Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2008

Open Access 01-12-2008 | Research article

Estimating adjusted prevalence ratio in clustered cross-sectional epidemiological data

Authors: Carlos Antônio ST Santos, Rosemeire L Fiaccone, Nelson F Oliveira, Sérgio Cunha, Maurício L Barreto, Maria Beatriz B do Carmo, Ana-Lucia Moncayo, Laura C Rodrigues, Philip J Cooper, Leila D Amorim

Published in: BMC Medical Research Methodology | Issue 1/2008

Login to get access

Abstract

Background

Many epidemiologic studies report the odds ratio as a measure of association for cross-sectional studies with common outcomes. In such cases, the prevalence ratios may not be inferred from the estimated odds ratios. This paper overviews the most commonly used procedures to obtain adjusted prevalence ratios and extends the discussion to the analysis of clustered cross-sectional studies.

Methods

Prevalence ratios(PR) were estimated using logistic models with random effects. Their 95% confidence intervals were obtained using delta method and clustered bootstrap. The performance of these approaches was evaluated through simulation studies. Using data from two studies with health-related outcomes in children, we discuss the interpretation of the measures of association and their implications.

Results

The results from data analysis highlighted major differences between estimated OR and PR. Results from simulation studies indicate an improved performance of delta method compared to bootstrap when there are small number of clusters.

