scholarly journals Estimation of treatment effect under non-proportional hazards and conditionally independent censoring

2012 ◽  
Vol 31 (28) ◽  
pp. 3504-3515 ◽  
Author(s):  
Adam P. Boyd ◽  
John M. Kittelson ◽  
Daniel L. Gillen
Keyword(s):  
Treatment Effect ◽  
Proportional Hazards ◽  
Independent Censoring

scholarly journals Impact of a non-constant baseline hazard on detection of time-dependent treatment effects: a simulation study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kim Jachno ◽  
Stephane Heritier ◽  
Rory Wolfe
Keyword(s):  
Clinical Trials ◽  
Simulation Study ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Proportional Hazards ◽  
Time Dependent ◽  
Time To Event ◽  
Baseline Hazard ◽  
Event Rates ◽  
Analytical Approaches

Abstract Background Non-proportional hazards are common with time-to-event data but the majority of randomised clinical trials (RCTs) are designed and analysed using approaches which assume the treatment effect follows proportional hazards (PH). Recent advances in oncology treatments have identified two forms of non-PH of particular importance - a time lag until treatment becomes effective, and an early effect of treatment that ceases after a period of time. In sample size calculations for treatment effects on time-to-event outcomes where information is based on the number of events rather than the number of participants, there is crucial importance in correct specification of the baseline hazard rate amongst other considerations. Under PH, the shape of the baseline hazard has no effect on the resultant power and magnitude of treatment effects using standard analytical approaches. However, in a non-PH context the appropriateness of analytical approaches can depend on the shape of the underlying hazard. Methods A simulation study was undertaken to assess the impact of clinically plausible non-constant baseline hazard rates on the power, magnitude and coverage of commonly utilized regression-based measures of treatment effect and tests of survival curve difference for these two forms of non-PH used in RCTs with time-to-event outcomes. Results In the presence of even mild departures from PH, the power, average treatment effect size and coverage were adversely affected. Depending on the nature of the non-proportionality, non-constant event rates could further exacerbate or somewhat ameliorate the losses in power, treatment effect magnitude and coverage observed. No single summary measure of treatment effect was able to adequately describe the full extent of a potentially time-limited treatment benefit whilst maintaining power at nominal levels. Conclusions Our results show the increased importance of considering plausible potentially non-constant event rates when non-proportionality of treatment effects could be anticipated. In planning clinical trials with the potential for non-PH, even modest departures from an assumed constant baseline hazard could appreciably impact the power to detect treatment effects depending on the nature of the non-PH. Comprehensive analysis plans may be required to accommodate the description of time-dependent treatment effects.

scholarly journals Statins use and COVID-19 outcomes in hospitalized patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256899
Author(s):  
Samuel K. Ayeh ◽  
Enoch J. Abbey ◽  
Banda A. A. Khalifa ◽  
Richard D. Nudotor ◽  
Albert Danso Osei ◽  
...  
Keyword(s):  
Treatment Effect ◽  
Proportional Hazards ◽  
Respiratory Infections ◽  
Invasive Mechanical Ventilation ◽  
Cox Proportional Hazards ◽  
Average Treatment Effect ◽  
Hospitalized Patients ◽  
Average Treatment ◽  
Increased Risk ◽  
Statin Use

Background There is an urgent need for novel therapeutic strategies for reversing COVID-19-related lung inflammation. Recent evidence has demonstrated that the cholesterol-lowering agents, statins, are associated with reduced mortality in patients with various respiratory infections. We sought to investigate the relationship between statin use and COVID-19 disease severity in hospitalized patients. Methods A retrospective analysis of COVID-19 patients admitted to the Johns Hopkins Medical Institutions between March 1, 2020 and June 30, 2020 was performed. The outcomes of interest were mortality and severe COVID-19 infection, as defined by prolonged hospital stay (≥ 7 days) and/ or invasive mechanical ventilation. Logistic regression, Cox proportional hazards regression and propensity score matching were used to obtain both univariable and multivariable associations between covariates and outcomes in addition to the average treatment effect of statin use. Results Of the 4,447 patients who met our inclusion criteria, 594 (13.4%) patients were exposed to statins on admission, of which 340 (57.2%) were male. The mean age was higher in statin users compared to non-users [64.9 ± 13.4 vs. 45.5 ± 16.6 years, p <0.001]. The average treatment effect of statin use on COVID-19-related mortality was RR = 1.00 (95% CI: 0.99–1.01, p = 0.928), while its effect on severe COVID-19 infection was RR = 1.18 (95% CI: 1.11–1.27, p <0.001). Conclusion Statin use was not associated with altered mortality, but with an 18% increased risk of severe COVID-19 infection.

Group sequential monitoring based on the maximum of weighted log-rank statistics with the Fleming–Harrington class of weights in oncology clinical trials

2020 ◽  
Vol 29 (12) ◽  
pp. 3525-3532
Author(s):  
Thomas J Prior
Keyword(s):  
Clinical Trials ◽  
Treatment Effect ◽  
Proportional Hazards ◽  
Rank Test ◽  
Event Data ◽  
Time To Event ◽  
Survival Curves ◽  
Delayed Treatment ◽  
Time To Event Data ◽  
Log Rank Test

Clinical trials in oncology often involve the statistical analysis of time-to-event data such as progression-free survival or overall survival to determine the benefit of a treatment or therapy. The log-rank test is commonly used to compare time-to-event data from two groups. The log-rank test is especially powerful when the two groups have proportional hazards. However, survival curves encountered in oncology studies that differ from one another do not always differ by having proportional hazards; in such instances, the log-rank test loses power, and the survival curves are said to have “non-proportional hazards”. This non-proportional hazards situation occurs for immunotherapies in oncology; immunotherapies often have a delayed treatment effect when compared to chemotherapy or radiation therapy. To correctly identify and deliver efficacious treatments to patients, it is important in oncology studies to have available a statistical test that can detect the difference in survival curves even in a non-proportional hazards situation such as one caused by delayed treatment effect. An attempt to address this need was the “max-combo” test, which was originally described only for a single analysis timepoint; this article generalizes that test to preserve type I error when there are one or more interim analyses, enabling efficacious treatments to be identified and made available to patients more rapidly.

