Switch to
| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
2
|
| pubmed:dateCreated |
1994-9-27
|
| pubmed:abstractText |
Case-control studies can often be made more efficient by using frequency matching, randomized recruitment, stratified sampling, or two-stage sampling. These designs share two common features: (1) some "first-stage" variables are ascertained for all study subjects, while complete variable ascertainment is carried out for only a selected subsample, and (2) the subsampling of subjects for "second-stage" variable ascertainment depends jointly on their disease status and their observed first-stage variables. Because first-stage variables alter the subsampling fractions, standard analyses require a multiplicative specification of any joint effects of a second- and a first-stage variable. We show that by making use of missing data methods, maximum likelihood estimates can be obtained for risk parameters of interest, even those characterizing interactions between first- and second-stage variables. Joint effects can thus be modelled flexibly, with allowance for both additive and multiplicative models. Preliminary data from a case-control study of lung cancer as related to age, sex, and smoking provide an example, leading to the suggestion that the combined effect of age and smoking is multiplicative.
|
| pubmed:language |
eng
|
| pubmed:journal | |
| pubmed:citationSubset |
IM
|
| pubmed:chemical | |
| pubmed:status |
MEDLINE
|
| pubmed:month |
Jun
|
| pubmed:issn |
0006-341X
|
| pubmed:author | |
| pubmed:issnType |
Print
|
| pubmed:volume |
50
|
| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
|
| pubmed:pagination |
350-7
|
| pubmed:dateRevised |
2006-11-15
|
| pubmed:meshHeading |
pubmed-meshheading:8068835-Adult,
pubmed-meshheading:8068835-Age Factors,
pubmed-meshheading:8068835-Aged,
pubmed-meshheading:8068835-Biometry,
pubmed-meshheading:8068835-Case-Control Studies,
pubmed-meshheading:8068835-Connecticut,
pubmed-meshheading:8068835-Female,
pubmed-meshheading:8068835-Humans,
pubmed-meshheading:8068835-Idaho,
pubmed-meshheading:8068835-Lung Neoplasms,
pubmed-meshheading:8068835-Male,
pubmed-meshheading:8068835-Middle Aged,
pubmed-meshheading:8068835-Neoplasms, Radiation-Induced,
pubmed-meshheading:8068835-Probability,
pubmed-meshheading:8068835-Radon,
pubmed-meshheading:8068835-Random Allocation,
pubmed-meshheading:8068835-Risk Factors,
pubmed-meshheading:8068835-Sex Factors,
pubmed-meshheading:8068835-Smoking,
pubmed-meshheading:8068835-Utah
|
| pubmed:year |
1994
|
| pubmed:articleTitle |
Flexible maximum likelihood methods for assessing joint effects in case-control studies with complex sampling.
|
| pubmed:affiliation |
Biostatistics Branch, National Cancer Institute, Rockville, Maryland 20852.
|
| pubmed:publicationType |
Journal Article,
Comparative Study
|