Turn on suggestions

Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

Showing results for

- Home
- /
- Analytics
- /
- Stat Procs
- /
- Re: How to interpret SE for lsmeans

Options

- RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Mute
- Printer Friendly Page

🔒 This topic is **solved** and **locked**.
Need further help from the community? Please
sign in and ask a **new** question.

- Mark as New
- Bookmark
- Subscribe
- Mute
- RSS Feed
- Permalink
- Report Inappropriate Content

Posted 03-21-2016 11:43 AM
(9817 views)

Hi All,

I am working on studying the variability of a new medical device. We took repeated measurements (12 on each of 30 subjects) on the device. Now I want to see the varaibility of measurements in gender groups, bmi groups etc. I ran a mixed model in sas with repeated measurements and got lsmeans for men, women, bmi groups and so on and their Standard errors. **I am not sure how do I interpret the SE from LSMEANS in this case to PI? or do I get the Standard Deviation from SE's (SE*SQRT(N)), but my total sample size is 30 and here DF for gender is 350, so what is N in my case?**

```
proc mixed data=icc; ;
class pt bmi gender bf skinfold;
model vp_dl=gender bmi bf gender*bf gender*bmi ;
repeated pt/ type=cs;
lsmeans gender bf bmi;
run;
```

Any help is greatly appreciated.

Type 3 Tests of Fixed Effects | ||||
---|---|---|---|---|

Effect | Num DF | Den DF | F Value | Pr > F |

gender | 1 | 350 | 144.60 | <.0001 |

BMI | 2 | 350 | 32.57 | <.0001 |

BF | 2 | 350 | 75.36 | <.0001 |

gender*BF | 2 | 350 | 5.38 | 0.0050 |

BMI*gender | 2 | 350 | 18.10 | <.0001 |

Least Squares Means | ||||||||
---|---|---|---|---|---|---|---|---|

Effect | BMI | gender | BF | Estimate | Standard Error | DF | t Value | Pr > |t| |

gender | 0 | 477.45 | 1.9367 |
350 | 246.53 | <.0001 | ||

gender | 1 | 438.69 | 2.5761 |
350 | 170.30 | <.0001 | ||

BF | 1 | 484.45 | 2.4906 |
350 | 194.51 | <.0001 | ||

BF | 2 | 468.57 | 2.8455 |
350 | 164.67 | <.0001 | ||

BF | 3 | 421.19 | 4.2231 |
350 | 99.74 | <.0001 | ||

BMI | 1 | 447.18 | 3.8360 |
350 | 116.57 | <.0001 | ||

BMI | 2 | 444.38 | 2.8957 |
350 | 153.46 | <.0001 | ||

BMI | 3 | 482.65 | 2.7025 |
350 | 178.60 | <.0001 |

1 ACCEPTED SOLUTION

Accepted Solutions

- Mark as New
- Bookmark
- Subscribe
- Mute
- RSS Feed
- Permalink
- Report Inappropriate Content

Tell the PI that standard deviations are for data. It is associated to the mean in the sense that a standard deviation provides a measure of how close some random future observation will be to the mean estimate.

In a similar way, you can think of a standard error as a way to think about how widely the parameter estimates will be expected to vary if you collect new data. A SE gives you a sense for how accurate your parameter estimate is.

If that is too abstract, you can also use the more familiar notion of a confidence interval. A confidence interval says that, given the data, the true parameter is probably within a certain interval (with some confidence). Standard errors are often used to construct confidence intervals. A big standard error leads to a wide CI; a small SE leads to a small CI.

3 REPLIES 3

- Mark as New
- Bookmark
- Subscribe
- Mute
- RSS Feed
- Permalink
- Report Inappropriate Content

The approximate standard errors for the LS-mean is computed as the square root of L*(X'*(V_hat)^-1*X)^-1*L'. The standard error is appropriate statistic for the LSMEANS not standard deviation. The standard deviation is a characteristic of the data itself, not of estimates such as the LS-means. If you want a standard deviation of a group of data, use the PROC MEANS.

The default is the denominator degrees of freedom taken from the "Type III Tests of Fixed Effects" table corresponding to the LS-means effect, 350 is the denominator degrees of freedom for the tests of fixed effects resulting from the MODEL. The documentation points out -

"The DDFM=BETWITHIN option is the default for REPEATED statement specifications (with no RANDOM statements). It is computed by dividing the residual degrees of freedom into between-subject and within-subject portions. PROC MIXED then checks whether a fixed effect changes within any subject. If so, it assigns within-subject degrees of freedom to the effect; otherwise, it assigns the between-subject degrees of freedom to the effect (see Schluchter and Elashoff 1990). " Check the documentation of DDFM = option on the MODEL statement in PROC MIXED procedure.

- Mark as New
- Bookmark
- Subscribe
- Mute
- RSS Feed
- Permalink
- Report Inappropriate Content

Ok, but I am not still not sure how do one explain SE from LSMEANS to a PI. They want to have an explanation about SE , because they understand std as how far a person is from the mean of the group. They try to understand SE in the same way.

Thanks

- Mark as New
- Bookmark
- Subscribe
- Mute
- RSS Feed
- Permalink
- Report Inappropriate Content

Tell the PI that standard deviations are for data. It is associated to the mean in the sense that a standard deviation provides a measure of how close some random future observation will be to the mean estimate.

In a similar way, you can think of a standard error as a way to think about how widely the parameter estimates will be expected to vary if you collect new data. A SE gives you a sense for how accurate your parameter estimate is.

If that is too abstract, you can also use the more familiar notion of a confidence interval. A confidence interval says that, given the data, the true parameter is probably within a certain interval (with some confidence). Standard errors are often used to construct confidence intervals. A big standard error leads to a wide CI; a small SE leads to a small CI.

**Available on demand!**

Missed SAS Innovate Las Vegas? Watch all the action for free! View the keynotes, general sessions and 22 breakouts on demand.

What is ANOVA?

ANOVA, or Analysis Of Variance, is used to compare the averages or means of two or more populations to better understand how they differ. Watch this tutorial for more.

Find more tutorials on the SAS Users YouTube channel.