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Indian Pediatrics Jun 2022Observational study designs are those where the investigator/researcher just observes and does not carry out any intervention(s)/actions to alter the outcome. The three... (Observational Study)
Observational Study
Observational study designs are those where the investigator/researcher just observes and does not carry out any intervention(s)/actions to alter the outcome. The three most common types of observational studies are cross-sectional, case control and cohort (or longitudinal). In cross-sectional studies, both the exposure/risk factor(s) and the outcome(s) are determined at a single time point. They can provide information on prevalence of a condition and snapshot of probable associations that can be used to generate hypothesis. Case-control studies are where subjects are selected based on presence/absence of outcome and the risk factors are determined during the study after enrolment of study subjects. The association between exposure and outcome is reported as odds ratio. These studies; however, have high risk of bias, which must be taken care of during study design. Cohort studies are prospective in nature, where subjects are selected based on presence/absence of exposure, and the outcome(s) is determined at the end of study. These studies can provide incidence of disease/outcome and the association between exposure and outcome is reported as relative risk. They are useful to ascertain causality. High dropouts of study participants and confounding can be problems encountered in these studies.
Topics: Case-Control Studies; Cohort Studies; Cross-Sectional Studies; Humans; Odds Ratio; Prospective Studies
PubMed: 35481482
DOI: No ID Found -
The Journal of Clinical Psychiatry Jun 2023Categorical outcome analyses in randomized controlled trials (RCTs) and observational studies are commonly presented as relative risks (RRs) and odds ratios (ORs). In...
Categorical outcome analyses in randomized controlled trials (RCTs) and observational studies are commonly presented as relative risks (RRs) and odds ratios (ORs). In some situations, these RRs and ORs may be misunderstood, resulting in wrong conclusions. How this may happen is explained in the context of a hypothetical RCT that compares potentially lifesaving drugs A and B with placebo. In this RCT, the RR for survival is 1.67 for A vs placebo and 1.42 for B vs placebo. Using these RR data, as a challenge, readers are invited to answer 2 questions either intuitively or by other means. First, by how much is A better than B? Second, if the absolute survival rate with B is 8.5%, using the answer obtained from the previous question, what is the absolute survival rate with A? In this same RCT, the OR for survival is 1.74 for A vs placebo and 1.46 for B vs placebo. Using the OR data instead of the RR data, readers are again invited to answer the 2 questions listed above. This article explains why it is easy for readers and even authors to arrive at wrong answers to the 2 questions and draw wrong conclusions about the results. This article also explains what the correct answers are and how they may be obtained. The explanations involve simple concepts and even simpler arithmetic.
Topics: Humans; Risk; Odds Ratio
PubMed: 37339361
DOI: 10.4088/JCP.23f14943 -
International Urology and Nephrology Dec 2023The assessment of risk and effect size of a specific endpoint associated to the presence/absence of a certain exposure is a hallmark in clinical and epidemiological... (Review)
Review
The assessment of risk and effect size of a specific endpoint associated to the presence/absence of a certain exposure is a hallmark in clinical and epidemiological research. In fact, before recommending any treatment, it is mandatory to investigate the magnitude of the benefits and harms between the exposure under investigation (e.g. a given treatment) and a specific disease or event. To do this, clinicians and statisticians use absolute (risk differences, number needed to treat, likelihood to be helped or harmed) and relative (risk ratio, incidence rate ratio, hazard ratio and odds ratio) measures of effect. Herein, using a series of clinical examples, we aim to present a step by step methodologic approach of measures of effect in the area of nephrology and urology.
Topics: Humans; Probability; Odds Ratio; Incidence; Language
PubMed: 37162698
DOI: 10.1007/s11255-023-03626-w -
BMC Medical Informatics and Decision... Jun 2024Pattern mining techniques are helpful tools when extracting new knowledge in real practice, but the overwhelming number of patterns is still a limiting factor in the...
BACKGROUND
Pattern mining techniques are helpful tools when extracting new knowledge in real practice, but the overwhelming number of patterns is still a limiting factor in the health-care domain. Current efforts concerning the definition of measures of interest for patterns are focused on reducing the number of patterns and quantifying their relevance (utility/usefulness). However, although the temporal dimension plays a key role in medical records, few efforts have been made to extract temporal knowledge about the patient's evolution from multivariate sequential patterns.
METHODS
In this paper, we propose a method to extract a new type of patterns in the clinical domain called Jumping Diagnostic Odds Ratio Sequential Patterns (JDORSP). The aim of this method is to employ the odds ratio to identify a concise set of sequential patterns that represent a patient's state with a statistically significant protection factor (i.e., a pattern associated with patients that survive) and those extensions whose evolution suddenly changes the patient's clinical state, thus making the sequential patterns a statistically significant risk factor (i.e., a pattern associated with patients that do not survive), or vice versa.
RESULTS
The results of our experiments highlight that our method reduces the number of sequential patterns obtained with state-of-the-art pattern reduction methods by over 95%. Only by achieving this drastic reduction can medical experts carry out a comprehensive clinical evaluation of the patterns that might be considered medical knowledge regarding the temporal evolution of the patients. We have evaluated the surprisingness and relevance of the sequential patterns with clinicians, and the most interesting fact is the high surprisingness of the extensions of the patterns that become a protection factor, that is, the patients that recover after several days of being at high risk of dying.
