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05-12-2016 04:44 PM

Hi,

I have a matched data set (shown below). I want to carry out the following analysis:

1. Descriptive table

2. conditional logistic regression unadjusted (outcome is fev)

3. conditional logistic regression adjusted (outcome is fev)

4. conditional logistic regression with interaction terms (opwo*race) (outcome is fev). Please guide me.

Thanks

Group | Type | Control Id | id | age | edu | race | parity | wt | ht | opwo | fev |

1 | Case | . | 5 | 32 | 9 | 1 | 0 | 90 | 5.2 | 1 | 1 |

1 | Control | 1 | 70 | 32 | 9 | 1 | 0 | 90 | 5.2 | 1 | 0 |

1 | Control | 2 | 99 | 35 | 10 | 1 | 0 | 90 | 5.2 | 0 | 0 |

2 | Case | . | 12 | 35 | 12 | 1 | 2 | 110 | 5 | 0 | 1 |

2 | Control | 1 | 43 | 35 | 12 | 1 | 1 | 110 | 5 | 0 | 0 |

2 | Control | 2 | 101 | 33 | 11 | 1 | 2 | 110 | 5 | 0 | 0 |

3 | Case | . | 22 | 25 | 14 | 3 | 0 | 110 | 5.4 | 0 | 1 |

3 | Control | 1 | 125 | 25 | 13 | 3 | 0 | 110 | 5.4 | 0 | 0 |

3 | Control | 2 | 51 | 28 | 12 | 3 | 0 | 110 | 5.4 | 0 | 0 |

4 | Case | . | 29 | 27 | 12 | 2 | 1 | 95 | 5 | 1 | 1 |

4 | Control | 1 | 32 | 29 | 12 | 2 | 2 | 125 | 6.2 | 1 | 0 |

4 | Control | 2 | 31 | 27 | 10 | 2 | 1 | 95 | 5.5 | 0 | 0 |

5 | Case | . | 40 | 28 | 10 | 3 | 2 | 130 | 6 | 1 | 1 |

5 | Control | 1 | 58 | 28 | 10 | 3 | 2 | 130 | 6 | 0 | 0 |

5 | Control | 2 | 93 | 27 | 11 | 3 | 2 | 130 | 6 | 1 | 0 |

Accepted Solutions

Solution

05-13-2016
09:27 AM

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Posted in reply to wajmsu

05-13-2016 03:43 AM

You can find some examples relate to conditional logistic in documentation.

Something like :

Example 72.11: Conditional Logistic Regression for Matched Pairs Data

```
data have;
infile cards expandtabs truncover;
input Group Type $ ControlId id age edu race parity wt ht opwo fev;
cards;
1 Case . 5 32 9 1 0 90 5.2 1 1
1 Control 1 70 32 9 1 0 90 5.2 1 0
1 Control 2 99 35 10 1 0 90 5.2 0 0
2 Case . 12 35 12 1 2 110 5 0 1
2 Control 1 43 35 12 1 1 110 5 0 0
2 Control 2 101 33 11 1 2 110 5 0 0
3 Case . 22 25 14 3 0 110 5.4 0 1
3 Control 1 125 25 13 3 0 110 5.4 0 0
3 Control 2 51 28 12 3 0 110 5.4 0 0
4 Case . 29 27 12 2 1 95 5 1 1
4 Control 1 32 29 12 2 2 125 6.2 1 0
4 Control 2 31 27 10 2 1 95 5.5 0 0
5 Case . 40 28 10 3 2 130 6 1 1
5 Control 1 58 28 10 3 2 130 6 0 0
5 Control 2 93 27 11 3 2 130 6 1 0
;
run;
proc logistic data=have ;
strata group;
model fev(event='1')= age edu race parity;
run;
```

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Posted in reply to wajmsu

05-12-2016 04:53 PM

What should the descriptive "table" contain? Counts, percents, Means, standard deviations, max, min or other statistics of single variables? Combinations of variables? within groups? Correlations?

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Posted in reply to ballardw

05-12-2016 07:19 PM

Thanks; I want to have counts with percents and mean with standard deviations and range.

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Posted in reply to wajmsu

05-12-2016 09:10 PM

Can you explain

2. conditional logistic regression **unadjusted** (outcome is fev)

3. conditional logistic regression **adjusted** (outcome is fev)

What is unadjusted and adusted ?

Did you check the example in documentation ,there are many examples about conditional logistic regression.

What is your STRATA(conditional) variable ? GROUP ?

About interaction terms opwo*race , Couldn't you write as

model ....=.... opwo*race ;

strata group;

or check EFFECT statement.

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Posted in reply to Ksharp

05-13-2016 01:16 AM

Sure.

Unadjusted means univatiate analysis (one variable assessed for the outcome, here fev)

Adjusted means multipvariate, when more than one independent variables are added to the model simultaneously where these variables adjust the effect/association of each other on the response variable.

My matching variables are: age, edu, race and parity. I think, not sure, these can be taken as 'strata'.

Thanks

Solution

05-13-2016
09:27 AM

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Posted in reply to wajmsu

05-13-2016 03:43 AM

You can find some examples relate to conditional logistic in documentation.

Something like :

Example 72.11: Conditional Logistic Regression for Matched Pairs Data

```
data have;
infile cards expandtabs truncover;
input Group Type $ ControlId id age edu race parity wt ht opwo fev;
cards;
1 Case . 5 32 9 1 0 90 5.2 1 1
1 Control 1 70 32 9 1 0 90 5.2 1 0
1 Control 2 99 35 10 1 0 90 5.2 0 0
2 Case . 12 35 12 1 2 110 5 0 1
2 Control 1 43 35 12 1 1 110 5 0 0
2 Control 2 101 33 11 1 2 110 5 0 0
3 Case . 22 25 14 3 0 110 5.4 0 1
3 Control 1 125 25 13 3 0 110 5.4 0 0
3 Control 2 51 28 12 3 0 110 5.4 0 0
4 Case . 29 27 12 2 1 95 5 1 1
4 Control 1 32 29 12 2 2 125 6.2 1 0
4 Control 2 31 27 10 2 1 95 5.5 0 0
5 Case . 40 28 10 3 2 130 6 1 1
5 Control 1 58 28 10 3 2 130 6 0 0
5 Control 2 93 27 11 3 2 130 6 1 0
;
run;
proc logistic data=have ;
strata group;
model fev(event='1')= age edu race parity;
run;
```

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Posted in reply to Ksharp

05-13-2016 09:27 AM

Thanks, it did work; will explore further. However, I can't take age edu race and parity in the model as they are matching variable and their effect has already been fixed.

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Posted in reply to Ksharp

05-13-2016 12:31 PM

Hi Ksharp,

What code should I use for count (with percents) for parity, race, opwo and for mean (with std deviation) for wt ht acros fev (1 and 0)?

Thanks.

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Posted in reply to wajmsu

05-14-2016 03:05 AM

Sorry. I don't know what you are talking about. Maybe you should start a new session to discuss this .