Course Introduction
The Survival and Event History Data Analysis Training Course provides participants with a comprehensive understanding of statistical methods for analyzing the timing and occurrence of events. Unlike conventional analyses that focus only on whether an event occurs, survival analysis examines when an event happens and how the risk of experiencing that event changes over time. The course develops practical skills for handling time-to-event datasets across medical, public health, social science, engineering, business, demographic, and operational research.
The training introduces the fundamental concepts required for working with event history data, including survival time, event indicators, censoring, truncation, risk sets, hazards, survival functions, and cumulative incidence. Participants learn why standard statistical techniques can be inappropriate when observations are censored or when follow-up periods differ between individuals. Through practical examples, the course demonstrates how study design, follow-up mechanisms, and event definitions influence analytical strategies and interpretation.
Participants develop practical expertise in non-parametric survival methods, including Kaplan-Meier estimation and life-table approaches. They learn how to estimate survival probabilities, construct and interpret survival curves, compare groups using appropriate statistical procedures, and investigate differences in event timing. The course also explains important assumptions and limitations associated with these techniques, helping participants distinguish descriptive survival patterns from conclusions requiring more advanced modeling.
The course provides extensive coverage of regression-based event history methods, particularly the Cox proportional hazards model. Participants learn how to specify models, interpret hazard ratios, evaluate covariates, assess proportional hazards assumptions, investigate interactions, and diagnose potential model problems. Parametric and flexible survival models are also introduced, allowing participants to understand how alternative assumptions about the underlying hazard function can influence estimation, prediction, and substantive interpretation.
Advanced topics address common challenges encountered in real-world survival datasets, including time-dependent covariates, competing risks, recurrent events, left truncation, interval censoring, frailty models, clustered observations, and multi-state event processes. Participants also explore approaches for dealing with missing data, non-proportional hazards, informative follow-up, and complex longitudinal event structures. Emerging applications involving machine learning, predictive modeling, explainable analytics, and AI-assisted survival analysis are considered within a framework of statistical validation and responsible interpretation.
By the end of the training, participants will be able to plan, conduct, interpret, and communicate survival and event history analyses using appropriate statistical methods. They will understand how to select analytical techniques based on event structure, censoring patterns, study design, and research objectives. Practical exercises reinforce the complete workflow from data preparation and exploratory analysis through model estimation, diagnostics, prediction, visualization, and reporting, enabling participants to produce transparent and methodologically defensible research findings.
Duration
5 days
Who Should Attend
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Researchers conducting longitudinal, cohort, panel, clinical, demographic, or event-based studies.
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Epidemiologists and public health professionals analyzing time-to-event outcomes and follow-up data.
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Medical and health researchers studying mortality, disease progression, recurrence, recovery, or treatment outcomes.
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Statisticians and data analysts seeking advanced methods for censored and time-dependent observations.
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Social scientists investigating transitions, employment events, migration, family formation, or other life-course processes.
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Economists studying duration, unemployment spells, business survival, default, or market-related events.
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Demographers conducting event history and population-transition research.
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Biostatisticians working with clinical trials, observational studies, registries, or longitudinal health datasets.
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Monitoring and evaluation professionals analyzing program duration, participant retention, failure, or transition events.
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Researchers working with engineering, reliability, maintenance, or equipment failure datasets.
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Data scientists developing predictive models for time-to-event outcomes and risk estimation.
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Postgraduate students and academic professionals developing quantitative research involving longitudinal event data.
Course Objectives
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Explain the statistical foundations of survival and event history analysis, including survival functions, hazards, censoring, truncation, risk sets, and event-time distributions.
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Prepare and structure time-to-event datasets correctly, distinguishing event observations from censored observations and identifying appropriate follow-up periods.
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Estimate and interpret survival functions using Kaplan-Meier and life-table techniques while understanding their assumptions and analytical limitations.
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Compare survival experiences across groups using appropriate statistical tests and graphical methods while distinguishing statistical evidence from substantive interpretation.
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Develop Cox proportional hazards models and interpret hazard ratios, covariate effects, interactions, confidence intervals, and model-specific statistical outputs.
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Evaluate proportional hazards and other model assumptions using graphical, statistical, and diagnostic procedures and determine appropriate responses to assumption violations.
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Apply parametric and flexible survival models when the research context requires alternative assumptions about hazard and survival-time distributions.
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Analyze advanced event structures involving time-dependent covariates, competing risks, recurrent events, frailty, left truncation, interval censoring, and multi-state processes.
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Apply appropriate approaches to missing data, model validation, prediction, discrimination, calibration, and sensitivity analysis within time-to-event research.
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Communicate survival analysis findings through clear statistical tables, survival curves, hazard estimates, model diagnostics, and reproducible methodological reporting.
Comprehensive Course Outline
Module 1: Foundations of Survival and Event History Analysis
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Conceptual foundations of survival time, event occurrence, risk sets, hazards, survival probabilities, and cumulative event processes.
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Differences between survival analysis, ordinary regression, longitudinal analysis, and other approaches to time-dependent research outcomes.
