Missing data and imputation

Navigation Menu

General issues of Vaccine
Cold chain Correlates of Protection Vaccines for Asplenia Vaccines for Pregnant women Vaccines for Immunocompromised hosts Vaccine hesitancy Additional materials of vaccine

General issues of Tropical med.

Definition of Tropical Medicine Matrices of tropical infection General issues of Helminths Neglected Tropical Diseases Sexually-transmitted infections Non-Communicable Diseases Maternal health and contraception Child health Malnutrition and Micronutrient Eosinophilia Fever in the tropics Diarrhea in the tropics Anemia in the tropics Dermatology in the tropics Ophthalmology in the tropics Neurology in the tropics Mental health in the tropics Surgery in the tropics Humanitarian emergency Epidemiology in outbreak Antimicrobial resistance Pathology of infectious diseases

General issues of Travel med.
Epidemiology of Travel health Last minute traveler Time zone issue High altitude medicine Diving medicine Pregnancy and travel Children and travel Elderly and travel

Immunology
Principle of human immune system Innate immunity Cellular immunity Humoral immunity Neutralizing antibody and its assay Antigenic Cartography Additional materials of immunology

Epi & Stats
Basics & Definition Epidemiology Odds in statistics and Odds in a horse race Collider bias Data distribution Statistical test Regression model Multivariate analysis Marginal effects Prediction and decision Table-related commands in STATA Missing data and imputation

Virus
HIV HIV-TB co-infection HIV-STI interaction Viral Hemorrhagic Fever Ebola Crimean-Congo hemorrhagic fever SFTS Rabies Polio Dengue Yellow fever Chikungunya Zika Japanese encephalitis Tick-borne encephalitis Viral hepatitis Measles Smallpox and Monkeypox Respiratory Syncytial virus

COVID-19
Bivalent BA.1/BA.4-5 mRNA vaccines Monovalent XBB-1.5 mRNA vaccine Private archives of the initial phase of the pandemic Private archives of lecture materials of COVID vaccine as of March 2021 厚生労働省が発出する保健行政関連の文書の読み解き方

Bacteria
Bacteriological tests Tuberculosis Tuberculosis in Children HIV-TB co-infection Leprosy Dermatological mycobacterium infecions Syphilis and Yaws Plague Pneumococcus Meningococcus Typhoid Salmonellosis Melioidosis Leptospirosis Brucellosis Bartonellosis Lyme disease and Relapsing fever Tularaemia Tetanus Diphtheria Anthrax Coxiellosis

Rickettsia
Rickettsiosis Scrub typhus Spotted fevers Epidemic typhus Murine typhus

Protozoa
Overview of protozoa Overview of medicine for protozoa Malaria Chagas disease African trypanosomiasis Leishmaniasis Trichomoniasis Toxoplasmosis Amoebiasis Giardiasis Cryptosporidiosis Cyclosporiasis Isosporiasis Pentatrichomoniasis Microsporidiasis Babesiosis

Fungi
General issues of fungi Coccidioidomycosis Paracoccidioidomycosis Histoplasmosis Talaromycosis Blastomycosis Sporotrichosis

Nematode (roundworm)
Nematode principles Lympatic filariasis Onchocerciasis Loiasis Microscopic differentiation of microfilariae Strongyloidiasis Ascariasis Ancylostomiasis (hookworm) Trichuriasis (whipworm) Enterobiasis (pinworm) Angiostrongyliasis (rat lungworm) Dracunculiasis (Guinea worm) Anisakiasis Trichinellosis (Trichinosis) Gnathostomiasis Spirurinasis Soil-transmitted helminths

Trematode (fluke, distoma)
General issues of Helminths Trematode principles Schistosomiasis Clonorchiasis Fascioliasis Paragonimiasis Metagonimiasis

Cestode (tapeworm)
General issues of Helminths Cestode principles Diphyllobothriasis Sparganosis Taeniasis Echinococcosis

Medical Zoology
Zoonosis Insectology Mosquitology Acarology Batology Snake toxicology Scorpion and spider toxicology Marine toxicology

Three mechanisms (assumptions) of data missing

Mechanisms (assumptions) of data missing

	Description	[Example] A cohort study in which participants self report their body weight every week via special app installed on their smartphones for 52 weeks in total
Missing not at random MNAR	Missing depends on observed data	Some participants intentionally deny to report higher body weights they don't like and only report lower body weights they like Data of those participants are missed because of the values in the variable body weight themselves Researchers cannot explain/predict the missing mechanism by
Missing at randam MAR	Missing does not depend on (other) unobserved data, but may depend on observed data The cause of missing can be explained by	15% of female participants deny to report their body weights after the agreement regardless of measured body weights, but only 1% of male deny to report: Data of female participants are more likely missed not because of data (body weights) themselves but because female are more likely reluctant to report their body weights In other words, values in the variable body weight themselves do not affect the missing but another variable sex affects the missing of values in the variable body weight
Missing completely at random MCAR	Missing does NOT depend on (other) observed values NOR on unobserved (missing) values themselves	A participant moved to outside cohort area because of family affair which has nothing to do with the study and is lost to follow up after the 10th week A participant fails to report body weights because of malfunction of the app between the 15th and 17th weeks The reasons (mechanisms) of missing have nothing to do with other observed (certainly reported) values, values in other variables, nor unobserved (missing) values themselves Missing mechanism is completely at random, i.e., completely free from either of observed values or unobserved (missing) values