scholarly journals Reflection on modern methods: planned missing data designs for epidemiological research

2020 ◽  
Vol 49 (5) ◽  
pp. 1702-1711 ◽  
Author(s):  
Charlie Rioux ◽  
Antoine Lewin ◽  
Omolola A Odejimi ◽  
Todd D Little
Keyword(s):  
Missing Data ◽  
Gold Standard ◽  
Epidemiological Research ◽  
Missing Data Treatments ◽  
Planned Missing Data ◽  
Missing Completely At Random ◽  
Modern Methods ◽  
Missing Items ◽  
Data Design ◽  
Planned Missing

Abstract Taking advantage of the ability of modern missing data treatments in epidemiological research (e.g. multiple imputation) to recover power while avoiding bias in the presence of data that is missing completely at random, planned missing data designs allow researchers to deliberately incorporate missing data into a research design. A planned missing data design may be done by randomly assigning participants to have missing items in a questionnaire (multiform design) or missing occasions of measurement in a longitudinal study (wave-missing design), or by administering an expensive gold-standard measure to a random subset of participants while the whole sample is administered a cheaper measure (two-method design). Although not common in epidemiology, these designs have been recommended for decades by methodologists for their benefits—notably that data collection costs are minimized and participant burden is reduced, which can increase validity. This paper describes the multiform, wave-missing and two-method designs, including their benefits, their impact on bias and power, and other factors that must be taken into consideration when implementing them in an epidemiological study design.

scholarly journals Planned missing data in early literacy interventions: A replication study with an additional gold standard

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0249175
Author(s):  
Ralph C. A. Rippe ◽  
Inge Merkelbach
Keyword(s):  
Missing Data ◽  
Early Literacy ◽  
Gold Standard ◽  
Convergent Validity ◽  
Late Preterm ◽  
Literacy Intervention ◽  
Literacy Interventions ◽  
Planned Missing Data ◽  
Standard Level ◽  
Planned Missing

Introduction In a digital early literacy intervention RCT, children born late preterm fell behind peers when in a control condition, but outperformed them when assigned to the intervention. Results did however not replicate previous findings. Replication is often complicated by resource quality. Gold Standard measures are generally time-intensive and costly, while they closely align with, and are more sensitive to changes in, early literacy and language performance. A planned missing data approach, leaving these gold standard measures incomplete, might aid in addressing the origin(s) of non-replication. Methods Participants after consent were 695 p Dutch primary school pupils of normal and late preterm birth. The high-quality measures, in additional to simpler but complete measures, were intentionally administered to a random subsample of children. Five definitions of gold standard alignment were evaluated. Results Two out of five gold standard levels improved precision compared to the original results. The lowest gold standard level did not lead to improvement: precision was actually diminished. In two gold standard definitions, an alphabetical factor and a writing-only factor the model estimates were comparable to the original results. Only the most precise definition of the gold standard level replicated the original results. Conclusion Gold standard measures could only be used to improve model efficiency in RCT-designs under sufficiently high convergent validity.

scholarly journals Planned missing data design: stronger inferences, increased research efficiency and improved animal welfare in ecology and evolution

2018 ◽  
Author(s):  
Daniel W.A. Noble ◽  
Shinichi Nakagawa
Keyword(s):  
Missing Data ◽  
Animal Welfare ◽  
Experimental Designs ◽  
List Type ◽  
Data Imputation ◽  
Data Types ◽  
Planned Missing Data ◽  
Research Questions ◽  
Data Design ◽  
Planned Missing

AbstractEcological and evolutionary research questions are increasingly requiring the integration of research fields along with larger datasets to address fundamental local and global scale problems. Unfortunately, these agendas are often in conflict with limited funding and a need to balance animal welfare concerns.Planned missing data design (PMDD), where data are randomly and deliberately missed during data collection, is a simple and effective strategy to working under greater research constraints while ensuring experiments have sufficient power to address fundamental research questions. Here, we review how PMDD can be incorporated into existing experimental designs by discussing alternative design approaches and evaluating how data imputation procedures work under PMDD situations.Using realistic examples and simulations of multilevel data we show how a variety of research questions and data types, common in ecology and evolution, can be aided by using a PMDD with data imputation procedures. More specifically, we show how PMDD can improve statistical power in detecting effects of interest even with high levels (50%) of missing data and moderate sample sizes. We also provide examples of how PMDD can facilitate improved animal welfare and potentially alleviate research costs and constraints that would make endeavours for integrative research challenging.Planned missing data designs are still in their infancy and we discuss some of the difficulties in their implementation and provide tentative solutions. Nonetheless, data imputation procedures are becoming more sophisticated and more easily implemented and it is likely that PMDD will be an effective and powerful tool for a wide range of experimental designs, data types and problems in ecology and evolution.

Assessing shortened safety climate measures: Simulating a planned missing data design in a field setting

2018 ◽  
Vol 104 ◽  
pp. 189-201 ◽  
Author(s):  
Huw Flatau Harrison ◽  
Mark A. Griffin ◽  
Marylene Gagne ◽  
Daniela Andrei
Keyword(s):  
Missing Data ◽  
Safety Climate ◽  
Planned Missing Data ◽  
Data Design ◽  
Field Setting ◽  
Planned Missing

Making the Most of Your Research Budget: Efficiency of a Three-Method Measurement Design With Planned Missing Data

Assessment ◽  
2018 ◽  
Vol 27 (5) ◽  
pp. 903-920 ◽  
Author(s):  
Mario Lawes ◽  
Martin Schultze ◽  
Michael Eid
Keyword(s):  
Cost Effectiveness ◽  
Missing Data ◽  
Research Design ◽  
Gold Standard ◽  
Statistical Power ◽  
Measurement Model ◽  
Additional Measure ◽  
Planned Missing Data ◽  
The Cost ◽  
Planned Missing

Planned missing data (PMD) designs are an elegant way to incorporate expensive gold standard methods (e.g., biomarker) and cheaper but systematically biased methods (e.g., questionnaires) in research designs while ensuring high statistical power and low research costs. This article outlines a PMD design with one expensive gold standard and two cheap but biased methods (three-method measurement [3-MM] design). The cost effectiveness of different 3-MM-PMD designs is investigated and compared with the cost effectiveness of corresponding same-price two-method measurement designs using a simulation study. The results underline that PMD designs yield higher statistical power compared with complete data designs in a wide variety of conditions. Adding a second cheap method to the measurement model (i.e., using a 3-MM-PMD design) can increase the statistical power of the research design even further while keeping costs constant, when the additional measure is inexpensive, shares only small amounts of bias variance with the initial cheap measure, and when the gold standard measure is highly expensive compared with the cheap measures. Recommendations as well as a computer program for finding the optimal research design are provided.

Planned Missing Data Design 2: Two-Method Measurement

Missing Data ◽  
2012 ◽  
pp. 295-323 ◽  
Author(s):  
John W. Graham ◽  
Allison E. Shevock
Keyword(s):  
Missing Data ◽  
Planned Missing Data ◽  
Data Design ◽  
Planned Missing

scholarly journals A novel item-allocation procedure for the three-form planned missing data design

MethodsX ◽  
2020 ◽  
Vol 7 ◽  
pp. 100941
Author(s):  
Kyle M. Lang ◽  
E. Whitney G. Moore ◽  
Elizabeth M. Grandfield
Keyword(s):  
Missing Data ◽  
Planned Missing Data ◽  
Allocation Procedure ◽  
Data Design ◽  
Planned Missing
Sign in / Sign up

Export Citation Format

Share Document