Analysis and design of randomised clinical trials involving competing risks endpoints

<p>Abstract</p> <p>Background</p> <p>In randomised clinical trials involving time-to-event outcomes, the failures concerned may be events of an entirely different nature and as such define a classical competing risks framework. In designing and analysing clinical trials involving such endpoints, it is important to account for the competing events, and evaluate how each contributes to the overall failure. An appropriate choice of statistical model is important for adequate determination of sample size.</p> <p>Methods</p> <p>We describe how competing events may be summarised in such trials using cumulative incidence functions and Gray's test. The statistical modelling of competing events using proportional cause-specific and subdistribution hazard functions, and the corresponding procedures for sample size estimation are outlined. These are illustrated using data from a randomised clinical trial (SQNP01) of patients with advanced (non-metastatic) nasopharyngeal cancer.</p> <p>Results</p> <p>In this trial, treatment has no effect on the competing event of loco-regional recurrence. Thus the effects of treatment on the hazard of distant metastasis were similar via both the cause-specific (unadjusted <it>csHR </it>= 0.43, 95% CI 0.25 - 0.72) and subdistribution (unadjusted <it>subHR </it>0.43; 95% CI 0.25 - 0.76) hazard analyses, in favour of concurrent chemo-radiotherapy followed by adjuvant chemotherapy. Adjusting for nodal status and tumour size did not alter the results. The results of the logrank test (<it>p </it>= 0.002) comparing the cause-specific hazards and the Gray's test (<it>p </it>= 0.003) comparing the cumulative incidences also led to the same conclusion. However, the subdistribution hazard analysis requires many more subjects than the cause-specific hazard analysis to detect the same magnitude of effect.</p> <p>Conclusions</p> <p>The cause-specific hazard analysis is appropriate for analysing competing risks outcomes when treatment has no effect on the cause-specific hazard of the competing event. It requires fewer subjects than the subdistribution hazard analysis for a similar effect size. However, if the main and competing events are influenced in opposing directions by an intervention, a subdistribution hazard analysis may be warranted.</p

Obesity and survival in operable breast cancer patients treated with adjuvant anthracyclines and taxanes according to pathological subtypes: a pooled analysis

IntroductionObesity is an unfavorable prognostic factor in breast cancer (BC) patients regardless of menopausal status and treatment received. However, the association between obesity and survival outcome by pathological subtype requires further clarification.MethodsWe performed a retrospective analysis including 5,683 operable BC patients enrolled in four randomized clinical trials (GEICAM/9906, GEICAM/9805, GEICAM/2003–02, and BCIRG 001) evaluating anthracyclines and taxanes as adjuvant treatments. Our primary aim was to assess the prognostic effect of body mass index (BMI) on disease recurrence, breast cancer mortality (BCM), and overall mortality (OM). A secondary aim was to detect differences of such prognostic effects by subtype.ResultsMultivariate survival analyses adjusting for age, tumor size, nodal status, menopausal status, surgery type, histological grade, hormone receptor status, human epidermal growth factor receptor 2 (HER2) status, chemotherapy regimen, and under-treatment showed that obese patients (BMI 30.0 to 34.9) had similar prognoses to that of patients with a BMI < 25 (reference group) in terms of recurrence (Hazard Ratio [HR] = 1.08, 95% Confidence Interval [CI] = 0.90 to 1.30), BCM (HR = 1.02, 0.81 to 1.29), and OM (HR = 0.97, 0.78 to 1.19). Patients with severe obesity (BMI ≥ 35) had a significantly increased risk of recurrence (HR = 1.26, 1.00 to 1.59, P = 0.048), BCM (HR = 1.32, 1.00 to 1.74, P = 0.050), and OM (HR = 1.35, 1.06 to 1.71, P = 0.016) compared to our reference group. The prognostic effect of severe obesity did not vary by subtype.ConclusionsSeverely obese patients treated with anthracyclines and taxanes present a worse prognosis regarding recurrence, BCM, and OM than patients with BMI < 25. The magnitude of the harmful effect of BMI on survival-related outcomes was similar across subtypes

Obesity and poor breast cancer prognosis: an illusion because of hormone replacement therapy?

High body mass index (BMI) and use of hormone replacement therapy (HRT) increase the risk of postmenopausal breast cancer. It has been shown that BMI modifies the effect of HRT, as its influence is most pronounced in lean women. We investigated the influence of BMI and HRT on prognosis in 2640 postmenopausal women diagnosed with breast cancer in Sweden in 1993–1995, taking into account HRT and mammography before diagnosis. Logistic and Cox regression were used. In non-users of HRT, obese women (BMI >30) compared with normal weight women (BMI <25) had a similar prognosis (hazard ratio (HR) 1.1, 95% confidence interval (CI) 0.8–1.6), despite larger tumours found in obese women. Obese HRT users had less favourable tumour characteristics and poorer prognosis compared with normal weight women (HR 3.7, 95% CI 1.9–7.2). The influence of BMI on breast cancer prognosis was similar whether diagnosed by mammographic screening or not. We found a similar prognosis of postmenopausal breast cancer-specific death regardless of BMI in non-users of HRT, but among HRT users obesity was associated with a poorer breast cancer prognosis

