Donna K. Pauler’s research while affiliated with Fred Hutch Cancer Center and other places

In clinical trials of advanced-stage disease it is often of interest to perform treatment comparisons for endpoints which are defined only for survivors. Examples include time on ventilation in ventilation studies, change in quality of life in health-related quality-of-life studies, and duration of response to therapy in therapeutic trials. Randomized treatment comparisons for these endpoints cannot be performed because the outcomes are only defined in the nonrandomly selected subgroup of survivors. We propose a new evaluation of the survivor average causal effect (SACE) for treatment comparisons of this nature. We provide an estimator of SACE in the presence of no unmeasured confounders, a nontestable assumption, which identifies SACE. We also outline a sensitivity analysis for exploring robustness of conclusions to deviations from this assumption. We apply this method to duration of ventilation in a clinical trial of acute respiratory distress syndrome.

We conducted a community-based pilot study to train Hispanic cancer survivors as promotoras (lay health educators) to encourage their social contacts to obtain breast and cervical cancer screening. Promotoras were recruited from a private oncologist's practice at a Minority-Based Community Clinical Oncology Program (MBCCOP). Five Hispanic women were trained to serve as promotoras by attending a 12-week course. They shared cancer screening information with family and social contacts and encouraged them to obtain Papanicolaou smears and/or mammograms. Study endpoints included the number of women recruited and trained to serve as promotoras, the number of contacts made per promotora, and the number of contacts who were screened; data were based on contact logs maintained for 1 year. Screening examinations were documented by a postcard returned by the contact or by review of community health clinic records. Five promotoras contacted 141 (range = 24-49 per promotora) women to share cancer screening information. Fifty Hispanic women obtained screening after contact with a promotora. Twenty-nine underwent mammography (ages 25-58) and 43 received a Papanicolaou smear (ages 23-62). Hispanic female cancer survivors can be trained as promotoras. Screening information conveyed by a promotora can successfully prompt Hispanic women to obtain mammography and Papanicolaou smears.

The optimal upper limit of the normal range for prostate-specific antigen (PSA) is unknown. We investigated the prevalence of prostate cancer among men in the Prostate Cancer Prevention Trial who had a PSA level of 4.0 ng per milliliter or less. Of 18,882 men enrolled in the prevention trial, 9459 were randomly assigned to receive placebo and had an annual measurement of PSA and a digital rectal examination. Among these 9459 men, 2950 men never had a PSA level of more than 4.0 ng per milliliter or an abnormal digital rectal examination, had a final PSA determination, and underwent a prostate biopsy after being in the study for seven years. Among the 2950 men (age range, 62 to 91 years), prostate cancer was diagnosed in 449 (15.2 percent); 67 of these 449 cancers (14.9 percent) had a Gleason score of 7 or higher. The prevalence of prostate cancer was 6.6 percent among men with a PSA level of up to 0.5 ng per milliliter, 10.1 percent among those with values of 0.6 to 1.0 ng per milliliter, 17.0 percent among those with values of 1.1 to 2.0 ng per milliliter, 23.9 percent among those with values of 2.1 to 3.0 ng per milliliter, and 26.9 percent among those with values of 3.1 to 4.0 ng per milliliter. The prevalence of high-grade cancers increased from 12.5 percent of cancers associated with a PSA level of 0.5 ng per milliliter or less to 25.0 percent of cancers associated with a PSA level of 3.1 to 4.0 ng per milliliter. Biopsy-detected prostate cancer, including high-grade cancers, is not rare among men with PSA levels of 4.0 ng per milliliter or less--levels generally thought to be in the normal range.

To examine the tolerability of single versus multiple daily dosing (SDD vs. MDD) of antidepressant drugs in clinical practice. Studies comparing single versus multiple daily dosing of antidepressants were reviewed. Since there were no numeric data available on the rates of adverse events for the SDD versus MDD arms, meta-analyses were carried out to compare rates of study completers (or rates of drop-outs) with single versus multiple daily dosing. The review process identified 22 studies meeting our inclusion criteria. This meta-analysis found no difference in the rates of study completers with SDD or MDD regime of antidepressants. Our analysis on rates of completers (or rates of drop-outs) gives us an estimation of the overall acceptability of treatment and of course, but has limited utility when compared to the rates of adverse events. Yet, the present analyses suggest that adverse events which are significant enough to result in drop-outs, are not more frequent with SDD than MDD. MDD strategy of antidepressants does not seem to be more advantageous for the acceptability of treatment and obviously is disadvantageous for compliance. Thus, a simplified treatment regimen may be practical to increase treatment success rates in depression.