CONCLUSIONS
Our proposed method with which to extract JDORSP generates a set of interpretable multivariate sequential patterns with new knowledge regarding the temporal evolution of the patients. The number of patterns is greatly reduced when compared to those generated by other methods and measures of interest. An additional advantage of this method is that it does not require any parameters or thresholds, and that the reduced number of patterns allows a manual evaluation.
Topics: Humans; Odds Ratio; Data Mining; Time Factors; Pattern Recognition, Automated; Delivery of Health Care; Electronic Health Records
PubMed: 38872146
DOI: 10.1186/s12911-024-02566-4 -
BMC Medical Research Methodology Nov 2022Mediation analysis aims at estimating to what extent the effect of an exposure on an outcome is explained by a set of mediators on the causal pathway between the...
BACKGROUND
Mediation analysis aims at estimating to what extent the effect of an exposure on an outcome is explained by a set of mediators on the causal pathway between the exposure and the outcome. The total effect of the exposure on the outcome can be decomposed into an indirect effect, i.e. the effect explained by the mediators jointly, and a direct effect, i.e. the effect unexplained by the mediators. However finer decompositions are possible in presence of independent or sequential mediators.
METHODS
We review four statistical methods to analyse multiple sequential mediators, the inverse odds ratio weighting approach, the inverse probability weighting approach, the imputation approach and the extended imputation approach. These approaches are compared and implemented using a case-study with the aim to investigate the mediating role of adverse reproductive outcomes and infant respiratory infections in the effect of maternal pregnancy mental health on infant wheezing in the Ninfea birth cohort.
RESULTS
Using the inverse odds ratio weighting approach, the direct effect of maternal depression or anxiety in pregnancy is equal to a 59% (95% CI: 27%,94%) increased prevalence of infant wheezing and the mediated effect through adverse reproductive outcomes is equal to a 3% (95% CI: -6%,12%) increased prevalence of infant wheezing. When including infant lower respiratory infections in the mediation pathway, the direct effect decreases to 57% (95% CI: 25%,92%) and the indirect effect increases to 5% (95% CI: -5%,15%). The estimates of the effects obtained using the weighting and the imputation approaches are similar. The extended imputation approach suggests that the small joint indirect effect through adverse reproductive outcomes and lower respiratory infections is due entirely to the contribution of infant lower respiratory infections, and not to an increased prevalence of adverse reproductive outcomes.
CONCLUSIONS
The four methods revealed similar results of small mediating role of adverse reproductive outcomes and early respiratory tract infections in the effect of maternal pregnancy mental health on infant wheezing. The choice of the method depends on what is the effect of main interest, the type of the variables involved in the analysis (binary, categorical, count or continuous) and the confidence in specifying the models for the exposure, the mediators and the outcome.
Topics: Female; Humans; Infant; Pregnancy; Causality; Mediation Analysis; Odds Ratio; Respiratory Sounds; Respiratory Tract Infections
PubMed: 36424556
DOI: 10.1186/s12874-022-01764-w -
Medicine Sep 2023To evaluate the effect of transplantation interval on patient and graft survival in liver retransplantation (reLT) using meta-analytical techniques. (Meta-Analysis)
Meta-Analysis
BACKGROUND AND AIM
To evaluate the effect of transplantation interval on patient and graft survival in liver retransplantation (reLT) using meta-analytical techniques.
METHODS
Literature search was undertaken until January 2022 to identify comparative studies evaluating patient survival rates, graft survival rates, and the interval time. Pooled hazard ratio (HR) or risk ratio (RR) and 95% confidence intervals (95% CI) were calculated with either the fixed or random effect model.
RESULTS
The 12 articles were included in this meta-analysis. The late reLT survival rate is better than the early reLT in the 30 days group, and there is no statistical significance in other time groups. The patient survival was significantly higher in late reLT than early reLT at 1 and 5 years (respectively: RR, 0.81 [95% CI, 0.73-0.89]; RR, 0.64 [95% CI, 0.46-0.88]). The graft survival was significantly higher in late reLT than early reLT at 1 year (RR, 0.75 [95% CI, 0.63-0.89]). The risk of death after reLT in early group was 1.43 times higher than that in late group (HR, 1.43 [95% CI, 1.21-1.71]).
CONCLUSIONS
Late reLT had significantly better survival rates than early reLT, and the transplantation interval was more reasonable to divide the early or late groups by 30 days.
Topics: Humans; Reoperation; Liver; Graft Survival; Odds Ratio
PubMed: 37713841
DOI: 10.1097/MD.0000000000035165 -
RMD Open Jul 2023To conduct a systematic review of the literature on the association between fibromyalgia and mortality and to pool the results in a meta-analysis. (Meta-Analysis)
Meta-Analysis
OBJECTIVE
To conduct a systematic review of the literature on the association between fibromyalgia and mortality and to pool the results in a meta-analysis.
