Introduction | Calculate Induction Delay | Data Setup | Mark Study Days with Administered Treatment Drugs | Measure Difference from Randomization Day to Day of First Dose | Partition Visit Weeks and Impute Missing Visits | "Backbone" Timeline of Clinical Protocol | CTN-0027 and CTN-0051 Protocols | CTN-0030 Protocol | Combined "Backbone" | Mark Missing Visits | Add First Randomization Day | Add on the Visit Days | Impute the Missing Visits | Clean up the Results | Summarize Weekly Substance Use Results | Participant's Assigned Treatment | Counting Days Since Randomization | Symbolically Summarize Participant UDS by Study Week | Symbolically Summarize Non-Randomized Participants UDS by Study Week | Final Product | Wrapping Up

Introduction \label | Motivation \label | Why use mvMonitoring | Multi- or Single-State AD-PCA | The msp Functions | Simulating Data with mspProcessData | Latent Feature Creation | Autocorrelated and Non-Stationary Error | The Non-Linear Latent Process | Single-State and Multi-State Features | Single State Features | Multi-State Features | Synthetic Fault Induction | Putting it all together... | Training with mspTrain | The xts Data Matrix | The Class Vector | Adaptive and Dynamic Modelling | Model Training | Monitoring Incoming Observations with mspMonitor | Issue a Warning with mspWarning | Example Simulation Workflow | Generating Synthetic Data | Train the Fault Detection Threshold | Test New Observations for Anomalies | Adding Lagged Features | Monitoring the Test Data | Warn Operators during Alarms | Conclusion

Introduction | Abstinence from Substance Use Endpoints | CTN-0094 Abstinence | @fiellin_counseling_2006 | @kosten_buprenorphine_1993 | @krupitsky_injectable_2011 (A) and (B) | Krupitsky et al., 2011 (A) | Krupitsky et al., 2011 (B) | @ling_buprenorphine_1998 | @lofwall_weekly_2018 | @mokri_medical_2016 | @schottenfeld_methadone_2005 | @schottenfeld_maintenance_2008 (A) and (B) | Schottenfeld, Chawarski, & Mazlan, 2008 (A) | Schottenfeld et al., 2008 (B) | @shufman_efficacy_1994 | @weiss_adjunctive_2011 [CTN-0030] | Computing Environment | References

Introduction | Substance Use Reduction Endpoints | @comer_injectable_2006 | @eissenberg_dose-related_1997 | @fiellin_counseling_2006 | @fudala_office-based_2003 | @haight_efficacy_2019 | @jaffe_methadyl_1972 | @johnson_controlled_1992 | @kosten_buprenorphine_1993 | @ling_buprenorphine_1998 (A) and (C) | Ling et al., 1998 (A) | Ling et al., 1998 (C) | @ling_buprenorphine_2010 | @ling_methadyl_1976 | Original | A Variant | @lofwall_weekly_2018 | @mattick_buprenorphine_2003 (A) and (B) | Mattick et al., 2003 (A) | Mattick et al., 2003 (B) | @pani_buprenorphine_2000 (A) and (B) | Pani et al., 2000 (A) | Pani et al., 2000 (B) | @petitjean_double-blind_2001 | @preston_methadone_2000 | @schottenfeld_methadone_2005 | @schwartz_randomized_2006 | @shufman_efficacy_1994 | @soyka_retention_2008 | @strain_dose-response_1993 | @strain_comparison_1994 | @strain_buprenorphine_1996 | @strain_moderate-_1999 | @strang_supervised_2010 | @strang_extended-release_2019 | @tanum_effectiveness_2017 | @wolstein_randomized_2009 | @woody_extended_2008 | @zaks_levomethadyl_1972 | Computing Environment | References

Introduction | "Relapse to Substance Use" Endpoints | CTN-0094 Endpoints | CTN-0094 Relapse | CTN-0094 Dropout | @johnson_controlled_1992 | @krupitsky_naltrexone_2004 and @krupitsky_naltrexone_2006 | @lee_extended-release_2016 | @lee_comparative_2018 [CTN-0051] | @schottenfeld_maintenance_2008 | Computing Environment | References

1. Overview | 2. GMT Files | 2.1 GMT Format Description | 2.2 Import GMT files with read_gmt | 2.3 Creating Your Own pathwayCollection List | 2.4 Importing a Pathway Collection from Wikipathways | 2.4 Writing a pathwayCollection Object to a .gmt File | 3. Import and Tidy an Assay Matrix | 3.1 Import with readr | 3.2 Tidy the Assay Data Frame | 3.3 Subsetting a Tidy Data Frame | 3.4 Data from a SummarizedExperiment Object | 4. Import and Join Response Data | 5. Example Tidy Assay and Pathways List | 6. Review

