Chemical Engineering Thermodynamic solution thermodynamic Introduction
Ternary Phase Diagram
LLE for Ternary System Water + Toluene + Benzaldehyde
Data Reliability
Use Proportional Hazards Regression Method To Analyze The Survival of Patient...Waqas Tariq
The Kaplan Meier method is used to analyze data based on the survival time. In this paper used Kaplan Meier procedure and Cox regression with these objectives. The objectives are finding the percentage of survival at any time of interest, comparing the survival time of two studied groups and examining the effect of continuous covariates with the relationship between an event and possible explanatory variables. The variables (Age, Gender, Weight, Drinking, Smoking, District, Employer, Blood Group) are used to study the survival patients with cancer stomach. The data in this study taken from Hiwa/Hospital in Sualamaniyah governorate during the period of (48) months starting from (1/1/2010) to (31/12/2013) .After Appling the Cox model and achieve the hypothesis we estimated the parameters of the model by using (Partial Likelihood) method and then test the variables by using (Wald test) the result show that the variables age and weight are influential at the survival of time.
Evaluating Racial Disparities in Survival after AIDS Diagnosis using Standard...fhardnett
The presentation was given at the Joint Statistical Meetings 2005 in Minneapolis, MN. The presentation describes the use of standardized Kaplan-Meier estimation to compare survival across population subgroups when covariate adjustment is necessary and the proportional hazards assumption does not hold.
Chemical Engineering Thermodynamic solution thermodynamic Introduction
Ternary Phase Diagram
LLE for Ternary System Water + Toluene + Benzaldehyde
Data Reliability
Use Proportional Hazards Regression Method To Analyze The Survival of Patient...Waqas Tariq
The Kaplan Meier method is used to analyze data based on the survival time. In this paper used Kaplan Meier procedure and Cox regression with these objectives. The objectives are finding the percentage of survival at any time of interest, comparing the survival time of two studied groups and examining the effect of continuous covariates with the relationship between an event and possible explanatory variables. The variables (Age, Gender, Weight, Drinking, Smoking, District, Employer, Blood Group) are used to study the survival patients with cancer stomach. The data in this study taken from Hiwa/Hospital in Sualamaniyah governorate during the period of (48) months starting from (1/1/2010) to (31/12/2013) .After Appling the Cox model and achieve the hypothesis we estimated the parameters of the model by using (Partial Likelihood) method and then test the variables by using (Wald test) the result show that the variables age and weight are influential at the survival of time.
Evaluating Racial Disparities in Survival after AIDS Diagnosis using Standard...fhardnett
The presentation was given at the Joint Statistical Meetings 2005 in Minneapolis, MN. The presentation describes the use of standardized Kaplan-Meier estimation to compare survival across population subgroups when covariate adjustment is necessary and the proportional hazards assumption does not hold.
ARTFICIAL INTELLIGENCE, SYSTEM ANALYSIS AND SIMULATION MODELING IN OPTIMIZATION OF TREATMENT FOR ESOPHAGEAL CANCER PATIENTS AFTER COMPLETE ESOPHAGECTOMIES
Application of Multivariate Regression Analysis and Analysis of VarianceKalaivanan Murthy
The work is done as part of graduate coursework at University of Florida. The author studied master's in environmental engineering sciences during the making of the presentation.
Gas Chromatography-Mass Spectrometry (GC-MS) is a powerful analytical technique used to separate, identify, and quantify components of complex mixtures. In GC-MS, a sample is first vaporized and injected into a gas chromatograph, where it undergoes separation based on differences in partitioning between a stationary phase and a mobile gas phase. The separated compounds then enter the mass spectrometer, where they are ionized, fragmented, and detected based on their mass-to-charge ratios. By comparing the mass spectra of the separated components with a database of known compounds, GC-MS can identify unknown substances with high specificity. Its sensitivity, versatility, and ability to analyze a wide range of compounds make GC-MS an indispensable tool in various fields including environmental analysis, pharmaceuticals, forensics, and metabolomics.
