T-stage. Men missing data for risk stage categorisation may be lacking one of thethree or a combination of at least two out of the three. Subgroups of these men will be analysedin a similar way, in order to understand the reasons to why they are missing data for risk stagecategorization.Statistical analysis involves univariate and multivariate logistic regression, along with survivaland competing risk analysis. Data will be presented in tables, gures and forest plots, includingodds ratios, 95% condence intervals and p-values.According to the study, men missing data of risk stage categorization were about 2.6% of allmen in the data base, and had most likely a low risk PCa. They had higher comorbidity levelsbut the overall probability of death was the same compared to other men. In addition, they hadsignicantly lower proportion of death by PCa and experienced a large proportion of death byother cancer, which concurs with the previous conclusions about comorbidity and low risk PCa,indicating that they had another disease, possibly cancer, that required more attention.Considering only men missing data for risk categorisation, a large proportion were missingPSA level (58.3%), and these men had higher comorbidity, were older, and had a large proportionof death by other cancer. Surprisingly, men missing Gleason level (20.3%) had increased oddsratios for lower comorbidity levels, were younger at time of diagnosis, and had a higher survivalprobability in general. Unexpectedly, men missing T-stage (32%) were more likely to being treatedby Radio Theraphy (RT), were less likely to attend university hospitals and more likely to attendprivate physicians. Men missing a combination of at least two out of three of Gleason, PSA, T-stage(19.6%) had higher comorbidity levels and were more likely to be treated by RT, less likely to attenduniversity hospitals, had a large proportion of death by other cancer, and a larger proportion ofdeath closer to the time of diagnosis.Lastly, there were some indications of variations of the proportions of missing data of riskstage categorisation when dividing it into the subgroups mentioned above, and viewed over year ofdiagnosis. There was an increase in missing data of risk stage categorization around 2006 and anexplanation of this could be the change of IT-system for registration, leaving a general increase ofmissing data behind it, perhaps due to a looser control during the transition and unfamiliarity ofthe new system.The main conclusion was that the reasons for missing data of risk stage categorisation are mostlikely high comorbidity levels, probably including another cancer in combination with a low riskPCa. It was most common to be missing data of risk stage categorisation due to missing PSAlevel, and those men had high comorbidity and were older. Surprisingly, private physicians and/ortreatment by RT were more likely to be missing T-stage, and younger men with low comorbiditywere more likely to be missing Gleason score

Prostate cancer incidence, treatment and mortality : Empirical longitudinal register-based studies and methods for handling missing data

The diagnostic activity for prostate cancer has increased substantially in Sweden, primarily due to increased use of prostate-specific antigen (PSA) testing in asymptomatic men, and this has led to a large increase in diagnoses. There have also been changes in the diagnostic workup, guidelines, treatment strategies, and more effective treatments have been introduced in different phases of the disease. This thesis aims to increase the understanding of consequences of changes in diagnostic activity and treatment, with a focus on empirical studies, methodological development, and handling of missing data. In paper I, the survival of men with metastatic prostate cancer was investigated across calendar time periods by use of Kaplan-Meier analyses and Cox regression. The median survival from diagnosis increased with six months comparing men diagnosed 1998-2001 with men diagnosed 2010-2015, while median PSA decreased. In paper II, a discrete time multivariate longitudinal model was combined with a proxy for the unobserved level of diagnostic activity to produce prognoses of incidence and mortality. Simulations indicated that a higher diagnostic activity was associated with fewer men diagnosed with metastatic disease and fewer prostate cancer deaths. In paper III, we looked for clinical variables predictive of the survival of men with castration-resistant prostate cancer (CRPC). A new data base was created including longitudinal data on prescriptions of hormonal treatment, PSA, and cause of death. We found that PSA doubling time and PSA at time of CRCP were highly predictive and could be used for treatment decision. In paper IV, we estimated annual incidence of metastatic prostate cancer using different methods for handling missing data in metastatic status (M stage). Missing data in M stage was high and varied over calendar time and risk groups, yet each method indicated a downward trend in incidence. Although men with unknown metastatic status cannot be assumed to have nonmetastatic disease in general, this may be reasonable among those with tumour characteristics that indicate a low risk of metastases. In paper V, the estimation of multivariate longitudinal models was considered in a context where some events are observed on a coarser level (e.g. grouped) at some time points, causing gaps in the data. The likelihood function, score and observed information were derived under an independent coarsening mechanism. A simulation study was conducted comparing properties of several estimators including direct maximum likelihood and Monte Carlo Expectation Maximisation

Prostate cancer incidence, treatment and mortality : Empirical longitudinal register-based studies and methods for handling missing data

The diagnostic activity for prostate cancer has increased substantially in Sweden, primarily due to increased use of prostate-specific antigen (PSA) testing in asymptomatic men, and this has led to a large increase in diagnoses. There have also been changes in the diagnostic workup, guidelines, treatment strategies, and more effective treatments have been introduced in different phases of the disease. This thesis aims to increase the understanding of consequences of changes in diagnostic activity and treatment, with a focus on empirical studies, methodological development, and handling of missing data. In paper I, the survival of men with metastatic prostate cancer was investigated across calendar time periods by use of Kaplan-Meier analyses and Cox regression. The median survival from diagnosis increased with six months comparing men diagnosed 1998-2001 with men diagnosed 2010-2015, while median PSA decreased. In paper II, a discrete time multivariate longitudinal model was combined with a proxy for the unobserved level of diagnostic activity to produce prognoses of incidence and mortality. Simulations indicated that a higher diagnostic activity was associated with fewer men diagnosed with metastatic disease and fewer prostate cancer deaths. In paper III, we looked for clinical variables predictive of the survival of men with castration-resistant prostate cancer (CRPC). A new data base was created including longitudinal data on prescriptions of hormonal treatment, PSA, and cause of death. We found that PSA doubling time and PSA at time of CRCP were highly predictive and could be used for treatment decision. In paper IV, we estimated annual incidence of metastatic prostate cancer using different methods for handling missing data in metastatic status (M stage). Missing data in M stage was high and varied over calendar time and risk groups, yet each method indicated a downward trend in incidence. Although men with unknown metastatic status cannot be assumed to have nonmetastatic disease in general, this may be reasonable among those with tumour characteristics that indicate a low risk of metastases. In paper V, the estimation of multivariate longitudinal models was considered in a context where some events are observed on a coarser level (e.g. grouped) at some time points, causing gaps in the data. The likelihood function, score and observed information were derived under an independent coarsening mechanism. A simulation study was conducted comparing properties of several estimators including direct maximum likelihood and Monte Carlo Expectation Maximisation

Pooling control in variable preparative chromatography processes.

Preparative chromatographic columns that run at high loads are highly sensitive to batch-to-batch disturbances of the process parameters, placing high demands on the strategy used for pooling of the product fractions. A new approach to pooling control is presented in a proof-of-concept study. A model-based sensitivity analysis was performed identifying the critical process parameters to product purity and optimal cut points. From this, the robust fixed cut points were found and pooling control strategies for variations in the critical parameters were designed. Direct measurements and indirect measurements based on the UV detector signal were used as control signals. The method is demonstrated for two case studies of preparative protein chromatography: hydrophobic interaction and reversed phase chromatography. The yield improved from 88.18 to 92.88% when changing from fixed to variable pooling in hydrophobic interaction chromatography, and from 35.15 to 76.27% in the highly sensitive reversed phase chromatography