Cardiovascular DiseasesNCT00005221
Analysis of Longitudinal Cardiopulmonary Data
An observational study of Cardiovascular Diseases and Heart Diseases, run by Brigham and Women's Hospital.
Trial summary
To define new national norms for pediatric blood pressure by adjusting the available data set of over 60,000 pediatric blood pressure readings for age, height and gender among children of normal body weight. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Cardiovascular Diseases, Heart Diseases, Lung Diseases
- How many people
- 60,000 participants
- Where
- No sites listed yet
- Study dates
- Apr 1988to May 2008
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Pediatric Task Force, combination of 11 different studies collecting pediatric blood pressure data.
No eligibility criteria
About this study
To define new national norms for pediatric blood pressure by adjusting the available data set of over 60,000 pediatric blood pressure readings for age, height and gender among children of normal body weight.
Read the detailed description
BACKGROUND:
Longitudinal designs are frequently encountered in epidemiologic research, particularly in the cardiopulmonary field. Many different statistical models have been proposed for the analysis of longitudinal data in the statistical literature. These include the general linear model, autoregressive models, random effects models, and simple models based on an analysis of slopes over time. Complex models are not widely used in the epidemiologic literature, due mainly to a lack of understanding of their underlying utility and the types of questions that could be answered with complex models that cannot be addressed using simple models. An additional problem is a lack of software available for fitting complex models. The study has important public health implications, since longitudinal data continue to accumulate rapidly and no guidelines are available as to the appropriate methods of analysis for specific research questions. Furthermore, it is often only through the modelling of longitudinal data that processes pertaining to change can be understood. The original aim of the study was to perform a comparative study of statistical models on datasets from nine large epidemiological studies in the cardiopulmonary field in order to develop tools for identifying appropriate classes of statistical models for use in analyzing longitudinal data.
The present study define new national norms for pediatric blood pressure by adjusting the available data set of over 60,000 pediatric blood pressure readings for age, height and gender among children of normal body weight. It has been increasingly accepted that there is a long-term correlation between blood pressure in childhood and adulthood. Hence, it is significant to monitor childhood blood pressure. The 90th and 95th percentiles of blood pressure for specific age, sex and height groups were presented in the Update for the 1987 Task Force Report on Blood Pressure control in children, arid widely distributed by the National High Blood Pressure Education Program. There has been a recent update to the Task Force Report (Pediatrics, 2004) which used more current height percentiles based on Center for Disease Control and Prevention (2000) growth charts and extends the percentiles provided to include the 50th, 90th, 95th, and 99th percentiles. However, the percentiles are based on all children including both obese and non-obese children.
DESIGN NARRATIVE:
The nine datasets used included: for pulmonary data, the Childhood Respiratory Disease Study, the Netherlands data from Vlagtwedde and Vlaardingen, the Boston Police Study, the Fletcher Study data from England; for cardiopulmonary data, the Veterans Administration Normative Aging Study; for blood pressure data, the Wales Study, the Zinner/Kass Study, the Lee/Zinner Study, and the East Boston Childhood Blood Pressure Study. For each of the nine datasets the following models were fitted and compared for adults, children, and for adults and children combined: autoregressive models both serial-correlation and state-dependence; random-effects models; regression models with intraclass correlation structure; general linear models; models based on fitting slopes to individual persons. New methods for analyzing longitudinal data were developed and included fitting of higher-order autoregressive models with unequally spaced data, nonparametric methods, familial and other clustering effects in the analysis of longitudinal pulmonary function data, robust methods, empirical Bayes methods for estimation of slopes, and hierarchial models based on old and new methods.
The study was renewed in 1996 to perform a comparative study of complex models on datasets from four large epidemiologic studies in the cardiopulmonary field. The models were compared as regards goodness of fit, ease of implementation, and interpretability. In addition, new statistical methods were developed to model phenomena which seemed poorly-fitted by existing models, including adult longitudinal bp and pulmonary function data. The overall goal was to develop tools for identifying appropriate classes of longitudinal statistical models. This has important public health implications, since longitudinal data continue to accumulate rapidly and no guidelines exist as to appropriate methods of analysis. Furthermore, it is often only through modelling of longitudinal data rather than through cross-sectional or separate two time-point analyses that underlying processes pertaining to change can be understood.
