scholarly journals Is more better? An analysis of toxicity and response outcomes from dose-finding clinical trials in cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kristian Brock ◽  
Victoria Homer ◽  
Gurjinder Soul ◽  
Claire Potter ◽  
Cody Chiuzan ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Dose Escalation ◽  
Regression Models ◽  
Dose Finding ◽  
Linear Regression Models ◽  
Cancer Treatments ◽  
High Doses ◽  
Cytotoxic Therapies ◽  
Higher Doses ◽  
Tolerable Dose

Abstract Background The overwhelming majority of dose-escalation clinical trials use methods that seek a maximum tolerable dose, including rule-based methods like the 3+3, and model-based methods like CRM and EWOC. These methods assume that the incidences of efficacy and toxicity always increase as dose is increased. This assumption is widely accepted with cytotoxic therapies. In recent decades, however, the search for novel cancer treatments has broadened, increasingly focusing on inhibitors and antibodies. The rationale that higher doses are always associated with superior efficacy is less clear for these types of therapies. Methods We extracted dose-level efficacy and toxicity outcomes from 115 manuscripts reporting dose-finding clinical trials in cancer between 2008 and 2014. We analysed the outcomes from each manuscript using flexible non-linear regression models to investigate the evidence supporting the monotonic efficacy and toxicity assumptions. Results We found that the monotonic toxicity assumption was well-supported across most treatment classes and disease areas. In contrast, we found very little evidence supporting the monotonic efficacy assumption. Conclusions Our conclusion is that dose-escalation trials routinely use methods whose assumptions are violated by the outcomes observed. As a consequence, dose-finding trials risk recommending unjustifiably high doses that may be harmful to patients. We recommend that trialists consider experimental designs that allow toxicity and efficacy outcomes to jointly determine the doses given to patients and recommended for further study.

scholarly journals Is More Better? An Analysis of Toxicity and Response Outcomes From Dose-Finding Clinical Trials in Cancer

2020 ◽  
Author(s):  
Kristian Brock ◽  
Victoria Homer ◽  
Gurjinder Soul ◽  
Claire Potter ◽  
Cody Chiuzan ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Dose Escalation ◽  
Regression Models ◽  
Dose Finding ◽  
Linear Regression Models ◽  
Cancer Treatments ◽  
High Doses ◽  
Cytotoxic Therapies ◽  
Higher Doses ◽  
Tolerable Dose

Abstract BackgroundThe overwhelming majority of dose-escalation clinical trials use methods that seek a maximum tolerable dose, including rule-based methods like the 3+3, and model-based methods like CRM and EWOC. These methods assume that the incidences of efficacy and toxicity always increase as dose is increased. This assumption is widely accepted with cytotoxic therapies. In recent decades, however, the search for novel cancer treatments has broadened, increasingly focusing on inhibitors and antibodies. The rationale that higher doses are always associated with superior efficacy is less clear for these types of therapies. MethodsWe extracted dose-level efficacy and toxicity outcomes from 115 manuscripts reporting dose-finding clinical trials in cancer between 2008 and 2014. We analysed the outcomes from each manuscript using flexible non-linear regression models to investigate the evidence supporting the monotonic efficacy and toxicity assumptions. ResultsWe found that the monotonic toxicity assumption was well-supported across most treatment classes and disease areas. In contrast, we found very little evidence supporting the monotonic efficacy assumption. ConclusionsOur conclusion is that dose-escalation trials routinely use methods whose assumptions are violated by the outcomes observed. As a consequence, dose-finding trials risk recommending unjustifiably high doses that may be harmful to patients. We recommend that trialists consider experimental designs that allow toxicity and efficacy outcomes to jointly determine the doses given to patients and recommended for further study.

scholarly journals Is more better? An analysis of toxicity and response outcomes from dose-finding clinical trials in cancer

2020 ◽  
Author(s):  
Kristian Brock ◽  
Victoria Homer ◽  
Gurjinder Soul ◽  
Claire Potter ◽  
Cody Chiuzan ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Dose Escalation ◽  
Regression Models ◽  
Dose Finding ◽  
Linear Regression Models ◽  
Cancer Treatments ◽  
High Doses ◽  
Cytotoxic Therapies ◽  
Higher Doses ◽  
Tolerable Dose

