{
  "test_id": "aft_weibull",
  "test_label": "Weibull accelerated failure time regression",
  "n": 227,
  "statistic": -4.0685738724332037,
  "parameter": "NA",
  "p_value": 4.7301764742366878e-05,
  "estimate": 0.71202796027127424,
  "conf_int": [0.60456095099173279, 0.83859835038370545],
  "effect_size": {
    "name": "time_ratio",
    "value": 0.71202796027127424,
    "ci": [0.60456095099173279, 0.83859835038370545],
    "ci_level": 0.94999999999999996
  },
  "seed_used": 20261029,
  "assumptions": [
    {
      "id": "events_per_variable",
      "check": "events per variable",
      "statistic": 54.666666666666664,
      "p_value": "NA",
      "passed": true,
      "note": "at or above the rule-of-thumb floor (a convention, not a guarantee of a stable model)"
    }
  ],
  "warnings": [],
  "flags": [
    {
      "severity": "note",
      "message": "Event indicator 'status' read as 0/1: '2' = 1 (event), '1' = 0 (censored). To flip which level counts as the event, recode 'status' in Prepare data.",
      "message_i18n": {
        "key": "analysis.coercion.event_indicator",
        "args": {
          "column": "status",
          "event": "2",
          "reference": "1"
        }
      }
    },
    {
      "severity": "note",
      "message": "Values beyond Tukey's 1.5xIQR fence in: 'ph.ecog' (1), 'time' (10). Descriptive only - nothing has been removed or changed; check whether these reflect genuine extreme cases or data-entry errors.",
      "message_i18n": {
        "key": "sanity.outlier_values",
        "args": {
          "columns": "'ph.ecog' (1), 'time' (10)"
        }
      },
      "code": "outlier_values",
      "citations": ["Tukey's 1.5xIQR fence is a descriptive convention for flagging unusually extreme values, not a definition of error or a license to remove data - some 'outliers' are genuine biological extremes (Tukey, J.W., 1977, Exploratory Data Analysis, Addison-Wesley)."]
    }
  ],
  "test_label_i18n": {
    "key": "test.aft_weibull.label"
  },
  "coefficients": [
    {
      "term": "(Intercept)",
      "estimate": 530.29595268927142,
      "std.error": 0.45357771080213277,
      "statistic": 13.831004263645111,
      "p.value": 1.6569102170388576e-43,
      "conf.low": 217.98783852194651,
      "conf.high": 1290.0435150207243
    },
    {
      "term": "ph.ecog",
      "estimate": 0.71202796027127424,
      "std.error": 0.083478415028547776,
      "statistic": -4.0685738724332037,
      "p.value": 4.7301764742366878e-05,
      "conf.low": 0.60456095099173279,
      "conf.high": 0.83859835038370545
    },
    {
      "term": "sex",
      "estimate": 1.4934524813191072,
      "std.error": 0.12373256651271886,
      "statistic": 3.2415923509708326,
      "p.value": 0.0011886391464190672,
      "conf.low": 1.1718446920919434,
      "conf.high": 1.9033241597711656
    },
    {
      "term": "age",
      "estimate": 0.99255243219469536,
      "std.error": 0.0067635076674074125,
      "statistic": -1.1052607280143312,
      "p.value": 0.26904662957082698,
      "conf.low": 0.97948175282140648,
      "conf.high": 1.0057975330503521
    }
  ],
  "alpha": 0.050000000000000003,
  "sidedness": "two-sided",
  "estimate_label": "ph.ecog",
  "imputation": {
    "strategy": "complete_case",
    "n_rows": 228,
    "n_dropped": 1,
    "n_cells_imputed": 0,
    "imputed_cols": []
  },
  "time_unit": "days",
  "n_events": 164,
  "conf_level": 0.94999999999999996,
  "term_tests": [
    {
      "term": "ph.ecog",
      "test_method": "LR",
      "statistic": 16.636555297248378,
      "df": 1,
      "p_value": 4.5270051484904737e-05
    },
    {
      "term": "sex",
      "test_method": "LR",
      "statistic": 11.337890251164936,
      "df": 1,
      "p_value": 0.00075941549142894099
    },
    {
      "term": "age",
      "test_method": "LR",
      "statistic": 1.244091586796003,
      "df": 1,
      "p_value": 0.26468395990312238
    }
  ],
  "model_test": {
    "test_method": "LR",
    "statistic": 29.978621981952529,
    "df": 3,
    "p_value": 1.3944207382668884e-06
  },
  "log_scale": -0.31319273029660688,
  "power": {
    "method": null,
    "n": "NA",
    "mdes": "NA",
    "mdes_metric": null,
    "mdes_metric_i18n": null,
    "required_n": "NA",
    "required_n_label": null,
    "required_n_label_i18n": null,
    "severely_underpowered": null,
    "note": "Power is not calculated for this test.",
    "call_code": null
  },
  "explanations": ["Each additional unit of (Intercept) is associated with 530.3x the expected survival time - longer survival (TR 530.3, 95% CI 218 to 1290; p < 0.001).", "Each additional unit of ph.ecog is associated with a 28.8% shorter expected survival time - shorter survival (TR 0.712, 95% CI 0.6046 to 0.8386; p < 0.001).", "Each additional unit of sex is associated with a 49.35% longer expected survival time - longer survival (TR 1.493, 95% CI 1.172 to 1.903; p = 0.00119).", "Each additional unit of age is associated with a 0.7448% shorter expected survival time - shorter survival (TR 0.9926, 95% CI 0.9795 to 1.006; p = 0.269).", "This result is statistically significant at the 0.05 level (p < 0.001, n = 227)."],
  "explanations_i18n": [
    {
      "key": "exp.aft.numeric_times_longer",
      "args": {
        "term": "(Intercept)",
        "ratio": "530.3",
        "extras": {
          "key": "exp.extras.ci_p",
          "args": {
            "ci": {
              "key": "exp.ci",
              "args": {
                "level": "95",
                "lower": "218",
                "upper": "1290"
              }
            },
            "p": "p < 0.001"
          }
        }
      }
    },
    {
      "key": "exp.aft.numeric_pct_shorter",
      "args": {
        "term": "ph.ecog",
        "percent": "28.8",
        "ratio": "0.712",
        "extras": {
          "key": "exp.extras.ci_p",
          "args": {
            "ci": {
              "key": "exp.ci",
              "args": {
                "level": "95",
                "lower": "0.6046",
                "upper": "0.8386"
              }
            },
            "p": "p < 0.001"
          }
        }
      }
    },
    {
      "key": "exp.aft.numeric_pct_longer",
      "args": {
        "term": "sex",
        "percent": "49.35",
        "ratio": "1.493",
        "extras": {
          "key": "exp.extras.ci_p",
          "args": {
            "ci": {
              "key": "exp.ci",
              "args": {
                "level": "95",
                "lower": "1.172",
                "upper": "1.903"
              }
            },
            "p": "p = 0.00119"
          }
        }
      }
    },
    {
      "key": "exp.aft.numeric_pct_shorter",
      "args": {
        "term": "age",
        "percent": "0.7448",
        "ratio": "0.9926",
        "extras": {
          "key": "exp.extras.ci_p",
          "args": {
            "ci": {
              "key": "exp.ci",
              "args": {
                "level": "95",
                "lower": "0.9795",
                "upper": "1.006"
              }
            },
            "p": "p = 0.269"
          }
        }
      }
    },
    {
      "key": "exp.closer.significant",
      "args": {
        "alpha": "0.05",
        "p": "p < 0.001",
        "n": "227"
      }
    }
  ]
}