1. Overview | 1.1 Packages | 1.2 Example Results | 2. Plot Pathway Significance Levels | 2.1 Trim Pathway Names | 2.2 Tidy the Pathway Results | 2.3 Plot Significant Survival Pathways for One Adjustment | 2.4 Plot Significant Survival Pathways for All Adjustments | 3. Inspecting the Driving Genes | 3.1 Extract Pathway Decomposition | 3.2 Gene Loadings | 3.2.1 Wrangle Pathway Loadings | 3.2.2 Plot the Gene Loadings | 3.3 Gene Correlations with PCs | 3.3.1 Calculate Pathway Correlations | 3.3.2 Plot the Assay-PC Correlation | 4. Plot patient-specific pathway activities | 5. Review

1. Introduction | Installing the Package | Stable Build | Development Build | Loading Packages | 2. Case study: identifying significant pathways in protein expressions associated with survival outcome in ovarian cancer data | 2.1. Ovarian cancer dataset | 2.2. Creating an Omics data object for pathway analysis | 2.2.1 Expression and Phenotype Data | 2.2.2 Pathway Collections | 2.2.3 Create an OmicsSurv Data Container | 2.3. Testing pathway association with phenotypes | 2.3.1 Method Description | 2.3.2 Implementation | 2.3.3 The pathway analysis results | 2.3.4 Extract relevant genes from significant pathways | 2.3.5 Subject-specific PCA estimates | 2.3.6 Extract analysis dataset for significant pathways | 3. Case study: an integrative multi-omics pathway analysis of ovarian cancer data | 3.1 Creating copy number Omics data object for pathway analysis | 3.2 Identifying significant pathways and relevant genes in both CNV and protein level | 3.3 An integrative view on patient-specific pathway activities | 4. Case study: analysis of studies with complex designs | 4.1 Data setup and AESPCA analysis | 4.2 Test for sex interaction with first PC | 4.3 Survival curves by sex interaction | 5. Further reading | 6. References

1. Overview | Installing the Package | Stable Build | Development Build | Loading Packages | 2. Import Data | 2.1 Import .gmt Files | 2.2 Import and Tidy Assay Data | 2.3 Import Phenotype Info | 2.4 Match the Phenotype and Assay Data | 3. Create an Omics Data Object | 3.1 Create an Object | 3.2 Inspect the Object | 3.3 Detailed Object Views | 3.3.1 View the Assay | 3.3.2 View the pathwayCollection List | 3.3.3 View the Event Time | 3.3.4 View the Event Indicator | 4. Test Pathways for Significance | 4.1 AES-PCA | 4.2 Supervised PCA | 5. Inspect Results | 5.1 Analysis Output Table | 5.2 Graph of Top Pathways | 5.2.1 Tidy Up the Data | 5.2.2 Graph Pathway Ranks | 6. Links to Detailed Information

1. Overview | 1.1 Outline | 1.2 Import Data | 2. Omics-Class Objects Defined | 2.1 Class Overview | 2.2 Review of Data Types in R | 3. Create New Omics Objects | 3.1 Overview of Subtypes | 3.2 Create a Survival Omics Data Object | 3.3 View the New Object | 3.4 Regression and Classification Omics Data Objects | 4. Inspecting and Editing Omics-Class Objects | 4.1 Example "Get" Function | 4.2 Example "Set" Function | 4.3 Table of Accessors | 4.4 Inspect the Updated pathwayCollection List | 5. Review

1. Overview | 1.1 Outline | 1.2 Load Packages | 1.3 Load Omics Data | 2. Pathway Testing Setup | 2.1 Pathway Significance Testing Overview | 2.2 Extract Pathway PCs | 2.3 Test Pathway Association | 2.4 Adjust the Pathway $p$-Values for FDR | 2.5 Output a Sorted Data Frame / Tibble | 3. AES-PCA | 3.1 Method Details | 3.1.1 AES-PCA Method Sources | 3.1.2 Calculate Pathway-Specific Model $p$-Values | 3.1.3 AES-PCA Pros and Cons | 3.2 AES-PCA Examples | 3.2.1 Survival Response | 3.2.2 Regression Response | 3.2.3 Binary Classification Response | 4. Supervised PCA | 4.1 Method Details | 4.1.1 Supervised PCA Method Sources | 4.1.2 Calculate Pathway-Specific Model $p$-Values | 4.1.3 Supervised-PCA Pros and Cons | 4.2 Supervised PCA Examples | 4.2.1 Survival Response | 4.2.2 Regression Response | 4.2.3 Binary Classification Response | 5. Inspect the Results | 5.1 Table of $p$-Values | 5.2 Pathway PC and Loading Vectors | 6. Review