Statistical Methods for Clinical Trials with Multiple Outcomes, HIV Surveillance, and Nonparametric Meta-Analysis

Central to the goals of public health are obtaining and interpreting timely and relevant information for the benefit of humanity. In this dissertation, we propose methods to monitor and assess the spread HIV in a more rapid manner, as well as to improve decisions regarding patient treatment options. In Chapter 1, we propose a method, extending the previously proposed dual-testing algorithm and augmented cross-sectional design, for estimating the HIV incidence rate in a particular community. Compared to existing methods, our proposed estimator allows for shorter follow-up time and does not require estimation of the mean window period, a crucial, but often unknown, parameter. The estimator performs well in a wide range of simulation settings. We discuss when this estimator would be expected to perform well and offer design considerations for the implementation of such a study. Chapters 2 and 3 are concerned with obtaining a more complete understanding of the impact of treatment in randomized clinical trials in which multiple patient outcomes are recorded. Chapter 2 provides an illustration of methods that may be used to address concerns of both risk-benefit analysis and personalized medicine simultaneously, with a goal of successfully identifying patients who will be ideal candidates for future treatment. Riskbenefit analysis is intended to address the multivariate nature of patient outcomes, while “personalized medicine” is concerned with patient heterogeneity, both of which complicate the determination of a treatment’s usefulness. A third complicating factor is the duration of treatment use. Chapter 3 features proposed methods for assessing the impact of treatment as a function of time, as well as methods for summarizing the impact of treatment across a range of follow-up times. Chapter 4 addresses the issue of meta-analysis, a commonly used tool for combining information for multiple independent studies, primarily for the purpose of answering a clinical question not suitably addressed by any one single study. This approach has proven highly useful and attractive in recent years, but often relies on parametric assumptions that cannot be verified. We propose a non-parametric approach to meta-analysis, valid in a wider range of scenarios, minimizing concerns over compromised validity

A versatile test for equality of two survival functions based on weighted differences of Kaplan-Meier curves

With censored event time observations, the logrank test is the most popular tool for testing the equality of two underlying survival distributions. Although this test is asymptotically distribution-free, it may not be powerful when the proportional hazards assumption is violated. Various other novel testing procedures have been proposed, which generally are derived by assuming a class of specific alternative hypotheses with respect to the hazard functions. The test considered by Pepe and Fleming (1989) is based on a linear combination of weighted differences of two Kaplan-Meier curves over time and is a natural tool to assess the difference of two survival functions directly. In this article, we take a similar approach, but choose weights which are proportional to the observed standardized difference of the estimated survival curves at each time point. The new proposal automatically makes weighting adjustments empirically. The new test statistic is aimed at a one-sided general alternative hypothesis, and is distributed with a short right tail under the null hypothesis, but with a heavy tail under the alternative. The results from extensive numerical studies demonstrate that the new procedure performs well under various general alternatives. The survival data from a recent cancer comparative study are utilized for illustrating the implementation of the process

A re-examination of the BEST Trial using composite outcomes, including emergency department visits

Objectives: The influence of choice of endpoint on trial size, duration, and interpretation of results was examined in patients with heart failure who were enrolled in BEST (Beta-blocker Evaluation of Survival Trial). Background: The choice of endpoints in heart failure trials has evolved over the past 3 decades. Methods: In the BEST trial, we used Cox regression analysis to examine the effect of bucindolol on the current standard composite of cardiovascular death or heart failure hospitalization (CVD/HFH) compared with the original primary mortality endpoint and the expanded composite that included emergency department (ED) visits. We also undertook an analysis of recurrent events primarily using the Lin, Wei, Ying, and Yang model. Results: Overall, 448 (33%) patients on placebo and 411 (30%) patients on bucindolol died (hazard ratio [HR]: 0.90; 95% confidence interval [CI]: 0.78 to 1.02; p = 0.11). A total of 730 (54%) patients experienced CVD/HFH on placebo and 624 (46%) on bucindolol (HR: 0.80; 95% CI: 0.72 to 0.89; p < 0.001). Adding ED visits increased these numbers to 768 (57%) and 668 (49%), respectively (HR: 0.81; 95% CI: 0.73 to 0.90; p < 0.001). A total of 568 (42%) patients on placebo experienced HFH compared with 476 (35%) patients on bucindolol (HR: 0.78; 95% CI: 0.69 to 0.89; p < 0.001), with a total of 1,333 and 1,124 admissions, respectively. With the same statistical assumptions, using the composite endpoint instead of all-cause mortality would have reduced the trial size by 40% and follow-up duration by 69%. The rate ratio for recurrent events (CVD/HFH) was 0.83 (95% CI: 0.73 to 0.94; p = 0.003). Conclusions: Choice of endpoint has major implications for trial size and duration, as well as interpretation of results. The value of broader composite endpoints and inclusion of recurrent events needs further investigation. (Beta Blocker Evaluation in Survival Trial [BEST]; NCT00000560

