Journal article
Inference following multiple imputation for generalized additive models: an investigation of the median p-value rule with applications to the Pulmonary Hypertension Association Registry and Colorado COVID-19 hospitalization data
BMC medical research methodology, Vol.22(1), pp.148-14
05/21/2022
DOI: 10.1186/s12874-022-01613-w
PMCID: PMC9123297
PMID: 35597908
Abstract
Missing data prove troublesome in data analysis; at best they reduce a study's statistical power and at worst they induce bias in parameter estimates. Multiple imputation via chained equations is a popular technique for dealing with missing data. However, techniques for combining and pooling results from fitted generalized additive models (GAMs) after multiple imputation have not been well explored.
We simulated missing data under MCAR, MAR, and MNAR frameworks and utilized random forest and predictive mean matching imputation to investigate a variety of rules for combining GAMs after multiple imputation with binary and normally distributed outcomes. We compared multiple pooling procedures including the "D2" method, the Cauchy combination test, and the median p-value (MPV) rule. The MPV rule involves simply computing and reporting the median p-value across all imputations. Other ad hoc methods such as a mean p-value rule and a single imputation method are investigated. The viability of these methods in pooling results from B-splines is also examined for normal outcomes. An application of these various pooling techniques is then performed on two case studies, one which examines the effect of elevation on a six-minute walk distance (a normal outcome) for patients with pulmonary arterial hypertension, and the other which examines risk factors for intubation in hospitalized COVID-19 patients (a dichotomous outcome).
In comparison to the results from generalized additive models fit on full datasets, the median p-value rule performs as well as if not better than the other methods examined. In situations where the alternative hypothesis is true, the Cauchy combination test appears overpowered and alternative methods appear underpowered, while the median p-value rule yields results similar to those from analyses of complete data.
For pooling results after fitting GAMs to multiply imputed datasets, the median p-value is a simple yet useful approach which balances both power to detect important associations and control of Type I errors.
Details
- Title: Subtitle
- Inference following multiple imputation for generalized additive models: an investigation of the median p-value rule with applications to the Pulmonary Hypertension Association Registry and Colorado COVID-19 hospitalization data
- Creators
- Matthew A Bolt - University of Colorado DenverSamantha MaWhinney - Colorado School of Public HealthJack W Pattee - Colorado School of Public HealthKristine M Erlandson - University of Colorado Anschutz Medical CampusDavid B Badesch - University of Colorado Anschutz Medical CampusRyan A Peterson - Colorado School of Public Health
- Resource Type
- Journal article
- Publication Details
- BMC medical research methodology, Vol.22(1), pp.148-14
- DOI
- 10.1186/s12874-022-01613-w
- PMID
- 35597908
- PMCID
- PMC9123297
- NLM abbreviation
- BMC Med Res Methodol
- ISSN
- 1471-2288
- eISSN
- 1471-2288
- Grant note
- R01 AG054366-05S1 / NIA NIH HHS UL1 TR002535 / NCATS NIH HHS
- Language
- English
- Date published
- 05/21/2022
- Academic Unit
- Biostatistics; Internal Medicine
- Record Identifier
- 9984914146802771
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