Designing a longitudinal clinical trial based on a composite endpoint: Sample size, monitoring, and adaptation.

Longitudinal clinical trials are often designed to compare treatments on the basis of multiple outcomes. For example in the case of cardiac trials, the outcomes of interest include mortality as well as cardiac events and hospitalization. For a COVID-19 trial, the outcomes of interest include mortality, time on ventilator, and time in hospital. Earlier work by these authors proposed a non-parametric test based on a composite of multiple endpoints referred to as the Finkelstein-Schoenfeld (FS) test (Finkelstein and Schoenfeld. Stat Med. 1999;18(11):1341-1354.). More recently, an estimate of the treatment comparison based on multiple endpoints (related to the FS test) was proposed (Pocock et al. Eur Heart J. 2011;33(2):176-182.). This estimate, which summarized the ratio of the number of patients who fared better vs worse on the experimental arm was coined the win ratio. The aim of this article is to provide guidance in the design of a trial that will use the FS test or the win ratio. The issues that will be considered are the sample size, sequential monitoring, and adaptive designs.

A framework for considering the risk-benefit trade-off in designing noninferiority trials using composite outcome approaches.

When a new treatment regimen is expected to have comparable or slightly worse efficacy to that of the control regimen but has benefits in other domains such as safety and tolerability, a noninferiority (NI) trial may be appropriate but is fraught with difficulty in justifying an acceptable NI margin that is based on both clinical and statistical input. To overcome this, we propose to utilize composite risk-benefit outcomes that combine elements from domains of importance (eg, efficacy, safety, and tolerability). The composite outcome itself may be analyzed using a superiority framework, or it can be used as a tool at the design stage of a NI trial for selecting an NI margin for efficacy that balances changes in risks and benefits. In the latter case, the choice of NI margin may be based on a novel quantity called the maximum allowable decrease in efficacy (MADE), defined as the marginal difference in efficacy between arms that would yield a null treatment effect for the composite outcome given an assumed distribution for the composite outcome. We observe that MADE: (1) is larger when the safety improvement for the experimental arm is larger, (2) depends on the association between the efficacy and safety outcomes, and (3) depends on the control arm efficacy rate. We use a numerical example for power comparisons between a superiority test for the composite outcome vs a noninferiority test for efficacy using the MADE as the NI margin, and apply the methods to a TB treatment trial.

Applying a Risk-benefit Analysis to Outcomes in Tuberculosis Clinical Trials.

Although it is common to analyze efficacy and safety separately in clinical trials, this could yield a misleading study conclusion if an increase in efficacy is accompanied by a decrease in safety. A risk-benefit analysis is a systematic approach to examine safety and efficacy jointly. Both the "rank-based" and "partial-credit" methods described in this paper allow researchers to create a single, composite outcome incorporating efficacy, safety, and other factors. The first approach compares the distribution of rankings between arms. In the second approach, a score can be assigned to each outcome category, considering its severity and comparing the mean or median scores of arms. The methods were applied to the A5279/Brief Rifapentine-Isoniazid Efficacy for TB Prevention study, and design considerations for future clinical trials are discussed, including the challenge of arriving at a consensus on rankings/scorings. If well designed, a risk-benefit analysis may allow for a superiority comparison and, therefore, avoid setting a noninferiority margin. Clinical Trials Registration. NCT01404312 (A5279).

Estimation for an accelerated failure time model with intermediate states as auxiliary information.

The accelerated failure time (AFT) model is a common method for estimating the effect of a covariate directly on a patient's survival time. In some cases, death is the final (absorbing) state of a progressive multi-state process, however when the survival time for a subject is censored, traditional AFT models ignore the intermediate information from the subject's most recent disease state despite its relevance to the mortality process. We propose a method to estimate an AFT model for survival time to the absorbing state that uses the additional data on intermediate state transition times as auxiliary information when a patient is right censored. The method extends the Gehan AFT estimating equation by conditioning on each patient's censoring time and their disease state at their censoring time. With simulation studies, we demonstrate that the estimator is empirically unbiased, and can improve efficiency over commonly used estimators that ignore the intermediate states.

One Month of Rifapentine plus Isoniazid to Prevent HIV-Related Tuberculosis.