Effectively Selecting a Target Population for a Future Comparative Study

When comparing a new treatment with a control in a randomized clinical study, the treatment effect is generally assessed by evaluating a summary measure over a specific study population. The success of the trial heavily depends on the choice of such a population. In this paper, we show a systematic, effective way to identify a promising population, for which the new treatment is expected to have a desired benefit, using the data from a current study involving similar comparator treatments. Specifically, with the existing data we first create a parametric scoring system using multiple covariates to estimate subject-specific treatment differences. Using this system, we specify a desired level of treatment difference and create a subgroup of patients, defined as those whose estimated scores exceed this threshold. An empirically calibrated group-specific treatment difference curve across a range of threshold values is constructed. The population of patients with any desired level of treatment benefit can then be identified accordingly. To avoid any ``self-serving\u27\u27 bias, we utilize a cross-training-evaluation method for implementing the above two-step procedure. Lastly, we show how to select the best scoring system among all competing models. The proposals are illustrated with the data from two clinical trials in treating AIDS and cardiovascular diseases. Note that if we are not interested in designing a new study for comparing similar treatments, the new procedure can also be quite useful for the management of future patients who would receive nontrivial benefits to compensate for the risk or cost of the new treatment

Efficacy of sacubitril/valsartan relative to a prior decompensation: the PARADIGM-HF trial

Objectives: This study assessed whether the benefit of sacubtril/valsartan therapy varied with clinical stability. Background: Despite the benefit of sacubitril/valsartan therapy shown in the PARADIGM-HF (Prospective Comparison of ARNI with ACEI to Determine Impact on Global Mortality and Morbidity in Heart Failure) trial, it has been suggested that switching from an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker should be delayed until occurrence of clinical decompensation. Methods: Outcomes were compared among patients who had prior hospitalization within 3 months of screening (n = 1,611 [19%]), between 3 and 6 months (n = 1,009 [12%]), between 6 and 12 months (n = 886 [11%]), >12 months (n = 1,746 [21%]), or who had never been hospitalized (n = 3,125 [37%]). Results: Twenty percent of patients without prior HF hospitalization experienced a primary endpoint of cardiovascular death or heart failure (HF) hospitalization during the course of the trial. Despite the increased risk associated with more recent hospitalization, the efficacy of sacubitril/valsartan therapy did not differ from that of enalapril according to the occurrence of or time from hospitalization for HF before screening, with respect to the primary endpoint or with respect to cardiovascular or all-cause mortality. Conclusions: Patients with recent HF decompensation requiring hospitalization were more likely to experience cardiovascular death or HF hospitalization than those who had never been hospitalized. Patients who were clinically stable, as shown by a remote HF hospitalization (>3 months prior to screening) or by lack of any prior HF hospitalization, were as likely to benefit from sacubitril/valsartan therapy as more recently hospitalized patients. (Prospective Comparison of ARNI with ACEI to Determine Impact on Global Mortality and Morbidity in Heart Failure [PARADIGM-HF]; NCT01035255)

The Myth Of Making Inferences For An Overall Treatment Efficacy With Data From Multiple Comparative Studies Via Meta-analysis

Meta analysis techniques, if applied appropriately, can provide a summary of the totality of evidence regarding an overall difference between a new treatment and a control group using data from multiple comparative clinical studies. The standard meta analysis procedures, however, may not give a meaningful between-group difference summary measure or identify a meaningful patient population of interest, especially when the fixed effect model assumption is not met. Moreover, a single between-group comparison measure without a reference value obtained from patients in the control arm would likely not be informative enough for clinical decision making. In this paper, we propose a simple, robust procedure based on a mixture population concept and provide a clinically meaningful group contrast summary for a well-defined target population. We use the data from a recent meta analysis for evaluating statin therapies with respect to the incidence of fatal stroke events to illustrate the issues associated with the standard meta analysis procedures as well as the advantages of our simple proposal