Statistical developments over the past several years are described in this review. Efforts in phase I studies have focused on efficient estimation of maximum tolerated dose. Issues investigated for phase II trials include incorporation of multiple endpoints and randomization. For phase III trials, methods to reduce time or use the sample size more efficiently have been investigated. However, design innovations come with costs, including possible increased risk of incorrect conclusions. Other recent challenging statistical developments in clinical trials relate to use of complementary outcomes such as quality of life and to associated biologic questions, including the emergence of the field of genomics.

This chapter discusses the identification and measurement of gene expressions as prognostic indicators for survival times. As the technology and field of bioinformatics has rapidly exploded in recent years, so has the need for tools to analyze outcome data with covariates of extreme high dimension, such as arises from the measurement of expression levels from large numbers of genes. The chapter outlines a variety of data-reduction and analysis techniques for correlating survival times with high-dimensional covariates. It compares four methods for correlating high-dimensional gene expression levels with survival outcome in the context of Cox's proportional hazards model. Each method consists of an initial data reduction step and a secondary model-fitting step. In the case of a single influential gene, the stepwise model did consistently capture the gene in the optimal model, but unfortunately it included a number of false positive genes as well. Increasing the number of designated genes but dampening their correlation with survival led to a decrease in the true positive rate and little to no improvement in predictive performance as measured by the average cross-validated log likelihood.

We describe the short and intermediate-term quality-of-life (QOL) outcomes in patients treated on a randomized clinical trial in early-stage Hodgkin's disease (Southwest Oncology Group [SWOG] 9133) comparing subtotal lymphoid irradiation (STLI) with combined-modality treatment (CMT). Two hundred forty-seven patients participated in the QOL study (SWOG 9208), completing several standardized instruments (Symptom Distress Scale; Cancer Rehabilitation Evaluation System - Short Form; Medical Outcomes Study 36-Item Short-Form Health Survey Vitality Scale; and a health perception item), as well as questions about work, marital status, and concerns about having children. This article reports on results from baseline before random assignment, at 6 months, and at 1 and 2 years after random assignment. Patients receiving CMT experienced significantly greater symptom distress (P <.0001), fatigue (P =.001), and poorer QOL (P =.015) at 6 months than the STLI patients, reflecting a shorter time since completion of therapy in the CMT arm. Importantly, patients in the two groups did not differ on any outcomes at the 1-and 2-year assessments. Both patient groups reported significantly more fatigue before treatment than healthy reference populations, and fatigue did not improve in either group after treatment. This study demonstrated that patients with early-stage Hodgkin's disease experience a short-term decrease in QOL and an increase in symptoms and fatigue with treatment, which is more severe with CMT; by 1 year, however, CMT and STLI patients report similar outcomes. Fatigue scores for both arms were lower at baseline than scores for the general population and did not return to normal levels 2 years after random assignment. The mechanisms responsible for this lingering problem warrant further investigation.

Analyses of longitudinal quality of life (QOL) for patients with advanced stage disease are frequently plagued by problems of non-random drop-out attributable to deteriorating health and/or death. As an example, Moinpour et al. cite specific challenges which limited their report and assessment of QOL for patients treated for advanced stage colorectal cancer in a clinical trial of several chemotherapeutic regimes performed by the Southwest Oncology Group. A particular source of confusion that arises in studies of advanced stage disease is whether or not to differentiate loss of follow-up due to death from drop-out where the patient is still alive but has dropped from the study. In this paper we examine exploratory data techniques for longitudinal QOL data with non-random missingness due to drop-out and censorship by death. We propose a pattern mixture model for longitudinal QOL, time of drop-out and survival, which allows for straightforward implementation of sensitivity analyses and explicit comparisons to the raw data. Our method is illustrated in the context of analysing the data and addressing the challenges posed by Moinpour et al.