Evidence map›Paper›PMID 29353911›Full record

ArticleBiometrika2017

Miscellanea Dependent generalized functional linear models.

S Jadhav, H L Koul, Q Lu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biometrika, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

S JadhavDepartment of Statistics & Probability, Michigan State University, 619 Red Cedar Road, East Lansing, Michigan 48824, U.S.A.
H L KoulDepartment of Statistics & Probability, Michigan State University, 619 Red Cedar Road, East Lansing, Michigan 48824, U.S.A.
Q LuDepartment of Epidemiology & Biostatistics, Michigan State University, B601West Fee Hall, 909 Fee Road, East Lansing, Michigan 48824, U.S.A.

Funding

Computational Efficient Statistical Tools for Analyzing Substance Dependence Sequencing DataR01DA043501 · NIDA · UNIVERSITY OF FLORIDA · PI LU, QING · 2017 to 2021
$2.1M
Gene-Gene/Gene-Environment Interactions Associated with Nicotine DependenceK01DA033346 · NIDA · MICHIGAN STATE UNIVERSITY · PI LU, QING · 2013 to 2017
$833k
NIDA NIH HHS K01 DA033346NIDA NIH HHS R01 DA043501
6 · The paper itself

Abstract

This paper considers testing for no effect of functional covariates on response variables in multivariate regression. We use generalized estimating equations to determine the underlying parameters and establish their joint asymptotic normality. This is then used to test the significance of the effect of predictors on the vector of response variables. Simulations demonstrate the importance of considering existing correlation structures in the data. To explore the effect of treating genetic data as a function, we perform a simulation study using gene sequencing data and find that the performance of our test is comparable to that of another popular method used in sequencing studies. We present simulations to explore the behaviour of our test under varying sample size, cluster size and dimension of the parameter to be estimated, and an application where we are able to confirm known associations between nicotine dependence and neuronal nicotinic acetylcholine receptor subunit genes.

Indexed as

Cluster dataFamily sequencing dataFunctional data analysisGeneralized estimating equation

Identifiers

PMID29353911
PMCPMC5771479

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.