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Survival Analysis Techniques Training Course

Online Training Download PDF
Upcoming Training Schedules 14 locations
Location Duration Next Start Date Dates Available Action
Nairobi, Kenya 10 days Jul 13, 2026 104 dates
Accra, Ghana 10 days Jul 27, 2026 31 dates
Addis Ababa, Ethiopia 10 days Jul 13, 2026 31 dates
Cape Town, South Africa 10 days Jul 13, 2026 52 dates
Dar es Salaam, Tanzania 10 days Jul 13, 2026 26 dates
Dubai, UAE 10 days Jul 27, 2026 52 dates
Istanbul, Turkey 10 days Jul 20, 2026 16 dates
Kampala, Uganda 10 days Jul 13, 2026 31 dates
Kigali, Rwanda 10 days Jul 20, 2026 52 dates
Kuala Lumpur, Malaysia 10 days Aug 10, 2026 31 dates
Mombasa, Kenya 10 days Jul 13, 2026 52 dates
Pretoria, South Africa 10 days Jul 13, 2026 52 dates
Singapore 10 days Jul 20, 2026 31 dates
Zanzibar, Tanzania 10 days Jul 20, 2026 16 dates

Survival Analysis Techniques Training Course

Course Introduction

The Survival Analysis Techniques Training Course is designed to equip participants with comprehensive knowledge and practical skills in applying survival analysis methods and time-to-event statistical techniques to research, healthcare studies, risk assessment, and evidence-based decision-making. In today's data-intensive and research-driven environment, governments, healthcare organizations, academic institutions, pharmaceutical companies, and development agencies increasingly rely on survival analysis techniques to investigate the occurrence and timing of events, estimate probabilities of survival, assess intervention effectiveness, and predict future outcomes. This course provides participants with practical competencies in survival modeling, event history analysis, censoring techniques, hazard modeling, and predictive analytics essential for high-quality research and strategic planning.

The course focuses on the fundamental and advanced principles of survival analysis, including time-to-event data structures, survival functions, censoring mechanisms, life table methods, Kaplan-Meier estimation, hazard functions, Cox proportional hazards models, parametric survival models, competing risks analysis, and statistical reporting techniques. Participants will gain practical experience in applying survival analysis methodologies to evaluate health outcomes, analyze customer retention, assess equipment failure rates, examine project sustainability, and generate evidence for organizational planning and policy formulation. The course emphasizes practical applications of survival analysis in public health, clinical research, epidemiology, engineering reliability studies, economics, social sciences, and business analytics.

As organizations increasingly adopt advanced analytics, predictive modeling systems, and evidence-based management frameworks, competencies in survival analysis have become indispensable for researchers, epidemiologists, biostatisticians, data scientists, monitoring and evaluation specialists, and organizational leaders. This training emphasizes analytical reasoning, quantitative problem-solving, statistical rigor, and evidence generation approaches that improve predictive capabilities, strengthen research quality, and support effective and informed decision-making processes.

Through presentations, practical exercises, computer-based applications, collaborative group work, and real-world case studies, participants will develop competencies necessary to design survival studies, analyze time-to-event data, interpret survival models, and communicate analytical findings effectively. Upon completion of this course, participants will be capable of applying survival analysis techniques to solve complex research challenges, evaluate interventions, develop predictive models, and contribute to improved organizational performance, healthcare outcomes, and policy development.

Course Objectives

Upon completion of this course, participants will be able to:

1.     Understand the principles and applications of survival analysis techniques.

2.     Organize and manage time-to-event datasets effectively.

3.     Apply life table and Kaplan-Meier estimation methods appropriately.

4.     Conduct hazard modeling and survival regression analyses.

5.     Analyze censored data and competing risk scenarios effectively.

6.     Develop and interpret Cox proportional hazards models.

7.     Utilize statistical software applications for survival analysis and reporting.

8.     Prepare professional analytical reports and evidence-based recommendations.

9.     Apply survival analysis findings to support research and policy development.

10.  Utilize predictive evidence to improve organizational planning and decision-making.

Organizational Benefits

Organizations that invest in this training will benefit by:

1.     Strengthening research quality and analytical rigor.

2.     Improving predictive analytics and evidence-based planning capabilities.

3.     Enhancing monitoring, evaluation, and performance measurement systems.

4.     Supporting policy formulation and program evaluation processes.

5.     Improving risk assessment and scenario planning frameworks.

6.     Strengthening healthcare and organizational outcome evaluations.

7.     Building staff competencies in advanced statistical modeling techniques.

8.     Enhancing forecasting and decision support capabilities.

9.     Improving resource allocation and operational efficiency.

10.  Promoting innovation, accountability, and continuous organizational learning.

Target Participants

This course is designed for researchers, epidemiologists, biostatisticians, data analysts, healthcare professionals, public health specialists, statisticians, monitoring and evaluation specialists, economists, policy analysts, market researchers, consultants, academicians, postgraduate students, project managers, program officers, engineers, reliability analysts, and professionals involved in predictive analytics, time-to-event research, and evidence-based decision-making.