The overwhelming majority of dose-escalation clinical trials use methods that seek a maximum tolerable dose, including rule-based methods like the 3+3, and model-based methods like CRM and EWOC. These methods assume that the incidences of efficacy and toxicity always increase as dose is increased. This assumption is widely accepted with cytotoxic therapies. In recent decades, however, the search for novel cancer treatments has broadened, increasingly focusing on inhibitors and antibodies. The rationale that higher doses are always associated with superior efficacy is less clear for these types of therapies. We extracted dose-level efficacy and toxicity outcomes from 115 manuscripts reporting dose-finding clinical trials in cancer between 2008 and 2014. We analysed the outcomes from each manuscript using flexible non-linear regression models to investigate the evidence supporting the monotonic efficacy and toxicity assumptions. We found that the monotonic toxicity assumption was well-supported across most treatment classes and disease areas. In contrast, we found very little evidence supporting the monotonic efficacy assumption. Our conclusion is that dose-escalation trials routinely use methods whose assumptions are violated by the outcomes observed. As a consequence, dose-finding trials risk recommending unjustifiably high doses that are harmful to patients. We recommend that trialists consider experimental designs that allow toxicity and efficacy outcomes to jointly determine the doses given to patients and recommended for further study.

scholarly journals Adaptive Dose-Finding Studies: A Review of Model-Guided Phase I Clinical Trials

2014 ◽  
Vol 32 (23) ◽  
pp. 2505-2511 ◽  
Author(s):  
Alexia Iasonos ◽  
John O'Quigley
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Dose Escalation ◽  
Late Onset ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Design Parameters ◽  
Phase I Trials ◽  
Phase I Clinical Trials ◽  
Trial Duration

Purpose We provide a comprehensive review of adaptive phase I clinical trials in oncology that used a statistical model to guide dose escalation to identify the maximum-tolerated dose (MTD). We describe the clinical setting, practical implications, and safety of such applications, with the aim of understanding how these designs work in practice. Methods We identified 53 phase I trials published between January 2003 and September 2013 that used the continual reassessment method (CRM), CRM using escalation with overdose control, or time-to-event CRM for late-onset toxicities. Study characteristics, design parameters, dose-limiting toxicity (DLT) definition, DLT rate, patient-dose allocation, overdose, underdose, sample size, and trial duration were abstracted from each study. In addition, we examined all studies in terms of safety, and we outlined the reasons why escalations occur and under what circumstances. Results On average, trials accrued 25 to 35 patients over a 2-year period and tested five dose levels. The average DLT rate was 18%, which is lower than in previous reports, whereas all levels above the MTD had an average DLT rate of 36%. On average, 39% of patients were treated at the MTD, and 74% were treated at either the MTD or an adjacent level (one level above or below). Conclusion This review of completed phase I studies confirms the safety and generalizability of model-guided, adaptive dose-escalation designs, and it provides an approach for using, interpreting, and understanding such designs to guide dose escalation in phase I trials.

scholarly journals Optimal biological dose: a systematic review in cancer phase I clinical trials

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
J. Fraisse ◽  
D. Dinart ◽  
D. Tosi ◽  
C. Bellera ◽  
C. Mollevi
Keyword(s):  
Systematic Review ◽  
Clinical Trials ◽  
Phase I ◽  
Dose Escalation ◽  
Phase 1 ◽  
Dose Finding ◽  
Cancer Clinical Trials ◽  
Phase I Clinical Trials ◽  
Cancer Trial ◽  
Biological Dose