Conclusion

We recommend the use of logistic model with random effects for analysis of clustered data. The choice of method to estimate confidence intervals for PR (delta or bootstrap method) should be based on study design.
Literature
1.
Lui K-J: Statistical Estimation of Epidemiological Risk. 2004, John Wiley & Sons LtdCrossRef
2.
Zhang J, Kai YF: What's the Relative Risk? A Method of Correcting the Odds Ratio in Cohort Studies of Common Outcomes. Journal of American Medical Association. 1998, 280: 1690-1691. 10.1001/jama.280.19.1690.CrossRef
3.
Barros AJD, Hirakata VN: Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Medical Research Methodology. 2003, 3: 21-33. 10.1186/1471-2288-3-21.CrossRefPubMedPubMedCentral
4.
Greenland S: Interpretation and choice of effect measures in epidemiologic analysis. American Journal of Epidemiology. 1987, 125: 761-768.PubMed
5.
Lee J, Chia KS: Estimation of prevalence rate ratios for cross-sectional data: an example in ocuppational epidemiology. Br J Ind Med. 1993, 50 (9): 861-862.PubMedPubMedCentral
6.
Lee J: Odds ratio or relative risks for cross-sectional data?. International Journal of Epidemiology. 1994, 23: 201-203. 10.1093/ije/23.1.201.CrossRefPubMed
7.
8.
Axelson O, Fredri M, Ekberg K: Use of prevalence ratio versus the prevalence odds ratio as a measure of risk in cross-sectional studies. Occupational Environmental Medicine. 1994, 51: 574-10.1136/oem.51.8.574.CrossRefPubMedPubMedCentral
9.
10.
Localio AR, Margolis DJ, Berlin JA: Relative risks and confidence intervals were easily computed indirectly from multivariate logistic regression. Journal of Clinical Epidemiology. 2007, 60: 874-882. 10.1016/j.jclinepi.2006.12.001.CrossRefPubMed
11.
Newcombe RG: A deficiency of the odds ratio as a measure of effect size. Statistics in Medicine. 2006, 25: 4235-4240. 10.1002/sim.2683.CrossRefPubMed
12.
Miettinen O: Estimability and estimation in case-referent studies. American Journal of Epidemiology. 1976, 103: 226-235.PubMed
13.
Greenland S, Thomas DC: On the need for rare disease assumption in case control in case-control studies. American Journal of Epidemiology. 1982, 116: 547-553.PubMed
14.
Rodrigues L, Kirkwood BR: Case-control designs in the study of common diseases: updates on the demise of the rare disease assumption and the choice of sampling scheme for controls. International Journal of Epidemiology. 1990, 19: 205-213. 10.1093/ije/19.1.205.CrossRefPubMed
15.
Zochetti C, Consonni D, Bertazzi PA: Relationship between prevalence rate ratios in cross-sectional studies. International Journal of Epidemiology. 1997, 26: 220-223. 10.1093/ije/26.1.220.CrossRef
16.
Greenland S: Model-based Estimation of Relative Risks and Other Epidemiologic Measures in Studies of Common Outcomes and in Case-Control Studies. American Journal of Epidemiology. 2004, 160: 301-305. 10.1093/aje/kwh221.CrossRefPubMed
17.
Blizard L, Hosmer DW: Parameter Estimates and Goodness-of-Fit in Log Binomial Regression. Biometrical Journal. 2006, 48: 5-22. 10.1002/bimj.200410165.CrossRef
18.
Wacholder S: Binomial Regression in GLIM: Estimating Risk Ratios and Risk Differences. American Journal of Epidemiology. 1986, 123 (1): 174-184.PubMed
19.
Amorim LD, Bangdiwala SI, McMurray RG, Creighton D, Harrell J: Intraclass correlations among physiologic measures in children and adolescents. Nurs Res. 2007, 56 (5): 355-360. 10.1097/01.NNR.0000289497.91918.94.CrossRefPubMed
20.
Barreto ML, Cunha SS, Alcântara-Neves N, Carvalho LP, Cruz AA, Stein RT, Gensen B, Cooper PJ, Rodrigues LC: Risk factors and immunological pathways for asthma and other allergic diseases in children: background and methodology of a longitudinal study in a large urban center in Northeastern Brazil (Salvador-SCAALA study). BMC Pulmonary Medicine. 2006, 6: 15-25. 10.1186/1471-2466-6-15.CrossRefPubMedPubMedCentral
21.
Moncayo AL, Vaca MG, Amorim L, Rodriguez A, Erazo S, Oviedo G, Quinzo I, Padilla M, Chico M, Lovato R, Gomez E, Barreto ML, Cooper PJ: Impact of long-term treatment with ivermectin on the prevalence and intensity of soil-transmitted helminth infections. PLoS Negleted Tropical Disease. 2: e293-10.1371/journal.pntd.0000293.
22.
Moineddin R, Matheson FI, Glazier RH: A simulation study of sample size for multilevel logistic regression models. BMC Medical Research Methodology. 2007, 7: 34-43. 10.1186/1471-2288-7-34.CrossRefPubMedPubMedCentral
23.
Wilcosky TC, Chambless LE: A comparison of direct adjustment and regression adjustment of epidemiologic measures. J Chron Dis. 1985, 34: 849-856. 10.1016/0021-9681(85)90109-2.CrossRef
24.
Lane PW, Nelder JA: Analysis of covariance and standardization as instances of prediction. Biometrics. 1982, 38: 613-621. 10.2307/2530043.CrossRefPubMed
25.
Flanders WD, Rhodes PH: Large sample confidence intervals for regression standardized risks, risk ratios and risk differences. J Chron Dis. 1987, 40: 697-704. 10.1016/0021-9681(87)90106-8.CrossRefPubMed
26.
Diggle PJ, Liang KY, Zeger SL: Analysis of Longitudinal Data. 1994, New York: Oxford University Press
27.
Larsen K, Petersen JH, Budtz-Jørgensen E, Endahl L: Interpreting Parameters in the Logistic Regression Model with Random Effects. Biometrics. 2000, 56: 909-914. 10.1111/j.0006-341X.2000.00909.x.CrossRefPubMed
