23 research outputs found

    Adjusting for treatment switching in the METRIC study shows further improved overall survival with trametinib compared with chemotherapy

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    Trametinib, a selective inhibitor of mitogen-activated protein kinase kinase 1 (MEK1) and MEK2, significantly improves progression-free survival compared with chemotherapy in patients with BRAF V600E/K mutation–positive advanced or metastatic melanoma (MM). However, the pivotal clinical trial permitted randomized chemotherapy control group patients to switch to trametinib after disease progression, which confounded estimates of the overall survival (OS) advantage of trametinib. Our purpose was to estimate the switching-adjusted treatment effect of trametinib for OS and assess the suitability of each adjustment method in the primary efficacy population. Of the patients randomized to chemotherapy, 67.4% switched to trametinib. We applied the rank-preserving structural failure time model, inverse probability of censoring weights, and a two-stage accelerated failure time model to obtain estimates of the relative treatment effect adjusted for switching. The intent-to-treat (ITT) analysis estimated a 28% reduction in the hazard of death with trametinib treatment (hazard ratio [HR], 0.72; 95% CI, 0.52–0.98) for patients in the primary efficacy population (data cut May 20, 2013). Adjustment analyses deemed plausible provided OS HR point estimates ranging from 0.48 to 0.53. Similar reductions in the HR were estimated for the first-line metastatic subgroup. Treatment with trametinib, compared with chemotherapy, significantly reduced the risk of death and risk of disease progression in patients with BRAF V600E/K mutation–positive advanced melanoma or MM. Adjusting for switching resulted in lower HRs than those obtained from standard ITT analyses. However, CI are wide and results are sensitive to the assumptions associated with each adjustment method

    Quality of Life in Hormone Receptor–Positive HER-2+ Metastatic Breast Cancer Patients During Treatment with Letrozole Alone or in Combination with Lapatinib

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    This paper presents analyses evaluating quality of life in patients with hormone receptor–positive human epidermal growth factor receptor 2–positive tumors receiving letrozole alone or in combination with lapatinib in clinical trial EGF30008

    Psychometric evaluation of the Osteoporosis Patient Treatment Satisfaction Questionnaire (OPSAT-Q™), a novel measure to assess satisfaction with bisphosphonate treatment in postmenopausal women

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    BACKGROUND: The Osteoporosis Patient Satisfaction Questionnaire (OPSAT-Q) is a new measure of patient satisfaction with bisphosphonate treatment for osteoporosis. The objective of this study was to evaluate the psychometric characteristics of the OPSAT-Q. METHODS: The OPSAT-Q contains 16 items in four subscales: Convenience, Confidence with Daily Activities, Side Effects, and Overall Satisfaction. All four subscale scores and an overall composite satisfaction score (CSS) can be computed. The OPSAT-Q, Osteoporosis Targeted Quality of Life (OPTQoL), and sociodemographic/clinical questionnaires, including 3 global items on convenience, functioning and side effects, were self-administered to women with osteoporosis or osteopenia recruited from four US clinics. Analyses included item and scale performance, internal consistency reliability, reproducibility, and construct validity. Reproducibility was measured using the intraclass correlation coefficient (ICC) via a follow-up questionnaire completed by participants 2 weeks post baseline. RESULTS: 104 women with a mean age of 65.1 years participated. The majority were Caucasian (64.4%), living with someone (74%), and not currently employed (58.7%). 73% had osteoporosis and 27% had osteopenia. 80% were taking weekly bisphosphonates and 18% were taking daily medication (2% missing data). On a scale of 0–100, individual patient subscale scores ranged from 17 to 100 and CSS scores ranged from 44 to 100. All scores showed acceptable internal consistency reliability (Cronbach's alpha > 0.70) (range 0.72 to 0.89). Reproducibility ranged from 0.62 (Daily Activities) to 0.79 (Side Effects) for the subscales; reproducibility for the CSS was 0.81. Significant correlations were found between the OPSAT-Q subscales and conceptually similar global measures (p < 0.001). CONCLUSION: The findings from this study confirm the validity and reliability of the OPSAT-Q and support the proposed composition of four subscales and a composite score. They also support the use of the OPSAT-Q to examine the impact of bisphosphonate dosing frequency on patient satisfaction

    Adjusting for treatment switching in the METRIC study shows further improved overall survival with trametinib compared with chemotherapy

