scholarly journals Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Alejandro Schuler ◽  
David Walsh ◽  
Diana Hall ◽  
Jon Walsh ◽  
Charles Fisher

Abstract Estimating causal effects from randomized experiments is central to clinical research. Reducing the statistical uncertainty in these analyses is an important objective for statisticians. Registries, prior trials, and health records constitute a growing compendium of historical data on patients under standard-of-care that may be exploitable to this end. However, most methods for historical borrowing achieve reductions in variance by sacrificing strict type-I error rate control. Here, we propose a use of historical data that exploits linear covariate adjustment to improve the efficiency of trial analyses without incurring bias. Specifically, we train a prognostic model on the historical data, then estimate the treatment effect using a linear regression while adjusting for the trial subjects’ predicted outcomes (their prognostic scores). We prove that, under certain conditions, this prognostic covariate adjustment procedure attains the minimum variance possible among a large class of estimators. When those conditions are not met, prognostic covariate adjustment is still more efficient than raw covariate adjustment and the gain in efficiency is proportional to a measure of the predictive accuracy of the prognostic model above and beyond the linear relationship with the raw covariates. We demonstrate the approach using simulations and a reanalysis of an Alzheimer’s disease clinical trial and observe meaningful reductions in mean-squared error and the estimated variance. Lastly, we provide a simplified formula for asymptotic variance that enables power calculations that account for these gains. Sample size reductions between 10% and 30% are attainable when using prognostic models that explain a clinically realistic percentage of the outcome variance.

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 1695-1695 ◽  
Author(s):  
Eric Padron ◽  
Najla H Al Ali ◽  
Deniz Peker ◽  
Jeffrey E Lancet ◽  
Pearlie K Epling-Burnette ◽  
...  

Abstract Abstract 1695 Introduction: CMML is a genetically and clinically heterogeneous malignancy characterized by peripheral monocytosis, cytopenias, and a propensity for AML transformation. Several prognostic models attempt to stratify patients into subcategories that are predictive for overall survival (OS), six models of which are specific to CMML. However, these models have either never been externally validated in the context of CMML or were externally validated prior to the use of hypomethylating agents. We externally validate and perform a detailed statistical comparison between the International Prognostic Scoring System (IPSS), MD Anderson Scoring System (MDASC), MD Anderson Prognostic Score (MDAPS), Dusseldorf Score (DS), and Spanish Scoring Systems (SS) in a large, single institution cohort. Methods: Data were collected retrospectively from the Moffitt Cancer Center (MCC) CMML database and charts were reviewed of patients that satisfied the WHO criteria for the diagnosis of CMML. The primary objective of the study was to validate the above prognostic models calculated at the time of initial presentation to MCC. All prognostic models were calculated as previously published. All analyses were conducted using SPSS version 15.0 (SPSS Inc, Chicago, IL). The Kaplan–Meier (KM) method was used to estimate median overall survival and the log rank test was used to compare KM survival estimates between two groups. Results: Between January 2000 and February 2012, 123 patients were captured by the MCC CMML database. The median age at diagnosis was 69 (30–90) years and the majority of patients were male (69%). By the WHO classification, the majority of patients had CMML-1 (84% vs. 16%) and most patients were subcategorized as MPN-CMML (59%) versus MDS-CMML (39%) by the FAB CMML criteria. The median overall survival of the entire cohort was 30 months and the rate of AML transformation was 44% (54). Twenty-two patients (18%) were treated with decitabine and 66 (54%) patients were treated with 5-azacitidine. Risk group stratification according to specific prognostic model is summarized in Table 1. The IPSS, MDASC, DS, and SS all predicted OS (p<0.05) while the MDASP could not be validated (p=0.924). When only patients who were treated with 5-azacitadine were considered, the MDASC, DS, and SS continued to predict OS (p<0.05) while the IPSS (p=0.15) and MDASP (p=0.239) did not. Previous reports have demonstrated that the MDASC provides further discrimination to refine stratification by the IPSS in Myelodysplastic Syndromes (MDS). Except for the low-risk DS patients, we grouped patients in our CMML cohort into lower and higher risk disease with each prognostic score and attempted to further stratify patients by the MDASC using KM and the log rank test. The MDASC was able to further risk stratify patients in each group for all prognostic models except those in the higher risk groups by the SS (p=0.07) and DS (P=0.45). When a similar statistical analysis was applied to each prognostic scoring system, only the MDASC was consistently able to further stratify the majority of risk groups as described in Table 2. The Dusseldorf scoring system was able to further stratify all lower risk groups regardless of model but was not able to do so in higher risk disease. Conclusions: This represents the first external validation of existing CMML prognostic models in the era of hypomethylating agent therapy. Except for the MDASP, we were able to validate the prognostic value all models tested. The MDASC represents the most robust model as it consistently refined the stratification of other models tested and remained predictive of OS in 5-azacitidine treated patients. Multi-institution collaboration is needed to construct a robust CMML specific prognostic model. Comparison to the IPSS-R is in progress. Disclosures: No relevant conflicts of interest to declare.