Determination of Elemental Impurities – Challenges of a Screening MethodSGS
On Dec. 16, 2014 the ICH Working Group published the elemental impurities guideline into the current version step 4. The aim of this control strategy is to track impurities that may contaminate pharmaceutical products that are potentially contributed by several sources. Additionally, the guideline also focuses on final drug product quality. To ensure that all components & all needed production steps required for a pharmaceutical product demonstrate regulatory compliance, risk assessment will become a priority for every pharmaceutical manufacturer. This approach of testing & documentation can become a major challenge, especially in the consideration of various potential sources.
EXPERIMENTAL IMPLEMENTATION OF EMBARRASINGLY PARALLEL PROCESS IN ANALYSIS OF ...ijesajournal
This paper explains the development of a embedded based parallel system to measure glucose concentration of the blood samples. The developed instrument works on the principle of absorbance transmittance photometry using ATmega32 microcontrollers. In order to handle more blood samples and reduce the response time of glucose analyzing process in large number of blood samples, the embarrassing parallel measurement operation is implemented. The proposed system architecture and the co-design of hardware and software are discussed in detail. The system is evaluated using the
parameters of Speedup Factor, Efficiency and Throughput are studied. The result shows that system attained the linear speedup in measurement of blood samples.
ARTFICIAL INTELLIGENCE, SYSTEM ANALYSIS AND SIMULATION MODELING IN OPTIMIZATION OF TREATMENT FOR ESOPHAGEAL CANCER PATIENTS AFTER COMPLETE ESOPHAGECTOMIES
Application of Multivariate Regression Analysis and Analysis of VarianceKalaivanan Murthy
The work is done as part of graduate coursework at University of Florida. The author studied master's in environmental engineering sciences during the making of the presentation.
Gas Chromatography-Mass Spectrometry (GC-MS) is a powerful analytical technique used to separate, identify, and quantify components of complex mixtures. In GC-MS, a sample is first vaporized and injected into a gas chromatograph, where it undergoes separation based on differences in partitioning between a stationary phase and a mobile gas phase. The separated compounds then enter the mass spectrometer, where they are ionized, fragmented, and detected based on their mass-to-charge ratios. By comparing the mass spectra of the separated components with a database of known compounds, GC-MS can identify unknown substances with high specificity. Its sensitivity, versatility, and ability to analyze a wide range of compounds make GC-MS an indispensable tool in various fields including environmental analysis, pharmaceuticals, forensics, and metabolomics.
Determination of Elemental Impurities – Challenges of a Screening MethodSGS
On Dec. 16, 2014 the ICH Working Group published the elemental impurities guideline into the current version step 4. The aim of this control strategy is to track impurities that may contaminate pharmaceutical products that are potentially contributed by several sources. Additionally, the guideline also focuses on final drug product quality. To ensure that all components & all needed production steps required for a pharmaceutical product demonstrate regulatory compliance, risk assessment will become a priority for every pharmaceutical manufacturer. This approach of testing & documentation can become a major challenge, especially in the consideration of various potential sources.
EXPERIMENTAL IMPLEMENTATION OF EMBARRASINGLY PARALLEL PROCESS IN ANALYSIS OF ...ijesajournal
This paper explains the development of a embedded based parallel system to measure glucose concentration of the blood samples. The developed instrument works on the principle of absorbance transmittance photometry using ATmega32 microcontrollers. In order to handle more blood samples and reduce the response time of glucose analyzing process in large number of blood samples, the embarrassing parallel measurement operation is implemented. The proposed system architecture and the co-design of hardware and software are discussed in detail. The system is evaluated using the
parameters of Speedup Factor, Efficiency and Throughput are studied. The result shows that system attained the linear speedup in measurement of blood samples.