The study was renewed in February 2000 to extend and enhance several techniques in the analyses of longitudinal data frequently encountered in epidemiological studies. The techniques include: methods for control for time-dependent confounding in epidemiological studies; developing an incidence model for benign breast disease using the Nurses' Health Study; analysis of incomplete longitudinal data from the Normative Aging Study; extending the penalized likelihood procedures for quantile regression to the repeated measures setting; and development of methods to estimate correlated ROC curves to measure the predictive accuracy of GEE regression models for longitudinal data.
The study was renewed in 2005 to define new national norms for pediatric blood pressure by adjusting the available data set of over 60,000 pediatric blood pressure readings for age, height and gender among children of normal body weight. The study will (a) broaden the percentiles presented so as to display the full range of blood pressure percentiles from the first to the 99th percentile (b) evaluate the percentiles for children of normal body weight so as to disentangle the relationship between hypertension and obesity (c) provide a algorithm using Microsoft Access for computation of a blood pressure percentile or Z-score in a user-friendly format (d) provide percentiles for both K4 and K5 diastolic blood pressure (the Task Force Report only includes percentiles for K5 DBF) and (e) compare childhood blood pressure percentiles by ethnic group. The study will also develop screening rules for detecting high blood pressure in children. Given the existence of new Task Force Norms that quantify percentile values of blood pressure in children by age, sex and height, an significant issue is how to use these norms to effectively screen children for high blood pressure, where high blood pressure is defined as having an average blood pressure > 95' percentile for a given age, sex and height based on task force percentiles over a large number of visits. The goal is to develop efficient screening rules that maximize accuracy with the fewest screening visits necessary per child. The screening rules will be assessed in simulation studies and tested in a sample of 16,000 Houston school children who were screened for high blood pressure
- Conditions
- Cardiovascular Diseases, Heart Diseases, Lung Diseases, Hypertension
- Medical subject headings
- Cardiovascular Diseases, Heart Diseases, Lung Diseases, Hypertension
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 May 2000 (estimated)
- Study startApr 1988
- Primary completionMay 2008
- Study completionMay 2008
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (18)Papers the sponsor has linked to this study.
- Other trials for Cardiovascular DiseasesStudies of the same condition, open and under way.
- More trials run by Brigham and Women's HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Time perspective
- Cross-sectional
- Enrolment
- 60,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- Brigham and Women's Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- National Heart, Lung, and Blood Institute (NHLBI)
- Responsible party
- Principal investigator, Bernard Rosner, Brigham and Women's Hospital
- Investigators
- Bernard Rosner, PhD, Principal investigator, Brigham and Women's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT00005221
- Sponsor's protocol ID
- 1100
- Other IDs
- R01HL040619
- Status last verified
- May 2012
- First submitted
- 25 May 2000
- Last updated
- 17 May 2012
- Results posted
- No
Publications
Rosner B. Multivariate methods for binary longitudinal data with heterogeneous correlation over time. Stat Med. 1992 Oct-Nov;11(14-15):1915-28. doi: 10.1002/sim.4780111412.
PubMed 1480881Munoz A, Carey V, Schouten JP, Segal M, Rosner B. A parametric family of correlation structures for the analysis of longitudinal data. Biometrics. 1992 Sep;48(3):733-42.
PubMed 1420837Rosner B. Multivariate methods for clustered binary data with multiple subclasses, with application to binary longitudinal data. Biometrics. 1992 Sep;48(3):721-31.
PubMed 1420836Beckett LA, Rosner B, Roche AF, Guo S. Serial changes in blood pressure from adolescence into adulthood. Am J Epidemiol. 1992 May 15;135(10):1166-77. doi: 10.1093/oxfordjournals.aje.a116217.
PubMed 1632426Rosner B, Langford HG. Judging the effectiveness of antihypertensive therapy in an individual patient. J Clin Epidemiol. 1991;44(8):831-8. doi: 10.1016/0895-4356(91)90138-y.