2021 ◽  
Vol 38 (5) ◽  
Author(s):  
Jia-qing Yan ◽  
Min Liu ◽  
Ying-lin Ma ◽  
Kai-di Le ◽  
Bin Dong ◽  
...  

AbstractIncreasing evidence demonstrated that alternative splicing (AS) plays a vital role in tumorigenesis and clinical outcome of patient. However, systematical analysis of AS in lung squamous cell carcinoma (LUSC) is lacking and greatly necessary. Thus, this study was to systematically estimate the function of AS events served as prognostic indicators in LUSC. Among 31,345 mRNA AS events in 9633 genes, we detected 1996 AS in 1409 genes which have significant connection with overall survival (OS) of LUSC patients. Then, prognostic model based on seven types of AS events was established and we further constructed a combined prognostic model. The Kaplan–Meier curve results suggested that seven types of AS signatures and the combined prognostic model could exhibit robust performance in predicting prognosis. Patients in the high-risk group had significantly shorter OS than those in the low-risk group. The ROC showed all prognostic models had high accuracy and powerful predictive performance with different AUC ranging from 0.837 to 0.978. Moreover, the combined prognostic model had highest performance in risk stratification and predictive accuracy than single prognostic models and had higher accuracy than other mRNA model. Finally, a significant correlation network between survival-related AS genes and prognostic splicing factors (SFs) was established. In conclusion, our study provided several potential prognostic AS models and constructed splicing network between AS and SFs in LUSC, which could be used as potential indicators and treatment targets for LUSC patients.


2019 ◽  
Vol 146 (3) ◽  
pp. 801-807 ◽  
Author(s):  
Di-Han Liu ◽  
Zheng-Hao Ye ◽  
Si Chen ◽  
Xue-Song Sun ◽  
Jing-Yu Hou ◽  
...  

Abstract Purpose We combined conventional clinical and pathological characteristics and pathological architectural grading scores to develop a prognostic model to identify a specific group of patients with stage I lung adenocarcinomas with poor survival following surgery. Methods This retrospective study included 198 patients with stage I lung adenocarcinomas recruited from 2004 to 2013. Multivariate analyses were used to confirm independent risk factors, which were checked for internal validity using the bootstrapping method. The prognostic scores, derived from β-coefficients using the Cox regression model, classified patients into high- and low-risk groups. The predictive performance and discriminative ability of the model were assessed by the area under the receiver operating characteristic curve (AUC), concordance index (C-index) and Kaplan–Meier survival analyses. Results Three risk factors were identified: T2 (rounding of β-coefficients = 81), necrosis (rounding of β-coefficients = 67), and pathological architectural score of 5–6 (rounding of β-coefficients = 58). The final prognostic score was the sum of points. The derived prognostic scores stratified patients into low- (score ≤ 103) and high- (score > 103) risk groups, with significant differences in 5-year overall survival (high vs. low risk: 49.3% vs. 88.0%, respectively; hazard ratio: 4.55; p < 0.001). The AUC for the proposed model was 0.717. The C-index of the model was 0.693. Conclusion An integrated prognostic model was developed to discriminate resected stage I adenocarcinoma patients into low- and high-risk groups, which will help clinicians select individual treatment strategies.


