I have a dataset containing longitudinal data related to deaths. The lived/died (Event) indicator is given by 0(live)/1(die).

The dataset contains 10,000,000 observations of 200,000 people. Every person experiences the Event at some time in the dataset. Therefore, there are 200,000

**Event**observations and 9,800,000

**No Event**Observations.

The data set contains fields laid out similar to the following:

Date | ID | Event | X1 | X2....X19 |

1/1/1900 | Alice | 0 | 1.2 | 34.5 |

2/1/1900 | Alice | 0 | 1.4 | 23.4 |

3/1/1900 | Alice | 1 | 1.6 | 12.5 |

7/1/1942 | Bob | 1 | 2.3 | 98.3 |

The are 19 possible regressors (X1,X2,X3,...,X19), 2 of them are categorical.

I decided to use Binary Logistic Regression to determine a model for likelihood of death, like so:

However, no matter what I do every possible regressor is being returned as significant, whether I try 1 regressor or all 19 of them. What fundamental assumption/error am I making?