Abstract Background Classical phase 1 dose-finding designs based on a single toxicity endpoint to assess the maximum tolerated dose were initially developed in the context of cytotoxic drugs. With the emergence of molecular targeted agents and immunotherapies, the concept of optimal biological dose (OBD) was subsequently introduced to account for efficacy in addition to toxicity. The objective was therefore to provide an overview of published phase 1 cancer clinical trials relying on the concept of OBD. Methods We performed a systematic review through a computerized search of the MEDLINE database to identify early phase cancer clinical trials that relied on OBD. Relevant publications were selected based on a two-step process by two independent readers. Relevant information (phase, type of therapeutic agents, objectives, endpoints and dose-finding design) were collected. Results We retrieved 37 articles. OBD was clearly mentioned as a trial objective (primary or secondary) for 22 articles and was traditionally defined as the smallest dose maximizing an efficacy criterion such as biological target: biological response, immune cells count for immunotherapies, or biological cell count for targeted therapies. Most trials considered a binary toxicity endpoint defined in terms of the proportion of patients who experienced a dose-limiting toxicity. Only two articles relied on an adaptive dose escalation design. Conclusions In practice, OBD should be a primary objective for the assessment of the recommended phase 2 dose (RP2D) for a targeted therapy or immunotherapy phase I cancer trial. Dose escalation designs have to be adapted accordingly to account for both efficacy and toxicity.

Investigation of the Association Between Treatment Effects On Progression Free Survival and Overall Survival in Multiple Myeloma Using Data From Published Literature

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 4241-4241
Author(s):  
Bin Zhang ◽  
Shannon Cartier ◽  
Virginia Rosen ◽  
April Teitelbaum ◽  
Ying Zhang ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Overall Survival ◽  
Clinical Trials ◽  
Linear Regression ◽  
Regression Models ◽  
Treatment Effects ◽  
Geometric Mean ◽  
Progression Free Survival ◽  
Linear Regression Models ◽  
Free Survival

Abstract Abstract 4241 Background: The primary goals in the development of new oncology therapies are treatments that improve quality of life and overall survival (OS). In recent years, advances in the treatment of multiple myeloma (MM) have led to improvements in OS. However, with improved treatment options and multiple rounds of therapy it is becoming an increasing challenge to demonstrate OS improvements in clinical trials. Thus, progression free survival (PFS) is widely used as a surrogate endpoint to demonstrate long term clinical benefit. However, to our knowledge, there has been very limited analysis of the association between treatment effects on PFS and treatment effects on OS in patients with multiple myeloma (MM). Purpose: To evaluate whether observed treatment effects on PFS are positively associated with treatment effects on OS in MM based on published data from clinical trials in patients with MM. Methods: A systematic literature review of MM identified 13 published clinical trials that reported HRs for the effects of treatment on PFS and separately on OS. The patients in 12 of the studies were previously untreated, and one study included patients with relapsed/refractory disease. The Pearson correlation coefficient (Pearson r) was used to estimate the association between the reported hazard ratios (HRPFS and HROS), as well as the log-transformed HRs (log(HRPFS) and log(HROS)), for the treatment effects on PFS and OS. Linear regression models were used to evaluate the relationship between the HR, and log(HR), of PFS and OS. A log transformation of the HRs for PFS and OS was used in order to minimize any skewness and normalize the data. R-squared values were estimated from the regression models. 95% Confidence intervals around the R-squared values were estimated using Olkin and Finn's approximation (I. Olkin and J. D. Finn, Psychological Bulletin, Vol 118(1), Jul 1995, 155–164) of the standard error and Student's T distribution. Sensitivity analyses included the estimation of additional weighted regression models to investigate the robustness of the R-squared estimates using two weighting methods. Individual study sample sizes were used as weights in one method to allocate more precision to studies with larger sample sizes. Estimates of the geometric mean of the variance of the HRs were also utilized as weights to account for the multi-directional error around the two HRs. The geometric mean estimates were calculated as the inverse of the product of the width of the two HRs. Results: Pearson r estimates between the HRs for the treatment effects on PFS and OS showed a strong positive correlation between HRPFS and HROS r=0.815 (CI: 0.53–0.93; p<0.0001), and between log(HRPFS) and log(HROS) r=0.80 (CI:0.50–0.92; p=0.0001), indicating that the treatment effects on PFS and treatment effects on OS in MM are positively associated. The HRPFS and log(HRPFS) were significantly associated with the HROS and log(HROS), respectively, based on the linear regression models. The R-squared values for these models were 0.66 (CI: 0.43–0.89) for the regression of HROS on HRPFS and R-squared values of 0.64 (CI: 0.40–88) for the regression of log(HROS) on log(HRPFS). Figure 1 displays the association between the log-transformed HRs. The sensitivity analysis results for the Pearson r and R-squared values were generally consistent with the primary analysis. Pearson r estimates ranged from 0.67 to 0.84, and the weighted regressions between HROS and HRPFS produced the extremes for the R-squared estimates: 0.71 and 0.45 when weighting by sample size and geometric mean of the variances, respectively. A separate regression model was considered that also adjusted for available covariates (age, population, length of follow-up). These covariates were not statistically significant predictors at the alpha = 0.05 level. With the addition of these covariates, the R-squared value for the linear regression of log(HROS) on log(HRPFS) was 0.695. Conclusion: This analysis based on published MM clinical trial results indicate that treatment effects on PFS may be positively associated with treatment effects on OS. Further studies involving patient-level data would be necessary to confirm these results. Disclosures: Zhang: BMS: Employment. Cartier:OptumInsight: Consultancy. Rosen:BMS: Consultancy. Teitelbaum:Optum Insight was hired to do literature review: Employment. Bartlett:Bristol-Myers Squibb: Employment. Kroog:Bristol-Myers Squibb: Employment. Mukhopadhyay:BMS: Employment. Wagner:Bristol-Myers Squibb: Employment.