2021 ◽  
Author(s):  
Gerardo Alvarez-Uria ◽  
Sumanth Gandra ◽  
Venkata R Gurram ◽  
Raghu P Reddy ◽  
Manoranjan Midde ◽  
...  

Previous COVID-19 prognostic models have been developed in hospital settings, and are not applicable to COVID-19 cases in the general population. There is an urgent need for prognostic scores aimed to identify patients at high risk of complications at the time of COVID-19 diagnosis. The RDT COVID-19 Observational Study (RCOS) collected clinical data from patients with COVID-19 admitted regardless of the severity of their symptoms in a general hospital in India. We aimed to develop and validate a simple bedside prognostic score to predict the risk of hypoxaemia or death. 4035 patients were included in the development cohort and 2046 in the validation cohort. The primary outcome occurred in 961 (23.8%) and 548 (26.8%) patients in the development and validation cohorts, respectively. The final model included 12 variables: age, systolic blood pressure, heart rate, respiratory rate, aspartate transaminase, lactate dehydrogenase, urea, C-reactive protein, sodium, lymphocyte count, neutrophil count and neutrophil/lymphocyte ratio. In the validation cohort, the area under the receiver operating characteristic curve (AUROCC) was 0.907 (95% CI, 0.892-0.922) and the Brier Score was 0.098. The decision curve analysis showed good clinical utility in hypothetical scenarios where admission of patients was decided according to the prognostic index. When the prognostic index was used to predict mortality in the validation cohort, the AUROCC was 0.947 (95% CI, 0.925-0.97) and the Brier score was 0.0188. If our results are validated in other settings, the RCOS prognostic index could help improve the decision making in the current COVID-19 pandemic, especially in resource limited-settings.


2019 ◽  
Vol 1 (Supplement_1) ◽  
pp. i33-i33
Author(s):  
Dhiego Bastos ◽  
Sujit Prabhu ◽  
Raymond Sawaya ◽  
Andrei Joaquim ◽  
Diane Liu ◽  
...  

Abstract INTRODUCTION: Prognostic scores have been developed to predict overall survival (OS) in patients with brain metastasis from breast cancer, comprising different combinations of prognostic factors. A new prognostic score including the number of brain metastases has been proposed. We aimed to evaluate the use of these prognostic scores on a neurosurgical population. METHODS: Retrospective study with consecutive patients with brain metastasis from breast cancer treated in the neurosurgery department. Clinical end point is overall survival estimated by the Kaplan Meier method. Univariate and multicovariate Cox proportional hazard models are applied to estimate the effect of covariates of interest on OS. We employ Bootstrap validation method to estimate the bias-corrected or over fitting-corrected predictive accuracy of Cox models, which is presented by concordance index(C-index). RESULTS: 315 consecutive patients with brain metastasis from breast cancer. Median OS was 14 months(95% CI 11–16.9), KPS, number of brain metastases, biological subtypes, age and presence of extracranial metastases were significantly associated with improved OS on the univariate analysis. Multivariate analysis showed that KPS, biological subtype, age and number of brain metastases were statistically significant for OS. The recursive partitioning analysis(RPA) classes, the graded prognostic assessment(GPA) score, the diagnostic specific GPA(DS-GPA) and the modified DS-GPA identified individual subgroups with different OS. RPA and DS-GPA had OS statistical significant between all groups with a C-index of 0.561 and 0.586 respectively. DS-GPA had a c-index of 0.639 and modified DS-GPA had an c-index of 0.637. DS-GPA and modified DS-GPA had a better performance in terms of discrimination when compared to RPA(p&lt; 0.001) and GPA(p=0.01). CONCLUSIONS: DS-GPA and modified DS-GPA were able to better discriminate subgroups OS, which most likely reflects the use of biological subtype in the score calculation. The incorporation of number of BM by the modified DS-GPA improved the distinction between the higher score and the lower score group.


2021 ◽  
Author(s):  
Jia-qing Yan ◽  
Min Liu ◽  
Yin-lin Ma ◽  
Kai-di Le ◽  
Bin Dong ◽  
...  

