Methodologic issues in the analysis of data from a population based osteoporosis study : adjusting for selection bias and measurement error
Notice bibliographique
Résumé
Two issues related to osteoporosis are addressed. (1) To estimate the prevalence of osteoporosis in Canada. (2) To estimate the effect of initial bone mass on bone mineral density (BMD) decline rates over a period of three years. We employ data from the Canadian Multicenter Osteoporosis Study (Cantos), a prospective, fixed cohort study comprised of 9,423 randomly selected subjects from nine different regions in Canada. For the first objective, Cantos had a relatively low participation rate (42%), so that selection bias is a concern. The Cantos study design, however, included a brief risk factor questionnaire for those invitees that declined further participation. These risk factors were then used to estimate the missing osteoporosis status for non-participants using Bayesian multiple imputation, thus adjusting for nonresponse bias. Both ignorable and non-ignorable imputation models are considered. Complicating study of the relationship between the initial BMD and rate of decline is the issue of measurement error, which can cause a spurious negative association between the rate of change in a variable and the initial value of the variable. A novel variation on the Bayesian methods of Richardson and Gilks (1993b) is used to adjust for measurement error in both the initial and year three BMD values. After adjusting for selection bias, prevalence increased from negligible in the youngest age group to 38.9% for women and 15.4% for men in the 80+ age group. Selection bias was estimated to be relatively small, except in the oldest age group, where the bias was 2.4% for women and 5.4% for men. For women the unadjusted relationship between the rate of decline of BMD and initial BMD is negative, -0.040 (95% CI = -0.053; -0.028). Correcting for measurement error results in estimates closer to zero. For example, using a hierarchical model with measurement error correction yields an estimate of -0.030 (95% CI = -0.050; -0.009). For men the unadjusted relationship between rate of decline and initial BMD was -0.026 (95% CI = -0.043; -0.009). Correcting for measurement error using the hierarchical model yields a much lower estimate of -0.013 (95% CI = -0.041; 0.016). It is clear that ignoring measurement error results in a heavily biased estimate of the effect of initial BMD on rate of decline of BMD for both women and men. The measurement error adjusted estimates were of a lesser magnitude than the unadjusted estimate, with reductions of approximately 25% to 50%. The results from this thesis can be used to make decisions about osteoporosis treatment in Canada. Knowledge of the prevalence rates in Canada is useful to public health planners in allocating resources. Knowledge of the relationship between initial levels of BMD and subsequent decline in BMD allows better public health decision making, for example, in deciding whether to focus on bone health early in life, or whether it is more important to prevent decline later in life.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,583 | 0,787 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,006 | 0,004 |
| Bibliométrie | 0,010 | 0,016 |
| Études des sciences et des technologies | 0,003 | 0,011 |
| Communication savante | 0,007 | 0,004 |
| Science ouverte | 0,009 | 0,005 |
| Intégrité de la recherche | 0,004 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».