28.
McCulloch CE, Searle SR: Generalized, Linear, and Mixed Models. 2001, New York: John Wiley & Sons Inc
29.
Urbach DR, Austin PC: Conventional models overestimate the statistical significance of volumeoutcome associations, compared with multilevel models. Journal of Clinical Epidemiology. 2005, 58: 391-400. 10.1016/j.jclinepi.2004.12.001.CrossRefPubMed
30.
Hardin JW, Hilbe JM: Generalized Estimating Equations. 2003, Boca Raton: Chapman & Hall/CRC
31.
Bishop YMM, Fienberg SE, Holland PW: Discrete Multivariate Analysis: Theory and Practice. 1975, Cambridge, Mass: MIT Press
32.
Efron B, Tibshirani RJ: An Introduction to the Bootstrap. 1993, New York: Chapman & HallCrossRef
33.
Fox J: An R and S-Plus Companion to Applied Regression. 2002, New York: Chapman & Hall
34.
Field CA, Welsh AH: Bootstrapping clustered data. Journal of Royal Statistical Society B. 2007, 69: 369-390. 10.1111/j.1467-9868.2007.00593.x.CrossRef
35.
ISAAC steering committee: Worldwide variation in prevalence of symptoms of asthma, allergic rhinoconjunctivitis, and atopic eczema: ISAAC. Lancet. 1998, 351: 1225-1232. 10.1016/S0140-6736(97)07302-9.CrossRef
36.
Ortega AN, Goodwin RD, McQuaid EL, Canino G: Parental mental health, childhood psychiatric disorders, and asthma attacks in island Puerto Rican youth. Ambul Pediatr. 2004, 4: 308-315. 10.1367/A03-169R1.1.CrossRefPubMed
37.
Weil CM, Wade SL, Bauman LJ, Lynn H, Mitchell H, Lavigne J: The relationship between psychosocial factors and asthma morbidity in inner-city children with asthma. Pediatrics. 1999, 104: 1274-1280. 10.1542/peds.104.6.1274.CrossRefPubMed
38.
Carmo MBB, Santos DN, Amorim LDAF, Fiaccone RL, Cunha SS, Rodrigues LC, Barreto ML: Minor psychiatric disorders in mothers and asthma in children. Soc Psychiatry Psychiatr Epidemiol. 2008, Epub ahead of print
39.
Mari JJ, Williams P: A validity study of a psychiatric screening questionnaire (SRQ 20) in primary care in the city of São Paulo. Br J Psychiatry. 1986, 148: 23-6. 10.1192/bjp.148.1.23.CrossRefPubMed
40.
Brooker S, Clements AC, Bundy DA: Global epidemiology, ecology and control of soil-transmitted helminth infections. Adv Parasitology. 2006, 62: 221-261. 10.1016/S0065-308X(05)62007-6.CrossRef
41.
Ranque S, Chippaux JP, Garcia A, Boussinesq M: Follow-up of Ascaris lumbricoides and Trichuris trichiura infections in children living in a community treated with ivermectin at 3-monthly intervals. Annals of Tropical Medicine Parasitology. 2001, 95: 389-93. 10.1080/00034980120065822.CrossRefPubMed
42.
Cooper PJ, Chico ME, Vaca MG, Rodriguez A, Alcantara-Neves NM, Genser B, Carvalho LP, Stein RT, Cruz AA, Rodrigues LC, Barreto ML: Risk factors for asthma and allergy associated with urban migration: background and methodology of a cross-sectional study in Afro-Ecuadorian school children in Northeastern Ecuador (Esmeraldas-SCAALA Study). BMC Pulmonary Medicine. 2006, 6: 24-45. 10.1186/1471-2466-6-24.CrossRefPubMedPubMedCentral
43.
44.
Andreozzi VL, Bailey TC, Nobre FF, Struchiner DJ, Barreto ML, Assis AMO, Santos LMP: Random-Effects Models in Investigating the Effect of Vitamin A in Childhood Diarrhea. Annals of Epidemiology. 2006, 16: 241-247. 10.1016/j.annepidem.2005.08.007.CrossRefPubMed
45.
Zeger SL, Liang K-Y, Albert PS: Models for longitudinal data: A generalized estimating equation approach. Biometrics. 1988, 44: 1049-1060. 10.2307/2531734.CrossRefPubMed
46.
Lindsey JK, Lambert P: On the appropriateness of marginal models for repeated measurements in clinical trials. Statistics in Medicine. 1998, 17: 447-469. 10.1002/(SICI)1097-0258(19980228)17:4<447::AID-SIM752>3.0.CO;2-G.CrossRefPubMed
47.
Petersen MR, Deddens JA: A comparison of two methods for estimating prevalence ratios. BMC Medical Research Methodology. 2008, 8: 9-18. 10.1186/1471-2288-8-9.CrossRefPubMedPubMedCentral
Metadata
Title
Estimating adjusted prevalence ratio in clustered cross-sectional epidemiological data
Authors
Carlos Antônio ST Santos
Rosemeire L Fiaccone
Nelson F Oliveira
Sérgio Cunha
Maurício L Barreto
Maria Beatriz B do Carmo
Ana-Lucia Moncayo
Laura C Rodrigues
Philip J Cooper
Leila D Amorim
Publication date
01-12-2008
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2008
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/1471-2288-8-80

Other articles of this Issue 1/2008

BMC Medical Research Methodology 1/2008 Go to the issue

Research article

Undue reliance on I 2 in assessing heterogeneity may mislead

Correspondence

The use of interim data and Data Monitoring Committee recommendations in randomized controlled trial reports: frequency, implications and potential sources of bias

Research article

Motives for (not) participating in a lifestyle intervention trial

Research article

Assessing the order of magnitude of outcomes in single-arm cohorts through systematic comparison with corresponding cohorts: An example from the AMOS study

Research article

Negative pressure wound therapy: Potential publication bias caused by lack of access to unpublished study results data

Research article

Engaging participants in a complex intervention trial in Australian General Practice