K81: Missing data: consequences and solutions

Although researchers do their best to avoid missing data, it is a common problem in medical and epidemiological studies. How large your missing data problem is and how to deal with it depends on how much data is missing and why your data are missing. This three-day course provides you with tools how to evaluate and handle missing data in medical and epidemiological studies with different missing data rates.

De voertaal van de cursus Missing data zal Nederlands zijn. Het cursusmateriaal is echter in het Engels.

De EpidM Wintercourse Missing data (WK81) is inhoudelijk gelijk aan deze cursus Missing data (K81), maar wordt in het Engels gegeven.
Alle informatie over Missing data (WK81) kunt u vinden op de website; www.epidm.nl/wintercourses.
Indien u besluit zich in te schrijven voor Missing data (WK81) moet u dit via de website van de Wintercourses doen.

Als bij een cursus [vol] staat, kunt u zich wel aanmelden, maar wordt u op een wachtlijst geplaatst. Zodra er een plek vrijkomt nemen we contact met u op. Op dat moment kunt u nog besluiten of u wilt deelnemen aan de cursus.

Datum:
17, 18, 19 september 2019
Plaats:
Amsterdam
Coördinator:
Dr. M.W. Heijmans
Voertaal:
Nederlands
Werkvorm:
Interactieve colleges en computerpractica
Toetsvorm:
Schriftelijk tentamen met computerexamen
Tentamendata:
Tentamendata: Zie rooster bij 'Tentamens'
Aantal EC:
2
Type cursus:
Verplicht vanaf cohort ME19
Periode:
Jaar 2
Niveau:
500
  • Course description and topics

    Although researchers do their best to avoid missing data, it is a common problem in medical and epidemiological studies. How large your missing data problem is and how to deal with it depends on how much data is missing and why your data are missing. This two-day course provides you with tools how to evaluate and handle missing data in medical and epidemiological studies with different missing data rates.

    There are various methods to deal with missing data. Simple solutions are that you ignore the missing values and delete all cases with missing values from the analysis or to use a regression model to estimate the missing values. There are also more advanced methods as Multiple Imputation. Multiple Imputation with the Multivariate Imputation with Chained Equations (MICE) procedure is a promising technique that works well in various missing data situations. With Multiple Imputation several complete datasets are generated. Data analysis has to be done in each dataset and results are pooled using special calculation rules (called Rubin’s rules). These steps will be discussed during the course as well as questions of how to use different missing data methods in medical and epidemiological datasets.

    Before you are going to use a method to handle missing data you must have to gain insight into the effect of missing data on your study results. Consequences of various rates of missing data for your study results will be explored and discussed during the course. In general there are three missing data mechanisms, missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). Knowledge about these mechanisms is important and provides information about how well you are able to estimate and replace the missing values and how well you are able to solve the missing data problem in your study. Furthermore it is important to check if your imputation strategy was successful (imputation diagnostics) which will also be discussed during the course.

    Each course day starts with lectures in the morning followed by computer exercises in the afternoon. During the computer exercises various ways to explore missing data problems as well as simple and more advanced missing data methods as Multiple Imputation will be trained using SPSS software. During the computer exercises you will work with real epidemiological and medical datasets.

    Although researchers do their best to avoid missing data, it is a common problem in medical and epidemiological studies. How large your missing data problem is and how to deal with it depends on how much data is missing and why your data are missing. This two-day course provides you with tools how to evaluate and handle missing data in medical and epidemiological studies with different missing data rates.

    There are various methods to deal with missing data. Simple solutions are that you ignore the missing values and delete all cases with missing values from the analysis or to use a regression model to estimate the missing values. There are also more advanced methods as Multiple Imputation. Multiple Imputation with the Multivariate Imputation with Chained Equations (MICE) procedure is a promising technique that works well in various missing data situations. With Multiple Imputation several complete datasets are generated. Data analysis has to be done in each dataset and results are pooled using special calculation rules (called Rubin’s rules). These steps will be discussed during the course as well as questions of how to use different missing data methods in medical and epidemiological datasets.

    Before you are going to use a method to handle missing data you must have to gain insight into the effect of missing data on your study results. Consequences of various rates of missing data for your study results will be explored and discussed during the course. In general there are three missing data mechanisms, missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). Knowledge about these mechanisms is important and provides information about how well you are able to estimate and replace the missing values and how well you are able to solve the missing data problem in your study. Furthermore it is important to check if your imputation strategy was successful (imputation diagnostics) which will also be discussed during the course.

    Each course day starts with lectures in the morning followed by computer exercises in the afternoon. During the computer exercises various ways to explore missing data problems as well as simple and more advanced missing data methods as Multiple Imputation will be trained using SPSS software. During the computer exercises you will work with real epidemiological and medical datasets.

  • Faculty
    Martijn W. Heymans, PhD , course coordinator
    Assistant professor
    Department of Epidemiology & Biostatistics. Amsterdam UMC, location VUmc
    Dr. Martijn Heymans expertise is in prognostic and prediction modeling, missing data and longitudinal data analysis. He (co)-authored more than 80 scientific publications and also teaches courses in epidemiology, applied biostatistics and regression techniques and works as a statistical consultant.

