I think that Royston and Wood is the best advice we have at present. What is really needed is a rule of thumb recognizing that having lots of complete cases can help to insulate against missings somewhat, as can the total sample size.
------------------------------ Frank E Harrell Jr Professor and Chairman School of Medicine Department of *Biostatistics* *Vanderbilt University* On Mon, Sep 12, 2016 at 2:50 AM, Hoogendoorn, Adriaan < [email protected]> wrote: > Originally, small numbers of imputations (3 or 5) were suggested, but > currently there seems to be preference for larger numbers. > > Many recent studies that do report numbers of imputation used between 20 > and 100 data sets (subjective view), but I’ve also seen as many as a > thousand. > > I guess that it depends on the amount of Monte Carlo error (the loss of > power to for testing an association) you are willing to accept. > > White, Royston and Wood (2010) suggest in their Statistics in Medicine > journal article as a rule of thumb to use “at least the percentage of > incomplete cases” (100 times the fraction of missing information), but they > also state that you might need more in specific settings. > > > > > Adriaan W. Hoogendoorn, PhD > > Senior Researcher – Statistician > > M: Room MB.02, A.J. Ernststraat 1187, 1081 HL Amsterdam, The Netherlands > T: + 31(0)20 788 4649 > > E: [email protected] > > Office hours: Mo-Tu-We-Th > > > > > > GGZ inGeest, Onderzoek en innovatie > Locatie A.J. Ernststraat, Amsterdam > www.ggzingeest.nl > <https://urldefense.proofpoint.com/v2/url?u=http-3A__www.ggzingeest.nl_&d=CwMGaQ&c=yHlS04HhBraes5BQ9ueu5zKhE7rtNXt_d012z2PA6ws&r=N9mDDDuK1isnKK-Q36bwNuZl066Rn4cNQtKxtVKMnWBnZ5yXlXHty3gF6wWXsdE6&m=uar2zvBt-CyCEgwFinsosPoZgBH_o6rJZAvxvVrWpeY&s=rOarCICWmLfxjAFAXCAKhMwIX7Cj-WlWK8neT8IgvxM&e=> > > > * GGZ inGeest, samen op eigen wijze* > > > > > > > > > > *Van:* Impute -- Imputations in Data Analysis [mailto:[email protected]. > NORTHWESTERN.EDU] *Namens *DAVID R JOHNSON > *Verzonden:* maandag 12 september 2016 0:13 > *Aan:* [email protected] > *Onderwerp:* Re: Typical number of imputations > > > > My experience is that authors who use MI seldom report the number of > imputations they used. > > > > ------------------------------------------------------------ > ------------------ > > David R. Johnson > Professor of Sociology, Human Development and Family Studies, and > Demography > Department of Sociology > 413 Oswald Tower > The Pennsylvania State University > University Park, PA 16802 > 814-865-9564 > [email protected] > ------------------------------------------------------------ > ------------------- > > > ------------------------------ > > *From: *"Allison, Paul D" <[email protected]> > *To: *"Impute" <[email protected]> > *Sent: *Saturday, September 10, 2016 7:58:55 AM > *Subject: *Re: Typical number of imputations > > > > Good question, but I'm not aware of any studies. > > > > > > Paul D. Allison, Professor > > Department of Sociology > > University of Pennsylvania > > 581 McNeil Building > > 3718 Locust Walk > > Philadelphia, PA 19104-6299 > > 610-715-5702 > > 419-818-1220 (fax) > > www.pauldallison.com > <https://urldefense.proofpoint.com/v2/url?u=http-3A__www.pauldallison.com_&d=CwMFAw&c=yHlS04HhBraes5BQ9ueu5zKhE7rtNXt_d012z2PA6ws&r=N9mDDDuK1isnKK-Q36bwNuZl066Rn4cNQtKxtVKMnWBnZ5yXlXHty3gF6wWXsdE6&m=WZhkIrvV_nPE4MPWJCbcERwh_uwCepoMW9A1CIlGono&s=jhCMas8sQAOyoeHxUbvqK4sCPETcmNiEkxGRSgzeBj8&e=> > > > ------------------------------ > > *From:* Impute -- Imputations in Data Analysis <[email protected]. > NORTHWESTERN.EDU> on behalf of Paul von Hippel < > [email protected]> > *Sent:* Friday, September 9, 2016 11:05 PM > *To:* [email protected] > *Subject:* Typical number of imputations > > > > Are there studies documenting how many imputations analysts typically use > in MI? I know the recommendations, but I'm interested in what users are > actually doing -- and whether users are using more imputations now than > previously. > > > Best wishes, > Paul von Hippel > LBJ School of Public Affairs > Sid Richardson Hall 3.251 > University of Texas, Austin > 2315 Red River, Box Y > Austin, TX 78712 > > > > ------------------------------ > Dit e-mailbericht is uitsluitend bestemd voor de geadresseerde. Als dit > bericht niet voor u bestemd is, wordt u verzocht dit aan de afzender te > melden en het bericht te vernietigen. Het is niet toegestaan de inhoud van > dit bericht verder te verspreiden of te gebruiken. Voor meer informatie > over GGZ inGeest: www.ggzingeest.nl. Denk aan het milieu voordat u deze > e-mail print. >