Abstract Increasing evidence demonstrated that alternative splicing (AS) played a vital role in tumorigenesis and clinical outcome of patient. However, systematically analysis of AS in lung squamous cell carcinoma (LUSC) is lacking and greatly necessary. Thus, this study was to systematically estimate the function of AS events served as prognostic indicators in LUSC. Among 31,345 mRNA AS events in 9,633 genes, we detected 1,996 AS in 1,409 genes which have significantly connection with overall survival (OS) of LUSC patients. Then, prognostic model based on seven types of AS events were established and we further constructed a combined prognostic model. The Kaplan-Meier curve results suggested that seven types of AS signatures and the combined prognostic model could exhibit robust performance in predicting prognosis. Patients in the high risk group had significantly shorter OS than those in the low risk group. The ROC showed all prognostic models had high accuracy and powerful predictive performance with different AUC ranging from 0.837 to 0.978. Moreover, the combined prognostic model had highest performance in risk stratification and predictive accuracy than single prognostic models. Finally, a significant correlation network between survival-related AS genes and prognostic splicing factors (SFs) was established. In conclusion, our study provided several potential prognostic AS models and constructed splicing network between AS and SFs in LUSC, which could be used as potential indicators and treatment targets for LUSC patients.


2021 ◽  
Vol 11 ◽  
Author(s):  
Qiang Liu ◽  
Xiangyi Kong ◽  
Zhongzhao Wang ◽  
Xiangyu Wang ◽  
Wenxiang Zhang ◽  
...  

Purpose: Nomogram prognostic models could greatly facilitate risk stratification and treatment strategies for cancer patients. We developed and validated a new nomogram prognostic model, named NCCBM, for breast cancer patients with brain metastasis (BCBM) using a large BCBM cohort from the SEER (Surveillance, Epidemiology, and End Results) database.Patients and Methods: Clinical data for 975 patients diagnosed from 2011 to 2014 were used to develop the nomogram prognostic model. The predictive accuracy and discriminative ability of the nomogram were determined by concordance index (C-index) and calibration curve. The results were validated using an independent cohort of 542 BCBM patients diagnosed from 2014 to 2015.Results: The following variables were selected in the final prognostic model: age, race, surgery, radiation therapy, chemotherapy, laterality, grade, molecular subtype, and extracranial metastatic sites. The C-index for the model described here was 0.69 (95% CI, 0.67 to 0.71). The calibration curve for probability of survival showed good agreement between prediction by nomogram and actual observation. The model was validated in an independent validation cohort with a C-index of 0.70 (95% CI, 0.68 to 0.73).Conclusion: We developed and validated a nomogram prognostic model for BCBM patients, and the proposed nomogram resulted in good performance.


2021 ◽  
Vol 11 ◽  
Author(s):  
Chaobin He ◽  
Chongyu Zhao ◽  
Jiawei Lu ◽  
Xin Huang ◽  
Cheng Chen ◽  
...  

BackgroundAccumulating evidence has indicated the vital role of inflammation-based score (IBS) in predicting the prognostic outcome of cancer patients. Otherwise, their value in intrahepatic cholangiocarcinoma (iCCA) remains indistinct. The present study aimed to evaluate whether IBSs were related to survival outcomes in iCCA patients.MethodClinical characteristics were retrospectively collected in 399 patients diagnosed with iCCA from cohorts of Sun Yat-sen University Cancer Center (SYSUCC) and the First Hospital of Dalian Medical University (FHDMU). The survival curves were constructed with the Kaplan-Meier method and compared with the log-rank test. Univariate and multivariate analyses were conducted to determine the prognostic factors of overall survival (OS) and progression-free survival (PFS). The concordance index and the area under the time-dependent receiver operating characteristic (ROC) curves (AUROCs) were used to compare the predictive value of inflammation-based scores in terms of survival outcomes.ResultsThe significant survival differences in OS and DFS were observed when patients were stratified by the modified Glasgow Prognostic Score (mGPS) (p&lt;0.001). Multivariate analysis demonstrated that higher mGPS score was independently associated with poor OS and DFS (p&lt;0.001). The predictive accuracy of the mGPS was superior to other IBSs (all p&lt;0.001) in survival prediction in iCCA patients. The findings were further supported by the external validation cohort.ConclusionThe mGPS is a sensitive, efficient, simple and widely applicable preoperative prognostic factor for iCCA patients. Thus, more effective therapy and frequent surveillance should be conducted after surgical resection in iCCA patients with higher mGPS scores.