Validation of bevacizumab elisa ich q2 ver3,0 dt14.03krishgen
This document presents a discussion of the characteristics of our KRIBIOLISA™
BEVACIZUMAB ELISA kit considered by us during the validation of this kit in accordance
with ICH Q2 (R1) guidelines. The document is prepared based on tests run in our laboratory
and does not necessarily seek to cover the testing that may be required at user’s end for
registration in, or regulatory submissions. The objective of this validation is to demonstrate
that it is suitable for its intended purpose – detection of Bevacizumab (Avastin)
36433 Topic HA W9 R1Number of Pages 1 (Double Spaced)N.docxrhetttrevannion
36433 Topic: HA W9 R1
Number of Pages: 1 (Double Spaced)
Number of sources: 2
Writing Style: APA
Type of document: Essay
Academic Level:Master
Category: Nursing
Language Style: English (U.S.)
Order Instructions: Attached
Shawna Harris
Wednesday Mar 6 at 3:38pm
Manage Discussion Entry
The U.S. Preventative Services Task Force has prostate screening recommendations. The U.S. Preventative Services Task Force suggests discussing with the patient the benefits and possible harm from obtaining a prostate specific antigen, PSA test (U.S. Preventative Services Task Force). There is a small percent of people for whom this test can correctly identify and thus reduce the risk of mortality from prostate cancer (U.S. Preventative Services Task Force). However, this test can often have false positives, which could result in obtaining an unnecessary biopsy (U.S. Preventative Services Task Force). Invasive procedures, such as biopsies always have risk factors of their own. Consequently, the U.S. Preventative Task Force recommends a PSA screening test for men ages fifty-five to sixty-nine, only if the patient is requesting this screening even after discussing benefits and possible harms from testing and biopsy. (U.S. Preventative Services Task Force).
The American Cancer Society, ACS, recommends that men age fifty and over discuss the benefits and risks of screening in order to make an informed decision with his provider (Wolf, Wender, Etzioni,….& Smith, 2010). ACS also recommends if a man is at a high risk, that this information is presented earlier than fifty (Wolf et al…2010). Those at a higher risk include African American men with a family history of prostate cancer occurring in a family member who is not elderly (Wolf et al…2010). ACS also does not recommend that men whose life expectancy is less than ten years be screened for prostate cancer (Wolf et al…2010). Providers need to provide men with the benefits of early detection and treatment with the risk factors of treatment for prostate cancer. The results of PSA testing are not conclusive and therefore, the ACS reiterates the importance of the patient having the knowledge and information to make an informed decision. The ACS provides educational brochures and handouts on PSA screening to help guide patients to a discussion of this subject with his provider.
References
U.S. Preventative Services Task Force, (Accessed March 2019). Screening Guidelines for Prostate. Retrieved from: https://www.uspreventiveservicestaskforce.org/Page/Document/RecommendationStatementFinal/prostate-cancer-screening1 (Links to an external site.)Links to an external site.
Wolf, A., Wender, R., Etzioni, R….& Smith, R. (2010). American Cancer Society Guideline for the Early Detection of Prostate Cancer: Update 2010. Retrieved from: https://onlinelibrary.wiley.com/doi/full/10.3322/caac.20066 (Links to an external site.)
