![]() Investigators may selectively report results for timepoints or measures that produced results consistent with their preconceived beliefs or results that were newsworthy and disregard results for timepoints or measures that produced results that were inconsistent with their beliefs or considered not newsworthy. Selective reporting arises when investigators selectively report results in studies in such a way so that the study report highlights or emphasizes evidence supporting a particular hypothesis and does not report or understates evidence supporting an alternative hypothesis. Last, credibility of a cohort study may be affected by the reporting of results. Participants with missing outcome data may differ importantly from those with complete data (e.g., they may be healthier or may not have experienced adverse events). ![]() Bias due to missing data in prospective and retrospective studies arises when follow up data are missing for individuals initially included in the study. Missing data may also affect the credibility of cohort studies. Bias in measurement of exposure/outcome, or detection bias, can arise when outcome assessors are aware of intervention status, different methods are used to assess outcomes in the different intervention groups, and/or the exposure status is misclassified differentially or non-differentially (i.e., the probability of individuals being misclassified is different or equal between groups in a study, respectively). Selection bias occurs when selection of participants is related to both the intervention and outcome. Inappropriate selection of participants into the cohort study can result in selection bias. Readers should be mindful, however, that possibility of residual confounding caused by unknown or unmeasured confounders always remains. Readers should assess whether the authors accounted for known confounders of the relationship under investigation in either their design or statistical analysis. Investigators can use various design (e.g., matching) and statistical methods (e.g., adjusted analyses based on regression methods) to deal with known, measured confounders. Confounding occurs when the exposure of interest is associated with another factor that also influences the outcome of interest. Factors that decrease the credibility of cohort studiesĬohort studies are at serious risk of confounding bias and so adjusting or accounting for confounding factors is a priority in these studies. Readers considering applying evidence from cohort studies should be mindful of the following factors that affect the credibility or internal validity of cohort studies. Examples of cohort studies in ophthalmology include evaluation of a possible association between exposure to ambient air pollution and age-related cataract or assessment of the impact of eye preserving therapies for patients with advanced retinoblastoma. The differentiating characteristics between observational (e.g., cohort study) and experimental (e.g., RCT) study designs are that in the former the investigator does not intervene and rather “observes” and examines the relationship or association between an exposure and outcome. Cohort studies can also be conducted to generate hypotheses and establishing questions for future RCTs. In such circumstances, well-designed observational studies, which include cohort studies, can play an important role in producing evidence to guide clinical care decisions in ophthalmology. Further, patients included in RCTs may not be representative of patients encountered in practice and the effectiveness of therapies in strict clinical trials may be different than when implemented in routine practice. While large well-designed randomized controlled trials (RCTs) represent the optimal design for making inferences about the effects of exposures or interventions on health outcomes, they are often not feasible to conduct-due to costs or challenges of recruiting patients with rare conditions and following patients for sufficient durations.
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