Evidence map›Paper›PMID 12181775›Full record

ArticleAmerican journal of human genetics2002

Power calculations for a general class of family-based association tests: dichotomous traits.

Christoph Lange, Nan M Laird

Abstract read
In one paragraph

Article in American journal of human genetics, 2002. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed, 1 pooled it
–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

54 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. Brain sciences · 2021
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  10. Causal inference in genetic trio studies.Proceedings of the National Academy of Sciences of the United States of America · 2020
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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

2 authors.

Christoph LangeDepartment of Biostatistics, Harvard School of Public Health, Boston, MA, 02115, USA. clange@hsph.harvard.edu
Nan M Laird

Funding

FAMILY BASED TESTS OF ASSOCIATION FOR COMPLEX DISEASESR01MH059532 · NIMH · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI LAIRD, NAN MCKENZIE · 1998 to 2010
$2.9M
NHLBI NIH HHS HL66383NHLBI NIH HHS HR16040NIMH NIH HHS MH59532NIMH NIH HHS R01 MH059532
6 · The paper itself

Abstract

Using large-sample theory, we present a unified approach to power calculations for family-based association tests. Currently available methods for power calculations are restricted to special designs or require approximations or simulations. Our analytical approach to power calculations is broadly applicable in many settings. We discuss power calculations for two scenarios that have high practical relevance and in which power previously could only be assessed by simulation studies or by approximations: (1) studies using both affected and unaffected offspring and (2) studies with missing parental information. When the population prevalence is high, it can be worthwhile to genotype unaffected offspring. For many scenarios, high power can be achieved with reasonable sample sizes, even when no parental information is available.

Indexed as

Nuclear FamilyChi-Square DistributionChromosome MappingComputer SimulationGene FrequencyGenetic MarkersGenotypeHumansModels, GeneticPhenotypeSample SizeGenetic Markers

Identifiers

PMID12181775
PMCPMC379194

What OpenQuestion holds

Textmetadata
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.