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    Abstract Trametinib, a selective inhibitor of mitogen-activated protein kinase kinase 1 (MEK1) and MEK2, significantly improves progression-free survival compared with chemotherapy in patients with BRAF V600E/K mutation-positive advanced or metastatic melanoma (MM). However, the pivotal clinical trial permitted randomized chemotherapy control group patients to switch to trametinib after disease progression, which confounded estimates of the overall survival (OS) advantage of trametinib. Our purpose was to estimate the switching-adjusted treatment effect of trametinib for OS and assess the suitability of each adjustment method in the primary efficacy population. Of the patients randomized to chemotherapy, 67.4% switched to trametinib. We applied the rank-preserving structural failure time model, inverse probability of censoring weights, and a two-stage accelerated failure time model to obtain estimates of the relative treatment effect adjusted for switching. The intent-to-treat (ITT) analysis estimated a 28% reduction in the hazard of death with trametinib treatment (hazard ratio [HR], 0.72; 95% CI, 0.52-0.98) for patients in the primary efficacy population (data cut May 20, 2013). Adjustment analyses deemed plausible provided OS HR point estimates ranging from 0.48 to 0.53. Similar reductions in the HR were estimated for the first-line metastatic subgroup. Treatment with trametinib, compared with chemotherapy, significantly reduced the risk of death and risk of disease progression in patients with BRAF V600E/K mutation-positive advanced melanoma or MM. Adjusting for switching resulted in lower HRs than those obtained from standard ITT analyses. However, CI are wide and results are sensitive to the assumptions associated with each adjustment method. signal-regulated kinase kinase (MEK) inhibitor that was approved in May 2013 in the United States. METRIC (MEK Versus Dacarbazine [DTIC] or Paclitaxel [Taxol] in Metastatic Melanoma) was a randomized, multicenter phase 3 trial evaluating the efficacy and safety of trametinib compared with standard chemotherapy (dacarbazine or paclitaxel) in patients with advanced or metastatic (stage IIIc or IV) BRAF V600E/K mutation-positive melanoma. The prespecified number of PFS events was reached in October 2011. An intent-to-treat (ITT) analysis (comparing groups as randomized, without adjustment for treatment switching), conducted in February 2012, estimated a 58% reduction in the hazard for progression with trametinib (hazard ratio [HR], 0.42; 95% CI, 0.29-0.59) When treatment switching is permitted, an ITT analysis can be confounded. If switching is permitted after PD, postprogression survival (PPS) in switching patients is likely to be extended compared with the PPS that would have been observed in the absence of switching. Therefore, an ITT analysis is likely to underestimate the OS effect of a novel treatment Statistical methods that adjust for treatment switching are available. However, naive &quot;per-protocol&quot; methods that simply exclude switchers from the analysis, or censor them at the time of switch, will produce biased results because the propensity to switch is likely to be correlated with patient prognosis In this study, we apply RPSFTM, IPCW, and two-stage methods to account for confounding associated with treatment switching in METRIC to obtain a more reliable estimate of the true OS treatment effect of trametinib compared with chemotherapy, using a May 2013 data cut. In line with recent methodological recommendations [6], we assessed the suitability of each adjustment method in the context of METRIC. Materials and Methods Patients Patients enrolled on METRIC were randomized 2:1 to receive trametinib 2 mg once daily or chemotherapy (dacarbazine or paclitaxel). A total of 322 patients were enrolled on the study Ethics The study was approved by the institutional review board, and all patients provided written, informed consent to participate in the study. Statistical analyses The RPSFTM estimates counterfactual survival times (i.e., survival times that would have been observed in the absence of treatment switching) The Iterative Parametric Estimation (IPE) adjustment method adapts the RPSFTM by using a parametric estimation procedure The IPCW method artificially censors patients at the point of treatment switch and estimates weights for the observations associated with remaining patients according to their baseline and time-varying demographic and diseaserelated characteristics to adjust for any potential confounding created by the artificial censoring The two-stage estimation method can be applied when switching occurs after a disease-related time point Censoring in counterfactual datasets can be problematic because the treatment received affects the probability that the survival time of an individual is censored by the study end date We used Stata version 13.1 software [27] to carry out all of our analyses, and we used the strbee command to apply the RPSFTM method Results In the primary efficacy population, 64 patients (67.4%) in the control group had switched to trametinib at the time of data cutoff. Of these, 62 patients switched after disease progression had occurred -hence, we deemed it unnecessary to attempt to adjust for the effect of switching on estimates of the treatment effect on PFS, but it is clear that control group OS is likely to be confounded. There were 109 deaths (61.2%) in the The ITT analysis for the primary efficacy population showed that OS was improved for patients randomized to trametinib compared to those randomized to chemotherapy (HR from a Cox proportional hazards regression model stratified for LDH level and prior chemotherapy for advanced or metastatic disease, 0.72; 95% CI, 0.52-0.98). The results of the adjustment analyses are