    Iris Eekhout, PhD
    Department of Epidemiology & Biostatistics. Amsterdam UMC, location VUmc
    Department Child Health, Netherlands Organisation for Applied Scientific Research (TNO), Leiden


    Iris Eekhout finished a master in Clinical Psychology and a master in Methodology and Statistics at the University of Leiden. She did a PhD project on missing data methods at the department of Epidemiology and Biostatistics of the VU University medical center, that focused on methods to handle missing questionnaire items and total scores. Currently, Iris teaches in several EpidM courses and works as a statistician at TNO.


  • Programme
    Missing data consequences

    Lectures:
    - Examples of Missing data in different Epidemiological and Medical research designs.
    - The meaning of missing data mechanisms (MCAR, MAR, MNAR).
    - Consequences and impact of missing data rates for statistical analyses.
    - Ways to evaluate various missing data situations and mechanisms.

    Missing data solutions
    Lectures:
    - The application of simple missing data methods.
    - The theory and practice of Multiple Imputation.
    - Data analysis after Multiple Imputation.
    - How to evaluate imputation success by using imputation diagnostics

    Practicals
    Software SPSS

  • Learning objectives
      1. The participant is able to distinguish between different missing data mechanisms called missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR).
      2. The participant can apply basic evaluation procedures to make a valid assumption about the missing data mechanism.
      3. The participant understands the working of the most frequently used methods to handle missing data in epidemiological and medical datasets.
      4. The participant recognizes the strengths and limitations of the most frequently used methods to handle missing data in various missing data situations.
      5. The participant is able to work with SPSS to investigate missing data and to work with the best missing data methods for various missing data situations.
      6. The participant is able to use Multiple Imputation by the Multivariate Imputation by Chained Equations (MICE) procedure in SPSS amd RStudio.
      7. The participant understands how multiple imputation works and how a multiple imputation model should be specified.
      8. The participant understands how to handle missing questionnaire data and can comprehend the difference between handling item scores at item level and at total score level.
      9. The participant understands the practical solutions to handle missing data in longitudinal studies.
      10. The participant is able to work with SPSS and RStudio to handle missing data in questionnaires and in longitudinal studies.
  • Doelgroep en ingangseisen

    Doelgroep

    De cursus is ontwikkeld voor onderzoekers, gezondheidsprofessionals en PhD studenten in de epidemiologie, geneeskunde, gezondheidszorg, psychologie en bewegingswetenschappen.

    De cursus is bedoeld voor iedereen die meer wil weten over 'ontbrekende data', bijvoorbeeld omdat je binnen je onderzoek te maken hebt met ontbrekende data en u moet beginnen met uw gegevensanalyse of als u wilt leren hoe u andere artikelen onderzoeksbeurzen kunt beoordelen waarin ontbrekende data aan rol spelen . Het is ook belangrijk om de impact van ontbrekende data op praktijkgerelateerd onderzoek te kunnen beoordelen.

    Ingangseisen

    Voor het volgen van deze cursus is vereist:

    - De cursus Regressietechnieken (V30) is gevolgd of dat u beschikt over aantoonbare kennis op dit niveau.

    - Kennis van basis commando's van SPSS.


  • Cursusmateriaal
    Cursisten ontvangen op de eerste cursusdag een reader met de hand-outs van de presentaties, de werkgroep opdrachten en de practicumopdrachten.

    Alle uitwerkingen van werkgroepen en computerpracticum, eventuele aanvullende literatuur, eventuele aanvullende lesstof en informatie over het tentamen kunt u op Canvas vinden, onze digitale leeromgeving.

    Een week voor aanvang van de cursus ontvangt u informatie over het aanmaken van een canvas account voor deze cursus.


  • Afsluiting, Beoordeling en EC's

    Een verklaring van deelname wordt afgegeven indien de cursus in zijn geheel gevolgd is. In bijzondere gevallen kan de cursuscoördinator, na voorafgaand overleg en bij een geldige reden, besluiten bij geringe afwezigheid (max. 20%) toch een certificaat uit te reiken.

    Deelnemers die deze cursus volgen als onderdeel van de Masteropleiding Epidemiologie ronden de cursus altijd af met een tentamen.
    Deelnemers die deze cursus als een afzonderlijke cursus volgen kunnen facultatief de cursus afsluiten met een tentamen. De kosten hiervan bedragen 150,- per tentamen of hertentamen.
    Alleen wanneer het tentamen met een voldoende resultaat wordt afgesloten ontvangt u een tentamenverklaring met daarop de studiepunten (EC’s)

    Voor deelname aan het tentamen moet zich men altijd aanmelden.

    Zie voor informatie en aanmelding: Tentamens



  • Accreditatie

    Deze cursus is geaccrediteerd voor:

    • Cluster 1: huisartsen, specialisten ouderengeneeskunde, artsen verstandelijk gehandicapten
    • Cluster 2: medisch specialisten
    • Cluster 3: sociaal geneeskundigen, bedrijfsartsen, verzekeringsartsen, artsen maatschappij en gezondheid

    De cursus 'Klinische predictiemodellen' (K81) is geaccrediteerd voor 15 uren.
    Om in aanmerking te komen voor de accreditatie-uren van deze cursus dient u de gehele cursus aanwezig te zijn geweest.

Als u doorsurft op deze website, gaat u akkoord met de plaatsing van cookies. Meer informatie Deze melding verbergen
U maakt gebruik van een verouderde browser, voor optimaal gebruik raden wij aan om uw browser te updaten.