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From |
John Taffe <john.taffe@med.monash.edu.au> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: A little problem with clustered data |

Date |
Mon, 03 May 2004 09:57:17 +1000 |

Ronan, it's unfortunate that depression scale is confounded with hospital. Were hospitals randomly assigned to scales? Are other known characteristics of hospitals possible confounders? Or factors related to how the scales were presented? It seems unfortunate also that a depression scale should be seen as 'threatening'. Perhaps a cognac to go with the pipes.

John Taffe

Ronán Conroy wrote:

I would like to show you the results of two logistic regressions and get

ideas on how to carry the analysis forward.

The situation is this: a survey was run in 38 hospitals. The survey used two

depression scales. Half the hospitals received the first scale, the other

half the second.

Not all patients completed a depression scale. The researcher suspected that

one of the scales was less likely to be returned (it was more threatening

than the other). So he ran a logistic regression, which confirmed his

suspicions.

. logistic depress_scale_ok which_scale

Logistic regression Number of obs = 1206

LR chi2(1) = 13.09

Prob > chi2 = 0.0003

Log likelihood = -758.99238 Pseudo R2 = 0.0086

---------------------------------------------------------------------------

depress_sc~k | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]

-------------+-------------------------------------------------------------

which_scale | 1.560516 .1927593 3.60 0.000 1.22497 1.987975

---------------------------------------------------------------------------

However, when he used -svyset- to set the PSU to hospital, to account for

patient clustering within hospitals, this is what happens (same point

estimate, but much wider confidence intervals)

. svylogit depress_scale_ok which_scale if which_scale <3, or

Survey logistic regression

pweight: <none> Number of obs = 1206

Strata: <one> Number of strata = 1

PSU: hospital_number Number of PSUs = 38

Population size = 1206

F( 1, 37) = 3.15

Prob > F = 0.0839

---------------------------------------------------------------------------

depress_sc~k | Odds Ratio Std. Err. t P>|t| [95% Conf. Interval]

-------------+-------------------------------------------------------------

which_scale | 1.560516 .390986 1.78 0.084 .9392774 2.592642

---------------------------------------------------------------------------

So it would seem that the variation between hospitals in the rate of return

is greater than the variation you would expect from a binomial process. This

accords with the researcher's experience. Some hospitals took a dislike to

the depression scales, and this was more likely to happen with the more

threatening one.

My question, finally, is what next? Clearly, one source of variability in

the return of completed depression scales is whether the hospital thinks

that it is a useful exercise or not. But are hospitals allocated the second

scale more likely to withhold their collaboration? How much of the poorer

return rate is the unwillingness of patients to fill in the scale, and how

much is the way in which the hospital handles the task of administering the

scale and making sure it is returned?

A good two-pipe problem, as my old metaphysics tutor used to say.

Ronan M Conroy (rconroy@rcsi.ie)

Lecturer in Biostatistics

Royal College of Surgeons

Dublin 2, Ireland

+353 1 402 2431 (fax 2764)

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