** Provide response writing with references. All references must be in APA format and p.
36433 Topic HA W9 R1Number of Pages 1 (Double Spaced)N.docx
Yisen Lin's Term 2 Presentation
1. University of Toronto | 1
Cox Proportional Hazards Model:
Modeling Overall Survival Rates with Multi-Covariates
Supervisor: Lisa Wang (Biostatistician)
Presentation at Dlsph – Yisen Lin (MSc Candidate)
April 7th 2016
2. University of Toronto | 2
Content
• Research Site: Princess Margaret Cancer Centre
• General Outline
• Model Development:
- Overview of The Data
- Coxph Model Introduction
- Covariates Selection
- Model Validation
• Conclusion
• Acknowledgement
3. University of Toronto | 3
Princess Margaret Cancer Centre:
• Largest Cancer Centre in Canada
• One of 5 Largest Cancer Centre in the world
Biostatistics Department at PMCC:
• Supporting approx. 150 biostatisticial request
• Closely affiliated with DLSPH of utoronto
4. University of Toronto | 4
General Outline
1. Read/Understand Completed Clinical Trial Protocol
2. Reproduce the result of Data Analysis Part of
Completed Clinical Trial Study
3. Model Development and Validation on the Given
Datasets
My second term work is mainly focusing on
Part 3
5. University of Toronto | 5
Model Development
Overview of the data
2 Datasets for one model development
Covariates Listing:
1. Hb – Male 140-180g/L, Female 120-160g/L
(Looking for the effect below lower limit normal, LLN)
2. Plt 150-400g/L (Looking for ULN)
3. Na 135-145mmol/L (Looking for LLN)
4. Albumin 38-50g/L (Looking for LLN)
5. LD 125-220U/L (Looking for ULN)
Note: Above continuous covariates would be convert to binary outcome in model
6. Age
7. Gender
8. ECOG
9. No. of Metastatic Sites
10. No. of systematic therapies
Looking for Predictors for Overall Survival Using Cox ph model
7. University of Toronto | 7
Kaplan Meier Plot for the OS
General look about the survival proportion without any model fitting
8. University of Toronto | 8
Cox Proportional Hazard Model
Introduction
Why this model?
1. Since we want to assess the effect of multiple covariates on overall Survival.
And Cox-proportional hazards is the most commonly used multivariable
survival method (we got 10)
2. Robustness: safe choice for many situation
3. The estimated hazard is always non-negative
4. Hazard rate and survival rate can be estimated under minimum assumptions
5. For example: Comparing with logistic model, it considers censoring info and
survival time. ( we are going to use logistic model for 90 day mortality for score
testing)
9. University of Toronto | 9
Covariates Selection
1. Using univariate analysis:
(with p-vlaue < 0.05 criteria to exclude non-significant covariates)
Hb Plt Na Albumin Ld
P-
value
0.0527 0.011 0.0779 2.07e^-5 0.0122
Age Gender Meta.no. Terapy.no. Ecog
P-
value
0.766 0.746 0.00237 0.0158
4.09e^
-9
(Hb, Na, Gender and Age have been removed from the model, and fit
Multivariate model with all the covariates p < 0.05)
10. University of Toronto | 10
Covariates Selection
2. Applied both forward and backward selection method:
(with the selection criteria smaller AIC and p-value < 0.05)
We get the same result (model is stable)
Covariates in Final Model
Albumin 38-50 g/L (LLN)
Number of Metastatic Sites
ECOG
For the final Model the C-index is 0.724
11. University of Toronto | 11
Covariates Selection
Model Diagnostics
The residual plot is almost symmetric around 0, which
indicates appropriate linearity assumption
12. University of Toronto | 12
Covariates Selection
Model Diagnostics
Testing For the Proportionality of all 3 covariates:
p value are all big enough implies there is no evidence for having
a non-proportional hazard. So the coxph we proposed is quite right
13. University of Toronto | 13
Model Validation On the 90 day
mortality
Using the same covariates as the coxph model on Overall Survival
Same dataset
Area under the curve is 0.6764,which is acceptable.
14. University of Toronto | 14
Model Validation On the 90 day
mortality
1. Note that the c-index for the logistic model is 0.724,which is already
quite close to 0.8 (we consider 0.8 as the boundary of the model having good
prediction ability .
2. In order to eliminate the effect of over optimism, we use bootstrap to find the modified c-index
(0.731), clearly even better
15. University of Toronto | 15
Conclusion
Current work
In addition to the work presented before, I also use the covariates
and the estimation of parameters generated from dataset1, and validate
the model in the dataset2.
(Since, from my view I am doing very similar thing as what I did on dataset1)
Result would be presented in the Report
A glance of the dataset 2
In order to make the definition of covariates consistent
We need to adjust the corresponding covariates from dataset 2
