Ostotapahtuman ennustemallin kehittäminen ajoneuvomarkkinoinnin tueksi

Perinteisesti ajoneuvojen markkinointikampanjoissa kohderyhmät muodostetaan yksinkertaisella kriteeristöllä koskien henkilön tai hänen ajoneuvonsa ominaisuuksia. Ennustavan analytiikan avulla voidaan tuottaa kohderyhmänmuodostukseen teknisesti kompleksisia mutta kuitenkin helppokäyttöisiä menetelmiä. Tässä työssä on sovellettu luokittelu- ja regressiomenetelmiä uuden auton ostajien joukkoon. Tämän työn menetelmiksi on rajattu tukivektorikone sekä Coxin regressiomalli. Coxin regression avulla on tutkittu elinaika-analyysien soveltuvuutta ostotapahtuman tapahtumahetken mallintamiseen. Luokittelu tukivektorikonetta käyttäen onnistuu tehtävässään noin 72% tapauksissa. Tukivektoriregressiolla mallinnetun hankintahetken virheen keskiarvo on noin neljä kuukautta. Työn tulosten perusteella myös elinaika-analyysin käyttö ostotapahtuman tapahtumahetken mallintamiseen on menetelmänä käyttökelpoinen.

Fractures in older people - incidence, predictors and consequences

In older populations, fractures are common and the consequences of fractures may be serious both for an individual and for society. However, information is scarce about the incidence, predictors and consequences of fractures in population-based unselected cohorts including both men and women and a long follow-up. The objective of this study was to analyse the incidence and predictors of fractures as well as functional decline and excess mortality due to fractures, among 482 men and 695 women aged 65 or older in the municipality of Lieto, Finland from 1991 until 2002. In analyses, Poisson’s, Cox proportional Hazards and Cumulative Logistic regression models were used for the control of several confounding variables. During the 12-year follow-up with a total of 10 040 person-years (PY), 307 (26%) persons sustained altogether 425 fractures of which 77% were sustained by women. The total incidence of fractures was 53.4 per 1000 PY (95% confidence intervals [95% CI]: 47.9 - 59.5) in women and 24.9 per 1000 PY (95% CI: 20.4 - 30.4) in men. The incidence rates of fractures at any sites and hip fractures were associated with increasing age. No significant changes in the ageadjusted incidence rates of fractures were found in either gender during the 12-year follow-up. The predictors of fractures varied by gender. In multivariate analyses, reduced handgrip strength and body mass index (BMI) lower than 30 in women and a large number of depressive symptoms in men were independent predictors of fractures. A compression fracture in one or more thoracic or upper lumbar vertebras on chest radiography at baseline was associated with subsequent fractures in both genders. Lower body fractures independently predicted both short- (0-2 years) and long-term (up to 8 years) functional decline in mobility and activities of daily living (ADL) performance during the 8-year follow-up. Upper body fractures predicted decline in ADL performance during longterm follow-up. In the 12-year follow-up, hip fractures in men (Hazard Ratio [HR] 8.1, 95% CI: 4.4-14.9) and in women (HR 3.0, 95% CI: 1.9-4.9), and fractures at the proximal humerus in men (HR 5.4, 95% CI: 1.6-17.7) were independently associated with excess mortality. In addition, leisure time inactivity in physical exercise predicted independently both functional decline and excess mortality. Fractures are common among older people posing serious individual consequences. Further studies about the effectiveness of preventing falls and fractures as well as improving care and rehabilitation after fractures are needed.

Statistical analysis of survey-based event history data with application to modeling of unemployment duration

Longitudinal surveys are increasingly used to collect event history data on person-specific processes such as transitions between labour market states. Surveybased event history data pose a number of challenges for statistical analysis. These challenges include survey errors due to sampling, non-response, attrition and measurement. This study deals with non-response, attrition and measurement errors in event history data and the bias caused by them in event history analysis. The study also discusses some choices faced by a researcher using longitudinal survey data for event history analysis and demonstrates their effects. These choices include, whether a design-based or a model-based approach is taken, which subset of data to use and, if a design-based approach is taken, which weights to use. The study takes advantage of the possibility to use combined longitudinal survey register data. The Finnish subset of European Community Household Panel (FI ECHP) survey for waves 1–5 were linked at person-level with longitudinal register data. Unemployment spells were used as study variables of interest. Lastly, a simulation study was conducted in order to assess the statistical properties of the Inverse Probability of Censoring Weighting (IPCW) method in a survey data context. The study shows how combined longitudinal survey register data can be used to analyse and compare the non-response and attrition processes, test the missingness mechanism type and estimate the size of bias due to non-response and attrition. In our empirical analysis, initial non-response turned out to be a more important source of bias than attrition. Reported unemployment spells were subject to seam effects, omissions, and, to a lesser extent, overreporting. The use of proxy interviews tended to cause spell omissions. An often-ignored phenomenon classification error in reported spell outcomes, was also found in the data. Neither the Missing At Random (MAR) assumption about non-response and attrition mechanisms, nor the classical assumptions about measurement errors, turned out to be valid. Both measurement errors in spell durations and spell outcomes were found to cause bias in estimates from event history models. Low measurement accuracy affected the estimates of baseline hazard most. The design-based estimates based on data from respondents to all waves of interest and weighted by the last wave weights displayed the largest bias. Using all the available data, including the spells by attriters until the time of attrition, helped to reduce attrition bias. Lastly, the simulation study showed that the IPCW correction to design weights reduces bias due to dependent censoring in design-based Kaplan-Meier and Cox proportional hazard model estimators. The study discusses implications of the results for survey organisations collecting event history data, researchers using surveys for event history analysis, and researchers who develop methods to correct for non-sampling biases in event history data.

Survival-time analysis of white spruce during spruce budworm defoliation

Abstract

Analyzing change in medication use - statistical approaches

The objective of this study was to gain an understanding of the effects of population heterogeneity, missing data, and causal relationships on parameter estimates from statistical models when analyzing change in medication use. From a public health perspective, two timely topics were addressed: the use and effects of statins in populations in primary prevention of cardiovascular disease and polypharmacy in older population. Growth mixture models were applied to characterize the accumulation of cardiovascular and diabetes medications among apparently healthy population of statin initiators. The causal effect of statin adherence on the incidence of acute cardiovascular events was estimated using marginal structural models in comparison with discrete-time hazards models. The impact of missing data on the growth estimates of evolution of polypharmacy was examined comparing statistical models under different assumptions for missing data mechanism. The data came from Finnish administrative registers and from the population-based Geriatric Multidisciplinary Strategy for the Good Care of the Elderly study conducted in Kuopio, Finland, during 2004–07. Five distinct patterns of accumulating medications emerged among the population of apparently healthy statin initiators during two years after statin initiation. Proper accounting for time-varying dependencies between adherence to statins and confounders using marginal structural models produced comparable estimation results with those from a discrete-time hazards model. Missing data mechanism was shown to be a key component when estimating the evolution of polypharmacy among older persons. In conclusion, population heterogeneity, missing data and causal relationships are important aspects in longitudinal studies that associate with the study question and should be critically assessed when performing statistical analyses. Analyses should be supplemented with sensitivity analyses towards model assumptions.