A joint test for progression and survival with interval-censored data from a cancer clinical trial

Time to Achieve the Minimal Clinically Important Difference in Primary Total Hip Arthroplasty: Comparison of Anterior and Posterior Surgical Approaches

BACKGROUND

Controversy remains over outcomes between total hip arthroplasty approaches. This study aimed to compare the time to achieve the minimal clinically important difference (MCID) for the Hip Disability and Osteoarthritis Outcome Score-Physical Function Short Form (HOOS-PS) and the Patient-Reported Outcomes Measurement Information System (PROMIS) Global-Physical for patients who underwent anterior and posterior surgical approaches in primary total hip arthroplasty.

METHODS

Patients from 2018 to 2021 with preoperative and postoperative HOOS-PS or PROMIS Global-Physical questionnaires were grouped by approach. Demographic and MCID achievement rates were compared, and survival curves with and without interval-censoring were used to assess the time to achieve the MCID by approach. Log-rank and weighted log-rank tests were used to compare groups, and Weibull regression analyses were performed to assess potential covariates.

RESULTS

A total of 2,725 patients (1,054 anterior and 1,671 posterior) were analyzed. There were no significant differences in median MCID achievement times for either the HOOS-PS (anterior: 5.9 months, 95% confidence interval [CI]: 4.6 to 6.4; posterior: 4.4 months, 95% CI: 4.1 to 5.1, P = .65) or the PROMIS Global-Physical (anterior: 4.2 months, 95% CI: 3.5 to 5.3; posterior: 3.5 months, 95% CI: 3.4 to 3.8, P = .08) between approaches. Interval-censoring revealed earlier times of achieving the MCID for both the HOOS-PS (anterior: 1.509 to 1.511 months; posterior: 1.7 to 2.3 months, P = .87) and the PROMIS Global-Physical (anterior: 3.0 to 3.1 weeks; posterior: 2.7 to 3.3 weeks, P = .18) for both surgical approaches.

CONCLUSIONS

The time to achieve the MCID did not differ by surgical approach. Most patients will achieve clinically meaningful improvements in physical function much earlier than previously believed.

LEVEL OF EVIDENCE

Level III, Retrospective Comparative Study.

Use of composite outcomes to assess risk-benefit in clinical trials

Before a novel treatment can be deemed a clinical success, an assessment of its risk-benefit profile must be made. One of the inherent challenges for this assessment comes from the multiplicity that arises from comparing treatment groups across multiple outcomes. Composite outcomes that summarize a patient's clinical status, or severity, across a prioritized list of safety and efficacy outcomes have become increasing popular. In this article, we review these approaches and illustrate through examples some of the challenges and complexities of a composite derived from prioritized outcomes, such as the win ratio. These challenges include the difficult tension between the analytical validity that comes from choosing a pre-specified outcome and an evaluation that is responsive to unexpected safety events that arise during the course of a trial. Other challenges include a sensitivity of the resulting test statistic to the underlying censoring distribution and other nuisance parameters. Approaches that resolve some of the difficulties of the analytical challenges associated with prioritized outcomes are then discussed. Ultimately, a composite outcome of net clinical benefit is another decision tool, but one to be used alongside more traditional analyses of efficacy and safety, and with the broader perspective that investigators, the data safety monitoring board, and regulators bring to an evaluation of risk-benefit.

Beyond Composite Endpoints Analysis: Semicompeting Risks as an Underutilized Framework for Cancer Research

BACKGROUND

Composite endpoints (CEP), such as progression-free survival, are commonly used in cancer research. Notwithstanding their popularity, however, CEP analyses suffer from a number of drawbacks, especially when death is combined with a nonterminal event (ie, progression or recurrence), exemplifying the semicompeting risks setting. We investigated the semicompeting risks framework as a complementary analysis strategy that avoids certain drawbacks of CEPs.

METHODS

The illness-death model under the semicompeting risks framework was compared with standard analysis approaches: CEP analyses and (separate) univariate analyses for each component endpoint. Data from a previously published phase III randomized clinical trial in metastatic colon cancer including 1419 participants in the N9741 trial (conducted between 1997 and 2003) were used to determine the impact of the loss of information associated with combining multiple endpoints, as well as of ignoring the potentially informative role of death. A simulation study was conducted to further explore these issues.

RESULTS

Failure to account for critical features of semicompeting risks data can lead to potentially severely misleading conclusions. Advantages of semicompeting risks analyses include a clear delineation of treatment effects on both events, the ability to draw conclusions about a patient's joint risk of the two events, and an assessment of the dependence between the two event types.

CONCLUSIONS

Embedding and analyzing component outcomes in the semicompeting risks framework, either as a supplement or alternative to CEP analyses, represents an important, underutilized, and feasible opportunity for cancer research.

A rank test for bivariate time-to-event outcomes when one event is a surrogate

In many clinical settings, improving patient survival is of interest but a practical surrogate, such as time to disease progression, is instead used as a clinical trial's primary endpoint. A time-to-first endpoint (e.g., death or disease progression) is commonly analyzed but may not be adequate to summarize patient outcomes if a subsequent event contains important additional information. We consider a surrogate outcome very generally as one correlated with the true endpoint of interest. Settings of interest include those where the surrogate indicates a beneficial outcome so that the usual time-to-first endpoint of death or surrogate event is nonsensical. We present a new two-sample test for bivariate, interval-censored time-to-event data, where one endpoint is a surrogate for the second, less frequently observed endpoint of true interest. This test examines whether patient groups have equal clinical severity. If the true endpoint rarely occurs, the proposed test acts like a weighted logrank test on the surrogate; if it occurs for most individuals, then our test acts like a weighted logrank test on the true endpoint. If the surrogate is a useful statistical surrogate, our test can have better power than tests based on the surrogate that naively handles the true endpoint. In settings where the surrogate is not valid (treatment affects the surrogate but not the true endpoint), our test incorporates the information regarding the lack of treatment effect from the observed true endpoints and hence is expected to have a dampened treatment effect compared with tests based on the surrogate alone. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.