METHODS
The authors searched the PubMed, Scopus, and Web of Science databases using the key words 'fibromyalgia' and 'mortality' to identify studies that addressed an association between fibromyalgia and mortality. Original papers that assessed associations between fibromyalgia and mortality (all or specific causes) and provided an effect measure (hazard ratio (HR), standardised mortality ratio (SMR), odds ratio (OR)) quantifying the relationship between fibromyalgia and mortality were included in the systematic review. Of 557 papers that were initially identified using the search words, 8 papers were considered eligible for the systematic review and meta-analysis. We used a Newcastle-Ottawa scale to assess the risk of bias in the studies.
RESULTS
The total fibromyalgia group included 188 751 patients. An increased HR was found for all-cause mortality (HR 1.27, 95% CI 1.04 to 1.51), but not for the subgroup diagnosed by the 1990 criteria. There was a borderline increased SMR for accidents (SMR 1.95, 95% CI 0.97 to 3.92), an increased risk for mortality from infections (SMR 1.66, 95% CI 1.15 to 2.38), and suicide (SMR 3.37, 95% CI 1.52 to 7.50), and a decreased mortality rate for cancer (SMR 0.82, 95% CI 0.69 to 0.97). The studies showed significant heterogeneity.
CONCLUSIONS
These potential associations indicate that fibromyalgia should be taken seriously, with a special focus on screening for suicidal ideation, accident prevention, and the prevention and treatment of infections.
Topics: Humans; Databases, Factual; Fibromyalgia; Odds Ratio
PubMed: 37429737
DOI: 10.1136/rmdopen-2023-003005 -
Journal of Global Health Sep 2023We noted that there remains some confusion in the health-science literature on reporting sample odds ratios as estimated rate ratios in case-control studies.
BACKGROUND
We noted that there remains some confusion in the health-science literature on reporting sample odds ratios as estimated rate ratios in case-control studies.
METHODS
We recap historical literature that definitively answered the question of when sample odds ratios (ORs) from a case-control study are consistent estimators for population rate ratios. We use numerical examples to illustrate the magnitude of the disparity between sample ORs in a case-control study and population rate ratios when sufficient conditions for them to be equal are not satisfied.
RESULTS
We stress that in a case-control study, sampling controls from those still at risk at the time of outcome event of the index case is not sufficient for a sample OR to be a consistent estimator for an intelligible rate ratio. In such studies, constancy of the exposure prevalence together with constancy of the hazard ratio (HR) (i.e., the instantaneous rate ratio) over time is sufficient for this result if sampling time is not controlled; if time is controlled, constancy of the HR will suffice. We present numerical examples to illustrate how failure to satisfy these conditions adds a small systematic error to sample ORs as estimates of population rate ratios.
CONCLUSIONS
We recommend that researchers understand and critically evaluate all conditions used to interpret their estimates as consistent for a population parameter in case-control studies.
Topics: Humans; Case-Control Studies; Odds Ratio; Research Personnel
PubMed: 37712381
DOI: 10.7189/jogh.13.04101 -
Pediatrics Apr 2022Children in PICUs experience negative sequelae of immobility; however, interprofessional staff concerns about safety are a barrier to early mobilization. Our objective...
BACKGROUND
Children in PICUs experience negative sequelae of immobility; however, interprofessional staff concerns about safety are a barrier to early mobilization. Our objective was to determine the safety profile of early mobilization in PICU patients.
METHODS
We conducted a secondary analysis of a 2-day study focused on physical rehabilitation in 82 PICUs in 65 US hospitals. Patients who had ≥72-hour admissions and participated in a mobility event were included. The primary outcome was occurrence of a potential safety event during mobilizations.
RESULTS
On 1433 patient days, 4658 mobility events occurred with a potential safety event rate of 4% (95% confidence interval [CI], 3.6%-4.7%). Most potential safety events were transient physiologic changes. Medical equipment dislodgement was rare (0.3%), with no falls or cardiac arrests. Potential safety event rates did not differ by patient age or sex. Patients had higher potential safety event rates if they screened positive for delirium (7.8%; adjusted odds ratio, 5.86; 95% CI, 2.17-15.86) or were not screened for delirium (4.7%; adjusted odds ratio, 3.98; 95% CI, 1.82-8.72). There were no differences in potential safety event rates by PICU intervention, including respiratory support or vasoactive support.
CONCLUSIONS
Early PICU mobilization has a strong safety profile and medical equipment dislodgement is rare. No PICU interventions were associated with increased potential safety event rates. Delirium is associated with higher potential safety event rates. These findings highlight the need to improve provider education and confidence in mobilizing critically ill children.
Topics: Child; Critical Illness; Heart Arrest; Hospitalization; Humans; Intensive Care Units, Pediatric; Odds Ratio
PubMed: 35352118
DOI: 10.1542/peds.2021-053432 -
Adicciones Sep 2019Editorial of vol. 31(4). (Comparative Study)
Comparative Study
Editorial of vol. 31(4).
Topics: Alcohol Drinking; Health Risk Behaviors; Humans; Male; Odds Ratio; Prevalence; Research Design
PubMed: 31634408
DOI: 10.20882/adicciones.1416