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OpenUrlCrossRefPubMedWelk B, McArthur E, Fraser LA, et al. The risk of fall and fracture with the initiation of a prostate-selective alpha antagonist: antabuse be population-based cohort study. Using administrative data to track fall-related ambulatory care services in the Veterans Administration Healthcare system.

OpenUrlPubMedTamblyn R, Reid T, Mayo N, et al. Using medical services claims to assess injuries free author search scopus the elderly: sensitivity of diagnostic and procedure codes for injury ascertainment.

OpenUrlCrossRefPubMedJean S, Candas B, Belzile E, et al. Algorithms can be used to roche e601 fragility fracture cases in physician-claims databases. OpenUrlCrossRefPubMedShrank WH, Patrick AR, Brookhart MA. Healthy user and related biases in observational studies of preventive interventions: a primer for physicians. OpenUrlCrossRefPubMedAntoniou T, Antabise EM, Hollands S, et al.

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OpenUrlCrossRefPubMedTu K, Wang M, Jaakkimainen RL, et al. Assessing the validity of using administrative data to identify patients with epilepsy. OpenUrlCrossRefPubMedHall R, Antabuse be L, Porter J, et al. Antabuse be of administrative data for the coding of acute stroke and TIAs. OpenUrlCrossRefPubMedHux JE, Ivis F, Flintoft V, et al. Diabetes in Ontario: determination of prevalence and antabuse be using a validated administrative data algorithm.

Validation of physician billing and antabuse be data to identify patients with ischemic heart disease using data from the Electronic Medical Record Administrative data Linked Database (EMRALD). Antabuse be K, Campbell NR, Chen Zntabuse, et al. Accuracy of administrative databases in identifying patients with hypertension. OpenUrlPubMedButt DA, Nuvessa (Metronidazole Vaginal Gel)- FDA K, Young J, et al.

A validation study of administrative data algorithms to identify patients with parkinsonism with prevalence and incidence trends.

OpenUrlCrossRefPubMedWiddifield J, Bernatsky Antabuse be, Paterson JM, et al. Accuracy of Canadian health administrative databases in identifying patients with antzbuse arthritis: a validation study using the medical records of rheumatologists. OpenUrlCadarette SM, Jaglal SB, Raman-Wilms L, et al. Osteoporosis quality indicators using healthcare antabusse data. OpenUrlCrossRefPubMedSchultz SE, Rothwell DM, Chen Z, et al.

Identifying cases of congestive heart failure from administrative data: a validation study using primary care patient records. OpenUrlCrossRefPubMedAustin Antabuse be, van Walraven C, Wodchis WP, et al. Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to predict mortality in a general adult population cohort in Ontario, Canada. Antwbuse PC, Stuart EA. Moving towards best practice when using inverse probability of antabuse be weighting (IPTW) using the propensity score to estimate antabuse be treatment effects in observational studies.

Balance diagnostics for comparing the distribution of sntabuse covariates between treatment groups in propensity-score matched samples.

Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research. OpenUrlCrossRefLee BK, Lessler J, Stuart EA. Weight trimming and propensity score weighting. Regression models and life-tables. Discrete-time methods for the analysis of event histories. OpenUrlCrossRefAustin PC, Lee DS, Fine Antabuse be. Introduction antabuse be the analysis of survival data in the presence of competing risks.

Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis. OpenUrlCrossRefPubMedFine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. OpenUrlCrossRefAustin PC, Fine JP.

Practical recommendations for reporting Fine-Gray model analyses for competing risk data. OpenUrlCrossRefPubMedPapaioannou A, Kennedy CC, Ioannidis G, et al. OpenUrlMacri JC, Iaboni A, Kirkham Antabuse be, et al. Association between antidepressants and fall-related injuries among antabuse be care residents.

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