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Understanding right censoring, left censoring, interval censoring, truncation, delayed entry, and their implications for statistical estimation.
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Defining event times, observation windows, competing outcomes, follow-up periods, and analysis populations within different research designs.
Module 2: Data Preparation and Exploratory Survival Analysis
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Structuring individual-level time-to-event datasets with entry times, exit times, event indicators, covariates, and appropriate observation periods.
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Identifying data-quality problems involving impossible dates, inconsistent event indicators, duplicate observations, follow-up gaps, and incorrect censoring classifications.
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Exploring event-time distributions, censoring patterns, follow-up completeness, risk-set composition, and differences in observation duration.
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Using descriptive statistics and graphical methods to identify important patterns before fitting formal survival models.
Module 3: Kaplan-Meier and Non-Parametric Methods
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Estimating Kaplan-Meier survival functions and understanding how event occurrences and censored observations contribute to survival probability estimates.
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Constructing and interpreting survival curves for different populations, treatment groups, exposure categories, demographic groups, or research conditions.
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Comparing survival distributions using log-rank and related procedures while considering their assumptions and sensitivity to different hazard patterns.
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Understanding median survival, restricted mean survival time, confidence intervals, and alternative summaries when median survival is not observed.
Module 4: Cox Proportional Hazards Regression
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Building Cox regression models for time-to-event outcomes and understanding partial likelihood, baseline hazards, covariate effects, and model interpretation.
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Interpreting hazard ratios for continuous, categorical, transformed, and standardized predictors within substantive research contexts.
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Selecting covariates using theoretical reasoning, study design considerations, confounding structures, and research objectives rather than purely automated procedures.
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Evaluating model specification, interactions, nonlinear relationships, influential observations, and potential sources of instability in estimated effects.
Module 5: Model Assumptions and Diagnostics
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Assessing the proportional hazards assumption using graphical approaches, residual-based diagnostics, statistical tests, and time-varying effect investigations.
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Interpreting Schoenfeld, Martingale, deviance, and related residuals to identify potential model specification and influential-observation problems.
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Addressing non-proportional hazards through stratification, time-dependent effects, alternative parameterizations, or other theoretically appropriate approaches.
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Conducting sensitivity analyses and robustness checks to determine whether substantive conclusions depend strongly on specific modeling assumptions.
Module 6: Parametric and Flexible Survival Models
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Understanding exponential, Weibull, Gompertz, log-normal, log-logistic, and other parametric approaches to modeling survival-time distributions.
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Selecting distributions based on substantive knowledge, empirical patterns, diagnostic evidence, and the objectives of estimation or prediction.
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Comparing parametric survival models with semi-parametric Cox approaches in terms of assumptions, interpretation, flexibility, and predictive applications.
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Applying flexible approaches such as spline-based hazard models to represent complex and non-linear patterns in event risk over time.
Module 7: Advanced Event History Methods
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Modeling time-dependent covariates and exposures when predictor values change during follow-up or when their timing affects event risk.
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Analyzing competing risks when individuals may experience different mutually exclusive event types that prevent occurrence of the primary event.
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Understanding recurrent event models for repeated failures, episodes, hospitalizations, purchases, transitions, or other repeated outcomes.
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Applying frailty and random-effects approaches to account for unobserved heterogeneity, clustering, dependence, and repeated observations.
Module 8: Complex Censoring and Multi-State Processes
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Addressing left truncation, delayed entry, interval censoring, informative observation, and other complex follow-up mechanisms in event history datasets.
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Developing multi-state models for transitions between several states such as healthy, diseased, recovered, inactive, employed, unemployed, or deceased.
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Distinguishing transition-specific hazards, state occupation probabilities, expected state durations, and other measures in multi-state event processes.
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Designing analytical strategies for complex longitudinal event structures while maintaining transparent definitions of states, transitions, and observation mechanisms.
Module 9: Prediction, Validation and Emerging Analytical Approaches
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Evaluating survival prediction models using discrimination, calibration, prediction error, time-dependent performance measures, and appropriate validation strategies.
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Exploring machine-learning approaches for survival prediction, including tree-based methods, regularized models, ensemble techniques, and neural approaches.
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Examining explainable AI and interpretable survival modeling while distinguishing predictive associations from causal or mechanistic explanations.
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Applying resampling, external validation, sensitivity analysis, and reproducible computational workflows to strengthen the credibility of predictive survival models.
Module 10: Applied Survival Analysis and Research Reporting
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Developing an end-to-end survival analysis workflow from research question formulation and data preparation through estimation, diagnostics, validation, and reporting.
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Selecting analytical methods according to study design, event definition, censoring mechanism, competing events, predictor structure, and substantive research objectives.
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Creating publication-quality survival curves, hazard-ratio tables, model summaries, diagnostic plots, and clear descriptions of analytical assumptions.
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Reporting survival findings transparently by documenting censoring, follow-up, missing data, model specifications, assumptions, limitations, and uncertainty.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