Effect of baseline carbohydrate antigen 19-9 (CA19-9) level on overall survival (OS) in NAPOLI-1 trial: A phase III study of MM-398 (nal-IRI), with or without 5-fluorouracil and leucovorin (5-FU/LV), versus 5-FU/LV in metastatic pancreatic cancer (mPAC) previously treated with gemcitabine-based therapy.

2016 ◽  
Vol 34 (4_suppl) ◽  
pp. 425-425 ◽  
Author(s):  
Li-Tzong Chen ◽  
Jens T Siveke ◽  
Andrea Wang-Gillam ◽  
Richard Hubner ◽  
Shubham Pant ◽  
...  
Keyword(s):  
Overall Survival ◽  
Treatment Effect ◽  
Proportional Hazards ◽  
Objective Response ◽  
Progression Free Survival ◽  
Cox Proportional Hazards ◽  
Response To Therapy ◽  
Phase Iii ◽  
Free Survival ◽  
Previously Treated

425 Background: CA19-9 has been shown to correlate with response to therapy and OS in patients with mPAC. NAPOLI-1, a randomized phase 3 study evaluated nal-IRI, a nanoliposomal formulation of irinotecan, with or without 5-FU/LV vs 5-FU/LV in patients with mPAC previously treated with gemcitabine-based therapy. Nal-IRI+5-FU/LV significantly improved OS (primary endpoint) vs 5-FU/LV (6.1 mo vs 4.2 mo; unstratified hazard ratio [HR] = 0.67; P = 0.012). CA19-9 response (≥50% decline from baseline) was superior with nal-IRI+5FU/LV compared with 5-FU/LV (29% vs 9%; P=0.0006). Nal-IRI alone did not show a statistical improvement in survival. Methods: Patients with a recorded baseline CA19-9 measurement were divided into quartiles to evaluate the treatment effect pattern of CA19-9 from nal-IRI+5-FU/LV and 5-FU/LV arms. Quartile ranges were based on 404 available CA19-9 values from randomized patients (N=417). Unstratified Cox proportional hazards regression was used to estimate HRs and corresponding 95% CIs. Effect of baseline CA19-9 on time to response, progression-free survival, and response will be presented. Results: Of patients randomized to receive nal-IRI+5-FU/LV (n = 117) or 5-FU/LV enrolled contemporaneously (n = 119), 218 received study drug and had a baseline CA19-9 measurement. Results show a greater treatment effect on OS with higher CA19-9 level relative to 5-FU/LV. Conclusions: In patients with mPAC previously treated with gemcitabine-based therapy, nal-IRI+5-FU/LV significantly improved OS supported by progression free survival and objective response rate. The CA19-9 serum level can provide important information with regards to overall survival. Clinical trial information: NCT01494506. [Table: see text]

scholarly journals A new modeling and inference approach for the Systolic Blood Pressure Intervention Trial outcomes

2018 ◽  
Vol 15 (3) ◽  
pp. 305-312 ◽  
Author(s):  
Song Yang ◽  
Walter T Ambrosius ◽  
Lawrence J Fine ◽  
Adam P Bress ◽  
William C Cushman ◽  
...  
Keyword(s):  
Treatment Effect ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Rank Test ◽  
Time To Event ◽  
New Approach ◽  
New Methods ◽  
Log Rank Test ◽  
Hazards Model ◽  
Average Hazard Ratio

Background/aims In clinical trials with time-to-event outcomes, usually the significance tests and confidence intervals are based on a proportional hazards model. Thus, the temporal pattern of the treatment effect is not directly considered. This could be problematic if the proportional hazards assumption is violated, as such violation could impact both interim and final estimates of the treatment effect. Methods We describe the application of inference procedures developed recently in the literature for time-to-event outcomes when the treatment effect may or may not be time-dependent. The inference procedures are based on a new model which contains the proportional hazards model as a sub-model. The temporal pattern of the treatment effect can then be expressed and displayed. The average hazard ratio is used as the summary measure of the treatment effect. The test of the null hypothesis uses adaptive weights that often lead to improvement in power over the log-rank test. Results Without needing to assume proportional hazards, the new approach yields results consistent with previously published findings in the Systolic Blood Pressure Intervention Trial. It provides a visual display of the time course of the treatment effect. At four of the five scheduled interim looks, the new approach yields smaller p values than the log-rank test. The average hazard ratio and its confidence interval indicates a treatment effect nearly a year earlier than a restricted mean survival time–based approach. Conclusion When the hazards are proportional between the comparison groups, the new methods yield results very close to the traditional approaches. When the proportional hazards assumption is violated, the new methods continue to be applicable and can potentially be more sensitive to departure from the null hypothesis.

Sign in / Sign up

Export Citation Format

Share Document