The prevalence of caries for this one time point or over a short period Cross-sectional studies are sometimes age group is monitored over time and Figure 1. They are usually conducted to carried out to investigate associations this information is used in public health estimate the prevalence of the outcome of between risk factors and the outcome of policy planning and in the development interest for a given population, com- interest.
They are limited, however, by of targeting strategies. Data can also be collected on the fact that they are carried out at one time point and give no indication of the Sample selection and response rates individual characteristics, including ex- sequence of events — whether exposure The sample frame used to select a sample posure to risk factors, alongside informa- occurred before, after or during the onset and the response rate determine how tion about the outcome.
In this way cross- of the disease outcome. The sample used the outcome and the characteristics asso- The next four publications of Evidence- in a large cross-sectional study is often ciated with it, at a specific point in time. This is Why carry out a cross-sectional study?
Neverthe- tive. In order for the results to be often in the form of a survey. Usually there representative of the population, how- less, cross-sectional studies indicate asso- is no hypothesis as such, but the aim is to ever, not only must the selected sample describe a population or a subgroup within ciations that may exist and are therefore useful in generating hypotheses for fu- be representative but so must the res- the population with respect to an outcome ture research. Nonresponse is a common and a set of risk factors.
The level of nonresponse is one con- cern, but a greater one still is that of biased response, where a person is more likely to respond when they have a particular characteristic or set of charac- teristics.
Bias will occur when the char- acteristic in question is in some way related to the probability of having the outcome. We can never eliminate sampling error entirely, and it is unrealistic to expect that we could ever eliminate nonsampling error. It is good research practice to be diligent in seeking out sources of nonsampling error and trying to minimize them.
The rest of this chapter will deal with the analysis of survey data. Data analysis involves looking at variables or "things" that vary or change.
A variable is a characteristic of the individual assuming we are studying individuals. The answer to each question on the survey forms a variable. For example, sex is a variable-some individuals in the sample are male and some are female.
Age is a variable; individuals vary in their ages. Looking at variables one at a time is called univariate analysis. This is the usual starting point in analyzing survey data. There are several reasons to look at variables one at a time. First, we want to describe the data. How many of our sample are men and how many are women? How many are black and how many are white? What is the distribution by age? How many say they are going to vote for Candidate A and how many for Candidate B?
How many respondents agree and how many disagree with a statement describing a particular opinion? Another reason we might want to look at variables one at a time involves recoding. Recoding is the process of combining categories within a variable. Consider age, for example. In the data set used in this module, age varies from 18 to 89, but we would want to use fewer categories in our analysis, so we might combine age into age 18 to 29, 30 to 49, and 50 and over.
We might want to combine African Americans with the other races to classify race into only two categories-white and nonwhite. Recoding is used to reduce the number of categories in the variable e.
The frequency distribution is one of the basic tools for looking at variables one at a time. A frequency distribution is the set of categories and the number of cases in each category.
Percent distributions show the percentage in each category. Table 3. Begin by looking at the frequency distribution for sex. There are three columns in this table.
The first column specifies the categories-male and female. The second column tells us how many cases there are in each category, and the third column converts these frequencies into percents. With these data we will use frequency distributions to describe variables one at a time. There are other ways to describe single variables. The mean, median, and mode are averages that may be used to describe the central tendency of a distribution.
The range and standard deviation are measures of the amount of variability or dispersion of a distribution. We will not be using measures of central tendency or variability in this module. Usually we want to do more than simply describe variables one at a time. We may want to analyze the relationship between variables. Morris Rosenberg suggests that there are three types of relationships: " 1 neither variable may influence one another In other words, the independent variable is the factor that influences the dependent variable.
For example, researchers think that smoking causes lung cancer. The statement that specifies the relationship between two variables is called a hypothesis see Hoover , for a more extended discussion of hypotheses. In this hypothesis, the independent variable is smoking or more precisely, the amount one smokes and the dependent variable is lung cancer.
