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Posted 04-15-2015 09:44 AM
(2585 views)

I use both SAS and STATA to run a log-linear regression with the same dataset. The coefficient magnitudes were somehow different. One of the variables in my dataset had 18% missing values. I was wondering whether it was because SAS applied imputation when running regression.

Anyone knows the difference between SAS and STATA in running regression for data with missing values?

Thanks a lot.

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Thanks, Ballard.

But when I run the data with missing values excluded, the result was different from that given by not excluding missing values on purpose. The key difference is that coefficient values were totally different.

I'm attaching my data in my thread. The way I used was:

proc reg data=temp;

model lcost_all_adj=age_diag_gp1 age_diag_gp2 age_diag_gp4 age_diag_gp5 mhi_grp2 mhi_grp3 mhi_grp4 days_debr_grp2 days_debr_grp3 days_debr_grp4 flap

negpthp days_dswi_grp2 days_dswi_grp3 days_dswi_grp4 los_cs_grp2 los_cs_grp3 los_cs_grp4 los_cs_grp5 comorbid_grp2 comorbid_grp3

sepsis transf_bleedcomp;

run;

This gave me corrected total of 1198, as shown below:

Number of Observations Read | 1198 |
---|---|

Number of Observations Used | 1198 |

Analysis of Variance | |||||

Source | DF | Sum of Squares | Mean Square | F Value | Pr>F |

Model | 23 | 441.70924 | 19.20475 | 24.58 | <.0001 |
---|---|---|---|---|---|

Error | 1174 | 917.16903 | 0.78123 | ||

Corrected Total | 1197 | 1358.87827 |

However, when I particularly excluded the missing values, as below

proc reg data=temp;

model lcost_all_adj=age_diag_gp1 age_diag_gp2 age_diag_gp4 age_diag_gp5 mhi_grp2 mhi_grp3 mhi_grp4 days_debr_grp2 days_debr_grp3 days_debr_grp4 flap

negpthp days_dswi_grp2 days_dswi_grp3 days_dswi_grp4 los_cs_grp2 los_cs_grp3 los_cs_grp4 los_cs_grp5 comorbid_grp2 comorbid_grp3

sepsis transf_bleedcomp;

where mhi_ctg^=.;

run;

I have

Number of Observations Read | 992 |
---|---|

Number of Observations Used | 992 |

Analysis of Variance | |||||

Source | DF | Sum of Squares | Mean Square | F Value | Pr > F |

Model | 23 | 381.081 | 16.56874 | 21.28 | <.0001 |

Error | 968 | 753.8316 | 0.77875 | ||

Corrected Total | 991 | 1134.913 |

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I figured out what the problem was. After I recoded mhi_ctg into four dummy variables mhi_grp1-4, mhi_grp1 was excluded from the regression, and thus the observations with missing values in mhi_grp1 were treated the same way as the ones taking 1 in the variable.

Thank you, Ballard and Paige!

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I was wondering whether it was because SAS applied imputation when running regression.

SAS does not impute missing values in regression. It does not include observations with missing values among the model terms in the regression calculations.

--

Paige Miller

Paige Miller

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