AI Assistant Module | Overview | Quick Start | 1. API Key Setup | 2. Basic Usage | Option A: Use with jsBasicGadget | Option B: Standalone Shiny App | Features | Code Generation | Multiple AI Providers | Export Options | Safety Features | Important Notes | Data Access | Allowed Packages | Variable Structure | API Key Resolution Order | API Configuration Modes | show_api_config = TRUE (Default) | show_api_config = FALSE | Advanced Usage | Custom Variable Structure | Analysis Context | Production Deployment | Troubleshooting | API Key Not Found | Code Execution Errors | Summary Results Too Fragmented | Text Output Shows Escape Sequences | Best Practices | 1. Be Specific in Questions | 2. Review Generated Code | 3. Provide Context | 4. Use Appropriate Model | 5. Iterative Refinement | Limitations | Examples | Example 1: Descriptive Statistics | Example 2: Survival Analysis | Example 3: Visualization | Example 4: Model Diagnostics | Security Considerations | Code Execution Security | Environment-Aware Execution (Development vs Production) | Basic Security Features | API Key Security | Best Practices by Deployment Type | API Key Storage Locations | Compliance Considerations | Recommended Security Setup | Support | License

AI Assistant 모듈 | 개요 | 빠른 시작 | 1. API 키 설정 | 2. 기본 사용법 | 옵션 A: jsBasicGadget과 함께 사용 | 옵션 B: 독립 실행형 Shiny 앱 | 주요 기능 | 코드 생성 | 다양한 AI 제공자 | 내보내기 옵션 | 안전 기능 | 중요 사항 | 데이터 접근 | 허용된 패키지 | 변수 구조 | API 키 우선순위 | API 설정 모드 | show_api_config = TRUE (기본값) | show_api_config = FALSE | 고급 사용법 | 사용자 정의 변수 구조 | 분석 컨텍스트 | 프로덕션 배포 | 문제 해결 | API 키를 찾을 수 없음 | 코드 실행 오류 | Summary 결과가 너무 조각남 | 텍스트 출력에 이스케이프 시퀀스 표시 | 모범 사례 | 1. 구체적인 질문하기 | 2. 생성된 코드 검토 | 3. 컨텍스트 제공 | 4. 적절한 모델 사용 | 5. 반복적 개선 | 제한사항 | 예제 | 예제 1: 기술통계 | 예제 2: 생존분석 | 예제 3: 시각화 | 예제 4: 모델 진단 | 보안 고려사항 | 코드 실행 보안 | 환경 인식 실행 (개발 vs 프로덕션) | 기본 보안 기능 | API 키 보안 | 배포 유형별 모범 사례 | API 키 저장 위치 | 규정 준수 고려사항 | 권장 보안 설정 | 지원 | 라이선스

Introduction | Preparing Your Data | GAM Models | Cox Proportional Hazards | Logistic Regression | Poisson Regression | GLM with Splines | Natural Splines (ns) | B-splines (bs) | Cox Models with Splines | Customizing Your Plots | Reference Values | Confidence Interval Styles | Histogram Options | Log Scale | Interaction Terms | Tips for Best Results | Conclusion

Install | Example | Survival probability | Cumulative incidence: 1- Survival probability | Landmark analysis | Competing risk analysis | Theme Jama | Theme Nejmoa | Weighted Kaplan-Meier plot - svykm.object in survey package | Theme | JAMA | NEJM

Display results of comepting risk analysis using jstable(Fine-Gray Method) | TableSubgroupMultiCox | When using the TableSubgroupMultiCox function, preprocessing the data with the finegray function from the survival package is required. The finegray function generates a new dataset containing fgstart, fgstop, fgstatus, and fgwt. The TableSubgroupMultiCox function then displays results based on the corresponding formula and weights. | cox2.display | As written above, preprocessing the data with finegray function is also required. By using corresponding formula and weights, cox2.display function will display table results.

Introducing count_by, event options in TableSubgroupMultiCox | TableSubgroupMultiCox | Counting the Number of Independent Variables for Comparison | The default option for count_by is set to NULL. By specifying an independent variable in the count_by option, the table will display the counts for each level of the independent variable. | Calculate crude incidence rate of event | The default value for the event option is set to FALSE. By setting event to TRUE, the table will display the crude incidence rate of events. This rate is calculated using the number of events as the numerator and the count of the independent variable as the denominator.(Different from Kaplan-Meier Estimates) | Using both count_by and event option is also available | By using both count_by and event option, the table will display crude incidence rate and the counts for each level of the independant variable. | Introducing pairwise option | Introducing pairwise, pairwise.showtest option in CreateTableOneJS | The default value for the pairwise option is FALSE. By setting pairwise to TRUE, the table will display p-values for pairwise comparisons of stratified groups. | By setting pairwise.showtest option to TRUE, the table will display test used to calculate p-values for pairwise comparisons of stratified groups. Default test for categorical variables are chi-sq test and continuous variables are t-test. | Introducing pairwise option in svyCreateTableOneJS | By setting pairwise.showtest option to TRUE, the table will display test used to calculate p-values for pairwise comparisons of stratified groups.

Subgroup Analysis | Subgroup analysis for Cox regression is available by selecting the event, time, group, and subgroup variables. | Subgroup analysis for linear regression is available by selecting the group, outcome, and subgroup variables. | Subgroup analysis for logistic regression is available by selecting the group, outcome, and subgroup variables. | Competing risk analysis | Competing risk analysis can be performed by selecting the regression tab and choosing the Cox model. After clicking on "Competing Risk Analysis," select the competing risk and competing time variables to display results using the Fine-Gray method | Kaplan-Meier plots are also available with competing risks reflected. After clicking on "Competing Risk Analysis," select the competing risk and competing time variables to generate Kaplan-Meier plots that account for competing risks. | Subgroup analysis for Cox regression with competing risks is also available. After clicking on "Competing Risk Analysis," select the competing risk and competing time variables to generate Kaplan-Meier plots that account for competing risks. | Web applications

Install | GLM Table | GEE Table: from geeglm object from geepack package | Mixed model Table: lmerMod or glmerMod object from lme4 package | Cox model with frailty or cluster options | Cox mixed effect model Table: coxme object from coxme package | GLM for survey data : svyglm object from survey package | Cox model for survey data :svycoxph object from survey package | Sub-group analysis for Cox/svycox model

Install | RStudio Addins | Basic statistics | Repeated measure analysis | Survey data analysis | Propensity score analysis(experimental) | Web applications