Introduction | Data extraction | Configuring the connection to the server | Preparing the exposures and outcome(s) | Extracting the data from the server | Saving the data to file | Defining the study population | Propensity scores | Fitting a propensity model | Propensity score diagnostics | Using the propensity score | Evaluating covariate balance | Inspecting select population characteristics | Generalizability | Follow-up and power | Outcome models | Fitting a simple outcome model | Adding interaction terms | Adding covariates to the outcome model | Inspecting the outcome model | Kaplan-Meier plot | Time-to-event plot | Acknowledgments

Introduction | Obtaining drivers | The JAR folder | Obtaining drivers for SQL Server, Oracle, PostgreSQL, PDW, Spark, RedShift, Snowflake, BigQuery, and Synapse | Obtaining drivers for Netezza, Impala and InterSystems IRIS | Obtaining drivers for SQLite or DuckDb | Creating a connection | Using Windows Authentication for SQL Server | Connecting to a SQLite database | Connecting with Windows authentication from a non-windows machine

Introduction | General approach | Preparation for the example | Preparing the exposures and outcome(s) | Specifying hypotheses of interest | Specifying analyses | Covariate balance | Executing multiple analyses | Restarting | Retrieving the results | Diagnostics | Empirical calibration and negative control distribution | Exporting to CSV | Acknowledgments

Introduction | Fields with minimum values | Tables

Introduction | Exposures, covariates of interest, and controls | Exposures-outcome-sets, analysis IDs and models | Fields with minimum values | Tables

Simulate data | Fit a model locally | Approximate the likelihood function at one site | Normal approximation | Adaptive approximation | Approximate at all sites | Synthesize evidence | Fixed-effects | Visualization | Random-effects

Introduction | Learning bias distributions from negative control outcomes | Perform Bayesian adaptive bias correction

Introduction | Simulating some data | Approximating the likelihood at each site | Generating approximations at all sites | Combining evidence across sites | Fixed-effects model. | Bayesian random-effects model. | Choice of prior | Forest plot

Introduction | General approach | Preparation for the example | Specifying exposures-outcome sets | Specifying analyses | Executing multiple analyses | Restarting | Retrieving the results | Diagnostics summary | Negative control distribution | Acknowledgments

Introduction | Terminology | Installation instructions | Overview | Studies with a single drug | Configuring the connection to the server | Preparing the exposure and outcome of interest | Extracting the data from the server | Saving the data to file | Creating the study population | Defining a simple model | Model fitting | Adding a pre-exposure window | Including seasonality, and calendar time | Removing the COVID blip | Considering event-dependent observation time | Studies with more than one drug | Adding a class of drugs | Adding all drugs | Diagnostics for the main SCCS assumptions | Rare outcome assumption diagnostic | Event-exposure independence assumption diagnostic | Event-observation independence assumption diagnostic | Modeling assumptions diagnostic | Additional diagnostics | Power calculations | Time from exposure start to event | Ages covered per subject | Acknowledgments

Introduction | Important concepts. | Creating a cluster | Single-node cluster | Executing in parallel | Stopping the cluster | Debugging, logging, and error handling | Support for Andromeda objects

Introduction | Permanence | Technology | Creating an Andromeda object | Closing Andromeda objects | Temporary file location | Querying data from an Andromeda object | Simple meta-data | Using SQL | Dates and times | Batch operations | Safe mode | Saving and loading Andromeda objects | Using Andromeda in your packages | Import dplyr | Import .data from rlang | Beware of variable name confusion

Introduction | SQL parameterization | Substituting parameter values | Default parameter values | If-then-else | Translation to other SQL dialects | Functions and structures supported by translate | String concatenation | Bitwise operators | Table aliases and the AS keyword | Temp tables | Implicit casts | Case sensitivity in string comparisons | Schemas and databases | Optimization for massively parallel processing | Debugging parameterized SQL | Developing R packages that contain parameterized SQL | Spark SQL

Introduction | Terminology | Creating a console logger | Shorthand | Creating a file logger | Creating an error report logger | Creating an e-mail logger | Messages, warnings and fatal errors | Logging when parallel processing | Creating loggers in functions | Shiny log viewer

Introduction | Querying | Querying using Andromeda objects | Querying different platforms using the same SQL | Inserting tables | Logging all queries

Introduction | Connecting | Querying | Using dbplyr | Date functions | Allowed table and field names in dbplyr | Temp tables | Temp tables in dbplyr | Cleaning up emulated temp tables | Closing the connection

Introduction | Simulated data | MaxSPRT | Log likelihood ratio | Critical value | MaxSPRT and empirical calibration | Likelihood profiles | Fitting a null distribution using likelihood profiles | Computing a calibrated critical value | Demonstrating type 1 error with and without calibration | Computing log likelihood ratios and (uncalibrated and calibrated) critical values | Computing overall type 1 error

Introduction | Negative controls | Positive controls | Plot control effect sizes | Systematic error model | Fitting the empirical null, and specifying an assumption | Fitting the systematic error model using positive controls | Evaluating the calibration | Confidence interval calibration | Calibrating the confidence interval | References

Introduction | Negative controls | Plot negative control effect sizes | Empirical null distribution | Fitting the null distribution | Evaluating the calibration | Plotting the null distribution | P-value calibration | Calibrating the p-value | Computing the credible interval | References