Validation of a 22-Gene Genomic Classifier in Patients With Recurrent Prostate Cancer An Ancillary Study of the NRG/RTOG 9601 Randomized Clinical Trial

IMPORTANCE: Decipher (Decipher Biosciences Inc) is a genomic classifier (GC) developed to estimate the risk of distant metastasis (DM) after radical prostatectomy (RP) in patients with prostate cancer. OBJECTIVE: To validate the GC in the context of a randomized phase 3 trial. DESIGN, SETTING, AND PARTICIPANTS: This ancillary study used RP specimens from the phase 3 placebo-controlled NRG/RTOG 9601 randomized clinical trial conducted from March 1998 to March 2003. The specimens were centrally reviewed, and RNA was extracted from the highest-grade tumor available in 2019 with a median follow-up of 13 years. Clinical-grade whole transcriptomes from samples passing quality control were assigned GC scores (scale, 0-1). A National Clinical Trials Network–approved prespecified statistical plan included the primary objective of validating the independent prognostic ability of GC for DM, with secondary end points of prostate cancer–specific mortality (PCSM) and overall survival (OS). Data were analyzed from September 2019 to December 2019. INTERVENTIONS: Salvage radiotherapy (sRT) with or without 2 years of bicalutamide. MAIN OUTCOMES AND MEASURES: The preplanned primary end point of this study was the independent association of the GC with the development of DM. RESULTS: In this ancillary study of specimens from a phase 3 randomized clinical trial, GC scores were generated from 486 of 760 randomized patients with a median follow-up of 13 years; samples from a total of 352 men (median [interquartile range] age, 64.5 (60-70) years; 314 White [89.2%] participants) passed microarray quality control and comprised the final cohort for analysis. On multivariable analysis, the GC (continuous variable, per 0.1 unit) was independently associated with DM (hazard ratio [HR], 1.17; 95% CI, 1.05-1.32; P = .006), PCSM (HR, 1.39; 95% CI, 1.20-1.63; P < .001), and OS (HR, 1.17; 95% CI, 1.06-1.29; P = .002) after adjusting for age, race/ethnicity, Gleason score, T stage, margin status, entry prostate-specific antigen, and treatment arm. Although the original planned analysis was not powered to detect a treatment effect interaction by GC score, the estimated absolute effect of bicalutamide on 12-year OS was less when comparing patients with lower vs higher GC scores (2.4% vs 8.9%), which was further demonstrated in men receiving early sRT at a prostate-specific antigen level lower than 0.7 ng/mL (−7.8% vs 4.6%). CONCLUSIONS AND RELEVANCE: This ancillary validation study of the Decipher GC in a randomized trial cohort demonstrated association of the GC with DM, PCSM, and OS independent of standard clinicopathologic variables. These results suggest that not all men with biochemically recurrent prostate cancer after surgery benefit equally from the addition of hormone therapy to sRT. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT0000287

Do breast implants after a mastectomy affect subsequent prognosis and survival?

In a large study, published in this issue of Breast Cancer Research, Le and colleagues report that women receiving implants after mastectomies for early-stage breast cancer experience lower breast cancer mortality than women not receiving implants. Assessment of survival patterns among women receiving reconstructive implants is complex given unique patient characteristics, disease attributes, and treatment patterns. The interpretation of reduced mortality from breast cancer must be assessed in light of significantly reduced risks of death from most other causes. In contrast, patients receiving post-mastectomy implants had elevated rates of suicide, consistent with findings among women with cosmetic implants. Additional well-designed investigations are needed to clarify survival patterns among women receiving reconstructive implants

Competing risks survival analysis applied to data from the Australian Orthopaedic Association National Joint Replacement Registry

BACKGROUND AND PURPOSE: The Kaplan-Meier (KM) method is often used in the analysis of arthroplasty registry data to estimate the probability of revision after a primary procedure. In the presence of a competing risk such as death, KM is known to overestimate the probability of revision. We investigated the degree to which the risk of revision is overestimated in registry data. PATIENTS AND METHODS: We compared KM estimates of risk of revision with the cumulative incidence function (CIF), which takes account of death as a competing risk. We considered revision by (1) prosthesis type in subjects aged 75–84 years with fractured neck of femur (FNOF), (2) cement use in monoblock prostheses for FNOF, and (3) age group in patients undergoing total hip arthroplasty (THA) for osteoarthritis (OA). RESULTS: In 5,802 subjects aged 75–84 years with a monoblock prosthesis for FNOF, the estimated risk of revision at 5 years was 6.3% by KM and 4.3% by CIF, a relative difference (RD) of 46%. In 9,821 subjects of all ages receiving an Austin Moore (non-cemented) prosthesis for FNOF, the RD at 5 years was 52% and for 3,116 subjects with a Thompson (cemented) prosthesis, the RD was 79%. In 44,365 subjects with a THA for OA who were less than 70 years old, the RD was just 1.4%; for 47,430 subjects > 70 years of age, the RD was 4.6% at 5 years. INTERPRETATION: The Kaplan-Meier method substantially overestimated the risk of revision compared to estimates using competing risk methods when the risk of death was high. The bias increased with time as the incidence of the competing risk of death increased. Registries should adopt methods of analysis appropriate to the nature of their data.Marianne H. Gillam, Philip Ryan, Stephen E. Graves, Lisa N. Miller, Richard N. de Steiger and Amy Salte