Clinical trials in kidney transplantation suffer from several major issues including: 1) Unfeasibility due to low short-term event rates of hard outcomes and 2) Reliance on a composite outcome that consists of unequal endpoints that may generate misleading results. This thesis attempts to explore and apply methods to solve these issues and ultimately, improve kidney transplantation trials. We present a secondary analysis of the ACE trial in kidney transplant using composites with alternative graft function surrogate endpoints. Typically, kidney transplant trials—including the ACE trial— use a time-to-event composite of death, end-stage renal disease (ESRD), and doubling of serum creatinine. Instead of doubling of serum creatinine, we investigated the use of percentage declines of estimate glomerular filtration rate (eGFR) within a time-to-event composite of death and ESRD. Additionally, we present an application of an innovative analysis method, the win ratio approach, to the ACE trial as a way of lessening concerns associated with unequal composite endpoints. Composites of death, ESRD, and either a 40%, 30% or 20% decline in eGFR did not alter original ACE trial results, interpretations, or conclusions. The win ratio approach, and the presentation of a win ratio, generated very comparable results to a standard time-to-event analysis while lessening the impact of unequal composite endpoints and making fewer statistical assumptions. This research provides a novel, trial-level application of alternative endpoints and analysis techniques within a kidney transplant trial setting.

Composite endpoints are widely used in clinical trials. The outcome of a clinical trial can affect many individuals and it is therefore of importance that the methods used are as effective and correct as possible. Improvements of the standard method of testing composite endpoints have been proposed and in this thesis, the alternative method using nonparametric combination methodology is compared to the standard method. Performing a simulation study, the power of three combining functions (Fisher, Tippett and the Logistic) are compared to the power of the standard method. The performances of the four methods are evaluated for different compositions of treatment effects, as well as for independent and dependent components. The results show that using the nonparametric combination methodology leads to higher power in both dependent and independent cases. The combining functions are suitable for different compositions of treatment effects, the Fisher combining function being the most versatile. The thesis is written with support from Statisticon AB.

Many breast cancer diagnoses and second cancers are associated with BRCA gene mutations. Early detection of cancer is necessary to improve health outcomes, particularly with second cancers. Little is known about the influence of risk factors on time to diagnosis of second primary cancers after diagnosis with BRCA-related breast cancer. The purpose of this cohort study was to examine the risk of diagnosis of second primary cancers among women diagnosed with breast cancer after adjusting for BRCA status, age, and ethnicity. The study was guided by the empirical evidence supporting the mechanism of action in the mutation of BRCA leading to the development of cancer. Composite endpoint was used to define second primary cancer occurrences, and Kaplan-Meier survival curves were used to compare the median time-to-event among comparison groups and BRCA gene mutation status. Cox proportional hazards was used to examine the relationships between age at diagnosis, ethnicity, BRCA gene mutation status, and diagnosis of a second primary cancer. The overall median time to event for diagnosis of second primary cancers was 14 years. The hazard ratios for BRCA2 = 1.47, 95% CI [1.03 - 2.11], White = 1.511, 95% CI [1.18 - 1.94], and American Indian/Hawaiian = 1.424, 95% CI [1.12 -1.81] showing positive significant associations between BRCA2 mutation status and risk of diagnosis of second primary colorectal, endometrial, cervical, kidney, thyroid, and bladder cancers. Data on risk factors for development of second cancers would allow for identification of appropriate and timely screening procedures, determining the best course of action for prevention and treatment, and improving quality of life among breast cancer survivors.