Biopsy positivity in prostate cancer patients undergoing mpMRI-targeted radiation dose escalation.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 336-336 ◽  
Author(s):  
Jessica Meshman ◽  
Benjamin Farnia ◽  
Radka Stoyanova ◽  
Isildinha Reis ◽  
Matthew Abramowitz ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Clinical Trials ◽  
High Risk ◽  
Dose Escalation ◽  
Prostate Biopsy ◽  
Background Radiation ◽  
Reference Group ◽  
Multivariable Analysis ◽  
Cancer Center ◽  
High Doses

336 Background: Radiation (RT) dose escalation improves prostate cancer outcomes, but when the whole gland is treated to high doses complications can arise. We used prostate multiparametric MRI (mpMRI) findings for targeted dose escalation (MTDE) in prospective clinical trials in which prostate biopsy at 2-3 years after completion of RT was planned. Biopsy positivity is a known predictor of biochemical failure. These findings are compared to those in another cohort in which standard whole gland RT doses were used. Methods: Patients enrolled on three investigator initiated clinical trials incorporating MTDE (n=30) were eligible for inclusion. All patients were assessed for response by prostate biopsy 2-3 years after RT. Patients were compared to a reference group treated with standard RT doses to the whole prostate on a randomized trial at Fox Chase Cancer Center (FCCC trial). Univariable and multivariable analysis (MVA) was performed to assess for correlation with biopsy positivity, defined as carcinoma with or without RT effect. Results: Of those treated with MTDE: 3 (10%) were low, 23 (77%) intermediate, and 4 (13%) high risk. Assuming an α/β ratio of 1.5, MTDE patients received an equivalent dose (EQD2) of 76 Gy to the prostate, with focal dose escalation to an EQD2 of 98-122 Gy to mpMRI lesions. The MTDE cohort was compared with 115 patients from the FCCC trial, where 23 (20%) were low, 74 (64%) intermediate, and 18 (16%) high risk. The FCCC trial patients received an EQD2 of 76 Gy (n=64) or 84.24 Gy (n=51) without boost. Median time from RT to biopsy was 2 years (range, 1.6-3.3). The post-treatment biopsy results were negative in 50% (n=73), atypical in 12% (n=17), carcinoma with RT effect in 31% (n=45) and frank carcinoma in 7% (n=10). On MVA, patients with tumor volume >20% were more likely to have positive post-RT biopsies (OR: 3.21, 95% CI: 1.34-7.68, p= 0.009). MTDE patients were less likely to have positive post-RT biopsies, 10% vs. 45%, (OR: 0.13, 95% CI: 0.03-0.46, p=0.002). Conclusions: Focal dose-escalation to mpMRI-defined lesions significantly reduces biopsy positivity, a measure associated with long term outcomes including distant metastasis.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

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