{
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  "Title": "Matching Adjusted Indirect Comparison",
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  "Description": "Facilitates performing matching adjusted indirect\ncomparison (MAIC) analysis where the endpoint of interest is\neither time-to-event (e.g. overall survival) or binary (e.g.\nobjective tumor response). The method is described by\nSignorovitch et al (2012) <doi:10.1016/j.jval.2012.05.004>.",
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    "maic_forest_plot",
    "maic_unanchored",
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    "ph_diagplot",
    "ph_diagplot_lch",
    "ph_diagplot_schoenfeld",
    "plot_weights_base",
    "plot_weights_ggplot",
    "process_agd",
    "set_time_conversion",
    "survfit_makeup"
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      "title": "Binary outcome data from single arm trial",
      "concept": [
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        "adrs_sat"
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    },
    {
      "page": "adrs_twt",
      "title": "Binary outcome data from two arm trial",
      "concept": [
        "anchored datasets"
      ],
      "topics": [
        "adrs_twt"
      ]
    },
    {
      "page": "adsl_sat",
      "title": "Patient data from single arm study",
      "concept": [
        "unanchored datasets"
      ],
      "topics": [
        "adsl_sat"
      ]
    },
    {
      "page": "adsl_twt",
      "title": "Patient data from two arm trial",
      "concept": [
        "anchored datasets"
      ],
      "topics": [
        "adsl_twt"
      ]
    },
    {
      "page": "adtte_sat",
      "title": "Survival data from single arm trial",
      "concept": [
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      "topics": [
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      ]
    },
    {
      "page": "adtte_twt",
      "title": "Survival data from two arm trial",
      "concept": [
        "anchored datasets"
      ],
      "topics": [
        "adtte_twt"
      ]
    },
    {
      "page": "agd",
      "title": "Aggregate effect modifier data from published study",
      "concept": [
        "anchored datasets",
        "unanchored datasets"
      ],
      "topics": [
        "agd"
      ]
    },
    {
      "page": "basic_kmplot",
      "title": "Basic Kaplan Meier (KM) plot function",
      "topics": [
        "basic_kmplot"
      ]
    },
    {
      "page": "basic_kmplot2",
      "title": "Basic Kaplan Meier (KM) plot function using ggplot",
      "topics": [
        "basic_kmplot2"
      ]
    },
    {
      "page": "bucher",
      "title": "Bucher method for combining treatment effects",
      "topics": [
        "bucher",
        "print.maicplus_bucher"
      ]
    },
    {
      "page": "center_ipd",
      "title": "Center individual patient data (IPD) variables using aggregate data averages",
      "topics": [
        "center_ipd"
      ]
    },
    {
      "page": "centered_ipd_sat",
      "title": "Centered patient data from single arm trial",
      "concept": [
        "unanchored datasets"
      ],
      "topics": [
        "centered_ipd_sat"
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    },
    {
      "page": "centered_ipd_twt",
      "title": "Centered patient data from two arm trial",
      "concept": [
        "anchored datasets"
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      "topics": [
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    },
    {
      "page": "check_weights",
      "title": "Check to see if weights are optimized correctly",
      "topics": [
        "check_weights",
        "print.maicplus_check_weights"
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    },
    {
      "page": "dummize_ipd",
      "title": "Create dummy variables from categorical variables in an individual patient data (ipd)",
      "topics": [
        "dummize_ipd"
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    },
    {
      "page": "estimate_weights",
      "title": "Derive individual weights in the matching step of MAIC",
      "topics": [
        "estimate_weights",
        "plot.maicplus_estimate_weights"
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    },
    {
      "page": "find_SE_from_CI",
      "title": "Calculate standard error from the reported confidence interval.",
      "topics": [
        "find_SE_from_CI"
      ]
    },
    {
      "page": "get_pseudo_ipd_binary",
      "title": "Create pseudo IPD given aggregated binary data",
      "topics": [
        "get_pseudo_ipd_binary"
      ]
    },
    {
      "page": "get_time_as",
      "title": "Convert Time Values Using Scaling Factors",
      "topics": [
        "get_time_as"
      ]
    },
    {
      "page": "glm_makeup",
      "title": "Helper function to summarize outputs from glm fit",
      "topics": [
        "glm_makeup"
      ]
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    {
      "page": "kmplot",
      "title": "Kaplan Meier (KM) plot function for anchored and unanchored cases",
      "topics": [
        "kmplot"
      ]
    },
    {
      "page": "kmplot2",
      "title": "Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot",
      "topics": [
        "kmplot2"
      ]
    },
    {
      "page": "maic_anchored",
      "title": "Anchored MAIC for binary and time-to-event endpoint",
      "topics": [
        "maic_anchored"
      ]
    },
    {
      "page": "maic_forest_plot",
      "title": "Forest Plot for One or More MAIC Objects",
      "topics": [
        "maic_forest_plot"
      ]
    },
    {
      "page": "maic_unanchored",
      "title": "Unanchored MAIC for binary and time-to-event endpoint",
      "topics": [
        "maic_unanchored"
      ]
    },
    {
      "page": "medSurv_makeup",
      "title": "Helper function to retrieve median survival time from a 'survival::survfit' object",
      "topics": [
        "medSurv_makeup"
      ]
    },
    {
      "page": "ph_diagplot",
      "title": "Diagnosis plot of proportional hazard assumption for anchored and unanchored",
      "topics": [
        "ph_diagplot"
      ]
    },
    {
      "page": "ph_diagplot_lch",
      "title": "PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time",
      "topics": [
        "ph_diagplot_lch"
      ]
    },
    {
      "page": "ph_diagplot_schoenfeld",
      "title": "PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit",
      "topics": [
        "ph_diagplot_schoenfeld"
      ]
    },
    {
      "page": "plot_weights_base",
      "title": "Plot MAIC weights in a histogram with key statistics in legend",
      "topics": [
        "plot_weights_base"
      ]
    },
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