Paper Time Machines 1st Edition by Maurice Suckling ISBN 1032416912 978-1032416915
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Product details:
ISBN 10: 1032416912
ISBN 13: 978-1032416915
Author: Maurice W. Suckling
James Dunnigan’s memorable phrase serves as the first part of a title for this book, where it seeks to be applicable not just to analog wargames, but also to board games exploring non-expressly military history, that is, to political, diplomatic, social, economic, or other forms of history. Don’t board games about history, made predominantly out of (layered) paper, permit a kind of time travel powered by our imagination? Paper Time Machines: Critical Game Design and Historical Board Games is for those who consider this a largely rhetorical question; primarily for designers of historical board games, directed in its more practice-focused sections (Parts Two, Three, and Four) toward those just commencing their journeys through time and space and engaged in learning how to deconstruct and to construct paper time machines.
More experienced designers may find something here for them, too, perhaps to refresh themselves or as an aid to instruction to mentees in whatever capacity. But it is also intended for practitioners of all levels of experience to find value in the surrounding historical contexts and theoretical debates pertinent to the creation of and the thinking around the making of historical board games (Parts One and Five). In addition, it is intended that the book might redirect some of the attention of the field of game studies, so preoccupied with digital games, toward this hitherto generally much neglected area of research.
Key Features:
- Guides new designers through the process of historical board game design
- Encapsulates the observations and insights of numerous notable designers
- Deeply researched chapters on the history and current trajectory of the hobby
- Chapters on selected critical perspectives on the hobby
Table of contents:
1. Notation and Typography
2. The Problem of Survival Analysis
2.1 Parametric Modeling
2.2 Semiparametric Modeling
2.3 Nonparametric Analysis
2.4 Linking the Three Approaches
3. Describing the Distribution of Failure Times
3.1 The Survivor and Hazard Functions
3.2 The Quantile Function
3.3 Interpreting the Cumulative Hazard and Hazard Rate
3.3.1 Interpreting the Cumulative Hazard
3.3.2 Interpreting the Hazard Rate
3.4 Means and Medians
4. Hazard Models
4.1 Parametric Models
4.2 Semiparametric Models
4.3 Analysis Time (Time at Risk)
5. Censoring and Truncation
5.1 Censoring
5.1.1 Right-Censoring
5.1.2 Interval-Censoring
5.1.3 Left-Censoring
5.2 Truncation
5.2.1 Left-Truncation (Delayed Entry)
5.2.2 Interval-Truncation (Gaps)
5.2.3 Right-Truncation
6. Recording Survival Data
6.1 The Desired Format
6.2 Other Formats
6.3 Example: Wide-Form Snapshot Data
7. Using stset
7.1 A Short Lesson on Dates
7.2 Purposes of the stset Command
7.3 Syntax of the stset Command
7.3.1 Specifying Analysis Time
7.3.2 Variables Defined by stset
7.3.3 Specifying What Constitutes Failure
7.3.4 Specifying When Subjects Exit from the Analysis
7.3.5 Specifying When Subjects Enter the Analysis
7.3.6 Specifying the Subject-ID Variable
7.3.7 Specifying the Begin-of-Span Variable
7.3.8 Convenience Options
7.4 After stset
7.4.1 Look at stset’s Output
7.4.2 List Some of Your Data
7.4.3 Use stdescribe
7.4.4 Use stvary
7.4.5 Perhaps Use stfill
7.4.6 Example: Hip Fracture Data
8. Nonparametric Analysis
8.1 Inadequacies of Standard Univariate Methods
8.2 The Kaplan-Meier Estimator
8.2.1 Calculation
8.2.2 Censoring
8.2.3 Left-Truncation (Delayed Entry)
8.2.4 Interval-Truncation (Gaps)
8.2.5 Relationship to the Empirical Distribution Function
8.2.6 Other Uses of sts list
8.2.7 Graphing the Kaplan-Meier Estimate
8.3 The Nelson-Aalen Estimator
8.4 Estimating the Hazard Function
8.5 Estimating Mean and Median Survival Times
8.6 Tests of Hypothesis
8.6.1 The Log-Rank Test
8.6.2 The Wilcoxon Test