BACKGROUND: Tuberculosis is the leading killer of patients with human immunodeficiency virus (HIV) infection. Preventive therapy is effective, but current regimens are limited by poor implementation and low completion rates. METHODS: We conducted a randomized, open-label, phase 3 noninferiority trial comparing the efficacy and safety of a 1-month regimen of daily rifapentine plus isoniazid (1-month group) with 9 months of isoniazid alone (9-month group) in HIV-infected patients who were living in areas of high tuberculosis prevalence or who had evidence of latent tuberculosis infection. The primary end point was the first diagnosis of tuberculosis or death from tuberculosis or an unknown cause. Noninferiority would be shown if the upper limit of the 95% confidence interval for the between-group difference in the number of events per 100 person-years was less than 1.25. RESULTS: A total of 3000 patients were enrolled and followed for a median of 3.3 years. Of these patients, 54% were women; the median CD4+ count was 470 cells per cubic millimeter, and half the patients were receiving antiretroviral therapy. The primary end point was reported in 32 of 1488 patients (2%) in the 1-month group and in 33 of 1498 (2%) in the 9-month group, for an incidence rate of 0.65 per 100 person-years and 0.67 per 100 person-years, respectively (rate difference in the 1-month group, -0.02 per 100 person-years; upper limit of the 95% confidence interval, 0.30). Serious adverse events occurred in 6% of the patients in the 1-month group and in 7% of those in the 9-month group (P = 0.07). The percentage of treatment completion was significantly higher in the 1-month group than in the 9-month group (97% vs. 90%, P<0.001). CONCLUSIONS: A 1-month regimen of rifapentine plus isoniazid was noninferior to 9 months of isoniazid alone for preventing tuberculosis in HIV-infected patients. The percentage of patients who completed treatment was significantly higher in the 1-month group. (Funded by the National Institute of Allergy and Infectious Diseases; BRIEF TB/A5279 ClinicalTrials.gov number, NCT01404312.).

Are rare cancer survivors at elevated risk of subsequent new cancers?

BACKGROUND: Although rare cancers account for 27% of cancer diagnoses in the US, there is insufficient research on survivorship issues in these patients. An important issue cancer survivors face is an elevated risk of being diagnosed with new primary cancers. The primary aim of this analysis was to assess whether a history of rare cancer increases the risk of subsequent cancer compared to survivors of common cancers. METHODS: This was a prospective cohort study of 16,630 adults with personal and/or family history of cancer who were recruited from cancer clinics at 14 geographically dispersed US academic centers of the NIH-sponsored Cancer Genetics Network (CGN). Participants' self-reported cancer histories were collected at registration to the CGN and updated annually during follow-up. At enrollment, 14% of participants reported a prior rare cancer. Elevated risk was assessed via the cause-specific hazard ratio on the time to a subsequent cancer diagnosis. RESULTS: After a median follow-up of 7.9 years, relative to the participants who were unaffected at enrollment, those with a prior rare cancer had a 23% higher risk of subsequent cancer (95% CI: -1 to 52%), while those with a prior common cancer had no excess risk. Patients having two or more prior cancers were at a 53% elevated risk over those with fewer than two (95% CI: 21 to 94%) and if the multiple prior cancers were rare cancers, risk was further elevated by 47% (95% CI: 1 to 114%). CONCLUSION: There is evidence suggesting that survivors of rare cancers, especially those with multiple cancer diagnoses, are at an increased risk of a subsequent cancer. There is a need to study this population more closely to better understand cancer pathogenesis.

Risk factors for early mortality on antiretroviral therapy in advanced HIV-infected adults.

BACKGROUND: Many HIV-infected individuals present with advanced HIV disease. These patients are at high risk of death after antiretroviral therapy (ART) initiation, but risk factors for death in these patients are unclear. METHODS: We used data from a multisite randomized trial comparing empiric vs. preventive tuberculosis therapy in HIV-infected adults initiating ART with CD4 T-cell counts less than 50 cells/µl to evaluate risk factors for death within 48 weeks after ART initiation. Cox proportional hazards models were fit to evaluate characteristics present at baseline and at 4 weeks after ART initiation, including the week 4 CD4 T-cell response and new opportunistic infections. RESULTS: Of 850 enrolled, the median pre-ART CD4 T-cell count was 18 cells/µl and 67 (7.9%) died. Baseline risk factors for death included lymphadenopathy, lower CD4 T-cell count, lower serum albumin, high white blood cell count, elevated neutrophil percentage, and lower hemoglobin. Among 746 participants with data at week 4, the median changes in CD4 T-cell count and viral load for those who died (n = 43) vs. survived were 26 vs. 56 cells/µl and -2.7 vs. -2.7 log10 copies/ml, respectively. Each 20 cell/µl lower change in week 4 CD4 T-cell count was associated with a 20% increased risk of post week-4 mortality (adjusted hazard ratio 1.20, 1.01-1.42, P = .038). CONCLUSION: Evidence of active infection and suboptimal immunologic response during the first month of ART are associated with death in the first year after ART initiation in those with advanced HIV disease taking tuberculosis preventive therapy. Strategies to reduce early mortality in this population warrant further investigation.

Global rank tests for multiple, possibly censored, outcomes.

