Prognostic and surrogate markers for outcome in the treatment of pulmonary tuberculosis

Phase III trials for new tuberculosis treatment regimens require large numbers of participants and can take over five years to complete. A surrogate marker for poor outcome (failure at end of treatment or recurrence following successful treatment), the established endpoint in such trials, could shorten trial duration and reduce trial size. Culture results after two months of treatment have shown the most promise but, prior to this research, no formal evaluation had been performed. In this thesis, culture results during treatment are evaluated as prognostic and surrogate markers for poor outcome using data on 6974 patients from twelve tuberculosis treatment randomised controlled multi-arm trials conducted in East Africa and East Asia. A strong association was found between culture results during treatment and poor outcome. Nevertheless, culture results were not good patient-specific predictors of poor outcome with low sensitivities and specificities. Existing meta-analytic methods for evaluating surrogate markers are not wholly suited to this setting of multi-arm trials with binary true and surrogate endpoints. Extending these methods, the two month culture was found to be a good surrogate marker using data from Hong Kong trials and the three month culture was found to be a good surrogate marker using data from East African trials. These results are an indication that cultures during treatment do capture some of the treatment effect. Further work is needed in understanding the differences between the Hong Kong and East African trials. The meta-analytic methods for evaluating surrogate markers in this thesis included a graphical representation that permitted a clear visual evaluation of the surrogate. Methods developed in this thesis for modelling the relationship between the treatment effects on the true and surrogate endpoints were not satisfactory. The deficiencies were not overcome with the two extensions proposed. Further work is needed in developing a more appropriate model

Stylistic atructures: a computational approach to text classification

The problem of authorship attribution has received attention both in the academic world (e.g. did Shakespeare or Marlowe write Edward III?) and outside (e.g. is this confession really the words of the accused or was it made up by someone else?). Previous studies by statisticians and literary scholars have sought "verbal habits" that characterize particular authors consistently. By and large, this has meant looking for distinctive rates of usage of specific marker words -- as in the classic study by Mosteller and Wallace of the Federalist Papers. The present study is based on the premiss that authorship attribution is just one type of text classification and that advances in this area can be made by applying and adapting techniques from the field of machine learning. Five different trainable text-classification systems are described, which differ from current stylometric practice in a number of ways, in particular by using a wider variety of marker patterns than customary and by seeking such markers automatically, without being told what to look for. A comparison of the strengths and weaknesses of these systems, when tested on a representative range of text-classification problems, confirms the importance of paying more attention than usual to alternative methods of representing distinctive differences between types of text. The thesis concludes with suggestions on how to make further progress towards the goal of a fully automatic, trainable text-classification system

Stylistic atructures: a computational approach to text classification

The problem of authorship attribution has received attention both in the academic world (e.g. did Shakespeare or Marlowe write Edward III?) and outside (e.g. is this confession really the words of the accused or was it made up by someone else?). Previous studies by statisticians and literary scholars have sought "verbal habits" that characterize particular authors consistently. By and large, this has meant looking for distinctive rates of usage of specific marker words -- as in the classic study by Mosteller and Wallace of the Federalist Papers. The present study is based on the premiss that authorship attribution is just one type of text classification and that advances in this area can be made by applying and adapting techniques from the field of machine learning. Five different trainable text-classification systems are described, which differ from current stylometric practice in a number of ways, in particular by using a wider variety of marker patterns than customary and by seeking such markers automatically, without being told what to look for. A comparison of the strengths and weaknesses of these systems, when tested on a representative range of text-classification problems, confirms the importance of paying more attention than usual to alternative methods of representing distinctive differences between types of text. The thesis concludes with suggestions on how to make further progress towards the goal of a fully automatic, trainable text-classification system