Course Outline

Module 1: Foundations of Survival Analysis

1.     Principles and concepts of survival analysis

2.     Importance of time-to-event analysis in research and decision-making

3.     Applications of survival analysis across disciplines

4.     Characteristics of survival and event history data

5.     Introduction to survival analysis software applications

6.     General Case Study: Evaluating patient survival outcomes following treatment interventions

Module 2: Time-to-Event Data and Censoring Techniques

1.     Principles of time-to-event data structures

2.     Types of censoring mechanisms

3.     Truncation and incomplete observations

4.     Data preparation and management procedures

5.     Interpretation of censored datasets

6.     General Case Study: Managing incomplete patient follow-up data in healthcare studies

Module 3: Life Tables and Survival Functions

1.     Principles of life table analysis

2.     Construction and interpretation of survival tables

3.     Estimation of survival probabilities

4.     Comparison of survival experiences among groups

5.     Applications of life tables in research studies

6.     General Case Study: Assessing employee retention rates using life table methods

Module 4: Kaplan-Meier Estimation Techniques

1.     Principles of Kaplan-Meier estimation

2.     Construction of survival curves

3.     Estimation of median survival times

4.     Comparison of survival functions between groups

5.     Interpretation of Kaplan-Meier outputs

6.     General Case Study: Comparing recovery rates among treatment groups in clinical research

Module 5: Hazard Functions and Risk Assessment

1.     Principles of hazard functions

2.     Instantaneous risk and failure rates

3.     Hazard ratio estimation techniques

4.     Applications in predictive risk assessment

5.     Interpretation of hazard measures

6.     General Case Study: Assessing risk factors associated with patient mortality and disease progression

Module 6: Cox Proportional Hazards Regression Models

1.     Principles of Cox proportional hazards models

2.     Model specification and estimation procedures

3.     Assumptions of proportional hazards regression

4.     Interpretation of hazard ratios and coefficients

5.     Predictive applications of Cox models

6.     General Case Study: Identifying determinants of treatment success using Cox regression models

Module 7: Parametric Survival Models

1.     Principles of parametric survival analysis

2.     Exponential and Weibull survival models

3.     Log-normal and log-logistic models

4.     Model selection and goodness-of-fit procedures

5.     Applications in predictive analytics and forecasting

6.     General Case Study: Modeling equipment failure rates using parametric survival techniques

Module 8: Competing Risks and Advanced Survival Models

1.     Principles of competing risk analysis

2.     Cumulative incidence functions

3.     Multi-state survival models

4.     Recurrent event analysis techniques

5.     Interpretation of advanced survival outputs

6.     General Case Study: Evaluating multiple causes of patient outcomes in healthcare systems

Module 9: Survival Analysis in Public Health and Clinical Research

1.     Applications in epidemiological investigations

2.     Survival analysis in disease surveillance systems

3.     Clinical trial analysis techniques

4.     Program evaluation and intervention assessment methods

5.     Applications in healthcare planning and policy development

6.     General Case Study: Evaluating effectiveness of public health interventions using survival methods

Module 10: Survival Analysis in Business and Organizational Research

1.     Applications in customer retention analysis

2.     Employee turnover and workforce studies

3.     Product reliability and operational risk assessment

4.     Predictive analytics and decision support systems

5.     Applications in organizational performance management

6.     General Case Study: Predicting customer churn and subscription retention rates

Module 11: Survival Analysis Using Statistical Software

1.     Introduction to survival analysis software applications

2.     Data preparation and management procedures

3.     Conducting survival analyses using statistical packages

4.     Visualization and interpretation of survival outputs

5.     Development of analytical reports and dashboards

6.     General Case Study: Performing survival analysis using organizational and healthcare datasets

Module 12: Emerging Trends in Survival Analysis and Predictive Analytics

1.     Big data and advanced survival analytics

2.     Artificial intelligence and machine learning applications

3.     Real-time predictive systems and forecasting methods

4.     Integration of survival analysis with digital health technologies

5.     Future trends in survival analysis and time-to-event modeling

6.     General Case Study: Designing predictive survival frameworks for strategic planning and organizational transformation

General Information

1.     Customized Training: All our courses can be tailored to meet the specific needs of participants.

2.     Language Proficiency: Participants should have a good command of the English language.

3.     Comprehensive Learning: Our training includes well-structured presentations, practical exercises, web-based tutorials, and collaborative group work. Our facilitators are seasoned experts with over a decade of experience.

4.     Certification: Upon successful completion of training, participants will receive a certificate from Foscore Development Center (FDC-K).

5.     Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training, customized to the client's schedule.

6.     Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of days.

7.     Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.

8.     Additional Services: Accommodation, pickup services, freight booking, and visa processing arrangements are available upon request at discounted rates.

9.     Equipment: Tablets and laptops can be provided to participants at an additional cost.

10.  Post-Training Support: We offer one year of free consultation and coaching after the course.

11.  Group Discounts: Register as a group of more than two and enjoy a discount ranging from 10% to 50%.

12.  Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.

13.  Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call us at +254712260031.

14.  Website: Visit our website at www.fdc-k.org for more information.

 

 

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