presented in The first-line metastatic subgroup included 176 patients (trametinib, n = 114; chemotherapy, n = 62). In this subgroup, 43 patients on chemotherapy (69.4%) switched to trametinib. There were 70 deaths (61.4%) in the trametinib group and 40 (64.5%) in the chemotherapy group (29 switchers and 11 nonswitchers). The first-line subgroup ITT analysis indicated that the treatment benefit of trametinib compared with chemotherapy was slightly greater than that for the entire primary efficacy population (HR from a Cox proportional hazards regression model stratified for LDH level, 0.67; 95% CI, 0.45-1.00), although the difference was not statistically significant. Switching adjustment methods again produced reduced HRs and a larger divergence between the Kaplan-Meier curves for the trametinib and chemotherapy groups Discussion Trametinib was previously shown to improve PFS and OS compared with chemotherapy in patients with BRAF V600E/K-mutant melanoma After adjustment for treatment switching, trametinib reduced the risk of death compared with chemotherapy, with HRs substantially lower than those from the ITT analysis, which estimated a 28% reduction in the hazard of death with trametinib. The most plausible RPSFTM, IPCW, and two-stage method analyses estimated reductions of between 47% and 52%. CI were wide and of borderline statistical significance, largely due to the design of the adjustment methods. The RPSFTM and two-stage methods retained the P value from the ITT analysis by design (though bootstrapping could alternatively be undertaken), thus, in situations when the point estimate of the HR is reduced, CI widen. The IPCW analysis does not retain the ITT analysis P value (P = 0.04 in the ITT analysis reduced to P = 0.02 in the IPCW analysis). The point estimate reductions in the OS HR were not unexpected; the majority of patients randomized to the chemotherapy group switched to trametinib (64 of 95 patients [67.4%]). Given the large PFS treatment effect associated with trametinib, it is reasonable to expect that switching patients will live longer than they would have if they had not switched treatments. Adjusting for the treatment switching observed in the majority of chemotherapy group patients would therefore be expected to have a substantial impact on the estimate of the OS treatment effect. Although adjustment methods such as the RPSFTM, IPCW, and two-stage estimation are likely to produce smaller bias than naive per-protocol adjustments The RPSFTM is reliant upon the common treatment effect assumption. This assumption may be implausible given that most switching patients received trametinib after PD, potentially resulting in a diminished capacity to benefit compared to patients who received trametinib immediately upon randomization. The comparison of counterfactual survival times estimated for the control group and the experimental group resulted in HRs of N. R. Latimer et al. Treatment Switching Adjustment in METRIC 1.00, suggesting that the RPSFTM analyses had worked well. However, a visual inspection of the counterfactual survival curves presented in The IPCW is reliant on the no unmeasured confounders assumption. Results are prone to substantial error with small sample sizes and large switching proportions The two-stage estimation method is reliant upon the no unmeasured confounders assumption at PD and assumes that no additional time-dependent confounding occurs between PD and the time of treatment switch. For the primary efficacy population analysis, models converged that incorporated all covariates; therefore, the no unmeasured confounders assumption is not unreasonable. However, in the first-line subgroup, EORTC QLQ-30 covariates were excluded for model convergence to be achieved; therefore, the results are less robust (although EQ-5D covariates were retained, and hence the no unmeasured confounders assumption may remain reasonable). Although Recensoring was a key issue in our METRIC analysis. Whilst the issues around recensoring have been previously considered These analyses that adjust for the switching observed in METRIC confirm that trametinib improved OS, compared with chemotherapy, in patients with MM with a V600E/K BRAF mutation. The size of the treatment effect was considerably larger than that estimated using a standard, unadjusted ITT analysis. Although adjustment methods have important limitations, they are likely to provide a more reliable estimate of the true treatment effect of trametinib than an ITT analysis. Given the particular characteristics of METRIC, the RPSFTM, and two-stage methods that do not incorporate recensoring may be more appropriate than those that do. A key advantage of the two-stage and IPCW analyses is that they do not require the common treatment effect assumption, although this assumption appears reasonable in this case. Two-stage analyses are potentially more prone to bias than IPCW analyses because they do not account for all time-dependent confounding, but it was possible to incorporate more covariates in the two-stage models than the IPCW models in the METRIC analysis; hence, the IPCW may be more prone to unmeasured confounding. In particular, for the first-line treatment subgroup, we believe that the IPCW provided implausible results -the estimated HR appears unrealistically low. The IPCW method is prone to error in scenarios similar to those exhibited by the first-line subgroup within the METRIC trial -with very low numbers of control group patients not switching and potential unmeasured confounding Acknowledgment We thank SciMentum for editorial assistance with this manuscript. Conflict of Interest Supporting Information Additional supporting information may be found in the online version of this article: Data S1. Implementation of adjustment methods
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