Impact of enzalutamide (ENZA) vs. bicalutamide (BIC) on health-related quality of life (HRQoL) of patients (pts) with castration-resistant prostate cancer (CRPC): STRIVE study.

2018 ◽  
Vol 36 (6_suppl) ◽  
pp. 234-234
Author(s):  
Raoul Concepcion ◽  
Andrew J. Armstrong ◽  
Lawrence Ivan Karsh ◽  
Stefan Holmstrom ◽  
Cristina Ivanescu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Multiple Imputation ◽  
Lower Risk ◽  
Missing At Random ◽  
Minimum Clinically Important Difference ◽  
Well Being ◽  
Castration Resistant Prostate Cancer ◽  
Imputation Model ◽  
Piecewise Exponential ◽  
Outcome Index

234 Background: In STRIVE pts with CRPC (M0 n = 139; M1 n = 257), median time to 10-point decrease from baseline in FACT-P total for ENZA vs. BIC was 8.4 vs. 8.3 months (hazard ratio [HR] 0.91; 95% confidence interval [CI] 0.70, 1.19; p = 0.49). That assumed missing data was missing at random (MAR) and censored pts with no deterioration in FACT-P at last assessment. As HRQoL may worsen after progression/adverse events, for all STRIVE pts we replaced the MAR assumption with assumptions more likely to reflect clinically plausible HRQoL decline. Methods: Analyses of HRQoL decline (minimum clinically important difference or higher decrease in FACT-P vs. baseline) used a missing not at random (MNAR) assumption using a pattern mixture model (PMM) via sequential modeling with multiple imputation when imputation varies by reason of treatment discontinuation. Analysis of time to first clinically meaningful deterioration vs. baseline used a piecewise exponential survival multiple imputation model with reason-specific ∆ adjustment patterns similar to PMM analysis. Results: PMM analysis showed differences at week 61 in mean HRQoL change from baseline favoring ENZA vs. BIC for 7 of 10 scores: physical (PWB), functional, emotional (EWB), and social (SWB) well-being; FACT-P trial outcome index; FACT-G total; FACT-P total (all clinically meaningful except PWB). In the piecewise exponential survival imputation model, ENZA had a significantly lower risk of first deterioration in FACT-P total (0.76 [0.60, 0.95]), FACT-G total (0.66 [0.52, 0.83]), Prostate Cancer Subscale (PCS) pain-related (0.78 [0.62, 0.97]), SWB (0.49 [0.38, 0.64]), and EWB (0.58 [0.45, 0.75]) vs. BIC. For remaining domain scores, ENZA reduces risk of first deterioration (HR < 1) but the 95% CI includes 1 (which means not significant); sensitivity analysis showed similar results. Conclusions: In STRIVE pts, declines in all FACT-P scores were smaller for ENZA vs. BIC up to week 61. Comparison of change from baseline at week 61 favored ENZA for 7 of 10 scores (6 clinically meaningful). ENZA had a significantly lower risk of first deterioration in FACT-P or FACT-G total, PCS pain-related, EWB, and SWB. Clinical trial information: NCT01664923.

Health-related quality of life (HRQoL) patient-reported outcomes (PROs) and survival (OS) in metastatic castration-resistant prostate cancer (mCRPC) patients (pts).

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 53-53
Author(s):  
Lorente David ◽  
Rebeca Lozano ◽  
Guillermo de Velasco ◽  
Nuria Romero-Laorden ◽  
Elena Castro ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cox Regression ◽  
Well Being ◽  
Castration Resistant Prostate Cancer ◽  
Kaplan Meier ◽  
Patient Reported ◽  
Trial Outcome Index ◽  
Related Quality ◽  
Health Related ◽  
Outcome Index

53 Background: HRQoL is a relevant endpoint in trials in advanced prostate cancer. An association between HRQoL and OS has been reported. The Functional Assessment of Cancer Therapy-Prostate (FACT-P) is a validated HRQoL PRO in mCRPC. Methods: We evaluated the association between FACT-P and OS in the COU-301 and COU-302 trials (abiraterone vs placebo in mCRPC pts). FACT-P scores, sub-scores (physical (PWB), emotional (EWB), functional (FWB), social (SWB) well-being, prostate cancer subscale (PCS) FACT-G and the Trial Outcome Index (TOI) were calculated. A decrease in 3 (PWB, EWB, SWB, FWB, PCS), 9 (FACT-G, TOI) or 10 (FACT-Total) points after 3 cycles was considered clinically relevant. The association between FACT-P and OS was evaluated with Kaplan-Meier, Cox-regression models and c-indices. Results: 2,177 pts (COU-301: 1,121 /COU-302: 1,056) had valid baseline (BL) FACT-P scores. Mean BL score was 106.6 (COU-301) and 122.3 (COU-302). BL total scores were associated with OS in both COU-AA-301 (p<0.001) and COU-302 (p<0.001), independent of treatment. All FACT-P sub-scores except SWB were associated with OS (Table). A decrease in FACTP scores was associated with decreased OS in COU-301 (19.6 vs 14.2m; HR: 1.8; p<0.001) and COU-302 (34.4 vs 27.7m; HR: 1.3; p=0.009) datasets. Conclusions: BL FACTP scores (except SWB subscale) are significantly associated with outcome. Early declines in HRQoL can be observed and are associated with worse outcome. Prospective evaluation of the significance of changes in HRQoL is needed. YODA Project 2018-3745.[Table: see text]

