More analysis on the German KBV data from A Midwestern Doctor, who calls it the most important dataset we have so far.

I've downloaded the data and there are columns by date for "code" and "nocode". Does anyone know what these mean?

My current analysis approach:

1. Stack the data and add together the code and nocode numbers for each quarter and ICD-10 code, and then take log2 of this sum to standardize it.
2. Join in the German ICD-10 code definitions (they are a bit different from other ICD-10 versions).
3. Split the data into wide form with one row per quarter, creating 15,650 columns for each of the ICD-10-GM code definitions
4. Create a binary variable "covid19_vax" with value 0 for all quarters prior to 2021 and 1 for all 2021 and 2022 quarters.
5. Perform 15,650 t-tests with the binary variable and adjust for multiple testing using false discovery rate (FDR).
6. Screen for effects that are positive and significant according to FDR.

I'm using JMP software (Response Screening platform), which is way more convenient than Excel and great for graphics and stats without any coding.

The signals light up like a Christmas tree with hundreds of significant differences, including the R9* death codes in the OP plot.

More analysis on the German KBV data from A Midwestern Doctor, who calls it the most important dataset we have so far.

I've downloaded the data and there are columns by date for "code" and "nocode". Does anyone know what these mean?

My current analysis approach:

1. Stack the data and add together the code and nocode numbers for each quarter, and then take log2 of this sum to standardize it.
2. Join in the German ICD-10 code definitions (they are a bit different from other ICD-10 versions).
3. Split the data into wide form with one row per quarter, creating 15,650 columns for each of the ICD-10-GM code definitions
4. Create a binary variable "covid19_vax" with value 0 for all quarters prior to 2021 and 1 for all 2021 and 2022 quarters.
5. Perform 15,650 t-tests with the binary variable and adjust for multiple testing using false discovery rate (FDR).
6. Screen for effects that are positive and significant according to FDR.

I'm using JMP software (Response Screening platform), which is way more convenient than Excel and great for graphics and stats without any coding.

The signals light up like a Christmas tree with hundreds of significant differences, including the R9* death codes in the OP plot.

More analysis on the German KBV data from A Midwestern Doctor, who calls it the most important dataset we have so far. I've downloaded the data and there are columns by date for "code" and "nocode". Does anyone know what these mean?

My current analysis approach:

1. Stack the data and add together the code and nocode numbers for each quarter, and then take log2 of this sum to standardize it.
2. Join in the German ICD-10 code definitions (they are a bit different from other ICD-10 versions).
3. Split the data into wide form with one row per quarter, creating 15,650 columns for each of the ICD-10-GM code definitions
4. Create a binary variable "covid19_vax" with value 0 for all quarters prior to 2021 and 1 for all 2021 and 2022 quarters.
5. Perform 15,650 t-tests with the binary variable and adjust for multiple testing using false discovery rate (FDR).
6. Screen for effects that are positive and significant according to FDR.

I'm using JMP software (Response Screening platform), which is way more convenient than Excel and great for graphics and stats without any coding.

The signals light up like a Christmas tree with hundreds of significant differences, including the R9* death codes in the OP plot.

More analysis on the KBV data from A Midwestern Doctor, who calls it the most important dataset we have so far. I've downloaded the data and there are columns by date for "code" and "nocode". Does anyone know what these mean?

My current analysis approach:

1. Stack the data and add together the code and nocode numbers for each quarter, and then take log2 of this sum to standardize it.
2. Join in the German ICD-10 code definitions (they are a bit different from other ICD-10 versions).
3. Split the data into wide form with one row per quarter, creating 15,650 columns for each of the ICD-10-GM code definitions
4. Create a binary variable "covid19_vax" with value 0 for all quarters prior to 2021 and 1 for all 2021 and 2022 quarters.
5. Perform 15,650 t-tests with the binary variable and adjust for multiple testing using false discovery rate (FDR).
6. Screen for effects that are positive and significant according to FDR.

I'm using JMP software (Response Screening platform), which is way more convenient than Excel and great for graphics and stats without any coding.

The signals light up like a Christmas tree with hundreds of significant differences, including the R9* death codes in the OP plot.

More analysis on the KBV data from A Midwestern Doctor, who calls it the most important dataset we have so far. I've downloaded the data and there are columns by date for "code" and "nocode". Does anyone know what these mean?

My current analysis approach:

1. Stack the data and add together the code and nocode numbers for each quarter, and then take log2 of this sum to standardize it.
2. Join in the German icd-10 code definitions (they are a bit different from other versions).
3. Split the data into wide form with one row per quarter, creating 15,650 columns for each of the icd-10-gm code definitions
4. Create a binary variable "covid19_vax" with value 0 for all quarters prior to 2021 and 1 for all 2021 and 2022 quarters.
5. Perform 15,650 t-tests with the binary variable and adjust for multiple testing using false discovery rate (FDR).
6. Screen for effects that are positive and significant according to FDR.

I'm using JMP software (Response Screening platform), which is way more convenient than Excel and great for graphics and stats without any coding.

The signals light up like a Christmas tree with hundreds of significant differences, including the R9* death codes in the OP plot.

More analysis on the KBV data from A Midwestern Doctor, who calls it the most important dataset we have so far. I've downloaded the data and there are columns by date for "code" and "nocode". Does anyone know what these mean?