PubMed 1941036Smith L, Rosner B. Estimation of variance components for censored data with applications to blood pressure variability. Stat Med. 1991 Sep;10(9):1441-52. doi: 10.1002/sim.4780100911.
PubMed 1925172D'Agostino RB Jr, Sparrow D, Weiss S, Rosner B. Longitudinal models for analysis of respiratory function. Stat Med. 1995 Oct 30;14(20):2205-16. doi: 10.1002/sim.4780142004.
PubMed 8552897Cook NR, Rosner BA. An optimal rule for screening subjects for clinical trials in the presence of within-person variability. Control Clin Trials. 1994 Jun;15(3):173-86. doi: 10.1016/0197-2456(94)90055-8.
PubMed 8039404Schmid CH, Rosner B. A Bayesian approach to logistic regression models having measurement error following a mixture distribution. Stat Med. 1993 Jun 30;12(12):1141-53. doi: 10.1002/sim.4780121204.
PubMed 8210818Cook NR, Gillman MW, Rosner BA, Taylor JO, Hennekens CH. Prediction of young adult blood pressure from childhood blood pressure, height, and weight. J Clin Epidemiol. 1997 May;50(5):571-9. doi: 10.1016/s0895-4356(97)00046-2.
PubMed 9180649Rockhill B, Colditz GA, Rosner B. Bias in breast cancer analyses due to error in age at menopause. Am J Epidemiol. 2000 Feb 15;151(4):404-8. doi: 10.1093/oxfordjournals.aje.a010220.
PubMed 10695599Cook NR, Gillman MW, Rosner BA, Taylor JO, Hennekens CH. Combining annual blood pressure measurements in childhood to improve prediction of young adult blood pressure. Stat Med. 2000 Oct 15;19(19):2625-40. doi: 10.1002/1097-0258(20001015)19:193.0.co;2-h.
PubMed 10986538Tishler PV, Carey VJ, Reed T, Fabsitz RR. The role of genotype in determining the effects of cigarette smoking on pulmonary function. Genet Epidemiol. 2002 Mar;22(3):272-82. doi: 10.1002/gepi.0186.
PubMed 11921087Carey VJ, Rosner BA. Analysis of longitudinally observed irregularly timed multivariate outcomes: regression with focus on cross-component correlation. Stat Med. 2001 Jan 15;20(1):21-31. doi: 10.1002/1097-0258(20010115)20:13.0.co;2-5.
PubMed 11135345Rosner B, Grove D. Use of the Mann-Whitney U-test for clustered data. Stat Med. 1999 Jun 15;18(11):1387-400. doi: 10.1002/(sici)1097-0258(19990615)18:113.0.co;2-v.
PubMed 10399203
Study at a glance
From the registry record, last updated by the sponsor on 17 May 2012. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Analysis of Longitudinal Cardiopulmonary Data
- ClinicalTrials.gov ID
- NCT00005221
- Acronym / sponsor ID
- 1100
- Conditions
- Cardiovascular Diseases, Heart Diseases, Lung Diseases, Hypertension
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 60,000 participants (actual)
- Lead sponsor
- Brigham and Women's HospitalUniversity, hospital or other
- Collaborators
- National Heart, Lung, and Blood Institute (NHLBI)
- Study start
- Apr 1988
- Primary completion
- May 2008 (actual)
- Study completion
- May 2008 (actual)
- First posted
- 26 May 2000 (estimated)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
To define new national norms for pediatric blood pressure by adjusting the available data set of over 60,000 pediatric blood pressure readings for age, height and gender among children of normal body weight.
Detailed description
BACKGROUND:
Longitudinal designs are frequently encountered in epidemiologic research, particularly in the cardiopulmonary field. Many different statistical models have been proposed for the analysis of longitudinal data in the statistical literature. These include the general linear model, autoregressive models, random effects models, and simple models based on an analysis of slopes over time. Complex models are not widely used in the epidemiologic literature, due mainly to a lack of understanding of their underlying utility and the types of questions that could be answered with complex models that cannot be addressed using simple models. An additional problem is a lack of software available for fitting complex models. The study has important public health implications, since longitudinal data continue to accumulate rapidly and no guidelines are available as to the appropriate methods of analysis for specific research questions. Furthermore, it is often only through the modelling of longitudinal data that processes pertaining to change can be understood. The original aim of the study was to perform a comparative study of statistical models on datasets from nine large epidemiological studies in the cardiopulmonary field in order to develop tools for identifying appropriate classes of statistical models for use in analyzing longitudinal data.