8.6.3 Other Tests
8.6.4 Stratified Tests
9. The Cox Proportional Hazards Model
9.1 Using stcox
9.1.1 The Cox Model Has No Intercept
9.1.2 Interpreting Coefficients
9.1.3 The Effect of Units on Coefficients
9.1.4 Estimating the Baseline Cumulative Hazard and Survivor Functions
9.1.5 Estimating the Baseline Hazard Function
9.1.6 The Effect of Units on the Baseline Functions
9.2 Likelihood Calculations
9.2.1 No Tied Failures
9.2.2 Tied Failures
- The Marginal Calculation
- The Partial Calculation
- The Breslow Approximation
- The Efron Approximation
9.2.3 Summary
9.3 Stratified Analysis
9.3.1 Obtaining Coefficient Estimates
9.3.2 Obtaining Estimates of Baseline Functions
9.4 Cox Models with Shared Frailty
9.4.1 Parameter Estimation
9.4.2 Obtaining Estimates of Baseline Functions
9.5 Cox Models with Survey Data
9.5.1 Declaring Survey Characteristics
9.5.2 Fitting a Cox Model with Survey Data
9.5.3 Some Caveats of Analyzing Survival Data from Complex Survey Designs
9.6 Cox Model with Missing Data-Multiple Imputation
9.6.1 Imputing Missing Values
9.6.2 Multiple-Imputation Inference
10. Model Building Using stcox
10.1 Indicator Variables
10.2 Categorical Variables
10.3 Continuous Variables
10.3.1 Fractional Polynomials
10.4 Interactions
10.5 Time-Varying Variables
10.5.1 Using stcox, tve() texp()
10.5.2 Using stsplit
10.6 Modeling Group Effects: Fixed-Effects, Random-Effects, Stratification, and Clustering
11. The Cox Model: Diagnostics
11.1 Testing the Proportional-Hazards Assumption
11.1.1 Tests Based on Reestimation
11.1.2 Test Based on Schoenfeld Residuals
11.1.3 Graphical Methods
11.2 Residuals and Diagnostic Measures
11.2.1 Determining Functional Form
11.2.2 Goodness of Fit
11.2.3 Outliers and Influential Points
- Reye’s Syndrome Data
12. Parametric Models
12.1 Motivation
12.2 Classes of Parametric Models
12.2.1 Parametric Proportional Hazards Models
12.2.2 Accelerated Failure-Time Models
12.2.3 Comparing the Two Parameterizations
13. A Survey of Parametric Regression Models in Stata
13.1 The Exponential Model
13.1.1 Exponential Regression in the PH Metric
13.1.2 Exponential Regression in the AFT Metric
13.2 Weibull Regression
13.2.1 Weibull Regression in the PH Metric Fitting Null Models
13.2.2 Weibull Regression in the AFT Metric
13.3 Gompertz Regression (PH Metric)
13.4 Lognormal Regression (AFT Metric)
13.5 Loglogistic Regression (AFT Metric)
13.6 Generalized Gamma Regression (AFT Metric)
13.7 Choosing Among Parametric Models
13.7.1 Nested Models
13.7.2 Nonnested Models
14. Postestimation Commands for Parametric Models
14.1 Use of Predict After streg
14.1.1 Predicting the Time of Failure
14.1.2 Predicting the Hazard and Related Functions
14.1.3 Calculating Residuals
14.2 Using stcurve
15. Generalizing the Parametric Regression Model
15.1 Using the ancillary() Option
15.2 Stratified Models
15.3 Frailty Models
15.3.1 Unshared Frailty Models
15.3.2 Example: Kidney Data
15.3.3 Testing for Heterogeneity
15.3.4 Shared Frailty Models
16. Power and Sample-Size Determination for Survival Analysis
16.1 Estimating Sample Size
16.1.1 Multiple-Myeloma Data
16.1.2 Comparing Two Survivor Functions Nonparametrically
16.1.3 Comparing Two Exponential Survivor Functions
16.1.4 Cox Regression Models
16.2 Accounting for Withdrawal and Accrual of Subjects
16.2.1 The Effect of Withdrawal or Loss to Follow-Up
16.2.2 The Effect of Accrual
16.2.3 Examples
16.3 Estimating Power and Effect Size
16.4 Tabulating or Graphing Results
17. Competing Risks
17.1 Cause-Specific Hazards
17.2 Cumulative Incidence Functions
17.3 Nonparametric Analysis
17.3.1 Breast Cancer Data
17.3.2 Cause-Specific Hazards
17.3.3 Cumulative Incidence Functions
17.4 Semiparametric Analysis
17.4.1 Cause-Specific Hazards
- Simultaneous Regressions for Cause-Specific Hazards
17.4.2 Cumulative Incidence Functions
- Using stcrreg
- Using stcox
17.5 Parametric Analysis
18. References
19. Author Index
20. Subject Index
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