12.Operations05.Event sequence (memoryless) - sporedata/researchdesigneR GitHub Wiki
1. Use cases: in which situations should I use this method?
- Markov models are used to evaluate systems where there is a sequence of states, future states depending on the current one but not previous ones. The latter characteristic is called a Markov property.
- Used in decision and cost-effectiveness analyses models with a number of repeated events that is too extensive to be modeled with a simple tree - see Cost-effectiveness of Sacubitril-Valsartan in Hospitalized Patients Who Have Heart Failure With Reduced Ejection Fraction
- When a patient condition evolves over time -- see Clinical Stage Transitions in Persons Aged 12 to 25 Years Presenting to Early Intervention Mental Health Services With Anxiety, Mood, and Psychotic Disorders
- Evaluation of healthcare disparities over time -- see A Fresh Perspective on a Familiar Problem: Examining Disparities in Knee Osteoarthritis Using a Markov Model
- Evaluation of multiple risk factors on the course of a condition - see A Comprehensive Multistate Model Analyzing Associations of Various Risk Factors With the Course of Breast Cancer in a Population-Based Cohort of Breast Cancer Cases
- Evaluation of a sequence of hidden (latent) states, or where the state is not measured by a single variable -- see Conjoint cognitive and emotional death‐preparedness states and their changes within cancer patients’ last 6 months
2. Input: what kind of data does the method require?
- Data with a sequence of condition states
3. Algorithm: how does the method work?
Model mechanics
- Discrete-time Markov models
- Continuous-time Markov models
- Generalized Markov models
- Hidden (latent) Markov models
Reporting guidelines for Methods
Data science packages
- seqHMM for Hidden Markov Models - see Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R
Suggested companion methods
Learning materials
- Books
- Articles
4. Output: how do I interpret this method's results?
Mock conclusions or most frequent format for conclusions reached at the end of a typical analysis.
Tables, plots, and their interpretation
Reporting guidelines for Results
5. SporeData-specific
Templates
Data science functions
* sdatools::EFAScreePlot
* sdatools::EFAVSSPlot