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Available methods adjust for selection bias using an array of mostly untestable assumptions, with a cost-effectiveness evaluation requiring consideration of a broader range of issues compared to other observational studies.
Standard statistical methods for the analysis of censored or incomplete observations all require the assumption of missing at random to some degree, and none of these methods adjust for the potential bias introduced by post hoc subset selection.
Several methods adjust for urine volume (Boeniger et al. 1993; Teass et al. 1998).
12 Existing methods adjust for age, sex, and comorbidities but do not adjust for severity of illness, as indicated by physiological measures or blood tests.
Existing methods adjust for sample cellularity, generally determining HPV gene copy numbers per unit mass of genomic DNA (gDNA), per copy of an independent host calibrator gene, or with reference to a cell line [ 5- 8, 11- 14].
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Analyses were repeated using propensity score methods adjusted for clinical and health care utilization variables.
It may well be that future methods adjusting for these biases change the number of positively selected coding regions, invalidating this first conclusion.
Where possible, we extracted the statistical methods used to analyse the trial along with details describing whether these methods adjusted for clustering or other covariates.
Comparisons between groups for continuous variables were performed using two-sample t tests and regression methods, adjusting for the baseline value of the variable.
Associations between multimorbid groups and direct healthcare costs were compared with their respective single disease groups using linear regression methods, adjusting for age, gender and deprivation.
Sensitivity analyses were conducted for worse eye and average eye by actual value and change from baseline in the ITT population with missing values imputed using multiple imputation methods, adjusting for baseline and without adjusting for investigator.
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