Flexible, Free Software for Multilevel Multiple Imputation: A Review of Blimp and jomo

2019 ◽  
Vol 44 (5) ◽  
pp. 625-641
Author(s):  
Timothy Hayes
Keyword(s):  
Multiple Imputation ◽  
Interaction Model ◽  
Missing At Random ◽  
Analysis Model ◽  
Imputation Model ◽  
Model Following ◽  
Software Packages ◽  
Random Intercept ◽  
Impute Data ◽  
Multilevel Multiple Imputation

Multiple imputation is a popular method for addressing data that are presumed to be missing at random. To obtain accurate results, one’s imputation model must be congenial to (appropriate for) one’s intended analysis model. This article reviews and demonstrates two recent software packages, Blimp and jomo, to multiply impute data in a manner congenial with three prototypical multilevel modeling analyses: (1) a random intercept model, (2) a random slope model, and (3) a cross-level interaction model. Following these analysis examples, I review and discuss both software packages.

scholarly journals Multiple Imputation with Survey Weights: A Multilevel Approach

2019 ◽  
Vol 8 (5) ◽  
pp. 965-989
Author(s):  
M Quartagno ◽  
J R Carpenter ◽  
H Goldstein
Keyword(s):  
Multiple Imputation ◽  
Missing At Random ◽  
Model Parameters ◽  
Imputation Model ◽  
Data Set ◽  
Weighted Analysis ◽  
Empirical Performance ◽  
Uk Millennium Cohort Study ◽  
The Uk ◽  
Variance Estimates

Abstract Multiple imputation is now well established as a practical and flexible method for analyzing partially observed data, particularly under the missing at random assumption. However, when the substantive model is a weighted analysis, there is concern about the empirical performance of Rubin’s rules and also about how to appropriately incorporate possible interaction between the weights and the distribution of the study variables. One approach that has been suggested is to include the weights in the imputation model, potentially also allowing for interactions with the other variables. We show that the theoretical criterion justifying this approach can be approximately satisfied if we stratify the weights to define level-two units in our data set and include random intercepts in the imputation model. Further, if we let the covariance matrix of the variables have a random distribution across the level-two units, we also allow imputation to reflect any interaction between weight strata and the distribution of the variables. We evaluate our proposal in a number of simulation scenarios, showing it has promising performance both in terms of coverage levels of the model parameters and bias of the associated Rubin’s variance estimates. We illustrate its application to a weighted analysis of factors predicting reception-year readiness in children in the UK Millennium Cohort Study.

scholarly journals Multiple imputation with missing data indicators

2021 ◽  
pp. 096228022110473
Author(s):  
Lauren J Beesley ◽  
Irina Bondarenko ◽  
Michael R Elliot ◽  
Allison W Kurian ◽  
Steven J Katz ◽  
...  
Keyword(s):  
Multiple Imputation ◽  
Regression Models ◽  
Missing Values ◽  
Final Analysis ◽  
Missing At Random ◽  
Imputation Model ◽  
General Technique ◽  
Breast Cancer Study ◽  
Sequential Regression ◽  
Imputation Procedure

Multiple imputation is a well-established general technique for analyzing data with missing values. A convenient way to implement multiple imputation is sequential regression multiple imputation, also called chained equations multiple imputation. In this approach, we impute missing values using regression models for each variable, conditional on the other variables in the data. This approach, however, assumes that the missingness mechanism is missing at random, and it is not well-justified under not-at-random missingness without additional modification. In this paper, we describe how we can generalize the sequential regression multiple imputation imputation procedure to handle missingness not at random in the setting where missingness may depend on other variables that are also missing but not on the missing variable itself, conditioning on fully observed variables. We provide algebraic justification for several generalizations of standard sequential regression multiple imputation using Taylor series and other approximations of the target imputation distribution under missingness not at random. Resulting regression model approximations include indicators for missingness, interactions, or other functions of the missingness not at random missingness model and observed data. In a simulation study, we demonstrate that the proposed sequential regression multiple imputation modifications result in reduced bias in the final analysis compared to standard sequential regression multiple imputation, with an approximation strategy involving inclusion of an offset in the imputation model performing the best overall. The method is illustrated in a breast cancer study, where the goal is to estimate the prevalence of a specific genetic pathogenic variant.

New combination therapies for castration-resistant prostate cancer

2019 ◽  
Author(s):  
Mitchell G Lawrence ◽  
Laura H Porter ◽  
Daisuke Obinata ◽  
Shahneen Sandhu ◽  
Luke A Selth ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
New Combination ◽  
Combination Therapies ◽  
Castration Resistant Prostate Cancer ◽  
Castration Resistant
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