The present study define new national norms for pediatric blood pressure by adjusting the available data set of over 60,000 pediatric blood pressure readings for age, height and gender among children of normal body weight. It has been increasingly accepted that there is a long-term correlation between blood pressure in childhood and adulthood. Hence, it is significant to monitor childhood blood pressure. The 90th and 95th percentiles of blood pressure for specific age, sex and height groups were presented in the Update for the 1987 Task Force Report on Blood Pressure control in children, arid widely distributed by the National High Blood Pressure Education Program. There has been a recent update to the Task Force Report (Pediatrics, 2004) which used more current height percentiles based on Center for Disease Control and Prevention (2000) growth charts and extends the percentiles provided to include the 50th, 90th, 95th, and 99th percentiles. However, the percentiles are based on all children including both obese and non-obese children.
DESIGN NARRATIVE:
The nine datasets used included: for pulmonary data, the Childhood Respiratory Disease Study, the Netherlands data from Vlagtwedde and Vlaardingen, the Boston Police Study, the Fletcher Study data from England; for cardiopulmonary data, the Veterans Administration Normative Aging Study; for blood pressure data, the Wales Study, the Zinner/Kass Study, the Lee/Zinner Study, and the East Boston Childhood Blood Pressure Study. For each of the nine datasets the following models were fitted and compared for adults, children, and for adults and children combined: autoregressive models both serial-correlation and state-dependence; random-effects models; regression models with intraclass correlation structure; general linear models; models based on fitting slopes to individual persons. New methods for analyzing longitudinal data were developed and included fitting of higher-order autoregressive models with unequally spaced data, nonparametric methods, familial and other clustering effects in the analysis of longitudinal pulmonary function data, robust methods, empirical Bayes methods for estimation of slopes, and hierarchial models based on old and new methods.
The study was renewed in 1996 to perform a comparative study of complex models on datasets from four large epidemiologic studies in the cardiopulmonary field. The models were compared as regards goodness of fit, ease of implementation, and interpretability. In addition, new statistical methods were developed to model phenomena which seemed poorly-fitted by existing models, including adult longitudinal bp and pulmonary function data. The overall goal was to develop tools for identifying appropriate classes of longitudinal statistical models. This has important public health implications, since longitudinal data continue to accumulate rapidly and no guidelines exist as to appropriate methods of analysis. Furthermore, it is often only through modelling of longitudinal data rather than through cross-sectional or separate two time-point analyses that underlying processes pertaining to change can be understood.
The study was renewed in February 2000 to extend and enhance several techniques in the analyses of longitudinal data frequently encountered in epidemiological studies. The techniques include: methods for control for time-dependent confounding in epidemiological studies; developing an incidence model for benign breast disease using the Nurses' Health Study; analysis of incomplete longitudinal data from the Normative Aging Study; extending the penalized likelihood procedures for quantile regression to the repeated measures setting; and development of methods to estimate correlated ROC curves to measure the predictive accuracy of GEE regression models for longitudinal data.
The study was renewed in 2005 to define new national norms for pediatric blood pressure by adjusting the available data set of over 60,000 pediatric blood pressure readings for age, height and gender among children of normal body weight. The study will (a) broaden the percentiles presented so as to display the full range of blood pressure percentiles from the first to the 99th percentile (b) evaluate the percentiles for children of normal body weight so as to disentangle the relationship between hypertension and obesity (c) provide a algorithm using Microsoft Access for computation of a blood pressure percentile or Z-score in a user-friendly format (d) provide percentiles for both K4 and K5 diastolic blood pressure (the Task Force Report only includes percentiles for K5 DBF) and (e) compare childhood blood pressure percentiles by ethnic group. The study will also develop screening rules for detecting high blood pressure in children. Given the existence of new Task Force Norms that quantify percentile values of blood pressure in children by age, sex and height, an significant issue is how to use these norms to effectively screen children for high blood pressure, where high blood pressure is defined as having an average blood pressure > 95' percentile for a given age, sex and height based on task force percentiles over a large number of visits. The goal is to develop efficient screening rules that maximize accuracy with the fewest screening visits necessary per child. The screening rules will be assessed in simulation studies and tested in a sample of 16,000 Houston school children who were screened for high blood pressure
Study design
- Time perspective
- Cross-sectional
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Pediatric Task Force, combination of 11 different studies collecting pediatric blood pressure data.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Not reported in the source record.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 May 2012.
