Efficient use of phase 1 information in two-phase case-control studies based on administrative databases

Population-based administrative databases are valuable data sources for scientific research. A common problem of studies based on secondary data is, however, missing confounder information. For instance in pharmacoepidemiological studies based on claims data of statutory health insurances, information on the potentially important confounders body mass index (BMI) and smoking behaviour is lacking. Obtaining additional information from another data source, e.g. from survey data, for at least a subsample of the study population resolves this problem. Two-phase designs can be employed for the combined analysis of both data sources. In logistic two-phase studies a dichotomous outcome and some covariate information are available for the whole population in phase 1 whereas the full vector of covariates is only observed for a subsample in phase 2. Information of phase 1 is utilised in the analysis of the phase 2 sample via stratification by phase 1 covariates. This approach allows for an unbiased parameter estimation and can improve efficiency of the estimation of covariates included in the stratification. An additional gain in efficiency can be achieved if the stratification is also used for sampling of the phase 2 data set and the most informative subjects are sampled with a high probability. Two-phase designs have been developed for field studies. Field studies usually comprise very few covariates in phase 1 because ascertainment of each additional covariate for the whole population is associated with additional costs. In these traditional two-phase studies, the stratification is simply defined by cross-classification of all available phase 1 covariates. Two-phase database studies include a multitude of phase 1 covariates. Cross-classification of all available covariates would therefore lead to a tremendously high number of strata and, due to the restricted size of the phase 2 sample, to empty cells. Since two-phase methods cannot be applied in presence of empty cells, new stratification strategies are needed which account for all relevant phase 1 covariates but do not result in empty cells. The aim of this thesis is the development of strategies for the efficient use of phase 1 information in logistic two-phase studies comprising a multitude of phase 1 covariates. An alternative stratification strategy is proposed, which is based on percentiles of a disease score. In this context, the disease score includes as a summary measure information on several phase 1 covariates in the stratification. The core of the thesis is the development of a design criterion which allows the identification of efficient stratifications at the planning stage of the study when only phase 1 information is available. Both the new stratification strategy and the design criterion are applied to an empirical two-phase database study investigating the risk of serious bleedings associated with phenprocoumon exposure. Additionally, the novel approaches are evaluated in a simulation study mimicking the empirical study. It is shown that those stratifications assessed as efficient by the design criterion result in the smallest standard errors for the estimates of most phase 1 covariates in the simulation study. The best stratifications regarding unbiased and efficient parameter estimation are defined by cross-classification of variables used for sampling of the phase 2 data and percentiles of the disease score. Moreover, empirically based recommendations for the planning of future two-phase studies are deduced from the results of the simulation study. An outlook on survey methodological approaches for the analysis of partially missing data completes the thesis. The multiple imputation approach is applied to the empirical study and compared to results of the two-phase analyses. It becomes evident that multiple imputation is much more efficient with respect to the estimation of coefficients for phase 1 covariates than two-phase methods. However, biased estimates are observed in a simulation study, when the imputation model does not sufficiently account for the selective sampling of the phase 2 data. A final assessment of the comparison between two-phase methods and survey methodological approaches is still missing.