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42/100

✓ Peer-reviewed alzhub usefulness score 42 out of 100. Treatments

Topic: Risk, prevention and care in population studies

We studied the validity of longitudinal statistical inferences of clinical trials using minimization, a dynamic randomization algorithm designed to minimize treatment imbalance for prognostic factors. Repeated measures analysis of covariance and the random intercept and slope models, were used to simulate longitudinal clinical trials randomized by minimization or simple randomization. The simulations represented a wide range of analyses in real-world trials, including missing data caused by dropouts, unequal allocation of treatment arms, and efficacy analyses on either the original outcome or its change from baseline. We also analyzed the database from the Dominantly Inherited Alzheimer Network (DIAN), and used the estimated parameters to simulate the ongoing DIAN trial. Our analyses demonstrated minimization had conservative type I errors when the prognostic factor used in the minimization algorithm had a relatively strong correlation with the outcome and was not adjusted for in analyses. In contrast, adjusted tests for the prognostic factor as a covariate resulted in type I errors close to the nominal significance level. In many simulation scenarios, the adjusted tests using minimization had slightly greater statistical power than those using simple randomization, whereas in the other scenarios, the power of adjusted tests using these two randomization methods are almost indistinguishable.

Key findings

Our analyses demonstrated minimization had conservative type I errors when the prognostic factor used in the minimization algorithm had a relatively strong correlation with the outcome and was not adjusted for in analyses.

Why it matters

This is a statistical methods study about a randomization technique called minimization, not a test of any Alzheimer's treatment. It found that when a prognostic factor strongly related to the outcome was not adjusted for, the analysis gave conservative error rates, and that adjusting for it brought error rates close to the intended level.

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Study details

Study designlongitudinal study
Evidence strengthHigh

How this study scored 42/100

Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for sample size, effect reported, studied in people and randomised / blinded because the record does not state it.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Biostatistics & epidemiology)
Journal's citation record	0 / 4	Biostatistics & Epidemiology: journal h-index 13 (OpenAlex, 2026-10-29)
Study design	7 / 12	A longitudinal study following the same people over time
Strength of evidence	5 / 5	Rated “high” in our summary
Peer-review validation	10 / 10	Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent	2 / 5	2017 — within 10 year(s) of the newest study here
Sample size	0 / 4	Participant count not stated — scores zero (not stated in the record — scored 0)
Effect reported	0 / 3	No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people	0 / 3	Not stated whether this was studied in people — scores zero (not stated in the record — scored 0)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	2 / 7	Cited 6 times (OpenAlex, 2026-10-10)
Citations per year	0 / 5	0.5 citations a year over 11 year(s) since 2017
Impact for its field and year	1 / 6	Cited 0.4× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	1 / 4	Cited 3 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	3 / 4	Last-listed author Chengjie Xiong: h-index 60 (OpenAlex, whole career)
Institution's track record	2 / 3	Washington University in St. Louis: institution h-index 1,962 (OpenAlex)
Multi-centre collaboration	0 / 3	OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete)
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 29250611)
Free to read	0 / 2	Behind a paywall — scores zero
Total	42 / 100	—

What each of these 20 components measures, and what this score is not →

Read the original

Full text: free to read — PubMed Central lists this copy as free to read: the link above (checked 10 Oct 2026).

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.

APA 7th

Weng, H., Bateman, R., Morris, J. C., & Xiong, C. (2017). Validity and power of minimization algorithm in longitudinal analysis of clinical trials. Biostatistics & epidemiology. https://doi.org/10.1080/24709360.2017.1331822

Vancouver

Weng H, Bateman R, Morris JC, Xiong C. Validity and power of minimization algorithm in longitudinal analysis of clinical trials. Biostatistics & epidemiology. 2017. doi:10.1080/24709360.2017.1331822. PMID: 29250611.

BibTeX

@article{weng2017-29250611,
  author  = {Weng, H. and Bateman, R. and Morris, J. C. and Xiong, C.},
  title   = {{Validity and power of minimization algorithm in longitudinal analysis of clinical trials}},
  journal = {Biostatistics \& epidemiology},
  year    = {2017},
  doi     = {10.1080/24709360.2017.1331822},
  pmid    = {29250611},
  url     = {https://doi.org/10.1080/24709360.2017.1331822}
}

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