- Background: PubMed 1480881Rosner B. Multivariate methods for binary longitudinal data with heterogeneous correlation over time. Stat Med. 1992 Oct-Nov;11(14-15):1915-28. doi: 10.1002/sim.4780111412.
- Background: PubMed 1420837Munoz A, Carey V, Schouten JP, Segal M, Rosner B. A parametric family of correlation structures for the analysis of longitudinal data. Biometrics. 1992 Sep;48(3):733-42.
- Background: PubMed 1420836Rosner B. Multivariate methods for clustered binary data with multiple subclasses, with application to binary longitudinal data. Biometrics. 1992 Sep;48(3):721-31.
- Background: PubMed 1632426Beckett LA, Rosner B, Roche AF, Guo S. Serial changes in blood pressure from adolescence into adulthood. Am J Epidemiol. 1992 May 15;135(10):1166-77. doi: 10.1093/oxfordjournals.aje.a116217.
- Background: PubMed 1941036Rosner B, Langford HG. Judging the effectiveness of antihypertensive therapy in an individual patient. J Clin Epidemiol. 1991;44(8):831-8. doi: 10.1016/0895-4356(91)90138-y.
- Background: PubMed 1925172Smith L, Rosner B. Estimation of variance components for censored data with applications to blood pressure variability. Stat Med. 1991 Sep;10(9):1441-52. doi: 10.1002/sim.4780100911.
- Background: PubMed 8552897D'Agostino RB Jr, Sparrow D, Weiss S, Rosner B. Longitudinal models for analysis of respiratory function. Stat Med. 1995 Oct 30;14(20):2205-16. doi: 10.1002/sim.4780142004.
- Background: PubMed 8039404Cook NR, Rosner BA. An optimal rule for screening subjects for clinical trials in the presence of within-person variability. Control Clin Trials. 1994 Jun;15(3):173-86. doi: 10.1016/0197-2456(94)90055-8.
- Background: PubMed 8210818Schmid CH, Rosner B. A Bayesian approach to logistic regression models having measurement error following a mixture distribution. Stat Med. 1993 Jun 30;12(12):1141-53. doi: 10.1002/sim.4780121204.
- Background: PubMed 9180649Cook NR, Gillman MW, Rosner BA, Taylor JO, Hennekens CH. Prediction of young adult blood pressure from childhood blood pressure, height, and weight. J Clin Epidemiol. 1997 May;50(5):571-9. doi: 10.1016/s0895-4356(97)00046-2.
- Background: PubMed 10695599Rockhill B, Colditz GA, Rosner B. Bias in breast cancer analyses due to error in age at menopause. Am J Epidemiol. 2000 Feb 15;151(4):404-8. doi: 10.1093/oxfordjournals.aje.a010220.
- Background: PubMed 10986538Cook NR, Gillman MW, Rosner BA, Taylor JO, Hennekens CH. Combining annual blood pressure measurements in childhood to improve prediction of young adult blood pressure. Stat Med. 2000 Oct 15;19(19):2625-40. doi: 10.1002/1097-0258(20001015)19:193.0.co;2-h.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Bernard Rosner, PhD principal investigator
Brigham and Women's Hospital
Questions about this trial
What is this clinical trial studying?
An observational study of Cardiovascular Diseases and Heart Diseases, run by Brigham and Women's Hospital.
The full registered description is in the About this study section and the official record.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 17 May 2012. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT00005221, which also lists 18 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 May 2012.