Consider another example. Political analysts think that income influences voting decisions, that rich people vote differently from poor people. In this hypothesis, income would be the independent variable and voting would be the dependent variable. In order to demonstrate that a causal relationship exists between two variables, we must meet three criteria: 1 there must be a statistical relationship between the two variables, 2 we must be able to demonstrate which one of the variables influences the other, and 3 we must be able to show that there is no other alternative explanation for the relationship.
As you can imagine, it is impossible to show that there is no other alternative explanation for a relationship. For this reason, we can show that one variable does not influence another variable, but we cannot prove that it does.
We can only show that it is more plausible or credible to believe that a causal relationship exists. In this section, we will focus on the first two criteria and leave this third criterion to the next section.
In the previous section we looked at the frequency distributions for sex and voting preference. All we can say from these two distributions is that the sample is 40 percent men and 60 percent women and that slightly more than half of the respondents said they would be willing to vote for a woman, and slightly less than half are not willing to.
We cannot say anything about the relationship between sex and voting preference. In order to determine if men or women are more likely to be willing to vote for a woman candidate, we must move from univariate to bivariate analysis. A crosstabulation or contingency table is the basic tool used to explore the relationship between two variables. In the lower right-hand corner is the total number of cases in this table Notice that this is not the number of cases in the sample.
There were originally cases in this sample, but any case that had missing information on either or both of the two variables in the table has been excluded from the table. Be sure to check how many cases have been excluded from your table and to indicate this figure in your report.
Also be sure that you understand why these cases have been excluded. The figures in the lower margin and right-hand margin of the table are called the marginal distributions. They are simply the frequency distributions for the two variables in the whole table. Here, there are males and females the marginal distribution for the column variable-sex and people who are willing to vote for a woman candidate and who are not the marginal distribution for the row variable-voting preference.
The other figures in the table are the cell frequencies. Since there are two columns and two rows in this table sometimes called a 2 x 2 table , there are four cells.
The numbers in these cells tell us how many cases fall into each combination of categories of the two variables. This sounds complicated, but it isn't. For example, males are willing to vote for a woman and females are willing to vote for a woman. Before we percentage Table 3. Remember that the independent variable is the variable we think might be the influencing factor. The independent variable is hypothesized to be the cause, and the dependent variable is the effect.
Another way to express this is to say that the dependent variable is the one we want to explain. Since we think that sex influences willingness to vote for a woman candidate, sex would be the independent variable. Once we have decided which is the independent variable, we are ready to percentage the table. Notice that percentages can be computed in different ways. In Table 3. These are called column percents.
If they sum across to , they are called row percents. If the independent variable is the column variable, then we want the percents to sum down to i. If the independent variable is the row variable, we want the percents to sum across to i. This is a simple, but very important, rule to remember. Our main ana- the covariables of interest as fixed effects and the lyses will be based on the major item approach allowing protocol as a random effect. We will use the same single vs multicentre RCTs , and reported methodo- approach to test our hypothesis that methodologically logical support yes vs no.
To analyse whether the fol- supported protocols involvement of the Clinical Trial lowing independent variables are associated with Unit or Clinical Research Organization improved less adherence to a larger proportion of SPIRIT items than RCT protocols without reported methodological dependent variable , we will use multivariable regres- support. All statistical tests will approved in are more comprehensive due to be performed at a significance level of 0. In addition, we will scriptive analysis of RCT characteristics and patient- assess how many registered RCTs were registered pro- reported outcome data.
We will further stratify our analyses by an additional independent variable. To test for an effect country of RECs and descriptively compare proportions modification with year of REC approval vs , to see whether there is any evidence for heterogeneity we will add a corresponding interaction term year of across countries. Our hypothesis is that RCT protocols with regu- two multivariable regression analyses. Our hypothesis is that less comprehensive proto- cols are correlated with poorly planned trials leading to a higher risk of recruitment failure and trial discontinu- Subproject 4 Subgroups study Descriptive analysis of ation.
Our hypothesis is that less compre- poses of routinely collected data to support RCTs. Discussions In an additional study, we will assess if specific RCT The ASPIRE study and the five outlined substudies have characteristics are different between approved protocols, the overall aim to monitor and ultimately inform im- information in trial registries, and publications.
