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Common Errors in Statistics and How to Avoid Them - Good P.I

Good P.I Common Errors in Statistics and How to Avoid Them - Wiley publishing , 2003. - 235 p.
Download (direct link): ńommonerrorsinstatistics2003.pdf
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Was randomization conventional, that is, was the allocation sequence generated in advance of screening any patients?
Was allocation concealed prior to its being executed? As Vance W.
Berger and Costas A. Christophi relate in a personal communication,
“This is not itself a reportable design feature, so a claim of allocation concealment should be accompanied by specific design features. For example, one may conceal the allocation sequence; and instead of using envelopes, patient enrollment may involve calling the baseline information of the patient to be enrolled in to a central number to receive the allocation.”
Was randomization restricted or unrestricted? Randomization is unrestricted if a patient’s likelihood of receiving either treatment is independent of all previous allocations and is restricted otherwise. If both treatment groups must be assigned equally often, then prior allocations determine the final ones. Were the proportions also hidden?
Were treatment codes concealed until all patients had been randomized and the database locked? Were there instances of codes being revealed accidentally? Senn [1995] warns, “investigators should delude neither themselves, nor those who read their results, into believing that simply because some aspects of their trial were double-blind that therefore all the virtues of such trials apply to all their conclusions.” Masking can rarely, if ever, be assured; see, also, Day [1998].
Was randomization simultaneous, block simultaneous, or sequential? A blocked randomization is block simultaneous if all patients within any given block are identified and assigned accession numbers prior to any patient in that block being treated.
And, not least, was intent to treat permitted?
Missing Data2
Every experiment or survey has its exceptions. You must report the raw numbers of such exceptions and, in some instances, provide additional
2 Material in this section is reprinted with permission from Manager’s Guide to Design and Conduct of Clinical Trials, Wiley, 2002.
analyses that analyze or compensate for them. Typical exceptions include the following:
Did Not Participate. Subjects who were eligible and available but did not participate in the study—this group should be broken down further into those who were approached but chose not to participate and those who were not approached. With a mail-in survey for example, we would distinguish between those whose envelopes were returned “address unknown” and those who simply did not reply.
Ineligibles. In some instances, circumstances may not permit deferring treatment until the subject’s eligibility can be determined.
For example, an individual arrives at a study center in critical condition; the study protocol calls for a series of tests, the results of which may not be back for several days; but in the opinion of the examining physician, treatment must begin immediately. The patient is randomized to treatment, and only later is it determined that the patient is ineligible.
The solution is to present two forms of the final analysis: one incorporating all patients, the other limited to those who were actually eligible.
Withdrawals. Subjects who enrolled in the study but did not complete it including both dropouts and noncompliant patients. These patients might be subdivided further based on the point in the study at which they dropped out.
At issue is whether such withdrawals were treatment related or not. For example, the gastrointestinal side effects associated with erythromycin are such that many patients (including both authors) may refuse to continue with the drug. Traditional statistical methods are not applicable when withdrawals are treatment related.
Crossovers. If the design provided for intent-to-treat, a noncompliant patient may still continue in the study after being reassigned to an alternate treatment. Two sets of results should be reported: the first for all patients who completed the trials (retaining their original treatment assignments for the purpose of analysis), the second restricted to the smaller number of patients who persisted in the treatment groups to which they were originally assigned.
Missing Data. Missing data is common, expensive, and preventable in many instances.
The primary endpoint of a recent clinical study of various cardiovascular techniques was based on the analysis of follow-up angiograms. Although more than 750 patients were enrolled in the study, only 523 had the necessary angiograms. Almost one-third of the monies spent on the trials had been wasted. This result is not atypical. Capaldi and Patterson
[1987] uncovered an average attrition rate of 47% in studies lasting 4 to 10 years.
You need to analyze the data to ensure that the proportions of missing observations are the same in all treatment groups. Again, traditional statistical methods are applicable only if missing data are not treatment related.
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