Clinical trials often collect multiple outcomes on each patient, as the treatment may be expected to affect the patient on many dimensions. For example, a treatment for a neurological disease such as ALS is intended to impact several dimensions of neurological function as well as survival. The assessment of treatment on the basis of multiple outcomes is challenging, both in terms of selecting a test and interpreting the results. Several global tests have been proposed, and we provide a general approach to selecting and executing a global test. The tests require minimal parametric assumptions, are flexible about weighting of the various outcomes, and are appropriate even when some or all of the outcomes are censored. The test we propose is based on a simple scoring mechanism applied to each pair of subjects for each endpoint. The pairwise scores are then reduced to a summary score, and a rank-sum test is applied to the summary scores. This can be seen as a generalization of previously proposed nonparametric global tests (e.g., O'Brien, 1984). We discuss the choice of optimal weighting schemes based on power and relative importance of the outcomes. As the optimal weights are generally unknown in practice, we also propose an adaptive weighting scheme and evaluate its performance in simulations. We apply the methods to analyze the impact of a treatment on neurological function and death in an ALS trial.

A model-informed rank test for right-censored data with intermediate states.

The generalized Wilcoxon and log-rank tests are commonly used for testing differences between two survival distributions. We modify the Wilcoxon test to account for auxiliary information on intermediate disease states that subjects may pass through before failure. For a disease with multiple states where patients are monitored periodically but exact transition times are unknown (e.g. staging in cancer), we first fit a multi-state Markov model to the full data set; when censoring precludes the comparison of survival times between two subjects, we use the model to estimate the probability that one subject will have survived longer than the other given their censoring times and last observed status, and use these probabilities to compute an expected rank for each subject. These expected ranks form the basis of our test statistic. Simulations demonstrate that the proposed test can improve power over the log-rank and generalized Wilcoxon tests in some settings while maintaining the nominal type 1 error rate. The method is illustrated on an amyotrophic lateral sclerosis data set.

Pregnancy-associated breast cancer in women from Shanghai: risk and prognosis.

Breast cancer (BC) has been associated with pregnancy if diagnosed within 5-10 years after delivery (pregnancy-associated BC, PABC). PABC carries a poor prognosis compared to sporadic BC in Western populations. Data are limited regarding PABC in Asian populations, where longer periods of breastfeeding, higher birth rates and a lower median age of BC at diagnosis have been noted, all of which are known to influence prognosis. We used two datasets of women treated for early BC in Shanghai 1990-2012 (n = 10,161 and n = 7,411). For the analysis of BC risk after pregnancy we compared the distribution of pregnancy in our dataset to that in Shanghai using age-specific fertility rates. For disease-free survival (DFS) evaluation, we restricted our data to women ≤45 years. Women <30 years had a significantly elevated BC risk within 5 years of completing a pregnancy compared to women who had not been pregnant in the previous 5 years. In women aged 20-24 the relative risk (RR) was 3.33 (P = 0.012), and for women aged 25-29 the RR was 1.76 (P = 0.0074). For women >30, the RR was decreased. Patients with PABC had a higher risk of recurrence or death (hazard ratio (HR) for DFS 1.72, P = 0.019) compared to women with non-PABC by univariable analysis. Age was eliminated from the multivariable model by backward selection, resulting in tumor stage (3 versus 1, HR 3.08, P < .001) and recent pregnancy (HR 1.62, P < 0.05) as significant independent prognosticators. Having had a full-term pregnancy in the previous 5 years was associated with a 62 % increased risk of recurrence. We show that recent full-term pregnancy significantly elevates BC risk in women <30 in Shanghai, and that women diagnosed with PABC have a particularly adverse prognosis. Health care providers and women in Asian populations should be made aware of these results.

Socioeconomic status and survival after an invasive breast cancer diagnosis.

BACKGROUND: Women who live in geographic areas with high poverty rates and low levels of education experience poorer survival after a breast cancer diagnosis than women who live in communities with indicators of high socioeconomic status (SES). However, very few studies have examined individual-level SES in relation to breast cancer survival or have assessed the contextual role of community-level SES independent of individual-level SES. METHODS: The authors of this report examined both individual-level and community-level SES in relation to breast cancer survival in a population-based cohort of women ages 20 to 69 years who were diagnosed with breast cancer in Wisconsin between 1995 and 2003 (N = 5820). RESULTS: Compared with college graduates, women who had no education beyond high school were 1.39 times more likely (95% confidence interval [CI], 1.10-1.76) to die from breast cancer. Women who had household incomes <2.5 times the poverty level were 1.46 times more likely (95% CI, 1.10-1.92) to die from breast cancer than women who had household incomes ≥5 times the poverty level. Adjusting the analysis for use of screening mammography, disease stage at diagnosis, and lifestyle factors eliminated the disparity by income, but the disparity by education persisted (hazard ratio [HR], 1.27; 95% CI, 0.99-1.61). In multilevel analyses, low community-level education was associated with increased breast cancer mortality even after adjusting for individual-level SES (HR, 1.57; 95% CI, 1.09-2.27 for ≥20% vs <10% of adults without a high school degree). CONCLUSIONS: The current results indicated that screening and early detection explain some of the disparity according to SES, but further research will be needed to understand the additional ways in which individual-level and community-level education are associated with survival.