Specific- provements in the planning, conduct, analysis, and ally, we will check for differences in the following reporting of RCTs. Trials Page 11 of 13 Strengths and limitations Significance Strengths of our proposed studies include full access to The impact of poorly planned RCTs is pervasive to the protocols and associated documents of all included entire research process, wastes scarce resources, and RCTs approved by RECs in Switzerland, the UK, may have harmful consequences for all stakeholders in- Germany, and Canada in and Involved re- cluding patients, decision makers, and the scientific searchers are formally trained methodologists, and we community, thus affecting society as a whole.
To better will use standardized methods of data collection. We will understand and ultimately improve the clinical research pre-pilot all data extraction forms with detailed instruc- process, and RCTs in particular, it is necessary to empir- tions and carry out calibration exercises to align study ically and systematically investigate the design, methods, processes.
The present inter- international study, the data will be highly representative national study of RCT protocols approved in or of Switzerland and will allow us to explore for differ- will provide information on the completeness of ences between Swiss and other RECs. We specifically trial protocols and potential changes between and planned to conduct a subgroup analysis to investigate Our plan of research will identify reporting defi- whether the completeness of Swiss RCT protocols is dif- ciencies and associated RCT characteristics and clarify ferent from non-Swiss RCT protocols, because a new whether protocol adherence to SPIRIT recommenda- federal Law on Research in Humans Human Research tions is associated with the proportion of prematurely Act and its subsidiary ordinances came into effect in discontinued RCTs or the proportion of unpublished January Consequently, the roles and operating RCTs.
In this context, new time; compare characteristics of RCTs testing regulated guidance documents for trial protocols that built on the interventions versus non-regulated interventions; exam- SPIRIT recommendations www. First, we are using tinely collected data to support RCTs. Data extraction form for the Adherence to SPIrit knowledge, they are not in any way particular. Finally, We are grateful to Prof.
EvE and MB acquired funding. RS developed the web-tool for data extractions. All authors approved the final version before submission. World Medical Association. WMA Declaration of Helsinki - ethical principles for medical research involving human subjects [cited ]. The 2. Constraints on publication rights in industry-initiated clinical trials. BS is supported by an ; 14 —6. Advanced Postdoc. Mobility grant from the Swiss National Science 3. SIL participates in this project during her W.
Ghost authorship in industry-initiated randomised trials. PLoS Med. Comparison of protocols and registry entries to published reports for randomised controlled trials. Cochrane Database Syst Rev. Availability of data and materials 5. The data supporting the conclusions of this article is included within the Comparison of descriptions of allocation concealment in trial protocols and article and its additional file. Ethics approval and consent to participate Reporting on blinding in trial protocols and corresponding publications was All participating ethics committees are project partners.
J Clin Epidemiol. Empirical Consent for publication evidence for selective reporting of outcomes in randomized trials: Not applicable. Selective reporting in clinical Competing interests trials: analysis of trial protocols accepted by The Lancet. BvN is currently employed by Roche Pharma AG, Basel, Switzerland. All other authors declare no financial relationships with any Frequency and reasons for outcome reporting bias in clinical trials: organization that might have an interest in the submitted work and no interviews with trialists.
Prevalence, characteristics, and submitted work. Author details Increasing value and reducing waste: addressing inaccessible research. Spitalstrasse 12, Basel, Switzerland. Rev Panam Salud Publica. Is a subgroup effect believable? Hospital, Vitoria-Gasteiz, Spain. PLoS One. Freiburg, Germany. BMJ Open. Reporting Hospital Basel, Basel, Switzerland.
Clin Epidemiol. Greifswald, Greifswald, Germany. Characteristics and dissemination of phase 1 trials approved by a UK 17 Department of Neurosurgery and Department of Biomedicine, University regional office in Design, analysis and reporting of multi-arm trials and strategies to address 19 Department of Anesthesia, McMaster University, Hamilton, Canada.
Int J Epidemiol. Dickersin K, Rennie D. Registering clinical trials. Ioannidis JP. Effect of the statistical significance of results on the time to of Basel, Basel, Switzerland. Trials Page 13 of 13 Dissemination Accessed 25 Oct
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