2020 ◽  
Vol 6 (1) ◽  
pp. 18-39
Author(s):  
Areena Zaini ◽  
Haryantie Kamil ◽  
Mohd Yazid Abu

The Electrical & Electronic (E&E) company is one of Malaysia’s leading industries that has 24.5% in manufacturing sector production. With a continuous innovation of E&E company, the current costing being used is hardly to access the complete activities with variations required for each workstation to measure the un-used capacity in term of resources and cost. The objective of this work is to develop a new costing structure using time-driven activity-based costing (TDABC) at . This data collection was obtained at E&E company located at Kuantan, Pahang that focusing on magnetic component. The historical data was considered in 2018. TDABC is used to measure the un-used capacity by constructing the time equation and capacity cost rate. This work found three conditions of un-used capacity. Type I is pessimistic situation whereby according to winding toroid core, the un-used capacity of time and cost are -14820 hours and -MYR2.60 respectively. It means the system must sacrifice the time and cost more than actual apportionment. Type II is most likely situation whereby according to assembly process, the un-used capacity of time and cost are 7400 hours and MYR201575.45 respectively. It means the system minimize the time and cost which close to fully utilize from the actual apportionment. Type III is optimistic situation whereby according to alignment process, the un-used capacity of time and cost are 4120 hours and MYR289217.15 respectively. It means the system used small amount of cost and time from the actual apportionment.


2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Kara-Louise Royle ◽  
David A. Cairns

Abstract Background The United Kingdom Myeloma Research Alliance (UK-MRA) Myeloma Risk Profile is a prognostic model for overall survival. It was trained and tested on clinical trial data, aiming to improve the stratification of transplant ineligible (TNE) patients with newly diagnosed multiple myeloma. Missing data is a common problem which affects the development and validation of prognostic models, where decisions on how to address missingness have implications on the choice of methodology. Methods Model building The training and test datasets were the TNE pathways from two large randomised multicentre, phase III clinical trials. Potential prognostic factors were identified by expert opinion. Missing data in the training dataset was imputed using multiple imputation by chained equations. Univariate analysis fitted Cox proportional hazards models in each imputed dataset with the estimates combined by Rubin’s rules. Multivariable analysis applied penalised Cox regression models, with a fixed penalty term across the imputed datasets. The estimates from each imputed dataset and bootstrap standard errors were combined by Rubin’s rules to define the prognostic model. Model assessment Calibration was assessed by visualising the observed and predicted probabilities across the imputed datasets. Discrimination was assessed by combining the prognostic separation D-statistic from each imputed dataset by Rubin’s rules. Model validation The D-statistic was applied in a bootstrap internal validation process in the training dataset and an external validation process in the test dataset, where acceptable performance was pre-specified. Development of risk groups Risk groups were defined using the tertiles of the combined prognostic index, obtained by combining the prognostic index from each imputed dataset by Rubin’s rules. Results The training dataset included 1852 patients, 1268 (68.47%) with complete case data. Ten imputed datasets were generated. Five hundred twenty patients were included in the test dataset. The D-statistic for the prognostic model was 0.840 (95% CI 0.716–0.964) in the training dataset and 0.654 (95% CI 0.497–0.811) in the test dataset and the corrected D-Statistic was 0.801. Conclusion The decision to impute missing covariate data in the training dataset influenced the methods implemented to train and test the model. To extend current literature and aid future researchers, we have presented a detailed example of one approach. Whilst our example is not without limitations, a benefit is that all of the patient information available in the training dataset was utilised to develop the model. Trial registration Both trials were registered; Myeloma IX-ISRCTN68454111, registered 21 September 2000. Myeloma XI-ISRCTN49407852, registered 24 June 2009.


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